Implementing FAIR Chemical Data Principles: A Guide for Environmental and Biomedical Research

Abstract

This article provides a comprehensive guide to implementing FAIR (Findable, Accessible, Interoperable, and Reusable) principles for chemical data in environmental and biomedical research. It explores the fundamental need for FAIR data in chemical risk assessment and management, presents practical methodologies and community-developed tools for implementation, addresses common challenges in data harmonization, and validates the approach through real-world use cases. Designed for researchers, scientists, and drug development professionals, this resource aims to bridge the gap between data management theory and practical application, enabling more efficient chemical safety evaluation, regulatory decision-making, and scientific discovery.

Why FAIR Data is Revolutionizing Chemical Environmental Science and Risk Assessment

The Critical Data Gap in Modern Chemicals Management

Anthropogenic chemicals and their transformation products are increasingly prevalent in the environment, with persistence being a major driver of chemical risk [1]. Accurately predicting the environmental fate of new compounds is paramount for regulators and industry to prevent future contamination crises. However, this predictive capability is severely hampered by a critical data gap: the lack of large, high-quality, machine-readable data sets on biotransformation pathways and kinetics [1]. This whitepaper examines the origins and implications of this gap, framing the discussion within the urgent need for the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in environmental science research. The current state of data reporting presents a fundamental obstacle to leveraging advanced computational models, including those powered by machine learning and artificial intelligence, which are essential for proactive chemical risk assessment [1].

The State of Chemical Data Reporting

Current Challenges and Limitations

Despite decades of research and increased regulatory pressure, the available data on chemical biotransformation are insufficient for robust predictive modeling. The core issues include:

• Limited Data Coverage: Existing data sets are often restricted in size and confined to specific chemical classes, such as pesticides or hydrocarbons, leaving significant portions of the chemical space unexplored [1].
• Inadequate Metadata: Many studies report dissipation kinetics without corresponding pathway information, or they lack critical details on the physical, chemical, and biological conditions of the test system [1]. This omission of experimental metadata prevents researchers from understanding the impact of environmental variables on transformation processes.
• Non-Machine-Readable Formats: The predominant method for reporting biotransformation pathways remains static 2D images within scientific publications [1]. These figures, while useful for human comprehension, are not programmatically translatable, creating a monumental bottleneck for data aggregation and meta-analysis.

The Regulatory and Industry Context

The consequences of these data gaps are not merely academic. They directly impact the ability of regulators to identify and restrict potentially persistent chemicals before they enter the market and environment [1]. The conference highlighted ongoing regulatory evolution, such as the postponed REACH revision in the EU, which continues to operate in a context of uncertainty [2]. Furthermore, securing the chemical supply chain in a changing world demands greater collaboration and innovation, which is predicated on reliable and accessible data [2].

The FAIR Data Principles as a Solution

The environmental science community can address these challenges by adopting the FAIR data principles, which have been widely accepted by major institutions like the European Commission and the U.S. National Institutes of Health [1]. FAIR provides a framework for making data:

• Findable: Easily located by both humans and computers through rich metadata and persistent identifiers.
• Accessible: Retrievable using standard, open protocols.
• Interoperable: Ready to be integrated with other data sets and applications.
• Reusable: Well-described with provenance and domain-relevant community standards to allow for future replication and use.

Applying these principles to biotransformation data will boost the quality and quantity of information available for model development and regulatory decision-making [1].

Standardized Reporting with the Biotransformation Reporting Tool (BART)

To operationalize FAIR principles, we present the Biotransformation Reporting Tool (BART), a freely available Microsoft Excel template designed to guide researchers in reporting biotransformation data in a standardized, machine-readable format [1]. BART structures data into specific tabs to ensure comprehensive capture of all necessary information, from chemical structures to experimental conditions.

BART Structure and Workflow

The following diagram illustrates the logical workflow and structure for using BART to create FAIR-compliant biotransformation data.

Key Experimental Metadata for Reporting

The table below summarizes the essential experimental parameters that must be reported alongside biotransformation pathways to ensure data utility and reusability. These are based on OECD guideline recommendations and are integral to the BART template [1].

Table 1: Key Experimental Parameters for Biotransformation Studies

General Parameters	Sludge Systems	Soil Systems	Sediment Systems
Inoculum provenance [1]	Biological treatment technology [1]	Soil origin [1]	Sediment origin [1]
Inoculum source [1]	Solids retention time [1]	Soil texture (% sand, silt, clay) [1]	Sampling depth [1]
pH [1]	Volatile suspended solids concentration (VSS) [1]	Cation exchange capacity (CEC) [1]	Cation exchange capacity (CEC) [1]
Temperature [1]	Oxygen demand [1]	Water holding capacity [1]	Oxygen content [1]
Spike concentration [1]	Redox condition [1]	Microbial biomass [1]	Sediment porosity [1]

The Researcher's Toolkit for Biotransformation Studies

Table 2: Essential Research Reagents and Materials

Item	Function/Benefit
SMILES Strings	Standardized representation of molecular structure for machine readability and cheminformatic analysis [1].
Schymanski/PCI Confidence Levels	Standardized annotation for identifying the level of confidence in the structure elucidation of transformation products using mass spectrometry [1].
Activated Sludge Inoculum	A common, relevant microbial community used to study the aerobic biodegradation of chemicals in wastewater treatment systems [1].
Defined Mineral Medium	Provides essential nutrients while avoiding the introduction of complex organic matter that could interfere with the analysis of the test chemical's fate [1].
High-Resolution Mass Spectrometer (HRMS)	Critical instrument for identifying and characterizing unknown biotransformation products with high mass accuracy [1].

Case Study: Application to PFAS Biotransformation

The utility of standardized reporting is powerfully demonstrated in the study of per- and polyfluoroalkyl substances (PFASs), a class of chemicals of intense regulatory and scientific interest due to their persistence. The application of the BART template to PFAS biotransformation data has enabled the creation of a structured, publicly available database on the enviPath platform [1]. This systematic aggregation allows researchers to efficiently answer critical questions about the environmental fate of PFASs, such as identifying common transformation pathways that lead to the accumulation of stable perfluoroalkyl acids (PFAAs) [1]. This case study underscores how community-driven efforts with standardized tools can illuminate prominent data gaps and accelerate the understanding of complex contaminant families.

The critical data gap in modern chemicals management is not merely a shortage of studies, but a systemic failure in how the resulting data is reported and shared. The adoption of FAIR data principles through standardized tools like BART is a necessary paradigm shift for the environmental research community. By committing to report biotransformation pathways and kinetics in a machine-readable format, enriched with essential experimental metadata, researchers can directly empower the development of predictive models. This, in turn, will provide regulators and industry with the robust tools needed to perform proactive chemical risk assessments, ultimately preventing the release of persistently hazardous substances into the environment.

The FAIR Guiding Principles represent a foundational framework for scientific data management and stewardship, designed to enhance the value and utility of digital research assets. Formally introduced in 2016 in the journal Scientific Data by Wilkinson et al., these principles provide a structured approach to managing the increasing volume, complexity, and creation speed of research data [3] [4]. The acronym FAIR stands for Findable, Accessible, Interoperable, and Reusable, with each principle addressing distinct challenges in the modern data landscape. Unlike initiatives that focus primarily on human users, a distinguishing feature of FAIR is its emphasis on machine-actionability—the capacity of computational systems to find, access, interoperate, and reuse data with minimal human intervention [3] [5]. This capability is becoming increasingly crucial as researchers across disciplines, including environmental science and chemistry, increasingly rely on computational support to handle complex datasets.

The genesis of FAIR principles can be traced to a 2014 workshop in Leiden, Netherlands, where experts gathered to address persistent challenges in data sharing and reuse [6]. The resulting framework has since gained substantial traction across scientific domains, supported by funders, publishers, and research institutions worldwide. Importantly, FAIR does not necessarily mean "open"—data can be FAIR without being freely accessible to everyone [4] [5]. Instead, the principles aim to ensure that data are structured and described in ways that maximize their potential for reuse, whether access is open or restricted through authentication and authorization procedures [7]. This nuanced understanding is particularly relevant for chemical and environmental research, where data may be subject to intellectual property concerns, privacy regulations, or security considerations.

The Core FAIR Principles Explained

The FAIR principles comprise four interconnected pillars, each with specific guidelines for implementation. The table below summarizes the core components of each principle:

Table 1: The Core FAIR Principles and Their Key Requirements

Principle	Core Objective	Key Requirements
Findable	Easy discovery by humans and computers	• Persistent identifiers (e.g., DOI)• Rich metadata• Indexing in searchable resources• Clear identifier inclusion in metadata
Accessible	Retrievable once found	• Standardized retrieval protocols (HTTP, FTP)• Authentication/authorization where needed• Persistent metadata accessibility• Open, free, universally implementable protocols
Interoperable	Integration with other data and systems	• Formal knowledge representation languages• FAIR-compliant vocabularies• Qualified references to other (meta)data• Use of community standards
Reusable	Optimization for future use	• Plurality of accurate attributes• Clear usage licenses• Detailed provenance information• Domain-relevant community standards

Findability

Findability represents the foundational first step in the data reuse process. For data to be findable, both humans and computers must be able to efficiently discover them amidst the vast landscape of digital resources. This requires assigning globally unique and persistent identifiers (PIDs) such as Digital Object Identifiers (DOIs) to datasets, ensuring they can be reliably referenced and accessed over time [7] [8]. These identifiers serve as permanent markers for datasets, similar to how ISBNs identify books, preventing link rot and reference ambiguity in scholarly communications.

Rich metadata—data about the data—forms another critical component of findability. Comprehensive metadata should describe numerous aspects of the dataset, including creation context, generation methods, interpretation guidance, data quality, licensing information, and relationships to other data [7]. This metadata must explicitly include the identifier of the data it describes and be registered or indexed in searchable resources [8]. For computational discoverability, metadata should be structured in machine-readable formats, enabling automated systems to parse and index the information efficiently. In practice, repositories facilitate this process by providing fillable application profiles that guide researchers in providing extensive and precise information about their deposited datasets [7].

Accessibility

The Accessibility principle ensures that once users identify desired data through metadata and identifiers, they can retrieve them using standardized protocols. This typically involves communications protocols like HTTP/HTTPS or FTP/SFTP that are open, free, and universally implementable [7] [8]. A crucial distinction in FAIR terminology is that accessibility does not mandate open access; rather, it requires transparency about how data can be accessed, even if through authentication and authorization procedures [4] [7]. This is particularly important for sensitive data in chemical and pharmaceutical research, where intellectual property concerns or privacy considerations may necessitate restricted access.

The accessibility principle also stipulates that metadata should remain accessible even when the data themselves are no longer available [7] [8]. This ensures a permanent record of the dataset's existence and characteristics, which is valuable for tracking research outputs and understanding the evolution of scientific knowledge. Repositories supporting FAIR data should have clear contingency plans for metadata preservation, ensuring that descriptive information persists even if the repository service ceases operations or the data become unavailable due to format obsolescence or storage limitations.

Interoperability

Interoperability addresses the need for data to be integrated with other data and to work effectively with applications or workflows for analysis, storage, and processing [3]. This requires that (meta)data use formal, accessible, shared, and broadly applicable languages for knowledge representation [7] [8]. In practical terms, this means employing standardized formats, controlled vocabularies, and community-established ontologies that reduce ambiguity and enable meaningful data exchange between different systems.

For interoperability to function effectively, the vocabularies and ontologies used must themselves adhere to FAIR principles, being well-documented and resolvable using persistent identifiers [7]. Additionally, (meta)data should include qualified references to other (meta)data, creating a web of interconnected research assets that computational agents can traverse to gather related information [8]. In chemistry, established standards like the crystallographic information file (CIF) format exemplify interoperability in action, providing a structured way to represent and exchange crystallographic data that both humans and machines can interpret unambiguously [9] [7].

Reusability

Reusability represents the ultimate goal of the FAIR principles—optimizing the potential for data to be replicated and/or combined in different settings [3]. This requires that metadata and data are thoroughly described with a plurality of accurate and relevant attributes, enabling potential users to assess their suitability for new contexts [7]. Reusability builds upon the previous three principles while adding specific requirements for comprehensive documentation, clear licensing, and detailed provenance information.

To enable true reusability, data must be released with clear and accessible usage licenses that specify the terms under which they can be reused [10] [8]. Additionally, they should be associated with detailed provenance information describing the origin and history of the data, including how they were generated or collected, and any processing steps applied [8]. Finally, reusability requires that (meta)data meet domain-relevant community standards, ensuring they align with established practices and expectations within specific research fields [8]. For chemical data, this might include providing machine-readable chemical structures and detailed experimental methodologies that enable other researchers to understand and build upon the reported work.

FAIR Principles in Practice: Workflow and Implementation

Implementing FAIR principles requires careful planning and execution throughout the research data lifecycle. The following diagram illustrates a generalized FAIRification workflow that can be adapted to various research contexts, including chemical and environmental science:

Diagram 1: FAIR Data Implementation Workflow

The Researcher's Toolkit for FAIR Chemical Data

Successful implementation of FAIR principles in chemistry and environmental science requires specific tools and resources. The table below outlines essential components of the FAIR chemical data toolkit:

Table 2: Essential Toolkit for FAIR Chemical Data Management

Tool Category	Specific Examples	Function in FAIR Implementation
Persistent Identifiers	Digital Object Identifiers (DOIs), International Chemical Identifiers (InChI), International Generic Sample Number (IGSN)	Provides unique, persistent references for datasets, chemical structures, and physical samples [9] [7]
Chemical Repositories	Cambridge Structural Database (CSD), Chemotion Repository, NMRShiftDB, RADAR4Chem	Discipline-specific repositories that provide preservation, identifier assignment, and metadata standards [9] [7]
Metadata Standards	DataCite Metadata Schema, chemical methods ontology (CHMO), chemical information ontology (CHEMINF)	Standardized frameworks for describing datasets with controlled vocabularies [7]
Data Formats	Crystallographic Information Files (CIF), JCAMP-DX for spectral data, nmrML for NMR data	Machine-readable, standardized formats for specific data types that support interoperability [9] [7]
Provenance Tools	Electronic Lab Notebooks (ELNs), workflow management systems	Track data origin, processing history, and transformation steps to support reusability [8]

Community-Centric Reporting Formats

In environmental and chemical sciences, community-centric reporting formats have emerged as practical tools for implementing FAIR principles. These formats provide instructions, templates, and tools for consistently formatting data within specific disciplines, bridging the gap between generic FAIR guidelines and domain-specific practices [11]. For example, the Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) repository has developed 11 reporting formats for diverse Earth science data types, including cross-domain metadata (dataset metadata, location metadata) and domain-specific formats for biogeochemical samples, soil respiration, and leaf-level gas exchange [11].

These community-developed formats balance pragmatism for scientists with the machine-actionability emblematic of FAIR data [11]. They typically include a minimal set of required metadata fields necessary for programmatic data parsing, along with optional fields that provide detailed spatial/temporal context useful for downstream scientific analyses. The development process for these formats often involves reviewing existing standards, creating crosswalks of terms across relevant ontologies, iterative template development with user feedback, and hosting documentation on platforms that support public access and ongoing updates [11]. This approach demonstrates how research communities can adapt FAIR principles to their specific needs while maintaining alignment with the broader FAIR framework.

FAIR in Chemical and Environmental Research

Chemical Data FAIRification

The chemistry community has made significant strides in implementing FAIR principles, building upon decades of experience in developing standards for chemical information. Chemical data presents unique challenges for FAIRification due to the diversity of data types (from molecular structures to reaction protocols and spectroscopic data) and the need for precise representation of chemical entities [12]. Key advancements in FAIR chemical data include:

• Structure Representation: The International Chemical Identifier (InChI) provides a standardized, machine-readable representation of chemical structures that serves as a persistent identifier for molecular entities [9]. This enables unambiguous structure searching and interconnection between different chemical databases and resources.

• Analytical Data Standards: Standard formats like JCAMP-DX for spectral data and CIF for crystallographic data enable interoperability across instrumental platforms and computational analysis tools [9] [7]. These standards facilitate the exchange of both primary data and associated metadata, including instrument parameters and processing methods.

• Electronic Lab Notebooks (ELNs): Modern ELNs support FAIR data practices by capturing experimental procedures, observations, and results in structured formats that can be exported with appropriate metadata [8]. When integrated with laboratory instrumentation and data repositories, ELNs help maintain provenance information throughout the data lifecycle.

Initiatives like the WorldFAIR Chemistry project and NFDI4Chem are working to address persistent challenges in chemical data FAIRification, including the development of practical guidance, training resources, and infrastructure components that support researchers in adopting FAIR practices [12] [7]. These efforts recognize that achieving FAIR chemical data requires both technical solutions and cultural change within the research community.

Interdisciplinary Integration for Environmental Science

Environmental science research increasingly requires integration of diverse data types from multiple disciplines, creating both challenges and opportunities for FAIR implementation. Research in this domain typically combines chemical, biological, geological, and climatological data, each with their own traditions of data management and reporting [11]. The FAIR principles provide a common framework for making these diverse data types interoperable and reusable.

Successful integration of FAIR principles in environmental science involves:

• Cross-Domain Metadata Standards: Developing metadata frameworks that span traditional disciplinary boundaries while accommodating domain-specific requirements. For example, the ESS-DIVE repository has created reporting formats for sample-based water and soil chemistry measurements that include spatial, temporal, and methodological context needed for interpretation and reuse [11].

• Semantic Interoperability: Using shared vocabularies and ontologies to ensure that data from different domains can be meaningfully integrated. This might involve mapping between discipline-specific terminologies or developing cross-disciplinary ontologies for environmental phenomena [11].

• Programmatic Data Access: Implementing standardized application programming interfaces (APIs) that enable computational access to diverse data types for integrated analysis. This supports the development of automated workflows that combine data from multiple sources to address complex research questions [12].

The benefits of FAIR implementation in environmental science include accelerated scientific discovery through more efficient data reuse, improved reproducibility of research findings, and enhanced ability to synthesize information across studies and domains [11]. As environmental challenges become increasingly complex, FAIR data practices will play a crucial role in enabling the interdisciplinary collaboration needed to address them.

Challenges and Future Directions

Despite significant progress in developing standards, tools, and infrastructure, implementing FAIR principles still faces substantial challenges. These include technical barriers related to fragmented data systems and formats, organizational challenges such as cultural resistance and lack of FAIR-awareness, and resource constraints involving the cost and time required to transform legacy data [4]. Additionally, balancing openness with legitimate access restrictions remains particularly challenging in fields with commercial applications or privacy concerns [4].

Future directions for FAIR implementation focus on enhancing machine-actionability, addressing semantic interoperability challenges, and developing more sophisticated approaches for assessing FAIR compliance [6]. Initiatives like FAIR 2.0 aim to extend the original principles to better address semantic interoperability, ensuring that data and metadata are not only accessible but also meaningful across different systems and contexts [6]. The development of FAIR Digital Objects (FDOs) seeks to standardize data representation, facilitating seamless data exchange and reuse globally [6].

For chemistry and environmental science, priorities include refining domain-specific standards, developing integrated workflows that support FAIR data practices from generation through publication, and creating sustainable infrastructure for long-term data preservation [12]. As these fields continue to generate increasingly complex and voluminous data, adherence to FAIR principles will be essential for maximizing the value of research investments and accelerating the pace of scientific discovery.

Global chemical management is increasingly driven by robust regulatory frameworks that demand comprehensive data collection and reporting. The European Union's Chemicals Strategy for Sustainability and the United States Environmental Protection Agency's (EPA) Chemical Data Reporting (CDR) under the Toxic Substances Control Act (TSCA) represent two pivotal regulatory drivers. When examined through the lens of FAIR principles (Findable, Accessible, Interoperable, Reusable), these regulations create a powerful imperative for researchers and drug development professionals to standardize chemical data management. The integration of FAIR principles is not merely a technical exercise but a fundamental requirement for advancing environmental science research, enabling data reuse, computational analysis, and cross-disciplinary collaboration in chemical safety and development.

The European Union Chemicals Strategy

Strategic Objectives and Key Initiatives

The EU Chemicals Strategy is a cornerstone of the European Green Deal, aiming to transition towards safer and more sustainable chemicals. A key development in July 2025 was the introduction of an Action Plan specifically designed to strengthen the EU chemical industry's competitiveness and modernization amidst challenges including high energy costs, unfair global competition, and weak demand [13] [14]. This strategy is intrinsically linked to the REACH regulation (Registration, Evaluation, Authorisation and Restriction of Chemicals), which is undergoing its most significant revision in over a decade, with a proposal expected in Q4 2025 [15].

The strategic pillars and their associated actions are detailed below:

Table: Key Pillars of the EU Chemicals Strategy Action Plan (2025)

Strategic Pillar	Key Actions	Relevance to Research & Development
Resilience & Level Playing Field	Establishment of a Critical Chemical Alliance; Application of trade defence measures [13] [14]	Identifies critical production sites and supply chain dependencies guiding policy and investment in R&D for strategic sectors.
Affordable Energy & Decarbonisation	Rapid implementation of the Affordable Energy Action Plan; Support for clean carbon sources (e.g., carbon capture) [13] [14]	Promotes R&D into sustainable production processes and alternative feedstocks, reducing the carbon footprint of chemical synthesis.
Lead Markets & Innovation	Fiscal incentives for clean chemicals; Launch of EU Innovation and Substitution Hubs; Funding via Horizon Europe (2025-2027) [13] [14]	Directly funds and accelerates the development of safer and more sustainable chemical substitutes, a key area for applied research.
Action on PFAS	Science-based restriction of PFAS; Investment in innovation for safer alternatives [13] [14]	Creates a urgent need for research into alternative substances and remediation technologies for per- and polyfluoroalkyl substances.
Simplification & Competitiveness	Streamlining legislation via the "6th Omnibus"; Reducing administrative burdens by €363 million annually [13] [14]	Simplifies regulatory compliance, allowing R&D resources to be focused on innovation rather than administrative overhead.

REACH Revision 2025: A Scientific Perspective

The upcoming REACH revision, guided by the motto "simpler, faster, bolder," aims to address shortcomings in the current system [15]. Key scientific and regulatory advancements under discussion include:

• Mixture Assessment Factor (MAF): A pivotal scientific debate centers on implementing a MAF to account for the combined effects of chemical mixtures. Traditional risk assessment of single substances is now considered insufficient. Proposed MAF values range from 2 to 500, with a factor of 5-10 being a central point of discussion for high-volume chemicals [15].
• Polymer Registration: The revision may expand registration requirements to include polymers, which have largely been exempt, representing a significant shift in regulatory scope [15].
• Digital Chemical Passport: This initiative aims to enhance supply chain transparency and data accessibility, directly supporting FAIR data principles [15].
• Streamlined Processes: The revision seeks to accelerate both the market entry of safe chemicals and the restriction of hazardous substances, addressing current asymmetries in regulatory speed [15].

However, the revision faces challenges. The European Commission's Regulatory Scrutiny Board issued a negative opinion on the initial impact assessment in early October 2025, potentially delaying the legislative timeline and reflecting political tensions between health/environmental protection and industrial competitiveness [15].

US EPA Chemical Data Reporting (CDR) Requirements

Framework and Reporting Mechanics

The Chemical Data Reporting (CDR) rule, under the Toxic Substances Control Act (TSCA), is a cornerstone of US chemical management policy [16] [17]. It requires manufacturers (including importers) to provide the EPA with fundamental exposure-related information on chemicals in commerce. The CDR database serves as the most comprehensive source of screening-level exposure information for the EPA, which uses it for risk screening, assessment, prioritization, and evaluation [17].

The reporting is conducted every four years, with the most recent period ending in 2024 and the next submission due in 2028. The core requirement is that manufacturers report if they meet specific production volume thresholds for any chemical substance at a single site [16] [17].

Table: US EPA CDR Reporting Thresholds and Requirements

Aspect	Standard Threshold	Reduced Threshold	Exemptions
Production Volume	25,000 lbs (≈11.3 tons) or more at any single site during any calendar year since the last reporting period [17]	2,500 lbs for substances subject to certain TSCA actions [17]	Chemicals for non-TSCA uses (e.g., pesticides, pharmaceuticals); water; naturally occurring substances; certain polymers, microorganisms, and natural gases [17]
Reporting Entity	Manufacturers and importers	Small manufacturers defined by TSCA: total sales < $12M (parent company included) OR total sales < $120M and production volume of a chemical substance ≤ 100,000 lbs [17]
Data Submission	Electronically via the e-CDRweb tool and EPA's Central Data Exchange (CDX) system. Roles include Authorized Official, Agent, and Support [17]

The TSCA Inventory is dynamically updated, with the 2025 release containing 86,847 chemicals, of which 42,495 are listed as active [18]. For existing substances, businesses must also comply with Significant New Use Notice (SNUR) rules, requiring a notification to the EPA 90 days before commencing a designated new use [18].

CDR Data Utilization in Environmental Research

The data collected through CDR is instrumental for environmental science. It allows the EPA and researchers to understand the types, quantities, and uses of chemicals in commerce, which is the first step in identifying potential exposure pathways and assessing ecological and human health risks [17]. The public availability of non-confidential CDR data provides a valuable resource for academic and independent researchers studying chemical flows, life-cycle assessments, and exposure models.

Implementing FAIR Data Principles in Chemical Regulation

The FAIR Framework Explained

The FAIR Guiding Principles were established to enhance the utility of digital assets in an era of exponentially growing data volume and complexity [3]. They emphasize machine-actionability to enable computational systems to find, access, interoperate, and reuse data with minimal human intervention [3]. The core principles are:

• Findable: Data and metadata must be easy to locate by both humans and computers, requiring persistent identifiers and inclusion in searchable resources [3] [9].
• Accessible: Data should be retrievable using standardized, open protocols, with authentication and authorization procedures where necessary [3] [9].
• Interoperable: Data must be formatted in shared, broadly applicable languages and must include qualified references to other data to enable integration with other datasets and analytical workflows [3] [9].
• Reusable: Data should be richly described with multiple, relevant attributes, clear licensing, and detailed provenance to enable replication and reuse in new contexts [3] [9].

Bridging Regulation and FAIR Compliance

Both the EU Chemicals Strategy and the US CDR rule implicitly and explicitly drive the adoption of FAIR data practices. The EU's proposed Digital Chemical Passport is a direct application of FAIR, designed to make chemical information findable and accessible throughout the supply chain [15]. Similarly, the structured, electronic reporting mandate of the CDR rule ensures data is collected in a consistent format, supporting interoperability.

The table below outlines how chemical data can be managed to satisfy both regulatory and FAIR requirements.

Table: FAIR Data Implementation for Chemical Compliance

FAIR Principle	Regulatory Driver	Implementation in Chemical Research
Findable	EU: Digital Chemical Passport [15]US: CDR database indexing [17]	Use International Chemical Identifiers (InChI) for structures [9]; Obtain DOIs for datasets from repositories (e.g., Dataverse, Figshare) [9]; Register data in chemistry-specific repositories (e.g., Cambridge Structural Database) [9].
Accessible	US: CDR data retrieval via CDX [17]	Use standard web protocols (HTTP/HTTPS) [9]; Clearly document access restrictions for sensitive data; Ensure metadata is always available, even if data is under embargo [9].
Interoperable	EU: Standardized data formats for REACH registration [15]	Use community standards: CIF for crystallography, JCAMP-DX and nmrML for spectra [9]; Structure synthesis routes in machine-readable formats; Use controlled vocabularies for processes and properties.
Reusable	EU/US: Requirement for robust substance identity and use information [17] [15]	Document full experimental conditions and instrument settings; Apply clear licenses (e.g., CC-BY); Provide detailed provenance for data generation and processing [9].

Adhering to FAIR principles addresses a critical inefficiency in research: approximately 80% of effort is often spent on "data wrangling" and preparation, leaving only 20% for actual research and analysis. Implementing FAIR from the point of data creation reverses this ratio, maximizing research impact [9].

Experimental Protocols for Compliant Chemical Data Reporting

Protocol 1: Substance Identification and Characterization

Objective: To unambiguously identify and characterize a chemical substance for regulatory submission (e.g., REACH, CDR) following FAIR principles.

Materials:

• Pure substance sample: For analytical characterization.
• International Chemical Identifier (InChI) software: An algorithm (e.g., from Open Babel, ChemDraw) to generate a standard InChI and InChIKey [9].
• Analytical instruments: NMR spectrometer, Mass Spectrometer, HPLC, etc., for purity and identity confirmation.
• Standard data formats: JCAMP-DX for spectral data, CIF for crystal structures [9].

Procedure:

• Generate Standard Identifiers: Input the chemical structure into InChI-generating software to produce a standard InChI string and its hashed InChIKey. This fulfills the Findable principle [9].
• Perform Structural Elucidation: Conduct analytical tests (e.g., NMR, MS) to verify the molecular structure. Save all raw and processed spectral data in standard, machine-readable formats (e.g., JCAMP-DX for NMR).
• Determine Purity and Composition: Use HPLC, GC-MS, or other relevant techniques to quantify the purity of the substance and identify the nature and quantity of any impurities.
• Compile Characterization Dossier: Assemble a digital dossier containing:
  • The InChI and InChIKey.
  • Analytical data files in standard formats (Interoperable).
  • A detailed description of the methods and instruments used, including calibration information (Reusable).

Protocol 2: Chemical Data Reporting (CDR) Workflow

Objective: To prepare and submit CDR data for a manufactured chemical substance exceeding the 25,000 lbs threshold, ensuring compliance with TSCA and FAIR principles.

Materials:

• TSCA Inventory: The official list to confirm the substance is an "existing" chemical [18].
• Production volume records: Detailed site-specific manufacturing data for the relevant years (2024-2027 for the 2028 submission) [17].
• e-CDRweb reporting tool: The EPA's web-based system for electronic submission [17].
• Use information: Data on industrial processing and use, and commercial/consumer use.

Procedure:

• Substance Eligibility Check: Confirm the substance is on the TSCA Inventory and is not exempt from reporting (e.g., not a polymer, pesticide, or intermediate) [17] [18].
• Volume Threshold Assessment: For each site, aggregate production and import volumes for each calendar year since the last reporting period. Confirm if the 25,000 lbs (or 2,500 lbs) threshold is met [17].
• Data Collection: Collect data on total production volume for each reporting year. Gather processing and use information, including codes from the EPA-provided list.
• Electronic Submission:
  • Register with the EPA's CDX system and assign appropriate user roles (Authorized Official, Agent, Support) [17].
  • Use the e-CDRweb tool to complete Form U, ensuring all data fields are accurately filled.
  • The Authorized Official or Agent must review and submit the form electronically [17].

Table: Key Resources for FAIR Chemical Data and Regulatory Compliance

Tool/Resource	Function	FAIR Principle Application
International Chemical Identifier (InChI)	Provides a standardized, machine-readable string for unique chemical structure identification [9].	Findable: Creates a persistent, unique identifier for a substance.
Digital Object Identifier (DOI)	Provides a persistent link to a digital object, such as a research dataset in a repository [9].	Findable, Accessible: Ensures a dataset can be permanently located and cited.
Crystallographic Information File (CIF)	A standard format for storing and exchanging crystallographic data [9].	Interoperable: Allows crystal structure data to be used by different software and databases.
JCAMP-DX / nmrML	Standardized data formats for spectroscopic data (e.g., NMR, IR) [9].	Interoperable, Reusable: Ensures spectral data is machine-readable and accompanied by necessary metadata.
TSCA Inventory	The official list of chemical substances manufactured or processed in the US [18].	Findable: The definitive resource for determining regulatory status for US compliance.
REACH IT / e-CDRweb	Official online portals for submitting data to ECHA and the US EPA, respectively [17].	Accessible: Provide standardized, secure protocols for data submission.

Logical Workflow and Data Relationships

The following diagram illustrates the integrated workflow for managing chemical data in compliance with regulatory drivers and FAIR principles, from initial substance characterization to final reporting and reuse.

FAIR Chemical Regulatory Workflow

The EU Chemicals Strategy and the US EPA CDR requirements are powerful, parallel forces shaping the global chemical industry and the environmental research that supports it. While their immediate objectives differ—the EU focusing on a systemic green transition and the US on comprehensive data collection—both create a non-negotiable demand for high-quality, standardized chemical data. By consciously implementing FAIR data principles, researchers and drug development professionals can not only meet these regulatory demands more efficiently but also unlock the latent value in their data. This approach transforms compliance from a cost center into a strategic asset, fostering innovation in safer and more sustainable chemicals and enabling a new era of data-driven environmental science. The journey toward fully FAIR chemical data is complex, but it is an essential investment for the future of chemical safety and sustainability.

The High Cost of Dark Data in Chemical Research and Development

In the competitive landscape of chemical research and development, a significant and often overlooked obstacle hinders innovation and efficiency: dark data. This term refers to the unstructured, inaccessible, and untapped data generated throughout the R&D lifecycle—from experimental procedures and laboratory notes to characterization data and failed experiment records. It is estimated that 55% of data stored by organizations is dark data, and a overwhelming 90% of global business and IT executives agree that extracting value from this unstructured data is essential for future success [19]. Within diversified chemistry R&D, this encompasses data from lab notebooks, LIMS, experimental reports, and literature references that are not incorporated into searchable databases [19].

The implications for chemical sciences are profound. Research output is growing by 8–9% annually, yet the methods for sharing and reusing experimental data have not kept pace [9]. This creates a cycle of inefficiency where approximately 80% of all effort regarding data goes into data wrangling and preparation, leaving only 20% for actual research and analytics [9]. For researchers and drug development professionals operating within the framework of FAIR (Findable, Accessible, Interoperable, Reusable) chemical data principles, addressing this dark data challenge is not merely an optimization task but a fundamental requirement for advancing environmental science research and sustainable chemical development.

Quantifying the Problem: Scale and Financial Impact

The Expanding Universe of Unstructured Chemical Data

The volume of dark data in chemical enterprises is staggering and growing exponentially. Global estimates suggest that by 2025, there will be 175 zettabytes of data globally, with 80% being unstructured and a remarkable 90% of this unstructured data never being analyzed [20]. This trend is particularly acute in chemical research, where diverse data types—from spectral information and synthetic procedures to formulation data and analytical results—accumulate in isolated silos without standardized organization or annotation.

The financial impact of this unutilized asset is equally significant. The dark analytics market, valued at USD 0.9 billion in 2025, is projected to reach USD 5.5 billion by 2035, registering a compound annual growth rate (CAGR) of 20.4% [21]. This rapid market expansion underscores both the recognized value of dark data and the substantial investments being made to address the challenge.

Table 1: Dark Analytics Market Forecast and Segmental Growth (2025-2035)

Metric	2025 Value	2035 Projected Value	CAGR
Overall Market Size	USD 0.9 billion	USD 5.5 billion	20.4%
Leading Analytics Segment (Predictive)	39.6% market share	-	-
Leading Data Type (Business)	42.8% market share	-	-
Leading End-User (BFSI)	34.7% market share	-	-

Source: Future Market Insights [21]

Operational Costs and Inefficiencies

The accumulation of dark data creates multiple operational costs that directly impact research productivity and innovation cycles:

• R&D Cycle Time: Dark data forces researchers to duplicate experiments, rediscover previous findings, and work with incomplete information, significantly extending development timelines for new chemicals, materials, and pharmaceuticals [19].
• Resource Allocation: Valuable researcher time is spent searching for or regenerating existing data rather than pursuing novel investigations. One analysis notes that without FAIR data infrastructure, organizations "will not be able to have all these data talk to each other" or extract the implicit knowledge the data contain [9].
• Missed Opportunities: Historical experimental data, often scattered and incomplete, can provide valuable insights for current and future projects when properly organized and analyzed [19]. Without access to this knowledge repository, chemical enterprises miss opportunities for innovation and process improvement.

The chemical industry's response to these inefficiencies has included widespread cost-reduction programs and asset rationalization, particularly in regions like Europe where operational challenges have been most pronounced [22]. However, these measures address symptoms rather than the fundamental data management issues underlying the problem.

FAIR Data Principles as a Strategic Framework

Core Principles and Chemical Implementation

The FAIR data principles provide a comprehensive framework for addressing the challenge of dark data in chemical research. These principles describe distinct considerations for contemporary data publishing environments with respect to supporting both manual and automated deposition, exploration, sharing, and reuse [9].

Table 2: FAIR Principles Implementation in Chemical Research

Principle	Technical Definition	Chemistry Implementation
Findable	Data and metadata with globally unique, persistent machine-readable identifiers	Chemical structures with InChIs; datasets with DOIs; rich experimental metadata
Accessible	Data retrievable from identifiers using standardized protocols	Repositories with HTTP/HTTPS; clear access conditions; metadata preservation
Interoperable	Data formatted in formal, shared, broadly applicable language	Standard formats (CIF, JCAMP-DX); community metadata standards; controlled vocabularies
Reusable	Data thoroughly described for replication and combination	Detailed experimental procedures; clear licenses; complete provenance tracking

Source: CMU LibGuides on FAIR Data in Chemical Sciences [9]

For environmental science research particularly, FAIR implementation enables the cross-disciplinary data exchange necessary to address complex challenges spanning chemical synthesis, environmental impact assessment, and sustainability metrics.

FAIR Compliance in Publishing and Research

Major scientific publishers and funding agencies have increasingly adopted FAIR data requirements, making compliance essential for contemporary chemical research. ACS Publications strongly endorses the FAIR Data Principles and supports related initiatives including the Center for Open Science's Transparency and Openness Promotion (TOP) Guidelines and the Joint Declaration of Data Citation Principles [23].

Similarly, the Royal Society of Chemistry requires that "any data required to understand and verify the research in an article must be made available on submission" [24]. This policy shift reflects growing recognition that proper data management is fundamental to research integrity and reproducibility rather than an administrative adjunct to publication.

Methodologies: Transforming Dark Data to FAIR Data

Data Identification and Inventory Process

The first critical step in addressing dark data is conducting a systematic inventory of existing data assets. This process involves:

• Data Profiling: Understanding the structure, content, and quality of existing data to determine their characteristics and potential value [20]. For chemical enterprises, this includes identifying valuable but underutilized data types such as historical experimental data, external data from academic papers and patents, and unstructured text data from scientific articles or laboratory notes [19].
• Source Classification: Categorizing data sources based on format (structured, semi-structured, unstructured), origin (internal, external), and potential value to current and future R&D efforts [19].
• Priority Assessment: Prioritizing data sources based on their potential value to R&D objectives. For example, scaling up a newly validated functional material might prioritize access to historical formulations and manufacturing data to help predict ideal conditions [19].

Implementation Workflow

The transformation of dark data into FAIR-compliant resources follows a systematic workflow that can be visualized and implemented across chemical research organizations:

Dark Data to FAIR Transformation Workflow

Knowledge Management Strategies

Implementing the transformation workflow requires specific knowledge management strategies tailored to chemical research environments:

• Custom Curation: Manual curation of chemical data by domain experts creates high-quality datasets specific to organizational needs. This approach ensures data accuracy, relevance, and proper connection of internal information to global scientific knowledge [19]. Expert-curated datasets are particularly valuable for empowering AI-based digital transformation initiatives, as they provide specially designed training sets for machine learning models [19].

• Semantic Frameworks: Standardized approaches for organizing and classifying concepts and relationships in chemistry—including specialized lexicons, ontologies, and taxonomies—provide a common language for understanding chemical data across an organization [19]. For example, researchers investigating novel materials for electronic devices can use specialized taxonomies to categorize materials by properties like electrical conductivity, optical characteristics, or thermal stability, enabling more informed decisions about research directions [19].

• Automated Data Mining: Machine learning and advanced analytics can uncover hidden patterns in large volumes of unstructured chemical data [19]. For instance, scanning thousands of research articles to extract information on material properties, synthesis methods, and performance metrics can identify correlations that lead to novel material discoveries [19].

• Collaboration Tools: Centralized databases and integrated LIMS systems break down data silos and facilitate knowledge sharing across research teams [19]. Modern digital ecosystems also support knowledge transfer between organizations, which is particularly valuable for joint academic-industrial projects and during mergers and acquisitions where researchers need to share knowledge of material characteristics or performance data [19].

The Scientist's Toolkit: Essential Solutions for FAIR Data Implementation

Successfully addressing the dark data challenge requires both technical solutions and strategic approaches. The following toolkit outlines essential resources for chemical researchers implementing FAIR data principles:

Table 3: Research Reagent Solutions for FAIR Data Implementation

Solution Category	Specific Tools/Approaches	Function/Purpose
Persistent Identifiers	InChI, DOIs, accession numbers	Provides unique, machine-readable identifiers for chemical structures and datasets
Repository Platforms	Cambridge Structural Database, NMRShiftDB, Dataverse, Zenodo	Discipline-specific and general repositories for data deposition and discovery
Data Standards	CIF (crystallography), JCAMP-DX (spectral data), nmrML (NMR)	Standardized formats for analytical data enabling interoperability
Semantic Frameworks	Specialized ontologies, taxonomies, controlled vocabularies	Organizes chemical concepts and relationships for consistent classification
Electronic Lab Notebooks	ELNs with FAIR support, LIMS integration	Captures experimental data with rich metadata at point of generation
Analytical Tools	Automated data mining, NLP, machine learning algorithms	Extracts insights from unstructured data sources and identifies patterns

Sources: CAS Insights, CMU LibGuides, ACS Research Data Guidelines [19] [9] [23]

Experimental Protocols: Standardized Reporting for Chemical Data

Compound Characterization and Analysis

Comprehensive characterization of chemical compounds is fundamental to reproducible research and represents a critical area where standardized protocols can prevent data from becoming "dark." Authoritative guidelines from major publishers specify that manuscripts should provide "exemplary characterization and purity data for key compounds, including 1H NMR, 13C NMR, and HRMS and preferably full characterization of all compounds described" [23]. Specific reporting requirements include:

• Melting Points: Presented as "mp 75°C (from EtOH)" with crystallization solvent in parentheses [24].
• Spectroscopic Data: NMR data should include instrument frequency, solvent, and standard, presented in the format "δH(100 MHz; CDCl3; Me4Si) 2.3 (3 H, s, Me), 2.5 (3 H, s, COMe)" [24].
• Mass Spectrometry: Data should be presented as "m/z 183 (M+, 41%), 168 (38)" with relative intensities in parentheses and indication of spectrum type [24].
• Elemental Analysis: Appropriate formats include "Found: C, 63.1; H, 5.4. C13H13NO4 requires C, 63.2; H, 5.3%" [24].

Data Sharing and Repository Deposition

To ensure long-term accessibility and utility of research data, experimental protocols must include provisions for data sharing and deposition:

• Repository Selection: Authors should deposit data in "discipline-specific, community-recognized repositories or in generalist repositories when no community resource is available" [23]. Repositories that issue persistent unique identifiers (DOIs, accession numbers) are preferred to facilitate discoverability and citation.
• Metadata Standards: Rich metadata describing experimental conditions, instrument parameters, and processing methods should accompany all deposited data. As emphasized in FAIR guidelines, "raw spectra files could be uploaded at the same time a journal article is submitted or accepted. The files could include experimental metadata (essentially, data about the data) to describe how the spectra were obtained" [9].
• Data Documentation: Complete experimental conditions and instrument settings must be documented to enable replication and reuse. As noted in the FAIR context, "if a synthesis route section is formatted properly, chemists could use the data or reproduce the protocol even if it was separated from the context of the paper" [9].

The high cost of dark data in chemical research and development represents both a significant challenge and a substantial opportunity for innovation. As the chemical industry navigates evolving market dynamics, including moderate production growth projections of 3.5% in 2025 [22], the ability to leverage previously untapped data assets will increasingly determine competitive advantage.

The transformation from dark data to FAIR-compliant resources requires concerted effort across multiple fronts: technological infrastructure, cultural practices, and strategic prioritization. However, the benefits are substantial—reduced R&D cycle times, identification of novel research opportunities, improved product formulations, and more informed decisions about research directions [19]. For environmental science research specifically, implementing FAIR principles enables the cross-disciplinary collaboration and data integration necessary to address complex sustainability challenges.

As chemical enterprises look toward a future shaped by artificial intelligence, high-throughput experimentation, and increasingly complex research questions, the principles outlined in this guide provide a pathway to unlocking the hidden potential within their existing data assets. By embracing these strategies, researchers, scientists, and drug development professionals can not only reduce the costs associated with dark data but also accelerate the discovery and innovation that drive scientific progress.

Comprehensive chemical risk assessment requires a robust integration of data on both a substance's inherent hazard and the potential for human or environmental exposure. Traditionally, data silos, non-standardized reporting, and inaccessible formats have impeded this integration, creating critical gaps in safety evaluations. The FAIR Guiding Principles—which stipulate that data should be Findable, Accessible, Interoperable, and Reusable—provide a transformative framework to tackle these challenges [4]. For researchers, scientists, and drug development professionals, implementing FAIR data practices is no longer merely an informatics ideal but a fundamental prerequisite for accurate, efficient, and predictive risk assessment in environmental science and beyond. This technical guide details how FAIR data bridges the gap from hazard identification to exposure analysis, enabling a more complete and reliable safety profile for anthropogenic chemicals.

The FAIR Principles: A Technical Foundation

The FAIR principles were established to enhance the reusability of data holdings by both humans and computational systems [4]. Their application is critical for managing the vast and complex datasets generated in modern environmental and chemical research.

• Findable: Data and metadata must be easy to locate by both humans and automated systems. This is achieved by assigning globally unique and persistent identifiers (e.g., DOIs, UUIDs) and enriching datasets with machine-actionable metadata that are indexed in searchable resources [4].
• Accessible: Data must be retrievable using standardized, open, and free communication protocols. Accessibility does not necessarily mean open; data can be restricted and behind authentication and authorization barriers, but the path to access must be clear [4].
• Interoperable: Data must be machine-readable and integrable with other datasets and applications. This requires the use of standardized vocabularies, ontologies, and formats that allow for seamless data exchange and combination [11] [4].
• Reusable: Data must be well-described and documented to enable replication and use in new contexts. This involves clear licensing, detailed provenance, and rich, descriptive metadata that provide context on how the data was generated and its quality [4].

It is crucial to distinguish FAIR data from open data. FAIR focuses on the technical structure and machine-actionability of data, which may be confidential and access-controlled, as is often the case with internal preclinical assay results in biotech. Open data, in contrast, is defined by its free availability to all but may lack the structured metadata required for computational use [4].

The Role of FAIR Data in Chemical Risk Assessment Workflows

Regulatory frameworks for chemical evaluation, such as the U.S. Toxic Substances Control Act (TSCA) and the EU's REACH regulation, require a thorough assessment of risk based on hazard and exposure [25] [26]. FAIR data directly enhances this process.

Enhancing Hazard Identification

Hazard identification relies on high-quality data concerning a chemical's toxicological properties, environmental fate, and biotransformation pathways. Machine-readable data on biotransformation products and kinetics are essential for predicting chemical persistence, a major driver of chemical risk [1]. When this data is FAIR, it can be aggregated into large, high-quality training sets for machine learning models, enabling more reliable prediction of hazardous transformation products for new chemicals [1].

Quantifying Exposure and Release

Exposure assessment requires data on the potential release of a chemical throughout its lifecycle and the resulting levels of human or environmental contact. Regulatory agencies like the U.S. EPA use established models and default assumptions to assess exposure when chemical-specific information is unavailable [25]. Providing FAIR-compliant, chemical-specific data on factors like container types or equipment residue quantities allows for the refinement of these generic exposure scenarios, leading to more accurate and less conservative risk assessments [25].

Enabling Integrated Risk Characterization

The final risk characterization integrates hazard and exposure data. The use of non-FAIR data in this phase can introduce significant bottlenecks, as manual effort is required to find, interpret, and reformat disparate data sources. FAIR data, by contrast, enables automated or semi-automated data integration, allowing for more complex analyses. For instance, synthesizing diverse data types—hydrological, geological, ecological, and climatological—is essential for complex environmental systems science, and such interdisciplinary integration is only practical with data that is interoperable by design [11].

Implementing FAIR Chemical Data: Reporting Standards and Tools

Moving from principle to practice requires community-driven tools and standardized reporting formats.

Community-Centric Reporting Formats

Reporting formats are instructions, templates, and tools for consistently formatting data within a discipline. They are a pragmatic solution to achieve interoperability without the decade-long timeline of formal accreditation processes [11]. The environmental and chemical sciences have developed numerous such formats to harmonize diverse data types.

Table: Community Reporting Formats for Environmental and Chemical Data

Reporting Format Category	Specific Examples	Primary Application in Risk Assessment
Cross-Domain Metadata	Dataset Metadata, Location Metadata, Sample Metadata [11]	Ensures fundamental context (what, where, when) is findable and reusable for all data.
File-Formatting Guidelines	CSV File Guidelines, File-Level Metadata, Terrestrial Model Data Archiving [11]	Promotes interoperability of core data files and model outputs for re-analysis.
Domain-Specific Formats	Water/Sediment Chemistry, Soil Respiration, Leaf-Level Gas Exchange [11]	Standardizes exposure-relevant measurements for reliable comparison and synthesis.
Biotransformation Data	Biotransformation Reporting Tool (BART) [1]	Captures machine-readable data on transformation pathways and kinetics for persistence and hazard modeling.

The Biotransformation Reporting Tool (BART)

BART is a specific Microsoft Excel template developed to report biotransformation data in a FAIR manner [1]. Its structure directly addresses the gaps in conventional reporting, which typically relies on static images of pathway figures that are not machine-translatable.

BART's tabs provide a structured framework for all essential data:

• Compounds Tab: Chemical structures are reported as Simplified Molecular-Input Line-Entry System (SMILES) strings, a standard for computational chemistry.
• Connectivity Tab: Represents the pathway structure as a list of biotransformations, linking reactants and products in a tabular format.
• Scenario Tab: Captures critical experimental metadata, such as inoculum source and environmental conditions, which influence the applicability of the data.
• Kinetics_Confidence Tab: Reports biotransformation kinetics and the identification confidence level for transformation products [1].

Table: Key Experimental Parameters for Biotransformation Testing in BART

Test System	Key Inoculum Parameters	Key System Parameters
Sludge Systems	WWTP purpose, solids retention time, volatile suspended solids [1]	Reactor configuration, aeration type, spike concentration [1]
Soil Systems	Soil origin, dissolved organic carbon, cation exchange capacity [1]	Experimental humidity, soil texture, water holding capacity [1]
Sediment Systems	Sediment origin, organic content, redox condition [1]	Column height, pH in water and sediment, sediment porosity [1]

A FAIR Data Workflow for Chemical Risk Assessment

The following diagram visualizes the logical workflow of how FAIR data principles are applied throughout the chemical risk assessment process, from data generation to final risk management.

FAIR Data in Risk Assessment Workflow

Successfully implementing FAIR data practices requires a combination of conceptual frameworks, digital tools, and standardized resources.

Table: Essential Resources for FAIR Chemical Data Reporting

Tool/Resource	Type	Function in FAIR Workflow
BART Template	Reporting Tool	Standardizes machine-readable reporting of biotransformation pathways and kinetics for interoperability and reuse [1].
Community Reporting Formats	Guidelines & Templates	Provide community-agreed templates for specific data types (e.g., water chemistry) to ensure consistency and interoperability [11].
enviPath Platform	Database & Platform	A public repository for biotransformation data that implements and promotes FAIR principles, enabling efficient data sharing and usage [1].
SMILES Notation	Standard Vocabulary	A line notation for representing molecular structures in a machine-readable string, crucial for interoperability in cheminformatics [1].
IUPAC Standards	Nomenclature & Terminology	Provides the authoritative global language for chemistry, forming the foundation for standardized vocabularies and ontologies [12].
ESS-DIVE Repository	Data Repository	A long-term archive for environmental data that hosts and promotes the use of community reporting formats to enhance reusability [11].

The transition from assessing hazard alone to conducting comprehensive risk assessments that fully integrate exposure science is critically dependent on data quality and availability. The FAIR principles provide the necessary framework to break down data silos and unlock the full potential of existing and future chemical data. For the research community, adopting community standards like reporting formats and tools such as BART is a practical and essential step. This not only accelerates scientific discovery and regulatory review but also builds the foundational data infrastructure needed to tackle persistent environmental challenges, from PFAS contamination to the assessment of complex transformation products. By making chemical data Findable, Accessible, Interoperable, and Reusable, we empower scientists to build more accurate models and support evidence-based decisions that effectively protect human health and the environment.

Practical Frameworks and Tools for Implementing FAIR Chemical Data

Community-Centric Reporting Formats for Diverse Data Types

Making Earth and environmental science data Findable, Accessible, Interoperable, and Reusable (FAIR) contributes to research that is more transparent and reproducible [11]. However, data interoperability and reuse remain major challenges, in part due to the immense diversity of data types across Earth science disciplines [27] [11]. While formal (meta)data standards accredited by large governing bodies are useful, they are available for only a few environmental data types and can take over a decade to establish [11]. In contrast, community-centric reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—have emerged as a pragmatic solution to make data more accessible and reusable without requiring lengthy standardization processes [11].

These reporting formats represent community efforts aimed at harmonizing diverse environmental data types without the oversight of formal governing bodies [11]. They are typically more focused within specific scientific domains and enable efficient collection and harmonization of information needed to understand and reuse specific types of data within a research community [11]. For chemical data specifically, the need for FAIR data is critical, as anthropogenic chemicals and their transformation products are increasingly found in the environment, with persistence being a major driver of chemical risk [1]. Predictive models for biotransformation products and dissipation kinetics require large, high-quality, machine-readable training data sets with detailed experimental parameters, which are currently lacking [1].

Table 1: Categories of Community-Centric Reporting Formats

Category	Description	Examples
Cross-domain Formats	Apply broadly to data across different scientific disciplines	Dataset metadata, location metadata, sample metadata, file-level metadata, CSV file guidelines, terrestrial model data archiving [11]
Domain-specific Formats	Apply to specific data types within a scientific domain	Amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements [11]
Chemical-specific Formats	Address the unique needs of chemical data reporting	Biotransformation Reporting Tool (BART) for biotransformation pathways and kinetics [1]

Implementation of Reporting Formats in Environmental Science

The ESS-DIVE Reporting Framework

The Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) repository has developed a comprehensive framework of 11 reporting formats that encompass a range of complex and diverse environmental systems science (meta)data fields [11]. This framework includes six cross-domain reporting formats that apply broadly to data across different scientific disciplines and five domain-specific reporting formats for specific data types [11]. All formats were developed with a minimal set of required metadata fields necessary for programmatic data parsing and optional fields that provide detailed spatial/temporal context about the sample useful to downstream scientific analyses [11].

Throughout the development process, the teams aimed to strike a balance between pragmatism for the scientists reporting data and machine-actionability that is emblematic of FAIR data [11]. The formats were designed to be flexible, modular, and integrated, accommodating new reporting formats in the future and enabling their findability and accessibility individually or collectively [11]. As part of the framework development, all teams created templates with harmonized terms and formats to be internally consistent as much as possible—for example, dates are always reported in YYYY-MM-DD format, and spatial data are harmonized as "latitude" and "longitude" reported in decimal degrees [11].

Standardized Reporting for Chemical Biotransformation Data

For chemical contaminants in the environment, a specialized Biotransformation Reporting Tool (BART) has been developed as a Microsoft Excel template to assist authors with reporting their biotransformation data in a FAIR and effective way [1]. BART is freely available on GitHub and includes tabs for four different types of information:

• Compounds tab: Compound structures should be reported as simplified molecular input line entry specifications (SMILES) [1]
• Connectivity tab: Contains the pathway structure as a list of biotransformations by indicating reactants and products in a tabular format [1]
• Scenario tabs: Information on the experimental setup and on environmental conditions [1]
• Kinetics_Confidence tab: Biotransformation kinetics and identification confidence [1]

This specialized approach addresses the challenge of conventional reporting of chemical contaminant biotransformation, which typically includes pathway figures consisting of 2D images of reactant and product compounds connected by arrows representing singular reaction steps [1]. While this visual representation is important for understanding and communicating structural changes, the reported images are generally not easily translated into a machine-readable format [1].

Diagram 1: BART workflow for chemical data.

Experimental Protocols and Methodologies

Key Parameters for Biotransformation Studies

For experimental studies on the biotransformation of chemicals in environmental systems, specific parameters must be reported to ensure data quality and reproducibility. The BART template provides detailed guidance on key experimental parameters that are frequently collected during experimentation and should be reported with pathway information [1]. These parameters vary depending on the test system but include critical metadata about the inoculum provenance, sample description, experimental setup, and surrounding conditions [1].

Table 2: Key Parameters for Biotransformation Testing Systems

Test System	Inoculum Provenance Parameters	Sample Description Parameters	Experimental Setup Parameters
Sludge	Sample location, biological treatment technology, purpose of WWTP, solids retention time	Ammonia uptake rate, dissolved oxygen concentration, volatile suspended solids concentration	pH, reactor configuration, initial amount of sludge in bioreactor, type of aeration [1]
Soil	Soil origin, sampling depth	Dissolved organic carbon, cation exchange capacity, microbial biomass, soil texture	Addition of nutrients, experimental humidity, initial mass of sediment [1]
Sediment	Sediment origin	Bulk density, microbial biomass in sediment, organic content in sediment, sediment porosity	Column height, pH in sediment, pH in water, redox potential [1]
General	Not applicable	Redox condition, oxygen demand, total organic carbon	Temperature, solvent for compound addition, spike concentration [1]

Protocol Development for Evidence Synthesis

In addition to data reporting formats, protocol development is a crucial component of rigorous environmental research. A protocol serves as a comprehensive plan that details the research question, methods, and processes to be followed in a synthesis project, ensuring that the project is transparent, rigorous, and objective from start to finish [28]. Protocol registration is a required reporting element for systematic evidence synthesis, and any final synthesis without an associated protocol should be critically reviewed as this can often signal that established guidelines were not consulted [28].

Key components of a robust research protocol include [28]:

• A well-defined research question formulated using established methods
• A comprehensive strategy for retrieving relevant studies, including databases, search terms, and grey literature sources
• Detailed eligibility criteria for study selection (inclusion and exclusion criteria)
• Systematic screening methods for title/abstract and full-text review
• Standardized data extraction and coding strategies
• Study validity assessment strategies for critically appraising study quality
• Clearly described data synthesis strategies, whether using narrative synthesis, meta-analysis, or other methods

Diagram 2: Evidence synthesis protocol workflow.

Data Visualization and Communication

Effective Visualization of Chemical Data

In the field of chemistry, the ability to visualize complex data is paramount for interpreting intricate patterns that govern the behavior of substances at a molecular level [29]. Data visualization transforms abstract numbers and statistical outputs into coherent visual representations that enhance comprehension and facilitate discovery [29]. Different types of data visualizations serve distinct functions in chemistry, ranging from simple charts to intricate graphical representations that highlight multi-dimensional data [29].

Commonly used visualizations in chemical and environmental research include:

• Bar graphs: Effective for comparing quantities of different chemical compounds or reaction yields [29]
• Line charts: Useful for showing changes in chemical parameters over time, such as reaction kinetics or concentration profiles [29]
• Scatter plots: Can reveal correlations between two variables, such as the relationship between chemical structure and activity [29]
• Heat maps: Particularly useful for illustrating variation in chemical concentrations across different spatial regions or experimental conditions [29]
• Molecular structures: Essential for visualizing complex molecular arrangements and their transformations [29]

Accessibility Considerations in Data Visualization

When creating visualizations for chemical and environmental data, it is essential to consider accessibility requirements to ensure that the content can be understood by all users, including those with color vision deficiencies or low vision [30]. Accessibility legislation relevant to public sector websites requires that all content meet the A and AA success criteria listed in the Web Content Accessibility Guidelines 2.2 [30].

Key accessibility principles for data visualization include [30]:

• Non-text contrast: All parts of graphics required to understand the content must have a contrast ratio of at least 3:1 against adjacent colors
• Use of color: Color should not be used as the only visual means of conveying information, indicating an action, prompting a response, or distinguishing a visual element
• Non-text content: All data visualizations need alternative text that communicates the message of the chart
• Contrast (minimum): The visual presentation of text and images of text must have a contrast ratio of at least 4.5:1
• Sensory characteristics: Instructions for understanding and operating content should not rely solely on sensory characteristics such as shape, color, size, or visual location

Research Reagent Solutions and Essential Materials

Table 3: Essential Tools and Resources for FAIR Environmental Research

Tool/Resource	Function	Application in Research
BART Template	Standardized reporting of biotransformation pathways and kinetics	Captures chemical structures, pathway connectivity, experimental scenarios, and kinetic data in machine-readable format [1]
enviPath Platform	Database for storing and accessing biotransformation pathway information	Provides a platform for sharing FAIR biotransformation data and enables efficient data usage within the research community [1]
ESS-DIVE Repository	Long-term archive for diverse environmental systems science data	Stores and provides access to data formatted according to community reporting formats, enhancing findability and accessibility [11] [31]
IGSN (International Generic Sample Number)	Persistent identifier for physical samples	Enables effective tracking of samples across online data systems and facilitates linking sample metadata to measurement data [11]
SMILES (Simplified Molecular Input Line Entry Specification)	Standardized notation for representing chemical structures	Allows machine-readable representation of molecular structures in biotransformation pathway reporting [1]
WebAIM Color Contrast Checker	Tool for verifying color contrast ratios in data visualizations	Ensures that graphical elements meet accessibility requirements for users with visual impairments [30]
PROCEED Registry	Protocol registry for environmental evidence synthesis	Enables registration of systematic review protocols to enhance transparency and reduce duplication of efforts [28]

Community Platforms and Data Repositories

The effectiveness of community-centric reporting formats depends on accessible platforms for sharing both the formats themselves and the data formatted according to these guidelines. The ESS-DIVE team has shared and archived all reporting formats in three complementary ways, each with a distinct use [11]:

• Public repository archiving: All reporting formats are published as datasets in the ESS-DIVE repository, which enables direct public download and citation upon use [11]
• Version control hosting: Each reporting format is hosted on GitHub, enabling ongoing edits and versioning while allowing users to provide feedback [11]
• Rendered documentation: The most up-to-date reporting format content from GitHub is rendered as a project website through GitBook, providing a user-friendly interface for researchers [11]

This multi-platform approach ensures that documentation is available in various digital formats to serve the needs of diverse user groups and stakeholders, from software engineers who may prefer GitHub to Earth science researchers who may find GitBook websites more accessible [11].

Community-centric reporting formats for diverse data types represent a practical and effective approach to addressing the challenges of making complex environmental and chemical data FAIR—Findable, Accessible, Interoperable, and Reusable. By developing standardized reporting formats that integrate with scientific workflows, research communities can accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable [27] [11]. The implementation of these formats across various environmental science disciplines demonstrates their versatility and effectiveness in improving data quality, accessibility, and reuse, ultimately contributing to more transparent and collaborative research practices.

In modern environmental science and drug development, research is increasingly characterized by high-throughput experiments and large-scale collaborative projects. This has led to a deluge of complex chemical data, making effective data management not merely an advantage but a necessity [32]. The immense potential of this data can only be unlocked if it is structured for seamless sharing, integration, and reuse. Framed within the broader thesis of FAIR (Findable, Accessible, Interoperable, and Reusable) chemical data reporting principles, this guide provides a technical roadmap for harmonizing metadata to ensure that chemical datasets can drive reproducible and impactful scientific discovery in environmental research and beyond [9].

The FAIR Principles in a Chemical Context

The FAIR Guiding Principles provide a structured framework for enhancing the utility of digital assets, with a strong emphasis on machine-actionability. This is critical in chemistry, where the volume and complexity of data necessitate computational support [3]. The core principles are distinctly applied to chemical sciences as shown in the table below.

Table 1: FAIR Principles and Their Application to Chemical Data

FAIR Principle	Technical Definition	Chemistry Context & Application
Findable	Data and metadata have globally unique and persistent identifiers [3].	Using International Chemical Identifiers (InChIs) for structures and Digital Object Identifiers (DOIs) for datasets [9].
Accessible	Data are retrievable by their identifier using a standardized protocol [3].	Data is accessible via HTTP/HTTPS; metadata remains available even if data is restricted [9].
Interoperable	Data and metadata use formal, broadly applicable languages and standards [3].	Using standard formats like CIF for crystallography or JCAMP-DX for spectral data [9].
Reusable	Data and metadata are richly described with multiple attributes [3].	Providing detailed experimental procedures, instrument settings, and clear licensing [9].

It is vital to understand that FAIR is not synonymous with "open." Even data with privacy, security, or intellectual property constraints can, and should, be managed according to FAIR principles to ensure they are technically accessible through proper channels [9].

Essential Metadata Elements for Chemical Datasets

Harmonizing metadata involves agreeing upon and implementing a common set of descriptive elements. While a one-size-fits-all standard can be challenging due to the diverse sub-disciplines in chemistry, establishing minimum requirements is feasible and essential [32].

Core Metadata Checklist

A practical checklist for creating harmonized chemical metadata is provided below, synthesizing community best practices [9].

Table 2: Minimum Required Metadata Checklist for Chemical Datasets

Category	Essential Elements	Examples & Standards
General Identifiers	Persistent Identifier, Dataset Title, Creator, Publisher, Publication Date	DOI, Researcher ORCID
Chemical Substance	Chemical Structure, Name, Formula, InChI/SMILES	InChIKey, Canonical SMILES, IUPAC Name
Experimental Description	Experimental Type, Protocol, Sample Preparation, Conditions	Synthesis protocol, growth medium, temperature
Instrumentation & Methods	Instrument Type, Model, Settings, Data Processing Methods	NMR field strength, DFT functional, software version
Provenance & Administration	Data License, Funding Source, Project Name	Creative Commons (CC-BY), Grant Number

The Role of Minimum Information Standards

Initiatives like the MIxS (Minimum Information about any (x) Sequence) checklists, developed by the Genomic Standards Consortium, provide a powerful model. These checklists define a core set of mandatory fields—such as geographic location, collection date, and investigation type—that enable the integration of diverse datasets, for instance, in microbiome research for environmental studies [32]. Adopting a similar philosophy for chemical data, where a base layer of required metadata is supplemented with domain-specific fields, is key to effective harmonization.

Methodologies for Metadata Harmonization

Implementing a robust metadata strategy requires a structured approach. The following workflow outlines the key steps from planning to sharing FAIR chemical data.

The FAIRification Workflow

The diagram above illustrates a generalized FAIRification workflow for chemical data. A critical first step is assessing current lab data practices to identify gaps against FAIR principles [9]. Subsequently, research objectives guide the selection of appropriate metadata standards and the creation of a Data Management Plan (DMP), which is increasingly mandated by funders [9]. The most crucial phase involves collecting metadata at the point of experiment execution to prevent loss of context. This is followed by validation and curation to ensure quality and consistency before deposition into a suitable repository that guarantees long-term Findability and Accessibility [9].

Hierarchical Data Organization

For complex chemical datasets, a hierarchical model for organizing data and metadata, as demonstrated by the QCML quantum chemistry dataset, is highly effective [33]. This structure allows for efficient data management and retrieval.

This hierarchical organization, moving from the abstract chemical graph (e.g., a SMILES string) to specific 3D conformations and finally to the results of quantum calculations, creates a clear and machine-actionable data relationship. Each level can be tagged with appropriate metadata, making the entire dataset optimally structured for reuse in training machine learning models or integrative analyses [33].

The Researcher's Toolkit for FAIR Chemical Data

Successfully implementing FAIR principles requires a suite of tools and resources. The following table details key solutions for chemical researchers.

Table 3: Essential Toolkit for FAIR Chemical Data Management

Tool/Resource Category	Example	Primary Function
Chemical Repositories	Cambridge Structural Database (CSD), NMRShiftDB	Discipline-specific repository for crystal structures or NMR data [9].
General Repositories	Zenodo, Figshare, Dataverse	FAIR-compliant repository for general scientific data, often generating DOIs [9].
Chemical Representation	International Chemical Identifier (InChI), SMILES	Provides a standardized, machine-readable representation of chemical structures [9].
Spectral Data Formats	JCAMP-DX, nmrML	Standardized formats for exchanging spectral data along with acquisition parameters [9].
Metadata Standards	MIxS Checklists	Defines minimum information standards for various types of investigations [32].

Harmonizing metadata is the foundational step toward realizing the full promise of the FAIR principles in chemical research. For environmental scientists and drug development professionals, this is not merely a technical exercise in data curation. It is a strategic imperative that enhances reproducibility, facilitates interdisciplinary collaboration, and maximizes the return on investment in research. By adopting the essential elements, methodologies, and tools outlined in this guide, the chemical research community can transform isolated data points into a deeply interconnected and powerful resource for solving complex global challenges.

Implementing Persistent Identifiers (PIDs) for Samples and Compounds

The effective management of research data in environmental science and chemistry is paramount for accelerating scientific discovery. The Findable, Accessible, Interoperable, and Reusable (FAIR) principles provide a framework for enhancing data utility, with Persistent Identifiers (PIDs) serving as a foundational component for achieving these goals [11]. PIDs are unique, long-lasting references to digital or physical resources that enable reliable location, identification, and verification of these resources over time [34]. Within chemistry and environmental science, PIDs help interconnect publications, datasets, and physical research materials, thereby addressing challenges in data integration and reproducibility [1] [35].

The implementation of PIDs is particularly crucial for samples and compounds, where precise identification enables tracking of chemical substances across studies, connects analytical data to physical samples, and supports the creation of machine-actionable data infrastructures [36] [35]. This technical guide provides a comprehensive framework for implementing PIDs for samples and compounds, specifically contextualized within FAIR chemical data reporting principles for environmental science research and drug development.

Core Concepts of Persistent Identifiers

Defining Persistent Identifiers

Persistent Identifiers are more than just unique codes; they are part of a system designed to ensure permanent access to identified resources. For an identifier to be considered a true PID, it must exhibit several key characteristics [37] [38]:

• Unique: A PID must be globally unique, ensuring it identifies exactly one entity without ambiguity across systems and time.
• Persistent: Once assigned, the identifier should never be changed, deleted, or reassigned to a different entity, even if the identified resource is no longer available.
• Resolvable: The PID should be actionable, typically through web resolution, leading to a landing page with meaningful information about the identified resource.
• Computer-readable: PIDs are designed primarily for machine interaction, enabling automated data integration and processing.

Unlike locally unique identifiers such as catalog numbers, PIDs are designed for global interoperability, making them essential for cross-disciplinary research and large-scale data integration [38].

The Role of PIDs in FAIR Chemical Data

PIDs directly support each of the FAIR principles in chemical and environmental research [11]:

• Findability: PIDs create permanent links to chemical data and samples, making them discoverable through global search systems. Each PID is associated with rich metadata that enhances searchability.
• Accessibility: PIDs typically resolve to landing pages containing access information and instructions for obtaining the actual data or material, even if the storage location changes.
• Interoperability: By providing stable reference points, PIDs enable linking between related resources such as publications, datasets, physical samples, and researchers.
• Reusability: The rich metadata and permanent access provided by PIDs ensure that data and materials can be understood and reused in new contexts, often years after their original creation.

PID Types and Specifications for Chemical Research

Identifier Schemes for Different Entities

Various PID systems have been developed to address the needs of different research entities. The table below summarizes the most relevant PIDs for chemical and environmental research:

Table 1: Persistent Identifier Types for Chemical Research

Identifier Name	Primary Usage	Registration/Resolution Agency	Format Example
DOI (Digital Object Identifier)	Publications, datasets, digital research objects [34]	DataCite, CrossRef [34]	https://doi.org/10.1000/182
IGSN (International Generic Sample Number)	Physical samples, environmental specimens [38] [35]	DataCite [38]	https://doi.org/10.21384/AU1234
ARK (Archival Resource Key)	Physical or digital objects, museum specimens [38]	ARK Alliance [38]	http://n2t.net/ark:/65665/3af2b96d2-a8a1-47c5-9895-b0af03b21674
ORCID iD (Open Researcher and Contributor ID)	People, researchers [34]	ORCID Inc. [34]	https://orcid.org/0000-0001-6514-963X
ROR (Research Organization Registry)	Organizations, institutions [34]	Research Organization Registry [34]	https://ror.org/03pnyy777
ePIC (Persistent Identifier for eResearch)	Unpublished digital research objects [34]	Handle.Net Registry [34]	Handle-based format
CETAF Stable Identifier	Natural history specimens [39] [38]	Consortium of European Taxonomic Facilities [38]	http://herbarium.bgbm.org/object/B100277113

Chemical Substance Identifiers

In addition to the PID systems above, chemical research relies on specialized structural identifiers that, while not always resolvable via the web, provide crucial unique representation of chemical entities [36]:

Table 2: Chemical Structure Identifiers

Identifier	Description	Usage Context
InChI (International Chemical Identifier)	A standardized, text-based identifier that encodes molecular structural information [36]	Machine-processing, database indexing, structure searching
InChIKey	A 27-character hash of the InChI, comprising skeleton, stereochemistry, and charge blocks [36]	Database lookup, quick structure comparison, web searching
SMILES (Simplified Molecular Input Line Entry System)	A line notation using ASCII strings to describe chemical structures [36]	Chemical informatics, database storage, structure searching
CAS RN (CAS Registry Number)	A numeric identifier assigned by the American Chemical Society [36]	Regulatory contexts, commercial databases, substance inventory

For comprehensive FAIR data reporting, chemical structures should be represented using both a standard identifier (such as InChIKey or SMILES) and a resolvable PID that links to additional metadata and contextual information [36].

Implementation Framework for Samples and Compounds

PID Implementation Workflow

The process of implementing PIDs for samples and compounds follows a systematic workflow that ensures proper identification, metadata collection, and integration with research data management systems.

Diagram 1: PID Implementation Workflow

The FAIR-FAR Sample Concept

For physical samples and compounds, a comprehensive approach called "FAIR-FAR" has been proposed, extending the FAIR principles to include physical accessibility and reusability [35]. This concept links the virtual representation of a sample (with FAIR metadata) with the physical sample itself, which should be Findable, Accessible, and Reusable (FAR).

Diagram 2: FAIR-FAR Sample Concept

Metadata Requirements for PID Registration

Comprehensive metadata is essential for making PID-identified resources truly FAIR. The table below outlines required and recommended metadata elements for samples and compounds:

Table 3: Metadata Requirements for Sample and Compound PIDs

Metadata Category	Required Elements	Recommended Elements	FAIR Principle Supported
Basic Identification	PID, Resource Type, Title	Alternative Identifiers, Version Information	Findability, Accessibility
Provenance	Creator, Creation Date, Creating Organization	Funding Source, Project Context, Synthesis Protocol	Reusability, Accessibility
Chemical Description	Chemical Structure (InChI/SMILES), Chemical Formula	Stereochemistry, Isotopic Information, Purity Assessment	Interoperability, Reusability
Physical Characteristics	Physical State, Quantity	Storage Conditions, Stability Information, Hazard Classification	Reusability, Accessibility
Administrative	Access Rights, License Information	Preservation Plan, Review Process	Accessibility, Reusability
Relationships	Related Publications, Related Datasets	Parent Compounds, Derivatives, Analytical Results	Findability, Interoperability

Technical Implementation Guidelines

Selecting Appropriate PID Schemes

The choice of PID scheme depends on the nature of the resource and its intended use within the research ecosystem:

• Digital Object Identifier (DOI) for published datasets, software, and digital research outputs that require formal citation [34].
• International Generic Sample Number (IGSN) for physical environmental samples, geological specimens, and research materials that require unambiguous tracking across studies [35].
• Archival Resource Key (ARK) for institutional collections, museum specimens, and resources requiring decentralized management [38].
• ePIC for unpublished research data and digital objects in progress [34].

For chemical compounds, a dual approach is recommended: using a structural identifier (InChIKey) for unambiguous chemical description coupled with a resolvable PID (DOI or IGSN) for resource access and metadata [36] [35].

Integration with Research Data Management

Effective PID implementation requires integration with laboratory information management systems (LIMS) and electronic lab notebooks (ELNs). The Chemotion repository provides an exemplary model, combining a research data repository with a molecular archive to link digital representations with physical samples [35]. Implementation steps include:

• System Integration: Establish protocols for exchanging information between the data repository and sample archive based on structural descriptors (e.g., InChIKey) [35].
• Linking Mechanism: Create bidirectional links between virtual sample representations (with PIDs) and physical sample registry numbers [35].
• Access Control: Implement visibility controls to manage public access while maintaining internal sample tracking [35].
• Curation Process: Establish manual curation steps to verify automated linking between virtual and physical representations [35].

Governance and Sustainability Considerations

Long-term PID persistence requires careful attention to governance and financial sustainability:

• Administrative Models: Institutions can administer PIDs through existing registration agencies, ally with established providers, or establish new registration authorities depending on scale and resources [40].
• Financial Planning: PID services typically involve initial setup costs and ongoing maintenance fees, which should be factored into research project budgets and institutional infrastructure planning [40].
• Persistence Assurance: Choose PID providers with clear commitments to long-term resolution and robust organizational backing to ensure identifier persistence beyond project lifecycles [37].

Experimental Protocols and Reporting Standards

Standardized Reporting for Biotransformation Data

For environmental chemistry applications, standardized reporting formats enhance data interoperability. The Biotransformation Reporting Tool (BART) provides a template for reporting biotransformation pathways and kinetics in a machine-readable format [1]. Key components include:

• Compounds Tab: Chemical structures as SMILES with alternative structures for unresolved isomers [1].
• Connectivity Tab: Pathway structure as reactant-product relationships, flagging multistep reactions [1].
• Scenario Tab: Experimental parameters including inoculum provenance, environmental conditions, and system setup [1].
• Kinetics_Confidence Tab: Transformation kinetics and identification confidence levels (e.g., Schymanski Confidence Levels) [1].

Essential Research Reagent Solutions

The implementation of PIDs for samples and compounds relies on specific technical infrastructure and services:

Table 4: Research Reagent Solutions for PID Implementation

Tool/Service	Function	Usage Context
DataCite	DOI registration agency for research data and samples [34]	Minting DOIs and IGSNs for research outputs
Handle System	Underlying infrastructure for DOI, ePIC, and other handle-based PIDs [40]	Technical resolution of persistent identifiers
InChI Tools	Software for generating standard InChI and InChIKey identifiers [36]	Creating chemical structure representations
Chemotion Repository	Domain-specific repository for chemical data with sample linking [35]	Managing chemical research data with PID support
BART Template	Standardized reporting for biotransformation data [1]	Environmental fate studies of chemical contaminants
ORCID Registry	Persistent identifiers for researchers [34]	Attributing chemical research to specific contributors
ROR API	Lookup service for research organization identifiers [34]	Institutional attribution in chemical data publication

Implementing Persistent Identifiers for samples and compounds represents a critical step toward realizing FAIR data principles in environmental science and chemistry research. By providing stable, unambiguous references to both digital and physical research resources, PIDs enable the connectivity and context necessary for data reuse and integration. The technical framework presented in this guide—encompassing identifier selection, metadata standards, system integration, and reporting protocols—provides researchers and institutions with a roadmap for deploying PIDs effectively within their research workflows. As the research community continues to embrace open science and data sharing, robust PID implementation will serve as foundational infrastructure for transparent, reproducible, and collaborative chemical research.

In the landscape of environmental science research, particularly in fields dealing with chemical data such as the study of anthropogenic contaminants, the selection of an appropriate data repository is a critical decision that extends beyond simple data archiving. This choice is foundational to implementing the FAIR principles—making data Findable, Accessible, Interoperable, and Reusable—which have become a standard across the open data landscape [3] [41]. For researchers investigating the environmental fate of chemicals, such as per- and polyfluoroalkyl substances (PFAS), effective data sharing is essential for building predictive models of biotransformation pathways and dissipation kinetics [1]. The decision between depositing data in a domain-specific repository tailored to a particular research community or a generalist repository that accepts data across all disciplines carries significant implications for data discovery, reuse, and scientific impact. This technical guide examines both approaches within the context of FAIR chemical data reporting, providing environmental scientists and drug development professionals with evidence-based criteria for repository selection.

Repository Typology and Key Characteristics

Data repositories can be fundamentally categorized into two primary types: domain-specific and generalist. Understanding their distinct characteristics, strengths, and limitations is the first step in making an informed selection decision.

Domain-specific repositories are designed to store data from a particular subject area or field of study. These repositories often accept limited data types or specific file formats, utilize specialized metadata standards and vocabulary, and may otherwise restrict submissions to maintain disciplinary focus [42]. Examples relevant to environmental and chemical sciences include:

• Gene Expression Omnibus (GEO): For functional genomics data
• Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC): For biologic specimens and associated data [42]
• enviPath: Specifically for biotransformation pathway data [1]
• ICPSR: For social and behavioral science data [43]

These repositories typically employ community-specific standards for metadata and data formatting, which enhances interoperability within the discipline but may require additional effort from researchers to align their data with these specifications.

In contrast, generalist repositories accept data regardless of subject matter or disciplinary origin [42]. They provide broad platforms for sharing and preserving research data without restrictions based on data type, format, or content [44]. Common examples include:

• Figshare: A commercial platform that accommodates diverse research outputs
• Dryad: Particularly focused on data associated with peer-reviewed publications
• Zenodo: Operated by CERN and widely used across disciplines [42] [45]
• ESS-DIVE: Specializing in environmental systems science data [11]

Generalist repositories typically offer more flexible metadata requirements but may provide less domain-specific curation than their specialized counterparts. The NIH Generalist Repository Ecosystem Initiative (GREI) includes seven such repositories that collectively serve as alternatives when domain-specific options are unavailable [44].

Table 1: Fundamental Characteristics of Repository Types

Characteristic	Domain-Specific Repositories	Generalist Repositories
Scope	Specific discipline or field [42]	All disciplines [42]
Data Types	Limited to specific types or formats [42]	Regardless of type or format [44]
Metadata Standards	Specialized, community-developed [11]	Broader, more flexible schemas
Interoperability	High within specific community	Cross-disciplinary
Examples	GEO, BioLINCC, enviPath [42] [1]	Figshare, Dryad, Zenodo [42]

Repository Selection Framework

Selecting an appropriate repository requires a systematic approach that considers funder requirements, disciplinary norms, and long-term preservation needs. The following decision workflow provides a structured methodology for researchers navigating this process, particularly those working with chemical data in environmental contexts.

Diagram 1: Repository selection workflow

The selection process begins with identifying any mandatory repository requirements from funding agencies or publishers. Many federal funders, including the NIH, now require data deposition in established repositories, and some specify particular ones for certain data types [44] [46]. When no specific repository is mandated, researchers should prioritize domain-specific repositories that align with their research community, as these typically enhance discoverability within their field and often implement community standards that better support interoperability [44] [46]. If no suitable domain-specific repository exists, researchers should then consider generalist repositories, which provide a valuable alternative for sharing and preserving research data [44].

Throughout this selection process, repositories should be evaluated against established criteria and the FAIR principles. Key considerations include:

• Persistent Identifiers: The repository should assign unique persistent identifiers such as Digital Object Identifiers (DOIs) to datasets to support reliable citation and tracking [45] [44].
• Metadata Support: Rich metadata using schemas appropriate to the research community are essential for discovery and reuse [45] [44].
• Long-Term Sustainability: The repository should have a viable plan for long-term data management, including maintaining integrity, authenticity, and availability of datasets [45] [44].
• Access Controls: For sensitive data, such as human subjects data or confidential chemical compounds, the repository must provide appropriate security and access control mechanisms [43] [45].

Table 2: Essential Repository Characteristics Based on NIH and NSTC Guidelines

Characteristic	Description	Importance for FAIR Compliance
Unique Persistent Identifiers	Assigns datasets a citable, unique persistent identifier (e.g., DOI) [45] [44]	Essential for Findability
Metadata	Ensures datasets have metadata to enable discovery, reuse, and citation [45] [44]	Critical for Findability and Reusability
Long-Term Sustainability	Has a plan for long-term management of data [45] [44]	Ensures ongoing Accessibility
Curation & Quality Assurance	Provides expert curation to improve accuracy and integrity [45] [44]	Enhances Reusability
Clear Use Guidance	Provides documentation describing terms of access and use [45] [44]	Supports Reusability
Common Format	Allows data in widely used, non-proprietary formats [45] [44]	Promotes Interoperability
Provenance	Has mechanisms to record origin and modifications [45]	Essential for Reusability
Security & Integrity	Has measures to prevent unauthorized access or modification [45] [44]	Critical for sensitive data

Domain-Specific Repositories for FAIR Chemical Data

Domain-specific repositories offer significant advantages for environmental chemistry research by implementing community standards that directly support FAIR data principles. These repositories typically provide specialized metadata schemas tailored to specific data types, which enhances both human understanding and machine-actionability—a core emphasis of the FAIR principles [3].

The enviPath platform exemplifies the domain-specific approach for biotransformation data, addressing the critical need for standardized reporting of chemical contaminant transformations in the environment [1]. For chemical data, domain-specific repositories often support specialized structural representations such as Simplified Molecular Input Line Entry Specifications (SMILES), which enable precise communication of molecular structures in both human- and machine-readable formats [1]. This capability is particularly valuable for modeling biotransformation pathways, where structural changes determine environmental fate and potential toxicity.

Community-developed reporting formats play a crucial role in enhancing data quality within domain-specific repositories. For example, the Biotransformation Reporting Tool (BART) provides a standardized template for reporting biotransformation pathways and kinetics [1]. Such tools address the challenge of extracting information from conventional pathway figures, which are typically presented as 2D images that are not easily translated into machine-readable formats [1]. The implementation of these standardized reporting formats within domain-specific repositories directly supports the FAIR principle of interoperability by using "a formal, accessible, shared, and broadly applicable language for knowledge representation" [41].

Experimental Protocol: Standardized Reporting of Chemical Biotransformation Data

The following methodology outlines the standardized approach for reporting biotransformation data using the BART template, demonstrating how domain-specific repositories enable FAIR compliance in environmental chemistry research.

Materials and Reagents:

• Chemical standards of target compounds and suspected transformation products
• Environmental inoculum (e.g., activated sludge, soil, or sediment samples)
• Analytical instrumentation (e.g., LC-MS/MS, HRMS)
• BART template (publicly available from GitHub: https://github.com/FennerLabs/BART)

Procedure:

• Experimental Documentation: Record key experimental parameters in the BART "Scenario" tab, including:
  • Inoculum source and provenance (e.g., wastewater treatment plant location)
  • Environmental conditions (pH, temperature, redox potential)
  • System-specific parameters (e.g., solids retention time for sludge reactors)
  • Spike compound structures and concentrations [1]

• Compound Characterization:

  • Determine chemical structures of parent compounds and transformation products
  • Represent structures as SMILES strings in the "Compounds" tab
  • For isomeric compounds, designate the most plausible structure as primary and include alternatives [1]

• Pathway Elucidation:

  • Map biotransformation pathways in the "Connectivity" tab by specifying reactants and products
  • Flag multistep reactions where multiple enzymatic steps are hypothesized but not fully elucidated
  • Annotate compounds with appropriate confidence levels (Schymanski Confidence Levels or PFAS Confidence in Identification Levels) [1]

• Kinetic Data Reporting:

  • Report biotransformation kinetics in the "Kinetics_Confidence" tab
  • Include half-life values where determined
  • Provide information on data quality and identification confidence [1]

• Data Submission:

  • Submit the completed BART template as Supporting Information alongside manuscript publication
  • Deposit data in a domain-specific repository such as enviPath
  • Ensure all persistent identifiers (DOIs) are included in the submission [1]

Table 3: Research Reagent Solutions for Biotransformation Studies

Reagent/Resource	Function	Application in Biotransformation Studies
BART Template	Standardized reporting format for biotransformation data [1]	Ensures consistent, machine-readable data structure for environmental fate studies
SMILES Strings	Chemical structure representation [1]	Enables precise communication of molecular structures and transformations
enviPath Platform	Domain-specific data repository [1]	Provides specialized infrastructure for storing and accessing biotransformation pathways
Schymanski Confidence Levels	Identification confidence framework [1]	Standardizes quality assessment for identified transformation products
Environmental Inocula	Source of transforming microorganisms	Represents relevant microbial communities for biodegradation testing

Generalist Repositories and FAIR Compliance

Generalist repositories provide a valuable alternative when domain-specific options are unavailable or unsuitable for the data type. These repositories support FAIR data principles through broad accessibility and cross-disciplinary discovery, making them particularly valuable for interdisciplinary research projects that span multiple domains [11] [46].

The ESS-DIVE repository exemplifies how generalist repositories can implement standardized reporting formats to enhance data interoperability. ESS-DIVE has developed 11 community reporting formats for diverse environmental data types, including cross-domain metadata and domain-specific guidelines for biogeochemical samples, soil respiration, and leaf-level gas exchange measurements [11]. This approach demonstrates how generalist repositories can incorporate standardized templates to improve data consistency while maintaining broad accessibility across disciplines.

Generalist repositories typically support the FAIR principle of accessibility by providing "broad, equitable, and maximally open access to datasets and their metadata free of charge in a timely manner after submission" [45]. Platforms such as Zenodo, Figshare, and Dryad assign persistent identifiers, support rich metadata, and provide public access to datasets, thereby addressing the core FAIR requirements for findability and accessibility [45] [44].

However, the metadata standards in generalist repositories are often less specialized than those in domain-specific repositories, which can present challenges for complex chemical data. To address this limitation, researchers depositing chemical data in generalist repositories should provide comprehensive documentation using community standards wherever possible, even if not explicitly required by the repository. This might include using established chemical identifiers, structured data formats, and detailed methodological descriptions that enable proper interpretation and reuse.

The selection between domain-specific and generalist repositories represents a critical decision point in the research data lifecycle, with significant implications for the practical implementation of FAIR principles. For environmental science researchers working with chemical data, domain-specific repositories generally offer superior support for specialized data types through community-developed standards, specialized metadata schemas, and enhanced interoperability within the research domain. These repositories are particularly valuable for complex data types such as chemical transformation pathways, where specialized representation methods like SMILES strings and standardized reporting tools like BART enhance both human understanding and machine-actionability.

Generalist repositories provide an essential alternative when domain-specific options are unavailable or when research spans multiple disciplines. These repositories excel in providing broad accessibility, cross-disciplinary discovery, and robust preservation services that meet fundamental FAIR requirements. The ongoing development of standardized reporting formats within generalist repositories, such as those implemented in ESS-DIVE, further enhances their utility for environmental and chemical data.

Ultimately, repository selection should be guided by both disciplinary requirements and the core FAIR principles. Researchers should prioritize repositories that assign persistent identifiers, support rich metadata, ensure long-term sustainability, and employ appropriate access controls. By making informed decisions about repository selection and employing community standards for data reporting, environmental scientists can significantly enhance the findability, accessibility, interoperability, and reusability of their chemical data—advancing both their individual research impact and the broader progress of environmental science.

Overcoming Common Implementation Challenges in FAIR Chemical Data Management

Balancing Data Standardization with Practical Research Realities

The escalating volume and complexity of scientific data have made standardization an essential component of modern environmental research. The FAIR principles—Findable, Accessible, Interoperable, and Reusable—provide a foundational framework for scientific data management, emphasizing machine-actionability to handle the increasing scale of digital assets [3]. In environmental science, where research increasingly requires integrating diverse data types across disciplines, the implementation of consistent (meta)data reporting formats enables more transparent and reproducible research [11]. However, the pursuit of ideal standardization often conflicts with practical research realities, including diverse disciplinary practices, resource constraints, and the inherent complexity of environmental systems. This technical guide examines strategies for balancing these competing demands within the context of FAIR chemical data reporting in environmental science, providing researchers with practical methodologies for navigating this complex landscape.

Theoretical Frameworks: Value Configurations for Balancing Standardization and Customization

The challenge of balancing standardization with practical needs extends beyond technical implementation to fundamental organizational structures. Research in chronic care management has identified three value configurations that provide a useful framework for understanding how to manage these competing demands in scientific research [47].

Table 1: Value Configurations for Operational Design in Research Data Management

Value Configuration	Primary Focus	Standardization Approach	Cost Efficiency	Research Application Examples
Shop	Customized problem-solving	Minimal procedural standardization	High cost per unit; tailored solutions	Specialized analytical methods, novel instrument development
Chain	Linked processes with minimal variation	High procedural standardization	Lower cost per unit; scale advantages	Routine water quality analysis, standardized sensor deployments
Network	Facilitating system-wide collaboration	Flexible standards enabling interoperability	Lowest cost per unit; significant scale advantages	Multi-investigator projects, data synthesis across sites

The shop configuration represents highly customized research approaches where professionals have liberty to design methods for specific problems. In contrast, the chain configuration employs standardized processes with little variation, benefiting from economies of scale. The network configuration focuses on facilitating collaboration among distributed actors, creating value through flexible connections [47]. Rather than viewing these configurations as mutually exclusive, research organizations can benefit from recognizing their coexistence and implementing them at appropriate levels of abstraction. This approach allows for maintaining standardization where it provides efficiency while permitting customization where necessary for scientific innovation.

FAIR Data Principles: From Theory to Implementation

The FAIR Framework

The FAIR principles were established to provide guidelines for improving the Findability, Accessibility, Interoperability, and Reuse of digital assets [3]. These principles emphasize machine-actionability—the capacity of computational systems to find, access, interoperate, and reuse data with minimal human intervention—which has become essential as data volume and complexity exceed human processing capabilities [3]. The four components of the FAIR framework include:

• Findable: Metadata and data should be easily discoverable by both humans and computers, requiring machine-readable metadata for automatic discovery
• Accessible: Once found, users need clear protocols for accessing data, which may include authentication and authorization procedures
• Interoperable: Data must be able to integrate with other data and applications through use of formal, accessible, shared languages and knowledge representations
• Reusable: The ultimate goal of FAIR is optimizing reuse of data through rich metadata and clear usage licenses [3]

Community-Centric Reporting Formats

Implementing FAIR principles in environmental science requires practical solutions that bridge theoretical ideals with research realities. The ESS-DIVE repository addressed this challenge by developing community-centric reporting formats that balance rigor with practicality [11]. This approach recognized that while formal standards accredited by governing bodies (like ISO standards) are valuable, they are unavailable for many environmental data types and can take over a decade to develop through formal consensus processes [11].

Reporting formats represent community-driven efforts to harmonize diverse environmental data types without requiring extensive governing protocols. These formats are typically more domain-focused than international standards while still enabling efficient collection and harmonization of information needed for data reuse [11]. For example, FLUXNET's reporting format for half-hourly flux and meteorological data has enabled consistent formatting of carbon, water, and energy flux data from thousands of global sampling locations [11].

Table 2: Community Reporting Formats for Environmental Science Data

Reporting Format Category	Specific Formats Developed	Key Applications	Required Metadata Fields
Cross-Domain Formats	Dataset metadata, location metadata, sample metadata, file-level metadata, CSV formatting, terrestrial model data archiving	Broad application across environmental disciplines	Spatial coordinates (decimal degrees), temporal data (YYYY-MM-DD), persistent identifiers
Domain-Specific Formats	Amplicon abundance tables, leaf-level gas exchange, soil respiration, water/sediment chemistry, sensor-based hydrologic measurements	Specific measurement types in biological, geochemical, and hydrological research	Sample IDs (e.g., IGSNs), instrument calibration data, methodological protocols

The development process for these reporting formats followed a structured approach: (1) reviewing existing standards and resources, (2) creating crosswalks to map existing resources and identify gaps, (3) iteratively developing templates with user feedback, (4) defining minimal required metadata, and (5) hosting documentation on accessible platforms [11]. This methodology successfully created formats that researchers actually adopted because they addressed genuine workflow needs while improving data interoperability.

Methodological Approaches: Implementing Balanced Standardization

Strategic Sampling and Recruitment

Balancing methodological ideals with practical realities often requires flexible, adaptive approaches to research design. In qualitative health research, this balance has been addressed through intersectional recruitment strategies that acknowledge power dynamics while maintaining feasibility [48]. Key considerations include:

• Selective Recruitment: Combining direct methods (database searches, targeted outreach) with indirect methods (snowball sampling, multiplier engagement) to achieve diverse representation [48]
• Iterative Assessment: Ongoing evaluation and adaptation of recruitment strategies during research implementation [48]
• Transparent Documentation: Comprehensive reporting of selection criteria and recruitment processes to enable critical evaluation of potential biases [48]

These principles translate well to environmental sciences, where practical constraints often limit ideal sampling designs. For example, in field-based environmental research, strategic site selection that balances ideal spatial distribution with accessibility constraints can maintain scientific validity while acknowledging practical limitations.

Handling Imbalanced Data

Medical research provides valuable methodologies for addressing data quality challenges highly relevant to environmental science. Class imbalance—where one class is significantly underrepresented in a dataset—presents serious challenges for machine learning applications in environmental research [49]. In medical diagnostics, imbalance occurs naturally since diseased individuals are typically outnumbered by healthy ones, similar to how rare environmental phenomena (e.g., contamination events, extreme weather) are inherently underrepresented in datasets [49].

The imbalance ratio (IR) quantifies this disproportion: IR = Nmaj/Nmin, where Nmaj and Nmin represent the number of instances in majority and minority classes respectively [49]. Conventional classifiers typically exhibit inductive bias favoring the majority class, which can lead to critical errors—in medical contexts, misclassifying diseased patients as healthy, and in environmental contexts, failing to detect rare but significant events [49].

Table 3: Approaches for Handling Imbalanced Data in Research

Approach Category	Specific Methods	Advantages	Limitations
Preprocessing Level	Undersampling majority class, oversampling minority class, hybrid approaches	Directly addresses data distribution; model-agnostic	Risk of losing important information; synthetic data may not reflect true patterns
Learning Level	Algorithmic modifications, cost-sensitive learning	No artificial data manipulation; incorporates domain knowledge	Algorithm-specific implementations; complex parameter tuning
Combined Techniques	Hybrid methods integrating multiple approaches	Potentially superior performance; addresses multiple aspects	Increased complexity; requires extensive validation

Method selection must consider domain-specific requirements. In medical diagnostics, the cost of misclassifying a diseased patient is far greater than misclassifying a healthy one [49]. Similarly, in environmental monitoring, falsely classifying a contaminated site as clean may have more severe consequences than the reverse error.

Practical Implementation: Workflows and Visualization

Experimental Workflow for Standardized Data Collection

The following diagram illustrates a standardized yet adaptable workflow for environmental data collection and management, balancing FAIR principles with practical research constraints:

Value Configuration Implementation Model

The effective implementation of value configurations for balancing standardization and customization requires a structured approach:

Research Reagent Solutions for Standardized Data Collection

Successful implementation of balanced standardization requires specific tools and resources. The following table details essential components for environmental researchers implementing FAIR data principles:

Table 4: Essential Research Toolkit for Implementing Balanced Standardization

Tool/Resource Category	Specific Examples	Primary Function	Implementation Considerations
Community Reporting Formats	ESS-DIVE reporting formats for samples, water chemistry, gas exchange data [11]	Provide templates for consistent (meta)data organization	Select formats aligned with research community; adapt as needed for specific projects
Metadata Standards	Dataset metadata, location metadata, sample metadata formats [11]	Ensure proper documentation for data findability and reuse	Implement required elements first; expand to optional elements as resources allow
Data Repository Platforms	ESS-DIVE, GitHub, GitBook [11]	Enable data preservation, sharing, and version control	Select repositories based on discipline standards, preservation commitment, and functionality
Sampling Design Tools	Intersectional recruitment frameworks [48]	Support representative sampling within practical constraints	Balance ideal statistical power with feasibility; document limitations transparently
Imbalance Handling Methods	SMOTE, cost-sensitive learning, hybrid approaches [49]	Address unequal class distribution in datasets	Evaluate multiple approaches; select based on domain-specific error costs

Balancing data standardization with practical research realities requires both methodological sophistication and pragmatic acceptance of constraints. The frameworks presented in this guide—value configurations, FAIR principles, community reporting formats, and adaptive workflows—provide researchers with structured approaches for navigating these competing demands. By implementing strategic standardization that respects disciplinary diversity, resource limitations, and scientific innovation needs, environmental scientists can enhance data interoperability and reuse while maintaining research feasibility. The ultimate goal is not perfect standardization but rather practical frameworks that improve research quality and impact through more systematic, transparent, and reusable data practices.

Addressing Confidential Business Information (CBI) in Public Data Sharing

The effective sharing of chemical data is fundamental to advancing environmental science and drug development. The FAIR principles—Findable, Accessible, Interoperable, and Reusable—provide a framework for maximizing the value of research data [11]. However, integrating Confidential Business Information (CBI) into this framework presents significant challenges, requiring careful balancing of transparency and protection. Under the Toxic Substances Control Act (TSCA), CBI encompasses information whose disclosure could cause substantial competitive harm to the submitter [50] [51]. This technical guide outlines methodologies and protocols for addressing CBI within public data-sharing initiatives, enabling compliance and fostering collaboration while safeguarding legitimate business interests.

Defining CBI in Chemical and Environmental Research

Regulatory Framework and CBI Claims

Confidential Business Information protections are legally defined, particularly under statutes like TSCA. The United States Environmental Protection Agency (EPA) provides specific procedures for asserting and maintaining CBI claims. A final rule issued on June 1, 2023, modernized these procedures, emphasizing electronic reporting and clearer substantiation requirements [51]. To be recognized as CBI, information must meet specific criteria and cannot include certain health and safety study data, for which the scope of CBI claims has been narrowed [51].

Key procedural requirements for CBI claims under TSCA include:

• Electronic Reporting: Nearly all CBI claims must be submitted electronically via the Central Data Exchange (CDX) [51].
• Substantiation: Submitters must answer a standard set of questions to support their CBI claim, detailing the rationale for confidentiality [51].
• Sanitized Copies: Submitters must provide a public version of the submission with the CBI redacted [51].
• Contact Information: Companies must maintain accurate contact information, particularly for Authorized Officials, to receive timely EPA communications regarding their claims [51].

Quantitative Trends in Data Sharing

Recent analyses quantify the use of anonymized and shared data in biomedical and related research. A systematic review of 1,084 PubMed-indexed studies (2018–2022) revealed a statistically significant yearly increase in papers utilizing anonymized data, with a slope of 2.16 articles per 100,000 when normalized against total PubMed articles (p = 0.021) [52]. This trend intensified during the COVID-19 pandemic, underscoring the critical role of data sharing in global health crises.

The geographical distribution of this research is highly uneven, indicating the impact of regional regulations and practices:

Table 1: Geographical Distribution of Studies Using Anonymized Data (2018-2022)

Region/Country	Percentage of Studies (Single-Country Data)	Normalized Ratio (per 1000 citable documents)
United States (US)	54.8%	0.345 (Core Anglosphere average)
United Kingdom (UK)	18.1%	0.345 (Core Anglosphere average)
Australia	5.3%	0.345 (Core Anglosphere average)
Continental Europe	8.7%	0.061
Asia	Not Specified	0.044

This data demonstrates that data sharing practices are most prevalent in the "Core Anglosphere" (US, UK, Australia, Canada), which operate under distinct regulatory frameworks like the HIPAA Privacy Rule in the US [52]. In contrast, sharing is less common in Continental Europe, which operates under the GDPR, highlighting how legal ambiguities can impede practice [52].

Technical Protocols for CBI Management and Anonymization

CBI Determination and Substantiation Workflow

The process of determining what information can be claimed as CBI and preparing a submission requires a structured approach. The following workflow, developed from EPA TSCA procedures, outlines key decision points [51].

Data Anonymization Techniques for CBI

When CBI protections preclude direct sharing, anonymization techniques can be applied to create usable datasets while mitigating re-identification risks. A multi-layered approach is considered best practice [53].

Table 2: Data Anonymization Techniques for Protecting CBI and Personal Data

Technique	Methodology	Best Use-Case	Utility vs. Privacy
Tokenization	Replaces sensitive data with a unique, non-decryptable identifier (token) [53].	Internal data processing; structured data fields [53].	High utility for referential integrity.
Data Masking	Static or dynamic obfuscation of specific data elements (e.g., replacing characters with symbols) [53].	Non-production environments; internal data sharing [53].	Moderate utility, depends on implementation.
Synthetic Data Generation	Algorithmically generates artificial data that mimics the statistical properties of the original dataset [53].	AI model training; high-fidelity testing without real data [53].	High utility if model is accurate.
K-anonymity	Generalizes data so each record is indistinguishable from at least k-1 other records [53].	Public data release; datasets with quasi-identifiers [53].	Balance depends on k value and generalization.
Differential Privacy	Adds calibrated mathematical noise to query results or datasets to prevent individual identification [53].	High-risk public data sharing; statistical databases [52].	Privacy guarantee is mathematically provable.

Experimental Protocol: Implementing a CBI Risk Assessment and Anonymization Pipeline

This protocol provides a detailed methodology for assessing disclosure risk and applying appropriate anonymization to a chemical dataset containing potential CBI.

1. Project Scoping and Legal Review

• Objective: Define the purpose of data sharing, intended users, and legal basis (e.g., TSCA compliance, research collaboration) [51] [54].
• Procedure:
  • Conduct a review with legal counsel to identify regulatory requirements and constraints.
  • Document the types of CBI present (e.g., chemical identity, process information, supplier details).
  • Define the minimum data elements required to fulfill the sharing objective, adhering to data minimization principles.

2. Data Identification and CBI Inventory

• Objective: Create a comprehensive inventory of all data elements and classify them based on sensitivity.
• Procedure:
  • Extract all variables from the dataset.
  • Classify each variable using the following categories:
    • Direct Identifier: Uniquely identifies an entity (e.g., company name, specific chemical identifier pre-publication) [55].
    • Indirect (Quasi-) Identifier: Could be linked with other information to identify an entity (e.g., precise geographic location of a facility, production volume ranges, specific catalyst used) [55].
    • Confidential Attribute: Sensitive business data that is not an identifier (e.g., exact profit margin, detailed synthesis pathway).
    • Non-Confidential Attribute: Data that can be shared publicly (e.g., aggregated toxicity endpoints, general chemical class).

3. Disclosure Risk Assessment

• Objective: Quantitatively evaluate the risk that an individual company or process can be re-identified in the dataset.
• Procedure:
  • For datasets with quasi-identifiers, perform a k-anonymity assessment. Determine the smallest k value where each combination of quasi-identifiers (e.g., chemical class, production volume bracket, region) appears in at least k records.
  • A low k value (e.g., 1 or 2) indicates high re-identification risk.
  • Use statistical software (e.g., R with sdcMicro package) to automate this analysis.

4. Anonymization Technique Selection and Application

• Objective: Apply techniques from [53] to mitigate risks identified in Step 3.
• Procedure:
  • Direct Identifiers: Remove or apply tokenization if a persistent but masked link is needed.
  • Indirect Identifiers:
    • Generalize: Recode continuous variables (e.g., production volume) into broader categories [55].
    • Reduce Precision: Convert precise coordinates to regional levels [55].
  • Confidential Attributes:
    • Consider differential privacy for aggregated statistics if the dataset will be queried.
    • Top-/Bottom-code extreme values to prevent inference about outliers.
  • Synthetic Data Generation: If the risk remains high, model the dataset and generate a fully synthetic version for sharing [53].

5. Validation and Documentation

• Objective: Ensure the anonymized dataset retains analytical utility and is properly documented.
• Procedure:
  • Re-run key statistical analyses on the anonymized data and compare results to the original to assess utility loss.
  • Document all applied techniques, parameters (e.g., k value, noise level), and the disclosure risk assessment process in a transparent methodology report.

Implementing FAIR Principles with CBI Constraints

The FAIR-CBI Integration Framework

Integrating CBI-protected data into the FAIR ecosystem requires tailored strategies. The following diagram illustrates the logical relationship between FAIR principles and the specific actions needed to implement them with CBI.

Reporting Formats and Standardization

The use of community-developed reporting formats is a powerful tool for achieving interoperability, a core FAIR principle. For instance, the Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) repository has developed 11 community reporting formats for diverse Earth science data [11]. These formats provide instructions, templates, and tools for consistently formatting data, making it more accessible and reusable.

A critical development in chemical regulation is the EPA's requirement to report health and safety studies using an appropriate Organisation for Economic Co-operation and Development (OECD) Harmonised Template (OHT) [51]. This requirement aligns TSCA submissions with international standards, promoting consistency and interoperability across borders. The IUCLID software is the recommended free tool for creating these templated files [51]. This harmonization is a practical step toward FAIR chemical data, as it uses standardized, machine-actionable formats.

The Researcher's Toolkit for CBI and FAIR Data

Table 3: Essential Tools and Resources for Managing CBI and FAIR Chemical Data

Tool/Resource	Type	Primary Function	Relevance to CBI/FAIR
CDX (Central Data Exchange)	Software Platform	EPA's electronic reporting portal [51].	Mandatory for submitting TSCA CBI claims; enables electronic substantiation.
IUCLID	Software Application	Tool for creating, storing, and exchanging data on chemicals [51].	Generates standardized OHTs for health and safety data, ensuring interoperability.
Virtual Data Enclave (VDE)	Secure Environment	A remote desktop for analyzing restricted data without downloading it [55].	Enables Accessible but controlled use of high-sensitivity data, preventing leakage.
LangChain/Pinecone	Programming Framework / Vector Database	Tools for building AI applications with memory and efficient data retrieval [53].	Can be implemented in anonymization pipelines (e.g., for managing synthetic data).
ESS-DIVE Reporting Formats	Documentation & Templates	Community guidelines for formatting specific environmental data types [11].	Provides a model for creating Findable and Interoperable datasets.
sdcMicro	R Statistical Package	Comprehensive toolkit for statistical disclosure control [52].	Implements k-anonymity, differential privacy, and other risk assessment methods.
OECD Best Practice Guide	Guidance Document	Recommendations for fair, transparent chemical data sharing between companies [54].	Addresses fairness, cost, and consistency in CBI-heavy data exchanges.

Integrating Confidential Business Information into the FAIR data paradigm is a complex but achievable goal. Success hinges on a multi-faceted approach: a firm understanding of the regulatory landscape, the strategic application of technical anonymization methods, and the adoption of community standards and reporting formats. By implementing the structured protocols and workflows outlined in this guide—from rigorous CBI substantiation and disclosure risk assessment to the use of secure data enclaves and harmonized templates—researchers and professionals can unlock the value of chemical data for environmental and health research. This enables a collaborative ecosystem that simultaneously upholds the pillars of transparency and confidentiality, driving scientific innovation while respecting legitimate business interests.

Technical Solutions for Legacy Data Conversion and Format Migration

In environmental science and drug development, the management of legacy chemical data presents a significant challenge. Research output grows by 8–9% annually, yet the methods for sharing and reusing experimental data have not kept pace [56]. The transition towards research frameworks guided by the FAIR principles—making data Findable, Accessible, Interoperable, and Reusable—demands robust technical solutions for converting and migrating legacy data [56]. This migration is not merely a technical routine but a foundational step for enabling data-driven discovery, ensuring that valuable historical research can be integrated with modern analytical workflows and contribute to future innovation.

Legacy data migration is formally defined as the process of moving data from obsolete storage systems into modern, up-to-date environments while preserving its value and complex dependencies [57]. The core technical challenge lies in transforming often unstructured or semi-structured historical records into structured, machine-actionable formats without losing critical scientific context. For chemical sciences, this is particularly crucial, as the data's utility depends on the accurate preservation of chemical structures, experimental conditions, and analytical parameters. When executed with a strategic approach, migration does more than change storage locations; it enhances data quality, ensures regulatory compliance, and unlocks the potential for advanced analytics and cross-disciplinary collaboration [57].

Foundational Principles and Pre-Migration Planning

Aligning Migration with FAIR Principles

The FAIR principles provide a critical framework for evaluating the success of any data migration project, especially within the chemical context. These principles emphasize machine-actionability, which is essential for handling the volume and complexity of modern research data [3] [56].

Table: Applying FAIR Principles to Chemical Data Migration

FAIR Principle	Technical Definition	Migration Implementation in Chemistry
Findable	Data and metadata have globally unique, persistent identifiers [3].	Assign DOIs to datasets; use International Chemical Identifiers (InChI) for all chemical structures [56].
Accessible	Data are retrievable by their identifier using a standardized protocol [3].	Use HTTP/HTTPS protocols; ensure metadata remains accessible even if data is restricted [56].
Interoperable	Data is formatted in a formal, shared, and broadly applicable language [3].	Use community standards like CIF for crystal structures, JCAMP-DX for spectral data, and nmrML for NMR data [56].
Reusable	Data and metadata are thoroughly described to allow replication and combination [3].	Document complete experimental procedures, instrument settings, and data processing steps [56].

Critical Pre-Migration Steps: Audit and Strategy

Before any data is moved, a methodical planning phase is essential for managing risks and resources. The first step is a comprehensive data audit, which involves assessing existing project data to verify its quality and completeness [58]. This audit reveals the structure and condition of the legacy data, which generally falls into three categories:

• Structured Data: Has a predefined schema and is consistently organized (e.g., relational databases, consistently formatted spreadsheets) [58].
• Semi-structured Data: Does not conform to a rigid structure but contains parsable patterns (e.g., spreadsheets with inconsistent columns, JSON API responses) [58].
• Unstructured Data: Neither parsable nor consistently organized (e.g., PDF reports, paper field notes, images), making it the most difficult and costly to migrate [58].

Following the audit, a migration strategy must be developed. This includes determining the project's objectives, scope, and milestones [58]. A highly effective technique for managing complexity and budget is compartmentalization—breaking down the migration into clearly defined subsets. Data can be grouped by distinct format (e.g., specific laboratory EDD formats) or by its value to project goals (e.g., a specific time period, sample medium, or operable unit) [58]. This allows for prioritization, provides clear cut-off points, and enables the development of uniform processing instructions for each category, thereby increasing efficiency.

Technical Migration Methodologies and Workflows

Data Extraction and Transformation Approaches

The core of the migration process is an Extract, Transform, Load (ETL) workflow. The extraction method is determined by the source system's capabilities and the data's scale and complexity [59].

• File Export-Import: This is often the most straightforward method for small to medium-sized datasets. Data is exported from the legacy system into a file, processed, and then imported into the new system. For chemical data, the Structure-Data File (SDF) format is widely recommended due to its support for a comprehensive range of chemistry features, including enhanced stereo notation. Simplified line notations like SMILES are not recommended for migration as they do not support all required chemical features [59].
• Direct Data Migration: For large, complex datasets, establishing a live connection between the source and target databases can be more efficient. This method avoids file-based intermediaries but requires a deep understanding of both systems' database schemas, structures, and data types to ensure compatibility. It also necessitates a stable database connection and may require making the source system inaccessible to users during the migration, which can impact operations [59].

A robust technical architecture for handling complex migrations involves using a Main Stage Table (MST). The MST acts as an immutable landing zone for all legacy data, preserving the original state of metadata, identifiers, and structural data. All subsequent data cleaning, standardization, and transformation steps are performed within the MST, which provides a single point of control for logging events, tracking data lineage, and preparing the final, validated dataset for loading into the new production system [59].

Data Curation and Standardization for Chemistry

A pivotal stage in the transformation process is the application of chemical-specific business rules to ensure data quality and consistency. This involves both automated and manual curation efforts [60].

• Chemical Structure Standardization: This is a non-negotiable step. Automated workflows must be employed to check and standardize chemical structures according to predefined, well-documented rules. This includes handling of tautomers, stereochemistry, isotopologues, and the treatment of salts and solvates [60]. The use of industry-standard, structure-based representations is critical for interoperability.
• Data Mapping and Cleaning: Each field from the legacy source must be precisely mapped to the corresponding field in the target system. This process often reveals data quality issues such as duplicates, missing values, and inconsistent formatting. Data cleansing activities correct these errors, while normalization activities standardize formats (e.g., dates, units of measure) to ensure consistency across the entire dataset [61].

Table: Essential Technical Tools for Chemical Data Migration

Tool Category	Specific Examples	Primary Function in Migration
Data Profiling Tools	—	Analyze source data to identify patterns, anomalies, and inconsistencies [61].
ETL Testing Tools	—	Validate the extraction, transformation, and loading process against business rules [61].
Data Quality Tools	—	Check for completeness, consistency, and reliability; identify duplicates and missing values [61].
Schema Comparison Tools	—	Compare database schemas between source and target to identify structural mismatches [61].
Data Comparison Tools	—	Perform post-migration validation by comparing source and target datasets for content integrity [61].

Diagram: End-to-End Legacy Chemical Data Migration Workflow. This flowchart illustrates the comprehensive process for migrating legacy data, highlighting critical stages from data audit through to loading into a FAIR-compliant system, including feedback loops for quality assurance.

Validation, Post-Migration Support, and Change Management

Ensuring Data Integrity through Validation

A rigorous validation process is critical to confirm that data has been migrated completely and accurately, without corruption. This requires close collaboration between data owners, architects, and cheminformatics experts to define and execute a comprehensive validation plan [59] [61]. Key practices include:

• Defining Validation Rules and Thresholds: Establish clear, quantitative rules for data quality, assessing formats, range values, and cross-field dependencies. Thresholds set the acceptable levels of data quality and completeness, providing a clear standard for a successful migration [61].
• Identifying Inconsistencies and Gaps: Use automated tools to scan the migrated dataset, comparing it with the source to flag discrepancies, anomalies, and missing information for review and resolution [61].
• Establishing a Quality Assurance (QA) Process: Post-migration, a formal QA process must be initiated. This involves scheduled audits, performance monitoring of the new system, and a continuous improvement cycle to address any long-term issues that emerge [61]. All validation results and identified inconsistencies should be documented in a validation report, which then fuels a cycle of review, correction, and re-validation until all issues are resolved [59].

Change Management for Long-Term Success

The technical migration is only part of the project; its long-term success depends on user adoption and effective support [60].

• Onboarding and Training: Develop a multi-level onboarding plan initiated during the project's planning phase. This should include basic training for all end-users and advanced, bespoke training for key users, administrators, and "power users." "Train the trainer" workshops can empower internal champions to facilitate a smoother transition [60].
• Continued Support and Documentation: Appoint a dedicated point of contact to answer questions and articulate the benefits of the new system. Comprehensive documentation—including how-to guides, FAQs, and details on business rules and system differences—is essential for ongoing reference and for onboarding future staff [60].
• Project Evaluation and Continuous Improvement: After migration, conduct a project evaluation to assess what went well and what did not. This "lessons learned" exercise, combined with regular check-ins and user feedback, helps identify high-impact areas for future improvement and ensures the system continues to meet the organization's evolving needs [60].

Table: Key Research Reagent Solutions for Data Migration

Item / Solution	Function in the Migration Process
International Chemical Identifier (InChI)	Provides a machine-readable, standardized representation of chemical structures, making them Findable and Interoperable [56].
Structure-Data File (SDF)	A widely supported file format for transferring chemical structures and associated metadata between systems during export-import migration [59].
Crystallographic Information File (CIF)	A community-standard machine-readable format for reporting crystal structures, ensuring Interoperability and Reusability [56].
JCAMP-DX File Format	A standard format for representing spectroscopic data (e.g., IR, NMR), enabling the exchange and interoperability of spectral archives [56].
Electronic Lab Notebook (ELN) with FAIR Support	A modern tool for capturing experimental data and metadata in a structured way from the point of generation, facilitating future migrations [56].
Main Stage Table (MST)	A database table used as an intermediate, immutable storage area during complex migrations to provide better control, logging, and tracking of data [59].

Diagram: Technical Implementation of FAIR Principles. This diagram breaks down the FAIR principles into concrete technical actions and standards that can be implemented during a data migration project to ensure the resulting data is Findable, Accessible, Interoperable, and Reusable.

The migration of legacy chemical data to a FAIR-compliant framework is a complex but indispensable undertaking for research organizations in environmental science and drug development. It transcends a simple data transfer, representing a strategic investment in the quality, utility, and longevity of critical scientific assets. A successful outcome hinges on a methodical approach that integrates meticulous pre-migration planning, robust ETL methodologies tailored to chemical data, rigorous validation, and a strong change management strategy. By viewing legacy data not as a burden but as a valuable resource and applying these technical solutions, researchers and organizations can unlock the full potential of their historical data, fostering reproducibility, collaboration, and accelerated scientific discovery.

With research data accumulating rapidly and increasing in complexity, the global scientific community faces a significant reproducibility crisis [62]. Implementing high-quality data management has therefore become a critical priority across scientific disciplines. The FAIR (Findable, Accessible, Interoperable, and Reusable) principles provide practical guidelines for maximizing research data value, but these principles must be integrated directly into research operations through computational workflows to achieve meaningful impact [62]. This is particularly crucial for environmental science research dealing with chemical data, where integrating diverse data types presents unique challenges for interdisciplinary research [11].

Workflows—systematic executions of series of computational tools—represent a fundamental component of effective data management [62]. When FAIR principles are embedded directly into these workflows, researchers can transform them from theoretical concepts into operational practices that enhance reproducibility, collaboration, and research efficiency. For chemical data in environmental contexts, this integration enables easier data discovery, integration with biological and toxicological data, and ultimately more effective chemical risk assessment [63]. This technical guide provides a comprehensive framework for embedding FAIR principles into research workflows, with specific applications for chemical data reporting in environmental science research.

Core FAIR Principles in Practice

Defining the FAIR Framework

The FAIR principles emphasize machine-actionability—the capacity of computational systems to find, access, interoperate, and reuse data with minimal human intervention—recognizing that humans increasingly rely on computational support due to the increasing volume, complexity, and creation speed of data [3]. These principles apply to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure [3].

Table 1: Core FAIR Principles and Their Technical Definitions

Principle	Technical Definition	Workflow Implementation Focus
Findable	Data and metadata should have globally unique, persistent identifiers and be registered in searchable resources [3].	Workflow registration, rich metadata, persistent identifiers [62].
Accessible	Data and metadata should be retrievable by their identifiers using standardized protocols [3].	Public code repositories, standard communication protocols, clear access conditions [62] [56].
Interoperable	Data and metadata should use formal, shared, and broadly applicable languages with cross-references [3].	Community standards, standardized data formats, formal knowledge representation [62] [56].
Reusable	Data and metadata should be thoroughly described with clear usage licenses and provenance [3].	Detailed documentation, clear licensing, provenance tracking, domain-relevant standards [62] [56].

Domain-Specific FAIR Considerations for Chemical Data

In chemical and environmental sciences, FAIR implementation requires specialized approaches. Chemical structures should have unique identifiers (InChIs), and datasets should have DOIs to ensure findability [56]. For interoperability, chemical data should use standard formats that other systems can interpret, such as CIF files for crystallographic data and standardized formats for NMR data [56]. Reusability requires detailed experimental procedures and properly documented spectra with metadata on acquisition parameters [56].

The environmental health sciences face particular challenges with metadata completeness. For example, a systematic review of per- and polyfluoroalkyl substances found that 19% of candidate animal studies did not adequately characterize exposure, while 34.5% of samples in smoking data sets were missing metadata for sex [64]. Such incompleteness severely restricts potential for data reuse and integration.

Implementing FAIR Workflows: Technical Framework

Findability Implementation Strategies

Workflow Registration and Persistent Identification: FAIR workflow development begins with ensuring findability through registration in public records, preferably those indexed by popular search engines [62]. Specialized workflow registries like WorkflowHub and Dockstore support multiple widely used workflow languages and provide persistent identifiers [62]. WorkflowHub, sponsored by the European Research Infrastructure ELIXIR, can assign digital object identifiers (DOIs) to workflows, making them easily citable, with new DOIs automatically minted for each version [62].

Rich Metadata Description: Describing workflows with rich metadata enables both humans and machines to understand what the workflow does and supports discovery by search engines [62]. The RO-Crate (Research Object Crate) specification provides a method for packaging research data with associated metadata [62]. For chemical workflows, metadata should include detailed information about experimental conditions, instrument parameters, and chemical identifiers [56].

Table 2: Essential Metadata Elements for FAIR Chemical Data Workflows

Metadata Category	Required Elements	Chemical Data Specific Examples
Provenance	Authors, creation date, funding source	Principal investigator, synthesis date, grant number
Experimental Conditions	Temperature, pressure, time parameters	Reaction temperature, pressure, duration
Chemical Identifiers	Unique compound identifiers	InChI, InChIKey, SMILES, CAS number
Instrument Parameters	Device settings, calibration data	NMR frequency, MS ionization method, HPLC column type
Data Processing	Transformation methods, algorithms	Baseline correction method, peak identification threshold

Accessibility Implementation Strategies

Public Code Repositories: Making workflow source code available in public code repositories like GitHub, GitLab, or Bitbucket ensures accessibility using commonly used communication protocols (HTTPS or SSH) [62]. The Git protocol, free of charge and implementable on any system, represents a recommended solution for workflow accessibility [62].

Example Data Provision: Providing example input data and results alongside workflows helps users understand functionality and improves reproducibility [62]. For sensitive chemical data, synthetic data can be generated that mimics original data distributions while protecting privacy [62]. Example data also verifies user configuration when moving workflows between computational environments.

Interoperability Implementation Strategies

Community Standards and Reporting Formats: Achieving interoperability requires using community-developed standards and reporting formats [11]. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—help make data more accessible and reusable [11]. For environmental science with chemical data, relevant reporting formats include guidelines for water and sediment chemistry, soil respiration, and leaf-level gas exchange [11].

Standardized Data Formats: Chemical workflows should employ established data formats such as JCAMP-DX for spectral data, CIF (Crystallographic Information Framework) for crystal structures, and nmrML for NMR data [56]. These standardized formats ensure that data can be interpreted across different computational systems and research groups.

Reusability Implementation Strategies

Comprehensive Documentation: Reusability requires thorough documentation of workflows, including detailed experimental conditions, instrument settings, and processing steps [56]. For chemical data, this includes complete information about sample preparation, reaction conditions, and purification methods [56].

Clear Licensing and Provenance: Applying clear, machine-readable licenses to all datasets and tracking complete data provenance ensures that reuse conditions are understood and data quality can be assessed [62] [56]. Provenance should document the complete data generation workflow from initial acquisition through all processing steps [56].

Workflow Architecture and Technical Specifications

Computational Workflow Components

Computational workflows are a special type of software characterized by: (1) the composition of multiple components that include other software, workflows, code snippets, tools, and services; and (2) the explicit abstraction from run mechanics in some form of high-level workflow language that specifies data flow between components [65]. A workflow management system (WMS) handles data flow and/or execution control, abstracting the workflow from underlying digital infrastructure [65].

FAIR Chemical Data Workflow Architecture: This diagram illustrates the integrated workflow architecture for implementing FAIR principles in chemical research, showing the progression from research planning through data acquisition, processing, and FAIR implementation.

Workflow Management Systems and Languages

Multiple workflow management systems exist with varying capabilities and specialization. The computational workflow ecosystem includes more than 350 different workflow management systems of varying maturity [65]. Common systems used in scientific research include:

• Nextflow: Enables scalable and reproducible workflows [62]
• Snakemake: Python-based workflow management system [62]
• Galaxy: Web-based platform for data-intensive biomedical research [62]
• Common Workflow Language (CWL): Standard for describing analysis workflows and tools [62]

These systems provide benefits including abstraction, scaling, automation, reproducibility, and provenance tracking [65]. They facilitate error handling and restarting, automatic data staging, provenance recording, handling of large datasets, and distributed task execution across computing environments [65].

Essential Tools and Infrastructure

Research Reagent Solutions for FAIR Workflows

Table 3: Essential Research Reagent Solutions for FAIR Chemical Data Workflows

Tool Category	Specific Solutions	Function in FAIR Workflows
Workflow Management Systems	Nextflow, Snakemake, Galaxy, CWL [62]	Orchestrate computational steps, ensure reproducibility, manage data flow
Chemical Registries	PubChem, NORMAN-SLE, MassBank [63]	Provide reference data, chemical identifiers, and spectral libraries
Persistent Identifier Services	DataCite, Zenodo [62]	Assign DOIs and other persistent identifiers to datasets and workflows
Metadata Standards	EDAM Ontology, ISA Framework, RO-Crate [62] [64]	Provide structured formats for rich metadata description
Repository Platforms	WorkflowHub, Dockstore, ESS-DIVE, Harvard Dataverse [62] [11] [66]	Host and share workflows, data, and associated research products
Chemical Structure Representation	InChI, SMILES, MInChI (for mixtures), NInChI (for nanomaterials) [56] [63]	Unambiguously represent chemical structures in machine-readable forms

Cross-Domain Reporting Formats

For environmental science with chemical data, community-developed reporting formats enable consistent formatting of diverse data types. These include both cross-domain formats applicable across scientific disciplines and domain-specific formats for particular data types [11]:

• Cross-domain formats: Dataset metadata, file-level metadata, CSV formatting guidelines, sample metadata, location metadata, and terrestrial model data archiving [11]
• Domain-specific formats: Amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, and sensor-based hydrologic measurements [11]

These reporting formats balance pragmatism for scientists with machine-actionability emblematic of FAIR data, including minimal required metadata fields necessary for programmatic data parsing and optional fields that provide detailed spatial/temporal context [11].

Experimental Protocols for FAIR Implementation

Protocol: Establishing FAIR Chemical Data Workflows

Objective: Implement an end-to-end workflow for chemical data generation, processing, and sharing that embeds FAIR principles throughout the research lifecycle.

Materials and Tools:

• Electronic Laboratory Notebook (ELN) system
• Workflow Management System (Nextflow, Snakemake, or similar)
• Standardized chemical data templates based on community reporting formats
• Repository platform supporting FAIR principles (e.g., WorkflowHub, ESS-DIVE)

Procedure:

• Research Planning Phase:
  • Document experimental design in ELN using community-standard templates
  • Pre-register study design where appropriate
  • Identify appropriate data repositories and required metadata standards

• Data Acquisition Phase:

  • Use standardized data collection templates aligned with reporting formats
  • Assign unique sample identifiers and link to experimental conditions
  • Capture instrument metadata automatically where possible

• Data Processing Phase:

  • Implement processing steps in workflow management system
  • Containerize tools using Docker or Singularity for reproducibility
  • Capture provenance information for all data transformations

• FAIR Implementation Phase:

  • Annotate final datasets with rich metadata using controlled vocabularies
  • Deposit data in appropriate FAIR-aligned repository
  • Obtain persistent identifiers and link to related publications

Validation:

• Verify workflow using example input data with known expected outputs
• Test portability across different computational environments
• Confirm that all FAIR principles are addressed in the workflow outputs

Protocol: Implementing IUPAC Identifiers for Chemical Data Interoperability

Objective: Ensure chemical structures are unambiguously identified and interoperable across systems using IUPAC standards.

Background: The IUPAC International Chemical Identifier (InChI) provides a machine-readable way of describing chemical structures that is essential for FAIR chemical data [56]. Extensions including MInChI for mixtures and NInChI for nanomaterials enable identification of more complex chemical entities [63].

Procedure:

• For each chemical substance in the study, generate standard InChI identifiers
• For complex mixtures, apply MInChI to capture composition information
• For nanomaterials, implement NInChI to represent composition, size, shape, and surface characteristics
• Include InChI identifiers in all dataset metadata and repository submissions
• Use InChI-based searching to link related chemical records across resources

Applications:

• Chemical risk assessment projects like PARC use InChI identifiers to link chemical metadata with datasets on environmental occurrence and toxicity [63]
• The NORMAN Suspect List Exchange uses InChI and InChIKey to annotate substances for environmental monitoring [63]
• Nanomaterials research utilizes NInChI to enable finding and matching related records across data sources [63]

Integrating FAIR practices into research operations requires both technical solutions and cultural shifts. Workflows provide the necessary bridge between FAIR principles as theoretical concepts and their practical implementation in daily research activities. For environmental science with chemical data, this means embedding FAIR practices directly into experimental design, data collection, processing, and sharing procedures.

The technical framework presented in this guide—encompassing workflow registration, rich metadata, standardized formats, and comprehensive documentation—provides a pathway for researchers to systematically implement FAIR principles. By leveraging community-developed standards, reporting formats, and workflow technologies, researchers can transform FAIR from an aspiration into standard practice, ultimately enhancing research reproducibility, collaboration, and impact.

As the research community continues to develop infrastructure and standards for FAIR data, workflow integration will play an increasingly critical role in ensuring that data management practices keep pace with data generation. The ongoing work of initiatives like WorldFAIR, NFDI4Chem, and various research data alliances demonstrates the global commitment to realizing the full potential of FAIR data through practical, implementable workflow solutions [63].

In environmental science and drug development, effectively managing the life cycle of chemical data—from discovery to dissemination—is paramount for accelerating scientific discovery and regulatory decision-making. The FAIR Guiding Principles (Findable, Accessible, Interoperable, and Reusable) provide a framework for enhancing the utility of research data [11]. However, the immense diversity of data types in environmental and chemical research presents a significant challenge for standardization [11]. Reporting formats and metadata templates have emerged as critical tools to bridge this gap, serving as practical, community-centric instruments that translate the high-level FAIR principles into actionable and consistent reporting workflows [67]. This guide explores the ecosystem of tools, from user-friendly applications like ezEML to programmable pipelines and customizable templates, that empower researchers to create FAIR chemical data.

The Metadata Toolbox for Researchers

A range of tools exists to assist researchers in creating high-quality metadata, each catering to different use cases and technical proficiencies. The table below summarizes the core tools available.

Table 1: Core Metadata Creation Tools for Environmental and Chemical Research

Tool Name	Type	Primary Use Case	Key Features	Best For
ezEML [68]	Web Application	Streamlined EML creation	Form-based wizard; collaboration features; built-in validation	Users new to EML or those who need a user-friendly, guided interface
EMLassemblyline [68]	R Package	Automated EML generation within scripts	Function-based metadata creation; integrates with R workflows; extensible with the EML R package	Researchers working programmatically in R or those automating metadata for large/data projects
CEDAR Embeddable Editor [69]	Web Component	Custom, field-specific metadata templates	Embeds specialized templates in platforms like OSF; machine-readable JSON output	Communities with specialized metadata needs (e.g., cognitive neuroscience, social science)
BART (Biotransformation Reporting Tool) [1]	Excel Template	Standardized reporting of biotransformation data	Templates for compounds, connectivity, kinetics, and experimental scenarios; designed for PFAS and other chemicals	Experimentalists reporting biotransformation pathways and kinetics for meta-analysis

User-Friendly Applications: ezEML

For researchers seeking a guided, form-based experience, ezEML is an online application designed to simplify the creation of Ecological Metadata Language (EML) files [68]. EML is an XML metadata standard optimized for the ecological and environmental sciences, detailing the who, what, when, where, and how of a dataset [68]. ezEML simplifies this complex standard by presenting users with a relatively small subset of fields required for many common data scenarios [68].

Key functionalities of ezEML include:

• Step-by-Step Wizard: Guides users through the EML document creation process, ensuring critical elements like <title>, <abstract>, <temporalCoverage>, and <geographicCoverage> are properly defined [68].
• Collaboration and Templates: Supports collaboration through import/export features and allows research groups to create and share boilerplate metadata templates [68].
• Validation: Includes features to check the generated EML for correctness and completeness before publication [68].

Programmatic and Automated Tools: EMLassemblyline

For researchers who operate within programmatic workflows or need to automate metadata generation for large or recurrent projects, EMLassemblyline is an R package that fills this need [68]. It automates the creation of EML metadata within R scripts and is extensible through the lower-level EML R package [68].

Key functionalities of EMLassemblyline include:

• Batch Processing: Efficiently generates metadata for multiple data objects or complex datasets.
• Workflow Integration: Seamlessly fits into data processing and analysis pipelines written in R.
• Flexibility: While optimized for automation, it is also effective for creating a single EML metadata file, offering more direct control than ezEML for advanced users [68].

Customizable and Embedded Templates: The CEDAR Approach

The limitation of general-purpose metadata schemas is their inability to capture the nuanced details required for specific scientific domains. The integration of the CEDAR Embeddable Editor into platforms like the Open Science Framework (OSF) addresses this by allowing the use of specialized, community-developed metadata templates [69].

How it works:

• Community experts develop a machine-readable metadata schema in CEDAR for their specific field (e.g., human cognitive neuroscience, educational research) [69].
• This schema is made available in OSF as a specialized template [69].
• Researchers select the template and fill out the form within OSF, annotating their research objects with high-quality, domain-specific metadata [69].
• The metadata can be displayed on public projects and downloaded in a JSON file, making it both human- and machine-readable [69].

Domain-Specific Reporting: BART for Biotransformation Data

The Biotransformation Reporting Tool (BART) is a prime example of a domain-specific template designed to make chemical data FAIR. It addresses the critical challenge of predicting the environmental fate and persistence of chemicals, which requires large, high-quality, machine-readable datasets of biotransformation pathways and kinetics [1]. BART is a Microsoft Excel template that standardizes the reporting of:

• Compound Structures using Simplified Molecular Input Line Entry System (SMILES) notations.
• Pathway Connectivity in a tabular format that defines reactants and products.
• Experimental Scenarios and key parameters (e.g., inoculum source, pH, temperature).
• Biotransformation Kinetics and identification confidence levels [1].

This structured approach prevents the common issue of data being "locked" in non-machine-readable pathway figures within publications, thereby enabling the development of predictive models for chemicals like PFAS [1].

Experimental Protocols for FAIR Biotransformation Data

The following protocol outlines the methodology for generating and reporting biotransformation data using standardized tools like BART, based on current best practices [1].

Experimental Workflow for Biotransformation Studies

The diagram below illustrates the key stages of a biotransformation study, from experimental design to data publication, highlighting steps critical for FAIR compliance.

Detailed Methodology

1. Experimental Design and Setup

• Test System Selection: Choose an environmentally relevant inoculum such as aerobic activated sludge, soil, or water-sediment systems [1].
• Scenario Configuration: In the BART template's Scenario tab, record key parameters. For an aerobic sludge system, this includes:
  • Inoculum Provenance: Location and type of wastewater treatment plant.
  • Process Parameters: Solids retention time, pH, temperature, and dissolved oxygen concentration [1].
  • Spike Compound: Provide the SMILES string and initial concentration of the test chemical.

2. Sample Analysis and Compound Identification

• Instrumental Analysis: Use high-resolution mass spectrometry (HR-MS) coupled with liquid or gas chromatography to detect the parent compound and its transformation products.
• Structural Annotation: For each tentatively identified compound, report its SMILES string in the BART Compounds tab. Annotate identification confidence using the Schymanski Confidence Levels or PFAS Confidence in Identification (PCI) Levels in the Kinetics_Confidence tab [1].

3. Data Curation and Pathway Elucidation

• Define Connectivity: In the BART Connectivity tab, represent the biotransformation pathway as a series of reactions. List each reactant and product using their unique compound identifiers from the Compounds tab. The tool allows for flagging multistep reactions and specifying multiple products [1].
• Kinetic Analysis: Calculate dissipation half-lives or transformation rate constants for the parent compound and significant transformation products. Report these values alongside their corresponding reaction in the Kinetics_Confidence tab.

A Framework for Community-Driven Reporting Formats

The development of effective reporting formats is most successful when driven by community consensus. The process undertaken by the ESS-DIVE repository to create 11 diverse (meta)data reporting formats offers a replicable model [11].

Table 2: Guidelines for Developing Community-Centric Reporting Formats [11]

Step	Description	Key Outcome
1. Review Existing Standards	Conduct a comprehensive review of pre-existing data standards, repositories, and systems relevant to the data type.	A crosswalk that maps terms and variables from existing resources, identifying gaps and essential elements.
2. Develop a Crosswalk	Create a tabular map comparing variables, terms, and metadata from the reviewed standards.	A clear understanding of which existing standards can be adopted and what new harmonization is needed.
3. Iterative Development	Develop templates and documentation iteratively, incorporating feedback from prospective users.	A practical and user-friendly reporting format that balances researcher pragmatism with machine-actionability.
4. Define Minimum Metadata	Assemble a minimal set of required (meta)data fields necessary for programmatic parsing and reuse.	Enhanced interoperability without overburdening data contributors. Optional fields can provide richer context.
5. Host and Mirror Documentation	Publish final documentation on multiple platforms (e.g., a repository for archiving, GitHub for versioning, GitBook for readability).	Increased findability, accessibility, and ease of maintenance for the reporting format.

The logical flow for selecting and applying a metadata tool based on the nature of the research task is summarized below.

Essential Research Reagent Solutions for Biotransformation Studies

The following table details key reagents and materials used in biotransformation experiments, which should be thoroughly documented in the metadata to ensure reproducibility.

Table 3: Key Research Reagents and Materials for Biotransformation Studies

Reagent/Material	Function in Experiment	Reporting Requirement in Metadata
Environmental Inoculum (e.g., activated sludge, soil, sediment)	Provides the microbial consortium responsible for compound biotransformation.	Report provenance, source, description (e.g., organic content, redox condition), and key parameters like solids retention time for sludge [1].
Chemical Spike Solution	Introduces the target contaminant (e.g., a PFAS compound) into the test system at a defined concentration.	Document the solvent used, spike compound structure (SMILES), and initial concentration [1].
Nutrient Media	Supports microbial health and activity during the assay, preventing bias due to nutrient limitation.	Specify the addition and composition of any nutrients (e.g., nitrogen, phosphorus) to the test system [1].
Internal Standards & Reference Compounds	Used in mass spectrometry for quantification, quality control, and confirming instrument performance.	While often method-specific, the use of specific stable isotope-labeled internal standards for target compounds should be noted in the general methods description.
Solvents & Reagents (HPLC-MS grade)	Used for sample preparation, extraction, and instrumental analysis to minimize background interference.	Report the grades and suppliers of critical solvents and reagents as part of the analytical methodology.

The path to truly FAIR chemical data in environmental science and drug development is paved with practical, community-adopted tools. From the user-friendly ezEML to the programmable EMLassemblyline, and onward to customizable templates via CEDAR and domain-specific standards like BART, researchers now have a robust toolkit at their disposal. The adoption of these tools, coupled with a community-driven approach to developing new reporting formats, is fundamental to overcoming the challenges of data diversity. By integrating these resources into their scientific workflows, researchers and drug development professionals can significantly enhance the interoperability, reusability, and overall impact of their valuable chemical data.

Measuring Success: Impact Assessment and Case Studies in FAIR Chemical Data

The adoption of the FAIR principles—making data Findable, Accessible, Interoperable, and Reusable—is revolutionizing research data management in the chemical and environmental sciences [9]. This paradigm shift addresses the growing volume and complexity of research data, with chemical research output increasing by 8-9% annually [9]. The FAIR framework provides a structured approach to ensure data can be effectively discovered, accessed, integrated, and reused by both humans and machines, creating a robust foundation for scientific progress [70]. This technical guide examines the quantitative benefits of implementing FAIR data practices, with a specific focus on data reuse patterns, citation advantages, and enhanced research efficiency within chemistry and related environmental disciplines.

The FAIR Principles in Chemical and Environmental Science

Core Principles and Chemical Context

The FAIR principles establish distinct technical requirements for each aspect of data management. Findable data must possess globally unique and persistent machine-readable identifiers, such as Digital Object Identifiers (DOIs) for datasets and International Chemical Identifiers (InChIs) for chemical structures [9]. Accessible data should be retrievable using standardized communication protocols like HTTP/HTTPS, with metadata remaining accessible even when data itself has restricted access. Interoperable data requires formal, shared languages and formats that enable integration across systems, exemplified by crystallographic information files (CIFs) for crystal structures and JCAMP-DX for spectral data [9]. Reusable data must be thoroughly described with detailed metadata, including experimental procedures, instrument settings, and processing steps to enable replication and combination in different settings [9].

Implementation in Chemistry

The chemistry community has developed specialized infrastructure to support FAIR implementation. Key repositories include the Cambridge Structural Database for crystal structures and NMRShiftDB for NMR data [9]. General-purpose repositories like Zenodo, Figshare, and Dryad also provide essential services for chemical data preservation [70]. The NFDI4Chem consortium is building specialized tools and infrastructures for FAIR chemical data, while the Go FAIR Chemistry Implementation Network collaborates with the International Union of Pure and Applied Chemistry to establish data standards and protocols [9].

Empirical evidence demonstrates a significant citation advantage for studies that make their data publicly available. A large-scale multivariate regression analysis of 10,555 gene expression microarray studies provides robust statistical evidence for this benefit.

Table 1: Citation Advantage for Studies with Publicly Available Data

Field	Sample Size	Citation Increase	Confidence Interval	Controlled Covariates
Gene Expression Microarray	10,555 studies	9%	5% to 13%	Publication date, journal impact factor, open access status, author count, author publication history, institutional factors, and study topic [71]

This analysis confirmed that studies depositing data in public repositories (Gene Expression Omnibus or ArrayExpress) received significantly more citations than similar studies that did not share data, even after controlling for numerous known citation predictors [71]. The benefit was most pronounced for papers published in 2004-2005, showing approximately a 30% citation advantage in that period [71].

Patterns of Data Reuse Over Time

The temporal patterns of data reuse reveal how scientific value accumulates beyond initial publication. Analysis of 9,724 instances of third-party data reuse through mentions of GEO or ArrayExpress accession numbers demonstrates distinct phases of data utility.

Table 2: Data Reuse Timeline for 100 Datasets Deposited in Year 0

Time Since Deposition	Cumulative Data Reuse Papers	Reuse Type
Year 2	~40 papers	Mixed self-reuse and third-party
Year 4	~100 papers	Primarily third-party
Year 5	>150 papers	Predominantly third-party [71]

Researchers typically publish most papers using their own datasets within the first two years, while third-party data reuse continues to accumulate for at least six years [71]. This demonstrates that the long-term impact of data often extends far beyond the original research team's direct use. By year 5, the intensity of data reuse had increased to over 150 publications per 100 deposited datasets, indicating growing recognition of value in existing data resources [71].

Repository Adoption and Use Across Disciplines

The adoption of data repositories varies significantly across scientific disciplines, reflecting field-specific practices and available infrastructure.

Figure 1: Data Repository Ecosystem Showing Primary Categories and Major Platforms

Analysis of repository references in scientific publications reveals accelerating adoption across domains. GitHub is overwhelmingly referenced in software and computational contexts, with nearly 50% of its references appearing in Information and Computing Sciences literature [70]. Domain-specific repositories like NOMAD and Materials Cloud show strong adoption in their target disciplines, with the majority of references coming from Chemical Sciences and Physical Sciences [70]. General repositories like Zenodo and Figshare demonstrate broad cross-disciplinary use, though Figshare shows particular strength in Biological Sciences [70].

Methodologies for Quantifying Data Reuse

Robust quantification of data reuse benefits requires careful methodological approaches that control for confounding variables.

Data Collection and Sample Identification

• Population Definition: Identify a homogeneous set of research outputs, such as 10,555 gene expression microarray studies published 2000-2009 [71]
• Citation Sourcing: Collect citation counts from established bibliographic databases (e.g., Scopus) to ensure comprehensive coverage [71]
• Data Availability Determination: Verify public data deposition through repository queries (e.g., GEO using "pubmed_gds [filter]" and ArrayExpress via database matching) [71]

Covariate Selection and Control

• Temporal Factors: Publication date accounting for citation accumulation time [71]
• Journal Factors: Impact factor, citation half-life, article volume, and open access policy [71]
• Author Factors: Corresponding author country, institutional citation history, publication experience of first and last authors [71]
• Content Factors: Research topic and methodological focus [71]

Statistical Analysis

• Multivariate Regression: Employ linear regression with logarithmically transformed citation counts as the dependent variable [71]
• Model Validation: Assess multicollinearity among covariates and model fit statistics [71]
• Sensitivity Analysis: Examine different time cohorts and repository-specific effects [71]

Experimental Protocol: Direct Data Reuse Tracking

Complementary to citation analysis, direct tracking of data reuse provides more granular understanding of patterns.

Accession Number Extraction

• Full-Text Mining: Identify repository accession numbers (e.g., GEO or ArrayExpress) within research articles beyond formal citation sections [71]
• Context Analysis: Manually review citation contexts to distinguish between reuse citations and other types of citations [71]

Reuse Metric Development

• Reuse Intensity: Calculate publications per dataset over time since deposition [71]
• Reuse Distribution: Determine the proportion of datasets reused at least once (e.g., 20% of datasets deposited 2003-2007) [71]
• Cross-Dataset Analysis: Track how many datasets are used together in reuse studies [71]

Research Efficiency Gains from FAIR Implementation

Reduced Data Wrangling Time

Implementation of FAIR principles fundamentally shifts researcher effort from data preparation to analysis and interpretation. Currently, approximately 80% of effort regarding data goes into data wrangling and preparation, with only 20% dedicated to actual research and analytics [9]. This inefficiency stems from non-standardized data formats, incomplete metadata, and inconsistent documentation practices. FAIR-aligned data management creates structured workflows that significantly reduce this overhead through standardized reporting formats, machine-readable metadata, and persistent identifiers.

Community Reporting Formats

Standardized reporting formats have emerged as powerful tools for enhancing research efficiency across Earth and environmental sciences, with direct applicability to chemical research [11]. These community-developed formats provide templates, instructions, and tools for consistently formatting data within specific disciplines [11]. The Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) has developed 11 reporting formats covering cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines, and domain-specific formats for biological, geochemical, and hydrological data [11].

Table 3: Essential Research Reagent Solutions for FAIR Data Implementation

Tool Category	Specific Solutions	Function	Chemical Science Application
Persistent Identifiers	Digital Object Identifiers (DOIs), International Chemical Identifier (InChI)	Provide globally unique, machine-readable identifiers for datasets and chemical structures [9]	Enables precise chemical structure searching and dataset linking
Repository Platforms	Zenodo, Figshare, Chemotion, Cambridge Structural Database	Long-term preservation and access to research data with citation capabilities [70] [9]	Domain-specific repositories for chemical structures and spectra
Metadata Standards	Crystallographic Information Files (CIF), JCAMP-DX, nmrML	Standardized machine-readable formats for specific data types [9]	Ensures interoperability of analytical data across platforms
Electronic Lab Notebooks	LabArchive, RSpace, eLabJournal	Structured data capture at point of generation with FAIR support [9]	Integrates data management into experimental workflow

Workflow Integration

Figure 2: FAIR Data Implementation Workflow Integrating with Research Lifecycle

The integration of FAIR practices directly into research workflows creates a virtuous cycle of efficiency. As shown in Figure 2, proper data management informed by FAIR principles accelerates data sharing and enables more effective reuse, which in turn informs future experimental design [11]. This approach is particularly valuable for interdisciplinary research integrating chemical, environmental, and biological data, where consistent formatting and documentation is essential for cross-domain synthesis [11].

Implementation Framework for FAIR Chemical Data

Practical Checklist for Research Groups

Successful adoption of FAIR principles requires systematic implementation at the research group level.

Findability Enhancements

• Assign DOIs or other persistent identifiers to all datasets through repositories like Dataverse, Figshare, or Dryad [9]
• Use International Chemical Identifiers (InChIs) for all chemical structures [9]
• Create comprehensive metadata describing experimental conditions and deposit in searchable resources [9]

Accessibility Protocols

• Ensure data retrieval via standard web protocols (HTTP/HTTPS) [9]
• Clearly document access restrictions and authentication requirements [9]
• Separate metadata from data to ensure metadata remains accessible even if data is restricted [9]

Interoperability Standards

• Use established chemistry data formats (CIF, JCAMP-DX, nmrML) [9]
• Apply community-agreed metadata standards and controlled vocabularies [9]
• Include structured experimental procedures in machine-readable formats [9]

Reusability Optimization

• Document complete experimental conditions, instrument settings, and calibration data [9]
• Apply clear, machine-readable licenses (e.g., CC-BY, CC0) to all datasets [9]
• Provide complete provenance of data transformation and processing steps [9]

Infrastructure and Policy Support

Effective FAIR implementation requires supporting infrastructure and policy frameworks. The Enabling FAIR Data project brought together more than 300 cross-sector leaders to improve data handling in earth, space, and environmental sciences, developing resources including a Repository Finder Tool and Data Management Training Clearinghouse [72]. Funding agencies are increasingly mandating FAIR-aligned data management plans, with organizations like the European Research Council and National Institutes of Health requiring open access and proper data management [9]. Journal publishers are implementing author guidelines that require data deposition in FAIR-aligned repositories, moving beyond supplementary information files [72].

The quantitative evidence demonstrates clear and measurable benefits from implementing FAIR data principles in chemical and environmental research. The 9% citation advantage for data-sharing studies, combined with the long-term accumulation of data reuse and significant efficiency gains from reduced data wrangling, provides a compelling case for adopting FAIR practices. The methodological frameworks for quantifying reuse and the practical implementation tools now available lower barriers to adoption. As research becomes increasingly data-intensive and interdisciplinary, FAIR principles provide the essential foundation for accelerating discovery, enhancing collaboration, and maximizing the return on research investments. The ongoing development of community standards, repository infrastructure, and policy support will further strengthen the ecosystem for FAIR chemical data in the coming years.

Per- and polyfluoroalkyl substances (PFAS) represent a class of over 4,000 human-made chemicals characterized by their extreme environmental persistence and potential bioaccumulation, earning them the colloquial name "forever chemicals." The environmental science community faces significant challenges in managing, sharing, and reusing PFAS biotransformation data due to inconsistent reporting formats and methodological approaches. This analysis examines the current state of PFAS biotransformation research within the framework of Findable, Accessible, Interoperable, and Reusable (FAIR) data principles, which emphasize machine-actionability to handle increasing data volume and complexity [3]. By implementing community-centric data reporting formats and standardization protocols, researchers can accelerate the development of predictive models for PFAS fate and transport, ultimately supporting improved regulatory decision-making and remediation strategies.

PFAS are structurally diverse chemicals containing at least one fully fluorinated methyl or methylene carbon atom, contributing to their exceptional stability and surfactant properties [73]. The strength of the carbon-fluorine bond (approximately 116 kcal/mol) presents the primary challenge for biotic and abiotic transformation, though certain microorganisms have demonstrated capability to biotransform specific PFAS structures under controlled conditions [73]. Current research has predominantly focused on a limited subset of PFAS, including 8:2 fluorotelomer alcohol (8:2 FTOH), 6:2 fluorotelomer alcohol (6:2 FTOH), perfluorooctanesulfonic acid (PFOS), and perfluorooctanoic acid (PFOA), leaving significant knowledge gaps for many emerging alternatives [73].

The environmental fate community confronts substantial data interoperability hurdles, as biotransformation studies typically present pathway information as 2D images of reactant and product compounds connected by arrows representing singular reaction steps [1]. These visual representations, while intuitively understandable to researchers, are not readily translatable to machine-readable formats essential for meta-analysis and predictive modeling. This formatting limitation creates a critical bottleneck in developing comprehensive understanding of PFAS environmental behavior, particularly as regulatory pressure increases to include hazardous transformation products in chemical risk assessment [1].

Quantitative Analysis of PFAS Biotransformation Literature

A comprehensive meta-analysis of 97 published studies from 1989 to 2023, encompassing 288 experimental conditions, revealed significant trends and gaps in PFAS biotransformation research [73]. The analysis examined more than 100 fluorinated compounds, with data extracted and standardized to enable statistical comparison across studies. The findings provide crucial insights for prioritizing future research directions and resource allocation.

Table 1: Factors Influencing PFAS Biotransformation Likelihood Based on Meta-Analysis [73]

Factor	Impact on Biotransformation Likelihood	Notes
Redox Conditions	Higher under aerobic conditions	Anaerobic transformation poorly characterized
Microbial Culture	Higher in defined/axenic cultures	Complex communities present identification challenges
PFAS Concentration	Higher with elevated concentrations	Dose-response relationships not fully quantified
Fluorine Content	Higher with fewer fluorine atoms	Fully saturated compounds most recalcitrant
Chain Length	Shorter chains generally more susceptible	Interaction with functional groups observed
Chain Branching	Geometry influences accessibility	Structural complexity impedes enzymatic attack
Headgroup Chemistry	Critical determinant of transformation pathways	Functional groups affect binding and recognition

Table 2: Research Focus Disparities in PFAS Biotransformation Studies [73]

Research Aspect	Current Status	Priority Knowledge Gaps
Anaerobic Studies	Scarce/lacking	Well-defined electron acceptors/donors, carbon sources, and oxidation-reduction potentials
Transformation Products	Incompletely characterized	Comprehensive identification and quantification of intermediates and terminal products
Microbial Identification	Limited	Microorganisms and enzymes responsible for biotransformation reactions
PFAS Structural Diversity	Narrow focus	Majority untested for biotransformation potential
Kinetic Parameters	Insufficient data	Half-lives and rate constants for predictive modeling

The meta-analysis identified that the literature is particularly scarce in anaerobic PFAS biotransformation experiments with well-defined electron acceptors, electron donors, carbon sources, and oxidation-reduction potentials [73]. This represents a critical research gap given the prevalence of anaerobic conditions in many contaminated subsurface environments where PFAS are frequently detected.

FAIR Data Principles and Reporting Standards

The FAIR principles provide a framework for enhancing data utility by emphasizing machine-actionability—the capacity of computational systems to find, access, interoperate, and reuse data with minimal human intervention [3]. This approach is particularly relevant for PFAS biotransformation data, given the rapid expansion of chemical space and the complexity of transformation pathways.

Community-Centric Reporting Formats

Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—serve as practical implementations of FAIR principles for specific research communities [11]. Unlike formal accredited standards, which can take over a decade to develop, reporting formats are community efforts aimed at harmonizing diverse environmental data types without extensive governing protocols [11]. For PFAS research, such formats can facilitate data sharing within research groups, provide guidelines for consistent data collection, enable streamlined scientific workflows, and support long-term preservation of knowledge that might not otherwise be captured [11].

The development of effective reporting formats typically follows a structured process including: (1) reviewing existing standards; (2) developing a crosswalk of terms across relevant standards or ontologies; (3) iteratively developing templates and documentation with user feedback; (4) assembling a minimum set of metadata required for reuse; and (5) hosting documentation on platforms that can be publicly accessed and updated easily [11].

Biotransformation Reporting Tool (BART)

The Biotransformation Reporting Tool (BART) represents a specialized implementation of FAIR principles for chemical contaminant biotransformation data [1]. This Microsoft Excel template provides standardized fields for reporting key experimental parameters and results in a machine-readable format. BART includes four primary components:

• Compounds Tab: Records chemical structures as Simplified Molecular Input Line Entry Specifications (SMILES) strings, enabling computational structure manipulation and analysis [1].
• Connectivity Tab: Captures pathway structure as a list of biotransformations in tabular format, indicating reactants and products for each step [1].
• Scenario Tabs: Documents experimental setup and environmental conditions using standardized terminologies [1].
• Kinetics_Confidence Tab: Records biotransformation kinetics and identification confidence levels, including Schymanski Confidence Levels or PFAS Confidence in Identification (PCI) Levels for mass spectrometry-based identifications [1].

The template accommodates complex scenarios such as multistep reactions where multiple enzymatic steps are hypothesized but not fully elucidated, and cases where stereoisomeric transformation products cannot be fully resolved [1].

FAIR Data Implementation Flow

Experimental Protocols for PFAS Biotransformation Studies

Standardized methodological approaches are essential for generating comparable data across laboratories and research initiatives. Based on analysis of current literature, the following protocols represent best practices for PFAS biotransformation research.

Aerobic Biotransformation Assays

Aerobic conditions have demonstrated higher likelihood of PFAS biotransformation based on meta-analysis findings [73]. Recommended protocols include:

• Inoculum Preparation: Source activated sludge from wastewater treatment plants with documented biological treatment technology, solids retention time, and operational parameters [1]. Characterize inoculum for organic content, redox condition, and microbial community composition where feasible.
• Experimental Setup: Maintain pH within environmental relevance range (5-9) using appropriate buffer systems [1]. Use bioreactor configurations with controlled aeration and continuous monitoring of dissolved oxygen concentration.
• Compound Addition: Prepare PFAS stock solutions in suitable solvents, with documentation of solvent type and concentration [1]. Spike concentrations should reflect both environmentally relevant levels (ng/L-μg/L) and higher concentrations (mg/L) to elucidate kinetics.
• Monitoring and Analysis: Collect time-series data for parent compound depletion and transformation product formation using liquid chromatography-mass spectrometry (LC-MS) [1]. Report identification confidence levels using established frameworks.

Anaerobic Biotransformation Assays

While less studied, anaerobic biotransformation represents a critical knowledge gap requiring standardized protocols:

• Redox Condition Control: Establish and maintain specific anaerobic conditions (nitrate-reducing, iron-reducing, sulfate-reducing, methanogenic) with documented electron acceptors and donors [73] [74].
• Sediment-Water Systems: Characterize sediment origin, sampling depth, bulk density, cation exchange capacity, and sediment texture (% sand, silt, clay) [1]. Document organic carbon content in both water layer and sediment phase.
• Analytical Considerations: Implement specialized sampling techniques to maintain anaerobic conditions during sample collection and processing. Include appropriate controls for abiotic transformation and sorption losses.

Analytical Framework for Transformation Product Identification

Comprehensive identification of transformation products remains a significant challenge in PFAS research. Recommended approaches include:

• High-Resolution Mass Spectrometry: Employ LC-QToF systems capable of accurate mass measurements for elemental composition determination [75].
• Confidence Level Reporting: Apply Schymanski Confidence Levels or PFAS-specific PCI Levels to communicate identification certainty [1].
• Multimodal Analysis: Combine complementary techniques including nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS) where feasible to resolve isomeric structures [75].

Data Harmonization Workflow Implementation

The transformation of disconnected PFAS biotransformation data into FAIR-compliant datasets requires systematic implementation of harmonization workflows. The following diagram illustrates the complete pathway from experimental data generation to reusable data resources.

PFAS Data Harmonization Pipeline

Compound Structure Encoding

Chemical structures must be represented in machine-readable formats to enable computational analysis and cross-study comparison. The use of Simplified Molecular Input Line Entry Specifications (SMILES) provides a compact, unambiguous representation that facilitates structure searching, similarity analysis, and property prediction [1]. For PFAS structures, special attention should be given to:

• Perfluoroalkyl Chain Length: Accurate representation of CF2 repeat units and branching patterns.
• Headgroup Variants: Standardized encoding of functional groups including carboxylic acids, sulfonic acids, sulfonamides, and alcohol moieties.
• Isomeric Forms: Differentiation of linear and branched isomers where applicable, with designated stereochemistry when known.

Pathway Connectivity Mapping

Biotransformation pathways should be represented as connected reaction networks rather than static images. The BART Connectivity tab enables this by documenting reactant-product relationships in tabular format, specifying:

• Primary Reactant: The compound undergoing transformation, referenced by unique identifier.
• Transformation Products: All identified products resulting from the biotransformation reaction.
• Reaction Stoichiometry: Molar relationships between reactants and products where quantified.
• Confidence Indicators: Level of evidence supporting each proposed transformation step.

Experimental Metadata Annotation

Comprehensive metadata collection is essential for data reinterpretation and cross-study analysis. Critical metadata categories for PFAS biotransformation studies include:

Table 3: Essential Metadata Categories for PFAS Biotransformation Studies [11] [1]

Metadata Category	Required Elements	FAIR Compliance Benefit
Inoculum Characteristics	Source, provenance, biological treatment technology, solids retention time	Enables experimental reproducibility and cross-study comparison
Environmental Parameters	pH, temperature, redox potential, oxygen demand	Supports extrapolation to field conditions
System Geometry	Reactor configuration, spike concentration, solvent details	Facilitates kinetic model parameterization
Analytical Methods	Extraction techniques, instrumentation, identification confidence levels	Allows appropriate data interpretation and uncertainty quantification
Temporal Framework	Sampling frequency, experiment duration, lag phases	Enables kinetic rate calculation and half-life determination

The Researcher's Toolkit: Essential Research Reagents and Materials

Standardized materials and analytical tools are fundamental for generating comparable PFAS biotransformation data across research laboratories. The following table summarizes critical components of the PFAS researcher's toolkit.

Table 4: Essential Research Reagents and Materials for PFAS Biotransformation Studies

Item Category	Specific Examples	Function and Application
Reference Standards	PFOA, PFOS, 6:2 FTOH, 8:2 FTOH, PFHxA, GenX	Analytical quantification, method calibration, and recovery determination
Mass Spec Internal Standards	(^{13}C)-labeled PFAS isotopes, mass-labeled analogs	Isotope dilution quantification, correction for matrix effects
Culture Inocula	Activated sludge, sediment slurries, defined microbial consortia	Biocatalyst source for transformation studies, community function assessment
Analytical Columns	C18 reverse phase, porous graphitic carbon, HILIC	Chromatographic separation of PFAS and transformation products
Extraction Materials	Solid-phase extraction cartridges (WAX, GCB, C18), solvents	Sample preparation, concentration, and cleanup prior to analysis
Quality Controls	Laboratory blanks, matrix spikes, duplicate samples	Data quality assurance, contamination monitoring, precision assessment

The harmonization of PFAS biotransformation data through FAIR principles and standardized reporting formats represents a critical step toward addressing the environmental challenges posed by these persistent contaminants. Current research indicates that PFAS biotransformation depends on multiple factors including chain length, chain branching geometries, headgroup chemistry, and environmental conditions [73]. However, significant knowledge gaps remain, particularly for anaerobic transformation pathways, emerging PFAS alternatives, and enzyme systems responsible for defluorination reactions.

Future research priorities should include:

• Expanded Structural Coverage: Systematic investigation of biotransformation potential across diverse PFAS classes beyond the currently studied compounds (8:2 FTOH, 6:2 FTOH, PFOS, PFOA) [73].
• Mechanistic Elucidation: Identification of specific microorganisms and enzymes responsible for PFAS biotransformation reactions, particularly defluorination mechanisms [74].
• Standardized Kinetics: Development of harmonized protocols for determining biotransformation rate constants and half-lives across different environmental matrices [1].
• Model Integration: Incorporation of standardized biotransformation data into predictive models for chemical fate and exposure assessment [1].

The implementation of community-driven reporting formats like BART, combined with increased data sharing through platforms such as enviPath, will substantially enhance the utility of PFAS biotransformation research for regulatory decision-making and remediation strategy development [1]. By adopting these standardized approaches, the environmental research community can transform fragmented data into predictive knowledge, ultimately supporting the development of safer chemical alternatives and effective remediation technologies for contaminated sites.

The Findable, Accessible, Interoperable, and Reusable (FAIR) principles have emerged as a foundational framework for managing scientific data in the era of data-intensive research. Within environmental science and chemical reporting, implementing these principles effectively presents both unique challenges and critical opportunities for advancing research on chemical contaminants. The accurate prediction of environmental fate for pollutants, such as per- and polyfluoroalkyl substances (PFAS), relies heavily on large, high-quality, machine-readable datasets for training predictive models [1]. Despite this need, available data sets are often limited in size, coverage of chemical space, and machine-readability, creating a significant bottleneck for environmental research and regulatory decision-making [1]. This review provides a comparative analysis of FAIR implementation strategies across major data repositories and community initiatives, with specific focus on applications in chemical data reporting for environmental science research. We examine technical architectures, methodological approaches, and emerging best practices that enable researchers to overcome current limitations in data interoperability and reuse, thereby facilitating more robust chemical risk assessment and management.

FAIR Principles in Environmental and Chemical Contexts

The FAIR principles represent a paradigm shift in scientific data management, emphasizing machine-actionability alongside human understanding. For environmental chemical data, this translates to specific technical requirements: chemical structures must be represented using standardized notations (e.g., SMILES), experimental conditions must be comprehensively documented, and transformation pathways must be encoded in machine-readable formats [1]. The environmental sciences face particular challenges in achieving cross-domain interoperability, as chemical fate data must often be integrated with biological, hydrological, and geological datasets to develop comprehensive environmental models [11].

Community-centric approaches to developing reporting formats have proven essential for addressing the unique metadata requirements of chemical and environmental data. These approaches typically involve reviewing existing standards, developing crosswalks of terms across relevant ontologies, iteratively developing templates with user feedback, and assembling a minimum set of metadata required for reuse [11]. The Biotransformation Reporting Tool (BART), for instance, exemplifies this approach by providing a standardized template for reporting biotransformation pathways and kinetics in a FAIR-compliant manner [1]. Such domain-specific implementations balance pragmatism for scientists with the machine-actionability required by modern data science approaches, effectively bridging the gap between laboratory research and computational analysis.

Comparative Analysis of Repository Implementation Strategies

Repository-Specific FAIR Implementation

Repository/Initiative	Primary Domain	Key FAIR Features	Chemical Data Specialization	Metadata Standards
enviPath [1]	Biotransformation Informatics	- Electronic pathway representation- Template-based data submission (BART)- Integration with prediction tools	PFAS biotransformation database; SMILES for chemical structures; reaction connectivity tables	Custom scenario parameters; Schymanski Confidence Levels; PFAS Confidence in Identification
ESS-DIVE [11]	Environmental Systems Science	- Community-developed reporting formats- Modular framework- GitHub-based version control	Sample-based water/soil chemistry; microbial amplicon tables; leaf-level gas exchange	Cross-domain metadata (dataset, location, sample); CSV formatting guidelines; terrestrial model archiving
Australian Research Data Commons [76]	Cross-Domain Research	- Thematic communities (people, planet, HASS)- Cloud-based infrastructure- Translation between domains	Emphasis on interoperability across disciplines; common standards for data integration	Standardized metadata descriptions; harmonized vocabularies; cross-domain mapping
CABI/Agricultural Development [77]	Agricultural Science	- FAIR Process Framework- Human-centered design- Context-specific adaptation	Soil Information Systems; pest and disease data; digital plant health services	Data Management and Access Plan (DMAP); FAIR Potential Assessment Tool

Technical Architectures and Workflows

The implementation of FAIR principles across repositories reveals diverse technical architectures optimized for specific research communities. enviPath employs a specialized data model for representing biotransformation pathways, including capabilities for handling multi-step reactions, stereoisomeric transformation products, and comprehensive experimental metadata [1]. The platform utilizes the BART template—a Microsoft Excel-based tool—to structure data submission, containing dedicated tabs for compounds (with SMILES notation), pathway connectivity, experimental scenarios, and kinetics/confidence measures [1].

ESS-DIVE has adopted a modular framework that accommodates multiple community-developed reporting formats for different data types. This architecture includes cross-domain reporting formats (e.g., dataset metadata, sample metadata, CSV file formatting) and domain-specific formats for biological, geochemical, and hydrological data [11]. The technical implementation involves mirroring documentation across multiple web platforms (GitHub for version control and collaborative development, GitBook for user-friendly presentation, and the ESS-DIVE repository for archival and citation) to serve different user needs and ensure sustainability [11].

Implementation Challenges and Adaptive Strategies

Despite consensus on the value of FAIR principles, repositories face significant implementation challenges that require adaptive strategies. Technical barriers include the diversity of data types across Earth science disciplines, lack of standardized metadata descriptions across domains, and the complexity of existing standards that limit adoption [11]. Cultural and institutional barriers further complicate implementation, including researchers' tendency to treat data as intellectual property, insufficient incentives for data sharing, and the high initial costs of implementing FAIR practices [76].

Successful repositories have developed responsive strategies to address these challenges. The FAIR Process Framework developed by CABI emphasizes a "human-first rather than technology-first" approach, with flexibility to adapt to local contexts, priorities, and capacities [77]. ESS-DIVE addressed disciplinary diversity by creating specialized reporting formats for different data types while maintaining harmonization of core elements like date formats (YYYY-MM-DD) and spatial coordinates (decimal degrees) [11]. enviPath balances practical utility for researchers with machine-actionability by maintaining visual pathway representations alongside structured data tables, acknowledging that both are essential for scientific communication and computational reuse [1].

Methodologies for FAIR Chemical Data Reporting

Experimental Protocols for Biotransformation Studies

The generation of FAIR chemical data requires standardized experimental protocols that comprehensively capture both the chemical transformations and the contextual metadata necessary for interpretation and reuse. For biotransformation studies of chemical contaminants, such as PFAS, key methodological considerations include:

• System Characterization: Documenting the inoculum source and provenance (e.g., activated sludge, soil, or sediment), including critical parameters such as solids retention time, organic content, redox conditions, and microbial community characteristics when available [1].

• Experimental Conditions: Precisely controlling and recording environmental conditions including pH, temperature, nutrient amendments, and reactor configuration. These parameters must be reported using standardized terminologies and units to enable cross-study comparisons [1].

• Chemical Analysis: Employing high-resolution mass spectrometry and related analytical techniques, with appropriate documentation of identification confidence levels using established frameworks such as Schymanski Confidence Levels or PFAS Confidence in Identification (PCI) Levels [1].

• Data Transformation: Converting experimental results into structured formats using tools like BART, which guides researchers in representing chemical structures as SMILES, encoding pathway connectivity in tabular format, and associating transformation kinetics with specific experimental scenarios [1].

The Researcher's Toolkit for FAIR Data Generation

Tool/Resource	Function	Application in Chemical/Environmental Research
BART Template [1]	Standardized reporting of biotransformation pathways and kinetics	Captures compound structures (SMILES), reaction connectivity, experimental metadata for environmental fate studies
ESS-DIVE Reporting Formats [11]	Community-developed guidelines for diverse environmental data types	Standardizes water/sediment chemistry, soil respiration, leaf-level gas exchange, and microbial data
FAIR Process Framework [77]	Six-step process for implementing FAIR data strategies	Guides agricultural development projects in data management planning and governance
Semantic Web Technologies [78]	Data modeling and querying using ontologies and SPARQL	Enables integration of rare disease data; applicable to chemical toxicity and environmental health data
CDX Reporting Tool [79]	EPA's electronic reporting application for PFAS	Facilitates regulatory compliance and data submission for toxic substances control

Metadata Requirements for Chemical Data Reuse

The interoperability and reusability of chemical and environmental data depend critically on comprehensive metadata collection. Essential metadata elements for chemical fate studies include:

• Chemical Structure Information: Representation using standardized notations (SMILES, InChI) and association with persistent identifiers (CASRN, InChIKey) when available [1].

• Experimental System Metadata: Detailed documentation of the test system, including for sludge systems—biological treatment technology, solids retention time, and oxygen demand; for soil systems—soil texture, cation exchange capacity, and water holding capacity; for sediment systems—bulk density, organic content, and redox condition [1].

• Analytical Method Documentation: Comprehensive description of analytical techniques, instrumentation, quality assurance/quality control procedures, and confidence levels for compound identification [1].

• Provenance Information: Clear attribution of data sources, reference to original publications (via DOI), and documentation of any transformations or processing steps applied to the data [11].

Emerging Trends and Future Directions

The implementation of FAIR principles in environmental and chemical data repositories is evolving to address emerging challenges and opportunities. Several key trends are shaping future directions:

• Beyond FAIR: Initiatives are extending beyond basic FAIR compliance to emphasize discoverability (serendipitous data discovery beyond simple retrieval), inclusive accessibility (via applications and automated workflows), cross-domain interoperability, and a culture of reuse that encompasses models and methods alongside data [76].

• AI Readiness: With the increasing application of machine learning and artificial intelligence to chemical and environmental research, repositories are prioritizing data structures that support model training, including standardized feature representation, comprehensive metadata for model contextualization, and appropriate licensing for AI applications [1] [76].

• Policy Alignment: Regulatory requirements, such as the EPA's TSCA PFAS reporting rule, are creating new drivers for standardized data submission, though these must be balanced against practical implementation burdens [79]. Simultaneously, European initiatives like the European Health Data Space are establishing new frameworks for health and environmental data governance [78].

• Human-Centered Implementation: Successful FAIR implementation increasingly recognizes that technical solutions alone are insufficient. The FAIR Process Framework emphasizes adaptation to local contexts, capacity building, and practical tool integration into researcher workflows [77].

As environmental and chemical research continues to confront complex challenges—from PFAS contamination to ecosystem-level impacts—the robust implementation of FAIR principles across data repositories will be essential for generating actionable knowledge. The repositories and approaches examined in this review demonstrate that while technical standardization is necessary, sustainable FAIR data ecosystems require complementary investments in community engagement, flexible governance models, and human capital development.

The essential-use approach provides a transformative framework for chemicals management, determining that chemicals of concern should only be employed when their function is necessary for health, safety, or society's functioning and no feasible alternatives exist [80]. Simultaneously, FAIR principles (Findable, Accessible, Interoperable, and Reusable) establish a critical foundation for modern chemical data management [56]. This technical guide examines the strategic integration of these two paradigms, demonstrating how FAIR chemical data systems enable robust essentiality determinations and advance informed chemical risk assessment and decision-making for researchers, scientists, and drug development professionals.

Current chemical regulatory systems face unprecedented challenges in assessing and managing the tens of thousands of chemicals in commerce [80]. Traditional risk assessment approaches often require a decade or more to complete for a single chemical and demand an inordinately high degree of proof of risk to enact regulatory controls [80]. This system has proven inadequate for preventing widespread contamination and harmful health effects from concerning chemicals.

The essential-use approach emerges as a strategic alternative, shifting the burden of proof from demonstrating harm to demonstrating necessity for chemicals of concern [80]. Concurrently, the growing volume and complexity of chemical research data creates an urgent need for improved data management practices [56]. FAIR principles address this need by providing a framework for making data Findable, Accessible, Interoperable, and Reusable for both humans and machines [56]. The integration of these approaches represents a paradigm shift in chemical safety evaluation and sustainable chemical design.

Theoretical Foundations

The Essential-Use Approach: Principles and Definitions

The essential-use approach establishes that chemicals of concern should be used only when their function in specific products is "necessary for health, safety or is critical for the functioning of society" and where feasible alternatives are unavailable [80]. This approach categorizes chemical uses into three distinct classifications:

• Non-essential uses: Applications that do not meet necessity criteria
• Substitutable uses: Functions that have viable alternative chemicals or technologies
• Essential uses: Applications where the chemical function is critical and no alternatives exist

This framework originated in the Montreal Protocol for addressing ozone-depleting substances and has gained recent traction for managing per- and polyfluoroalkyl substances (PFAS) and other concerning chemical classes [80].

FAIR Data Principles in Chemical Sciences

The FAIR principles establish distinct considerations for contemporary data publishing environments [56]. The table below outlines the technical requirements and chemical science applications for each principle:

Table 1: FAIR Principles Framework for Chemical Data Management

Principle	Technical Definition	Chemistry Application
Findable	Data and metadata have globally unique, persistent machine-readable identifiers	Chemical structures with InChIs; datasets with DOIs [56]
Accessible	Data retrievable via standardized protocols with authentication/authorization	Repository access via HTTP/HTTPS; metadata remains accessible even if data is restricted [56]
Interoperable	Data formatted in formal, shared, broadly applicable language	Standard formats (CIF files, JCAMP-DX spectra) with cross-references [56]
Reusable	Data thoroughly described for replication and combination	Detailed experimental procedures; properly documented spectra with acquisition parameters [56]

Methodological Framework: Integrating FAIR Data into Essential-Use Assessments

Data Collection Protocols for Essentiality Determinations

Implementing the essential-use approach requires systematic data collection on chemical identity, function, and alternatives. The following experimental protocols ensure robust data generation for essentiality assessments:

Chemical Hazard Trait Assessment Protocol:

• Objective: Identify chemicals of concern based on comprehensive hazard traits
• Methodology: Evaluate chemicals against broad hazard traits including human toxicity, ecological toxicity, persistence, bioaccumulation, mobility, and impediments to material circularity [80]
• Data Requirements: Traditional toxicity data, physicochemical properties, environmental fate parameters, structural alerts
• Reporting Standards: Standardized hazard classification systems (GHS); structured data formats using JSON or XML

Chemical Functionality Assessment Protocol:

• Objective: Determine the specific function a chemical provides in a product or process
• Methodology: Systematic analysis of chemical role in product system; identification of performance requirements
• Data Requirements: Technical performance specifications, application conditions, concentration thresholds
• Reporting Standards: Controlled vocabularies for chemical functions; quantitative performance metrics

Alternatives Assessment Protocol:

• Objective: Identify and evaluate potential alternatives to chemicals of concern
• Methodology: Comparative assessment of alternative chemicals or technologies across multiple criteria
• Data Requirements: Alternative chemical structures, performance data, cost information, availability
• Reporting Standards: Structured alternatives assessment frameworks; standardized comparison metrics

FAIR Data Implementation for Chemical Substances

Moving beyond traditional molecular representations, modern chemical data management requires chemical substance models that handle real-world complexity [81]. The classical cheminformatics paradigm of (structure, properties, descriptors) proves insufficient for regulatory and industrial applications where substances are frequently multicomponent mixtures [81].

Table 2: Evolution from Molecular to Substance Data Models

Data Model Aspect	Classical Molecule Paradigm	Chemical Substance Paradigm
Representation Focus	Well-defined molecule	Potentially multi-component material [81]
Structure	Single connection table	Multiple components with roles and relations [81]
Metadata	Limited	Extensive experimental and procedural context [81]
Regulatory Application	Limited	Comprehensive for REACH, nanomaterial assessments [81]

The Ambit/eNanoMapper data model exemplifies this evolution, extending traditional molecular representations to encompass complex substances, metadata, and ontology annotations required for FAIR compliance [81].

Integrated Workflow: From FAIR Data to Essential-Use Decisions

The strategic integration of FAIR chemical data management with essential-use assessment creates a robust decision-making framework. The following workflow visualization illustrates this integrated process:

Figure 1: Integrated Workflow Combining FAIR Data Management with Essential-Use Assessment. This process ensures chemical decisions are based on comprehensive, well-documented data following FAIR principles.

Research Applications and Implementation Tools

Table 3: Research Reagent Solutions for FAIR Chemical Data Management

Tool/Category	Function	Implementation Example
Chemical Identifiers	Unique machine-readable structure identification	International Chemical Identifier (InChI); SMILES notation [56]
Data Repositories	Persistent, citable data storage	Discipline-specific (Cambridge Structural Database); General-purpose (Zenodo, Figshare) [56]
Standard Formats	Interoperable data exchange	JCAMP-DX (spectral data); CIF (crystallography); nmrML (NMR) [56]
Metadata Standards	Contextual data documentation	Minimum Information standards; Domain-specific schemas [11]
Electronic Lab Notebooks	Provenance tracking; Workflow documentation	FAIR-supporting ELNs with metadata capture [56]

Chemical Data Management Infrastructure

Implementing FAIR data practices requires robust infrastructure components:

Repository Selection Criteria:

• Support for persistent identifiers (DOIs)
• Domain-relevant metadata schemas
• Standardized API access
• Long-term preservation commitment
• Appropriate access controls

Metadata Framework Requirements:

• Cross-reference capabilities to related data and publications
• Structured experimental procedures in machine-readable formats
• Controlled vocabularies and ontologies for chemical processes
• Detailed instrument settings and calibration data
• Complete provenance of data transformation steps [56]

Case Study: FAIR Data in PFAS Essential-Use Assessment

The application of the essential-use approach to per- and polyfluoroalkyl substances (PFAS) demonstrates the critical role of FAIR data in chemical decision-making. Following the framework proposed by Cousins et al., PFAS uses are categorized as non-essential, substitutable, or essential [80].

Data Requirements for PFAS Assessment:

• Findable: PFAS structures with standard InChI identifiers; datasets with DOIs
• Accessible: Standardized data on environmental occurrence and health effects
• Interoperable: Consistent measurement data formats across studies
• Reusable: Complete experimental details on analytical methods and effect concentrations

Assessment Outcome: The implementation of this approach has informed policy decisions, including Maine's legislation banning PFAS in all products by 2030, except for uses determined as "currently unavoidable" [80]. This case demonstrates how FAIR chemical data enables transparent, evidence-based essentiality determinations.

Emerging Frontiers and Implementation Challenges

Machine Learning and AI Integration

Machine learning is reshaping how environmental chemicals are monitored and evaluated [82]. Bibliometric analysis reveals an exponential publication surge in ML applications for environmental chemicals since 2015, with China and the United States leading research output [82]. Key ML applications include:

• Predictive Toxicology: Model development for toxicity endpoint prediction
• Chemical Prioritization: Identification of chemicals requiring essentiality assessment
• Alternatives Identification: AI-enabled discovery of safer chemical substitutes

The successful integration of ML into essential-use assessment requires FAIR chemical data to train and validate predictive models [82].

Implementation Barriers and Solutions

Technical Challenges:

• Legacy data FAIRification requires significant resource investment
• Complex substance representations exceed traditional molecular paradigms
• Interoperability across disciplinary boundaries remains difficult

Strategic Solutions:

• Develop automated data curation tools for historical data
• Adopt progressive data management plans in research workflows
• Establish community-driven reporting formats for specific data types [11]
• Implement electronic lab notebooks with built-in FAIR support

The integration of FAIR data principles with the essential-use approach creates a powerful framework for transforming chemical management practices. This synergy enables:

• Evidence-Based Essentiality Determinations: Comprehensive, well-documented data supports transparent decisions
• Accelerated Chemical Assessment: FAIR data facilitates rapid evaluation of chemicals of concern
• Informed Alternatives Identification: Structured data enables comparative assessment of substitute chemicals
• Scientific Reproducibility: Detailed experimental data and provenance supports validation

Future advancement requires continued development of domain-specific reporting formats, enhanced computational tools for chemical data management, and broader adoption of FAIR practices across the chemical research lifecycle. As these frameworks mature, they promise more sustainable chemical innovation and enhanced protection of human and ecological health.

For researchers and drug development professionals, embracing this integrated approach represents both an opportunity and responsibility to advance chemical safety through superior data practices.

The FAIR Guiding Principles—that data and resources should be Findable, Accessible, Interoperable, and Reusable—were established in 2016 to provide a framework for improving the stewardship of digital assets [3]. These principles emphasize machine-actionability, which is the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention, recognizing the increasing volume, complexity, and creation speed of data that necessitates computational support [3]. In the specific context of environmental science research and the sub-domain of FAIR chemical data reporting, evaluating adherence to these principles through standardized metrics becomes crucial for ensuring that data can effectively drive scientific discovery and regulatory decision-making.

The need for standardized metrics is particularly acute in environmental science, where researchers generate multidisciplinary data such as hydrological, geological, ecological, biological, and climatological data [11]. The integration of these diverse data types presents unique challenges for data interoperability and reuse, including inconsistent use of terms, formats, and metadata across disciplines [11]. Community-centric approaches to developing reporting formats have emerged as a critical strategy for moving environmental data archiving toward achieving FAIR principles, though limitations remain in full implementation [11]. This technical guide provides a comprehensive framework for evaluating FAIR compliance in environmental data repositories, with specific application to chemical data reporting, to address these challenges and promote the development of high-quality, machine-readable data sets essential for predictive modeling and meta-analyses [1].

Core FAIR Principles and Their Operationalization

The FAIR principles are structured across four interconnected dimensions, each with specific guidelines for implementation. Findability ensures that data and metadata are easy to locate by both humans and computers, requiring that (meta)data are assigned globally unique and persistent identifiers, described with rich metadata, and registered or indexed in searchable resources [3]. Accessibility focuses on ensuring that data can be retrieved by their identifier using a standardized communication protocol, potentially including authentication and authorization procedures [3]. Interoperability requires that data can be integrated with other data and interoperate with applications or workflows for analysis, storage, and processing, achieved through the use of formal, accessible, shared languages and knowledge representations [3]. Reusability, as the ultimate goal of FAIR, optimizes the reuse of data by requiring that metadata and data are thoroughly described with multiple relevant attributes, released with clear usage licenses, and associated with detailed provenance information [3].

The operationalization of these principles in environmental data repositories involves implementing specific technical features and governance policies. For example, the SwissEnvEO repository addresses the challenge of making Earth Observation (EO) data FAIR-compliant by implementing a Spatial Data Infrastructure complemented with digital repository capabilities [83]. This approach facilitates the publication of "Ready to Use" information products derived from satellite EO data available in an EO Data Cube in full compliance with FAIR principles [83]. Similarly, in the chemical data domain, the BART (Biotransformation Reporting Tool) provides a standardized Microsoft Excel template to assist researchers in reporting biotransformation data in a FAIR and effective way, with specific tabs for compounds, connectivity, experimental scenarios, and kinetics/confidence information [1]. These domain-specific implementations demonstrate how the core FAIR principles can be adapted to address the particular challenges of environmental and chemical data types while maintaining alignment with the overarching FAIR framework.

Quantitative Metrics for FAIR Compliance Assessment

Systematic assessment of FAIR compliance requires the application of standardized, quantitative metrics that can evaluate the implementation of each FAIR principle. The FAIR-IMPACT project has refined and extended the seventeen minimum viable metrics originally proposed by the FAIRsFAIR project for the systematic assessment of FAIR data objects [84]. These metrics are based on indicators proposed by the RDA FAIR Data Maturity Model Working Group, on the WDS/RDA Assessment of Data Fitness for Use checklist, and on prior work conducted by project partners such as FAIRdat and FAIREnough [84]. The following tables summarize these core metrics across the four FAIR dimensions, providing a structured framework for evaluating repository compliance.

Table 1: Findability Metrics for FAIR Compliance Assessment

Metric ID	Metric Name	Description	Assessment Criteria
FsF-F1-01D	Globally Unique Identifier	Metadata and data are assigned a globally unique identifier	Identifier should be associated with only one resource at any time (e.g., IRI, URI, URL, URN, DOI, Handle, ARK, UUID, Hash code)
FsF-F1-02MD	Persistent Identifier	Metadata and data are assigned a persistent identifier	Identifiers based on Handle System, DOI, ARK that are both globally unique and persistent, maintained for long-term stability and resolvability
FsF-F2-01M	Descriptive Core Metadata	Metadata includes descriptive core elements to support data findability	Creator, title, data identifier, publisher, publication date, summary, and keywords based on common data citation guidelines
FsF-F3-01M	Data Identifier in Metadata	Metadata includes the identifier of the data it describes	Metadata explicitly specifies the identifier of the data content, such as links to downloadable data files or services
FsF-F4-01M	Metadata Indexing	Metadata is offered to be registered or indexed by search engines	Metadata available via methods consumable by well-known catalogs and search engines (e.g., Google, Bing) according to their requirements

Table 2: Accessibility, Interoperability, and Reusability Metrics

Metric ID	FAIR Dimension	Metric Name	Description
FsF-A1-01M	Accessibility	Metadata Access Information	Metadata contains access level and conditions of the data (public, embargoed, restricted, metadata-only)
FsF-A1-02MD	Accessibility	Identifier Resolution	Metadata and data are retrievable by their identifier (identifiers resolve to actual data or metadata)
FsF-A1.1-01MD	Accessibility	Standard Communication Protocol	A standardized communication protocol is used to access metadata and data (HTTP, HTTPS, FTP, SFTP, etc.)
FsF-A1.2-01MD	Accessibility	Protocol with Authentication	Metadata and data are accessible through a protocol supporting authentication (HTTPS, FTPS)
FsF-I1-01M	Interoperability	Formal Knowledge Representation	Metadata is represented using a formal knowledge representation language (RDF, RDFS, OWL with serializations like RDF/XML, RDFa, Notation3)
FsF-I1-02M	Interoperability	Standardized Vocabulary	Metadata uses standardized vocabulary from FAIR registries, following interoperability principles I2 and I3
FsF-I2-01M	Interoperability	Qualified References	Metadata includes qualified references to other metadata (e.g., references to related datasets using persistent identifiers)
FsF-I3-01M	Interoperability	References to Related Entities	Metadata includes references to related entities using identifiers in a specific relationship manner
FsF-R1-01M	Reusability	Detailed Provenance	Metadata includes detailed provenance information about the data creation process
FsF-R1.1-01M	Reusability	License Information	Metadata includes license information under which the data can be reused
FsF-R1.2-01M	Reusability	Provenance Linking	Metadata links to the provenance of the data creation process
FsF-R1.3-01M	Reusability	Domain-Specific Metadata	Metadata follows a community-standard or is based on a cross-domain standard for data representation

These metrics provide a comprehensive framework for assessing repository compliance with FAIR principles. When applying these metrics, it is important to consider their specific implementation in environmental and chemical data contexts. For example, the SwissEnvEO repository implements these principles by providing ARD (Analysis Ready Data) that are pre-processed to minimum requirements for immediate analysis, significantly enhancing findability and accessibility for environmental researchers [83]. Similarly, in chemical data reporting, the use of standardized vocabularies for chemical compounds and transformation processes enhances interoperability across different studies and platforms [1].

Domain-Specific Application: Environmental and Chemical Data

The implementation of FAIR principles in environmental and chemical data repositories requires domain-specific adaptations to address the particular characteristics and challenges of these data types. In environmental science, the diversity of data types—including hydrological, geological, ecological, biological, and climatological data—presents significant challenges for data interoperability and reuse [11]. Community reporting formats have emerged as a practical solution to harmonize diverse environmental data types without the oversight of formal governing protocols or working groups [11]. For example, the ESS-DIVE repository has developed 11 community reporting formats for a diverse set of Earth science (meta)data, including cross-domain metadata and domain-specific reporting formats for biological, geochemical, and hydrological data [11].

In the chemical data domain, specifically for biotransformation data reporting, standardized approaches are needed to address challenges in predicting biotransformation products and dissipation kinetics of chemical contaminants in the environment [1]. The BART template provides a standardized approach for reporting biotransformation data, with specific components for compound structures (reported as SMILES), pathway connectivity, experimental scenarios, and kinetics/confidence information [1]. This standardized approach enables the aggregation of data across studies and facilitates the answering of relevant questions on the environmental fate of chemicals, such as perfluoroalkyl and polyfluoroalkyl substances (PFASs) [1].

Table 3: Key Parameters for Reporting Environmental Biotransformation Data

Parameter Category	Specific Parameters	Reporting Standard
General Parameters	Inoculum provenance, sample location, sample description, redox condition, oxygen demand, total organic carbon (TOC)	Based on OECD guidelines and enviPath parameter terminologies
Sludge Systems	Biological treatment technology, purpose of WWTP, solids retention time, ammonia uptake rate, volatile suspended solids concentration (VSS)	Parameters highlighted in bold recommended according to OECD Test Nos. 303, 307, and 308
Soil Systems	Soil origin, sampling depth, dissolved organic carbon, cation exchange capacity (CEC), soil texture (% sand, silt, clay), water holding capacity	Detailed descriptions of each parameter provided in standardized templates
Sediment Systems	Sediment origin, bulk density, microbial biomass in sediment, organic content in sediment, sediment porosity, oxygen content in water layer	Community-developed reporting formats for specific data types
Experimental Setup	pH, reactor configuration, type of compound addition, solvent for compound addition, spike concentration, temperature, redox potential	Minimum set of required metadata fields for programmatic data parsing

The implementation of these domain-specific reporting formats follows a community-centric development process that includes reviewing existing standards, developing crosswalks of terms across relevant standards or ontologies, iteratively developing templates with user feedback, assembling a minimum set of (meta)data required for reuse, and hosting documentation on platforms that can be publicly accessed and updated easily [11]. This approach balances pragmatism for scientists reporting data with the machine-actionability that is emblematic of FAIR data [11].

Methodologies for FAIR Compliance Evaluation

The evaluation of FAIR compliance in environmental data repositories requires systematic methodologies that can consistently assess implementation across the four FAIR dimensions. The FAIR-IMPACT assessment methodology involves a structured approach to evaluating each metric through automated and manual checks [84]. For findability metrics, this includes verifying the presence and resolution of persistent identifiers, assessing the completeness of core descriptive metadata, and evaluating the availability of metadata through search engine optimization techniques [84]. For accessibility metrics, assessment focuses on testing identifier resolution, verifying the use of standardized communication protocols, and checking authentication and authorization mechanisms where applicable [84].

A critical aspect of FAIR compliance evaluation is the assessment of machine-actionability, which requires that metadata is represented in formal knowledge representation languages such as RDF, RDFS, and OWL [84]. This enables computational systems to process metadata in a meaningful way and facilitates data exchange across different systems and platforms [84]. Additionally, the use of standardized vocabularies from FAIR registries is essential for ensuring interoperability, as it enables consistent understanding and interpretation of data across different research communities and systems [84].

The evaluation of reusability involves assessing the completeness and clarity of provenance information, license specifications, and domain-specific metadata that provide context for proper data interpretation and reuse [84]. For environmental and chemical data, this includes domain-specific metadata schemas that capture essential parameters about experimental conditions, measurement techniques, and environmental contexts that influence data interpretation and reuse [11] [1]. The alignment of these assessment methodologies with international standards such as the CoreTrustSeal Requirements for Trustworthy Digital Repositories provides additional validation of repository trustworthiness and sustainability [84].

Visualization of FAIR Assessment Workflow

The process of evaluating FAIR compliance in environmental data repositories can be visualized as a systematic workflow encompassing multiple assessment stages and decision points. The following diagram illustrates the key steps in this assessment process, highlighting the interconnected nature of the four FAIR dimensions and the specific evaluation criteria at each stage.

FAIR Compliance Assessment Workflow

The assessment workflow begins with the Findability Assessment, which evaluates the implementation of persistent identifiers, metadata richness, and search engine indexing capabilities. This is followed by the Accessibility Assessment, which tests identifier resolution, protocol standardization, and authentication mechanisms. The Interoperability Assessment then examines knowledge representation, vocabulary standards, and qualified references between related data entities. Finally, the Reusability Assessment reviews provenance information, license clarity, and domain-specific metadata completeness. The results from these assessments are synthesized in the Compliance Analysis phase, where overall FAIR scores are calculated, compliance gaps are identified, and improvement recommendations are generated. The process concludes with the generation of a comprehensive Assessment Report that documents metric compliance and provides implementation guidance for addressing identified deficiencies.

Implementation Tools and Resource Ecosystem

The effective implementation of FAIR principles in environmental data repositories is supported by a growing ecosystem of tools, resources, and infrastructure components. These resources provide practical solutions for addressing the technical challenges of FAIR implementation and facilitating compliance assessment. The following table summarizes key resources and their functions in supporting FAIR implementation for environmental and chemical data.

Table 4: FAIR Implementation Tools and Resources for Environmental Data

Tool/Resource Name	Type	Primary Function	Domain Application
BART (Biotransformation Reporting Tool)	Reporting Template	Standardized reporting of biotransformation pathways and kinetics using Microsoft Excel template	Chemical data reporting for environmental fate studies
ESS-DIVE Reporting Formats	Community Reporting Formats	11 standardized formats for diverse Earth science (meta)data including cross-domain and domain-specific types	Environmental systems science research data
enviPath Platform	Database Platform	Electronic transcription of pathway and kinetic information from literature into machine-readable format	Biotransformation research data management and sharing
SwissEnvEO	Spatial Data Infrastructure	FAIR-compliant repository for Earth Observation data with digital repository capabilities	National environmental monitoring and reporting
FAIR-IMPACT Metrics	Assessment Framework	17 minimum viable metrics for systematic assessment of FAIR data objects	Domain-agnostic with environmental science applications
Earth Observations Data Cube (EODC)	Analytical Platform	Cloud-based platform for handling and analyzing large volumes of satellite EO data as Analysis Ready Data	Earth observation data processing and analysis

The BART template exemplifies domain-specific FAIR implementation tools, providing a structured approach for reporting biotransformation data that includes tabs for compounds (with structures reported as SMILES), connectivity (pathway structure as reactions), experimental scenarios, and kinetics/confidence information [1]. This template enables researchers to report complex biotransformation pathways in a machine-readable format while maintaining the visual representations important for human understanding [1]. Similarly, the ESS-DIVE reporting formats provide community-developed guidelines for consistently formatting data within specific Earth science disciplines, making data more accessible and reusable across research projects and synthesis activities [11].

Infrastructure platforms like enviPath and SwissEnvEO provide implementation examples of FAIR-compliant repositories for specific environmental data types. The enviPath platform has evolved from earlier efforts to systematically organize biotransformation information into a platform that implements and promotes FAIR principles, enabling efficient data usage and sharing within the field of biotransformation research [1]. SwissEnvEO addresses the specific challenge of making Earth Observation data FAIR-compliant by implementing a Spatial Data Infrastructure with digital repository capabilities, demonstrating how FAIR principles can be adapted for large-volume, complex environmental data streams [83].

The evaluation of FAIR compliance in environmental data repositories requires a systematic approach that combines standardized metrics with domain-specific adaptations to address the particular characteristics of environmental and chemical data types. The FAIR-IMPACT metrics provide a comprehensive framework for assessing compliance across the four FAIR dimensions, while community-developed reporting formats and implementation tools like BART and ESS-DIVE guidelines offer practical solutions for addressing domain-specific challenges. The continuing evolution of these assessment frameworks and implementation resources will play a critical role in advancing FAIR adoption across environmental science domains.

Future directions in FAIR compliance evaluation will likely involve increased automation of assessment processes, development of more sophisticated domain-specific metrics, and enhanced integration with repository certification frameworks like CoreTrustSeal. Additionally, as artificial intelligence and machine learning technologies advance, there will be growing opportunities to leverage these technologies for more efficient extraction and aggregation of FAIR-compliant data from diverse sources [1]. However, the continued development of high-quality, standardized reporting formats will remain essential for providing the ground-truth data sets needed for training and validating these AI tools [1]. The environmental and chemical data research communities can accelerate progress toward these goals by actively participating in the development and adoption of standardized reporting formats, contributing to public data platforms, and implementing FAIR assessment metrics in their data management practices.

Conclusion

The implementation of FAIR principles for chemical data represents a transformative shift in environmental and biomedical research, enabling more transparent, efficient, and collaborative science. By establishing foundational understanding, providing practical methodologies, addressing implementation challenges, and validating through real-world applications, this framework supports crucial advancements in chemical risk assessment and safety evaluation. The future of chemical research depends on robust data ecosystems where information flows seamlessly between disciplines and regulatory frameworks. As FAIR practices become increasingly embedded in research culture and supported by evolving tools and standards, they will accelerate the development of safer chemicals and more effective risk management strategies, ultimately contributing to better protection of human health and the environment. Researchers and institutions that embrace these principles now will be positioned at the forefront of data-driven scientific discovery.

References

• 1. FAIR and Effective Communication of Data on Chemical ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12613814/]
• 2. CIRS Chemical Regulation Conference 2025: Key Highlights [https://www.complianceandrisks.com/cr-news/from-molecules-to-mandates-insights-from-the-cirs-global-chemical-regulation-conference-2025/]
• 3. FAIR Principles [https://www.go-fair.org/fair-principles/]
• 4. FAIR data principles: What you need to know [https://tiledb.com/blog/fair-data-principles-explained]
• 5. The FAIR Principles - The Turing Way [https://book.the-turing-way.org/reproducible-research/rdm/rdm-fair/]
• 6. A Guide to the FAIR Principles [https://www.openscience.eu/article/infrastructure/guide-fair-principles]
• 7. FAIR Data Principles [https://knowledgebase.nfdi4chem.de/knowledge_base/docs/fair/]
• 8. FAIR principles for Research - Data Management for Research [https://guides.library.cmu.edu/researchdatamanagement/FAIR_principles]
• 9. FAIR Data in Chemical Sciences - Chemistry - CMU LibGuides [https://guides.library.cmu.edu/c.php?g=215398&p=10975561]
• 10. Understanding and Practicing FAIR Data [https://mdpiblog.wordpress.sciforum.net/2025/07/01/fair-data/]
• 11. Enabling FAIR data in Earth and environmental science ... [https://www.nature.com/articles/s41597-022-01606-w]
• 12. Digital recommendations for Chemistry FAIR data policy and ... [https://worldfair-project.eu/2023/06/01/d3-1-digital-recommendations-for-chemistry-fair-data-policy-and-practice/]
• 13. Plan for stronger EU chemical industry - European Commission [https://commission.europa.eu/news-and-media/news/plan-stronger-eu-chemical-industry-2025-07-08_en]
• 14. EC Action Plan for the Chemicals Industry Includes ... [https://www.lawbc.com/ec-action-plan-for-the-chemicals-industry-includes-measures-to-strengthen-the-sector/]
• 15. REACH Revision 2025: A Critical Moment for European ... [https://www.setac.org/resource/reach-revision-2025-a-critical-moment-for-european-chemical-safety.html]
• 16. Chemical Data Reporting under the Toxic Substances ... [https://www.epa.gov/chemical-data-reporting]
• 17. Basic Information about Chemical Data Reporting [https://www.epa.gov/chemical-data-reporting/basic-information-about-chemical-data-reporting]
• 18. US EPA Releases Updated 2025 TSCA Inventory | News [https://www.chemradar.com/news/detail/ecsu9bnz8ykg]
• 19. Dark data in chemistry R&D: Strategies for success [https://www.cas.org/resources/cas-insights/unlocking-power-dark-data-chemistry-rd-strategies-success]
• 20. Huge Potential of Dark Data – Revealing Untapped ... [https://www.usdsi.org/data-science-insights/huge-potential-of-dark-data-revealing-untapped-opportunities]
• 21. Dark Analytics Market | Global Market Analysis Report - 2035 [https://www.futuremarketinsights.com/reports/dark-analytics-market]
• 22. 2025 Chemical Industry Outlook [https://www.deloitte.com/us/en/insights/industry/chemicals-and-specialty-materials/chemical-industry-outlook/2025.html]
• 23. ACS Research Data Guidelines [https://researcher-resources.acs.org/publish/data_guidelines]
• 24. Experimental reporting [https://www.rsc.org/publishing/publish-with-us/publish-a-journal-article/experimental-reporting]
• 25. EPA Releases Key TSCA Risk Default Values Assessment [https://natlawreview.com/article/epa-publishes-default-values-used-new-chemical-risk-assessments-under-tsca]
• 26. Federal Register :: Procedures for Chemical Evaluation Under... Risk [https://www.federalregister.gov/documents/2025/09/23/2025-18431/procedures-for-chemical-risk-evaluation-under-the-toxic-substances-control-act-tsca]
• 27. Enabling FAIR data in Earth and environmental science with ... [https://www.ornl.gov/publication/enabling-fair-data-earth-and-environmental-science-community-centric-metadata-reporting]
• 28. Evidence Synthesis: Protocol Development [https://library.noaa.gov/evidencesynthesis/protocols]
• 29. Using Data Visualization to Understand Chemical Patterns [https://sbnsoftware.com/blog/using-data-visualization-to-understand-chemical-patterns/]
• 30. Data visualisation: colours - Government Analysis Function [https://analysisfunction.civilservice.gov.uk/policy-store/data-visualisation-colours-in-charts/]
• 31. Enabling FAIR data in Earth and environmental science ... [https://www.osti.gov/pages/biblio/1879414]
• 32. Metadata harmonization–Standards are the key for a better ... [https://environmentalmicrobiome.biomedcentral.com/articles/10.1186/s40793-022-00425-1]
• 33. The QCML dataset, Quantum chemistry reference data ... [https://www.nature.com/articles/s41597-025-04720-7]
• 34. persistent identifiers (PID) [https://knowledgebase.nfdi4chem.de/knowledge_base/docs/pid/]
• 35. Linking Research Data with Physically Preserved ... [https://www.nature.com/articles/s41597-025-04404-2]
• 36. Reporting Chemical Data in the Environmental Sciences [https://pmc.ncbi.nlm.nih.gov/articles/PMC12447227/]
• 37. Identifiers for the 21st century: How to design, provision ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5490878/]
• 38. Persistent Identifiers | The Society for the Preservation of ... [https://spnhc.org/persistent-identifiers/]
• 39. Persistent Identifiers - DiSSCo Digitisation Guides [https://dissco.github.io/DataManagement/IdentifierRecs.html]
• 40. A choice of persistent identifier schemes for the Distributed ... [https://riojournal.com/article/67379/]
• 41. Data Repositories for Research Data [https://libguides.uta.edu/datareps]
• 42. Choosing a Repository - Data Repositories [https://hslguides.med.nyu.edu/datarepositories/choosing]
• 43. Data Selection & Appraisal Criteria - ICPSR [https://www.icpsr.umich.edu/sites/icpsr/about/policies/colldev/selection]
• 44. Selecting a Data Repository - NIH Policy for Data ... [https://libguides.ecu.edu/c.php?g=1268513&p=9358698]
• 45. Selecting a data repository [https://lib.arizona.edu/research/data/data-management/best-practices/repositories]
• 46. Best Practices: Choosing a Repository - UB Libraries [https://library.buffalo.edu/research/rds/education/repository.html]
• 47. Value configurations for balancing standardization and ... [https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-021-06844-z]
• 48. Balancing between reality, ideality, and equity [https://pmc.ncbi.nlm.nih.gov/articles/PMC11555920/]
• 49. Handling imbalanced medical datasets: review of a decade ... [https://link.springer.com/article/10.1007/s10462-024-10884-2]
• 50. Confidential Business Information under TSCA [https://www.epa.gov/tsca-cbi]
• 51. Final Rule: Requirements for Confidential Business ... [https://www.epa.gov/tsca-cbi/final-rule-requirements-confidential-business-information-claims-under-tsca]
• 52. A quantitative analysis of the use of anonymization in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12078711/]
• 53. Deep Dive into Data Anonymization Techniques 2025 [https://sparkco.ai/blog/deep-dive-into-data-anonymization-techniques-2025]
• 54. Best Practice Guide on Chemical Data Sharing Between ... [https://www.oecd.org/en/publications/best-practice-guide-on-chemical-data-sharing-between-companies_c5f3c668-en.html]
• 55. ICPSR's Approaches to Confidentiality - University of Michigan [https://www.icpsr.umich.edu/sites/icpsr/about/policies/confidentiality]
• 56. FAIR in Chemical Sciences - Data Management for Research [https://guides.library.cmu.edu/researchdatamanagement/FAIR_chemistry]
• 57. How to Successfully Complete Legacy Database Migration [https://acropolium.com/blog/legacy-data-migration/]
• 58. Data Migration Best Practices - EDM - ITRC [https://edm-1.itrcweb.org/data-migration-best-practices/]
• 59. Chemical Data Migration best practices - Stage 3 [https://chemaxon.com/blog/chemical-data-migration-best-practices-stage-3-data-migration]
• 60. Chemical Data Migration best practices - Stage 4 [https://chemaxon.com/blog/chemical-data-migration-best-practices-stage-4-post-migration-validation-and-support]
• 61. What Are the Data Migration Validation Best Practices? [https://www.cloudficient.com/blog/what-are-the-data-migration-validation-best-practices]
• 62. Ten quick tips for building FAIR workflows - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC10538699/]
• 63. Reusing Chemical Data Across Disciplines: Initiatives and Common Challenges [https://www.degruyterbrill.com/document/doi/10.1515/ci-2025-0203/html]
• 64. A Case for Accelerating Standards to Achieve the FAIR of... Principles [https://pmc.ncbi.nlm.nih.gov/articles/PMC10289218/]
• 65. Applying the FAIR Principles to computational workflows [https://www.nature.com/articles/s41597-025-04451-9]
• 66. FAIR principles in workflows: A GIScience ... [https://www.sciencedirect.com/science/article/pii/S1569843225001244]
• 67. Metadata templates demystify the FAIR principles [https://communities.springernature.com/posts/metadata-templates-demystify-the-fair-principles]
• 68. Creating Metadata for Publication [https://edirepository.org/resources/creating-metadata-for-publication]
• 69. Introducing Field-Specific Metadata Templates for OSF [https://www.cos.io/blog/cedar-embeddable-editor]
• 70. The rise of data repositories in materials chemistry [https://www.nature.com/articles/s42004-024-01143-0]
• 71. Data reuse and the open data citation advantage - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC3792178/]
• 72. Advancing FAIR Data in Earth, Space, and Environmental ... [https://eos.org/agu-news/advancing-fair-data-in-earth-space-and-environmental-science]
• 73. Biotransforming the “Forever Chemicals”: Trends and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11948467/]
• 74. Bird's-Eye View: Current Understanding and Future ... [https://www.sciencedirect.com/science/article/pii/S2589914725000556]
• 75. A review of omics-based PFAS exposure studies reveals ... [https://www.sciencedirect.com/science/article/pii/S0048969722043534]
• 76. Data Sharing: Let's Do More Than Just What's FAIR [https://www.socialsciencespace.com/2025/02/data-sharing-lets-do-more-than-just-whats-fair/]
• 77. Making data work better in agriculture with FAIR principles [https://blog.cabi.org/2025/05/27/making-data-work-better-in-agriculture-with-fair-principles/]
• 78. FAIR Training Program 2025 - 2027 Part 1 opens registration [https://erdera.org/news/fair-training-program-2025-2027-part-1-opens-registration/]
• 79. EPA Proposes Major Rollbacks to TSCA PFAS Reporting ... [https://www.hunton.com/the-nickel-report/epa-proposes-major-rollbacks-to-tsca-pfas-reporting-rule-including-exemption-for-article-importers]
• 80. Optimizing Chemicals Management in the United States ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9893722/]
• 81. FAIR Data Model for Chemical Substances [https://www.intechopen.com/chapters/85839]
• 82. Mapping the Rise in Machine Learning in Environmental ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12567794/]
• 83. SwissEnvEO: A FAIR National Environmental Data Repository ... [https://datascience.codata.org/articles/10.5334/dsj-2021-022]
• 84. Metrics for data [https://fair-impact.eu/metrics-data]