# Applying FAIR Data Principles to Proteomics: A Practical Framework for Making Your Data Findable, Accessible, Interoperable, and Reusable Proteomics research generates complex datasets from mass spectrometry instruments, and those datasets lose most of their scientific value when they remain locked in local storage with incomplete annotations. The FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable, provide a concrete framework for managing proteomics data so that other researchers can locate it, retrieve it, combine it with other datasets, and use it for new analyses. This article gives laboratory professionals and biology students a step-by-step framework for applying FAIR principles to proteomics data, covering standard file formats such as mzML and mzIdentML, public repository deposition, persistent identifier assignment, and the practical decisions that determine whether a dataset is genuinely reusable or merely stored. ## The Reproducibility Problem in Proteomics Mass spectrometry-based proteomics produces raw instrument files, peak lists, identification results, quantification tables, and spectral libraries. Each of these data types has distinct structure and meaning, and each requires different handling to remain interpretable after the original experimenter has moved on. The challenge of sharing biological data is complicated by the fact that different scientific fields use different data types, and establishing a single standard for public accessibility is difficult across such varied formats [7]. Proteomics sits in a particularly demanding position because its data pipeline involves multiple proprietary instrument formats, numerous software tools for peptide identification, and quantification strategies that vary between label-free and labeled approaches. The practical consequence of poor data management is that published proteomics findings often cannot be verified or extended. A researcher who reads a paper describing a candidate biomarker has no way to reanalyze the underlying spectra if those spectra exist only on a laboratory computer with no accession number and no descriptive metadata. The FAIR principles address this problem directly by giving researchers a checklist against which they can assess their own data management practices. The principles do not require a single universal standard, which is important because the heterogeneity of wet lab technologies, data, metadata, software, and computational workflows makes uniform compliance genuinely difficult [8]. Instead, FAIR provides a framework for making deliberate, documented choices that maximize the long-term utility of each dataset. The omics revolution has transformed biomedical research by equipping scientists with the ability to interrogate complex biological phenomena at an unprecedented level, and the volume of big data generated by proteomics and other omics studies has led to the concurrent development of computational tools for in silico analysis and data deconvolution [9]. Given the intensive resources and high costs required to generate and analyze big data, centralized collaborative efforts have emerged to make data and analysis tools freely available to benefit the wider research community [9]. The FAIR principles provide the guiding framework for these data sharing efforts, and proteomics researchers who adopt them position their work within this broader ecosystem of open science. ## What FAIR Means for Proteomics Data The FAIR principles translate into specific actions when applied to proteomics. Findable means that a dataset has a globally unique and persistent identifier, that it is described with rich metadata, and that it is indexed in a searchable resource. For proteomics, this means depositing data in a public repository that assigns accession numbers and supports metadata search. Accessible means that the data can be retrieved using the identifier through a standardized protocol, and that the access conditions are clear even when authentication is required. Interoperable means that the data uses formal, shared vocabularies and formats that allow integration with other datasets. Reusable means that the data has detailed provenance information, clear usage licenses, and metadata that meets domain-relevant standards. The FAIR principles are not the same as open data. A dataset can be FAIR while remaining under controlled access, provided that the access conditions are explicit and the retrieval mechanism works. This distinction matters for proteomics because some datasets contain human clinical information that cannot be shared openly due to privacy obligations. The key is that the data must be discoverable and the access procedure must be documented, not that every spectrum must be downloadable by anyone without restriction. The National Cancer Institute's Cancer Research Data Commons demonstrates how FAIR principles operate across a large-scale data sharing ecosystem. Since 2014, the NCI has launched a series of data commons housing genomic, proteomic, imaging, and clinical data to support cancer research and promote data sharing of NCI-funded studies [10]. Each data commons implements FAIR principles and promotes data sharing in support of the NIH Data Management and Sharing Policy [10]. This example shows that FAIR implementation is not a theoretical exercise but a practical infrastructure decision that major research funders have already embraced. ## At a Glance: FAIR Implementation Decisions for Proteomics | FAIR Principle | Proteomics Implementation | Common Repository or Format | Practical Check | | --- | --- | --- | --- | | Findable | Deposit raw and processed data in a public proteomics repository with a persistent identifier | ProteomeXchange partner repositories such as PRIDE or MassIVE | Dataset has an accession number that is cited in the associated publication | | Accessible | Use standard retrieval protocols and document any access restrictions | Repository download interfaces and FTP endpoints | A reviewer can retrieve the dataset using only the accession number | | Interoperable | Convert proprietary instrument files to open standard formats | mzML for raw spectra, mzIdentML for identification results | Files open in multiple software tools without format conversion | | Reusable | Provide complete experimental metadata, sample annotations, and analysis parameters | Submission forms and metadata templates from the repository | A new analyst can interpret the dataset without contacting the original authors | ## Core Principles for Proteomics Data Management ### Persistent Identifiers and Repository Selection The first decision in making proteomics data FAIR is choosing where to deposit it. Public repositories provide the persistent identifiers that make data findable and accessible. The National Center for Biotechnology Information maintains a range of data resources including search systems and sequence databases that support biological research [1], and the European Bioinformatics Institute provides training and data resources that help researchers understand how to use these systems effectively [2]. For proteomics specifically, the ProteomeXchange consortium coordinates deposition across partner repositories, which means that a single submission can make data available through multiple access points. The choice of repository depends on the type of data being shared. Raw mass spectrometry files belong in a proteomics-specific repository where they can be paired with the appropriate metadata templates. Processed identification results can accompany the raw data in the same submission. If the study includes additional data types such as genomics or clinical information, the researcher may need to deposit those components in separate specialized repositories and link them through the metadata. The Cancer Research Data Commons ecosystem approach demonstrates this pattern, housing genomic, proteomic, imaging, and clinical data in separate but connected data commons that implement FAIR principles and promote data sharing for funded studies [10]. ### Standard File Formats Proprietary instrument formats create interoperability barriers because each mass spectrometer manufacturer uses its own file structure. Converting raw data to the open mzML format removes this barrier by providing a vendor-neutral representation of the spectra. Similarly, mzIdentML provides a standard format for peptide and protein identification results that can be read by multiple analysis tools. The conversion step should happen early in the data management workflow, and the converted files should be deposited alongside or instead of the proprietary originals. The decision to convert formats involves a tradeoff. The converted files preserve the spectral information needed for most reanalysis purposes, but some instrument-specific details may be lost in conversion. Researchers should document the conversion software and version used, and they should retain the original proprietary files in their local storage even after depositing the converted versions. The deposited standard-format files serve the FAIR purpose of interoperability, while the local proprietary files preserve the complete original record. ### Metadata Completeness Metadata is the descriptive information that makes a dataset interpretable. For proteomics, essential metadata includes the biological sample description, the organism, the tissue or cell type, the sample preparation method, the digestion protocol, the fractionation strategy, the instrument model and settings, the acquisition mode, the search engine and database used for identification, the search parameters, and the false discovery rate threshold applied. Each of these details affects whether another researcher can meaningfully reuse the data. The challenge of metadata management is that the volume and variety of data generated by omics studies requires rigorous standards and effective management to ensure accurate and reliable data exchange, curation, validation, and standardization [11]. Deep metadata annotations and the use of qualified data standards enable integration with external resources [11]. Repository submission forms guide researchers through the required metadata fields, but the quality of the annotations depends on the care taken during submission. A dataset with complete spectral files but vague sample descriptions has limited reuse value because no one can determine what biological condition the data represents. ## Practical Workflow for Making Proteomics Data FAIR ### Step 1: Plan Data Management Before Acquisition Data management decisions should be made before the first sample is run on the mass spectrometer. The experimental design determines what metadata needs to be recorded, and the analysis plan determines what file formats will be produced. A data management plan that identifies the target repository, the required metadata fields, and the file conversion steps should be written at the project outset. The German Network for Bioinformatics Infrastructure has documented that the heterogeneity of wet lab technologies, data, metadata, software, and computational workflows makes FAIR compliance challenging, and that a broad service landscape with distributed teams requires careful planning to achieve consistent data management [8]. Early planning reduces the burden of retrospective annotation, which is both time-consuming and error-prone. The data management plan should also address the sustainability of the data product. The LINCS Data Coordination and Integration Center experience demonstrates that end-to-end data processing and management generates a high-quality and persistent product that aligns with big data initiatives and concords with emerging data science best practices including the FAIR principles [11]. A well-designed plan anticipates the full data lifecycle from acquisition through deposition and long-term preservation. ### Step 2: Record Complete Experimental Metadata During Acquisition The laboratory notebook should capture all parameters that affect data interpretation. This includes instrument settings such as resolution, scan range, and collision energy, as well as sample handling details such as lysis buffer composition, digestion time, and fractionation method. The metadata should be recorded in a structured format that can be transferred directly to the repository submission form. Free-text notes in a laboratory notebook are difficult to convert into searchable metadata, so researchers should use templates or electronic laboratory notebooks that enforce consistent field naming. The LINCS Consortium experience shows that the large volume and variety of data generated by high-throughput projects necessitate rigorous data standards and effective data management including modular data processing pipelines and end-user interfaces to facilitate accurate and reliable data exchange, curation, validation, standardization, aggregation, integration, and end user access [11]. Even smaller-scale proteomics projects benefit from the same discipline, because the metadata requirements for repository deposition are identical regardless of project size. ### Step 3: Convert Proprietary Files to Standard Formats After acquisition, convert raw instrument files to mzML using the appropriate conversion tool. Document the conversion tool version and any parameters used during conversion. If the analysis pipeline produces identification results, export those in mzIdentML format where the search engine supports it. The converted files should be validated by opening them in a second software tool to confirm that the conversion preserved the spectral content. The conversion step should be treated as a data processing operation with its own quality controls. The converted files should be compared against the originals for file size consistency and spectral count. Any discrepancies should be investigated before deposition. The conversion documentation should be included in the repository submission so that users understand the provenance of the standard-format files. ### Step 4: Document the Analysis Workflow The analysis workflow includes every computational step from raw files to final results. This includes the search engine and version, the protein database and its version, the search parameters, the false discovery rate estimation method, and any downstream statistical analysis. Reproducibility requires that another researcher can rerun the analysis with the same inputs and obtain the same outputs. Workflow documentation can take the form of a written protocol, a version-controlled script, or a workflow definition file. The Galaxy Training Network provides accessible workflow training and analysis tutorials that emphasize reproducibility [4], and the nf-core documentation describes community pipeline standards for reproducible workflow configuration [5]. Researchers should adopt one of these structured approaches instead of relying on undocumented command sequences. The Carpentries lessons provide foundational training in Git and other computing skills that support reproducible analysis [6], and the European Bioinformatics Institute offers training pathways that help researchers build the skills needed for effective data management [2]. ### Step 5: Deposit Data in a Public Repository Prepare the repository submission using the converted files, the complete metadata, and the analysis documentation. The submission should include both the raw spectral data and the processed identification results. The repository will assign a persistent identifier, typically in the form of an accession number. This accession number must be included in any publication that reports the data, and it should also be linked to any related datasets deposited in other repositories. The submission process should be tested before the final deposit. Researchers should review the repository's submission guidelines, prepare all required files in advance, and verify that the metadata fields are complete. The repository help desk can provide assistance with technical issues, but the researcher is responsible for the accuracy and completeness of the submission. ### Step 6: Link the Dataset to the Publication The final step is connecting the dataset identifier to the published article. The accession number should appear in the manuscript text, and the repository record should include the publication citation once the article is available. This bidirectional linking ensures that readers of the paper can find the data and that users of the data can find the paper. The link also enables citation tracking, which provides measurable evidence of data reuse. The publication link should be established as early as possible. Many journals require data availability statements that include accession numbers, and the repository record should be updated with the citation when the article is published. The bidirectional link is a critical component of findability because it connects the two primary discovery routes for research outputs. ## Options and Tradeoffs in FAIR Implementation ### Repository Choice Proteomics repositories differ in their submission requirements, data retention policies, and access controls. Some repositories specialize in raw mass spectrometry data, while others accept processed results or integrated multi-omics datasets. The choice of repository should match the data type and the anticipated reuse audience. A general-purpose repository may be appropriate for supplementary data files, but the primary proteomics dataset belongs in a domain repository where it will be indexed alongside comparable datasets and where proteomics-specific metadata standards are enforced. The National Center for Biotechnology Information maintains a range of data resources that support biological research [1], and the European Bioinformatics Institute provides training and data resources that help researchers understand how to use these systems effectively [2]. Researchers should familiarize themselves with the available repository options before starting their data management planning. ### Open Access Versus Controlled Access The decision to make data openly downloadable or to require approval for access depends on the nature of the samples. Data from public cell lines and model organisms can typically be shared openly. Data derived from human clinical samples may require controlled access to protect participant privacy. The FAIR principles accommodate both approaches, provided that the access conditions are clearly stated and the retrieval mechanism functions. A controlled-access dataset that can be requested through a documented procedure is more FAIR-compliant than an openly accessible dataset with no metadata, because the controlled dataset is discoverable and its reuse conditions are explicit. The sharing of biological data has a crucial role in the advancement of biological science, and specific suggestions exist for how to share different kinds of biological data including proteomics data [7]. These suggestions include guidance on specialist and general repositories for depositing data and a checklist to ensure that data is shared in standards consistent with the FAIR guiding principles [7]. Researchers should consult this guidance when making access decisions. ### Raw Data Versus Processed Results Depositing only the final quantification table is insufficient for FAIR compliance because it prevents reanalysis of the underlying spectra. The raw data enables verification of identifications, reanalysis with different search parameters, and integration with new analytical methods. However, raw data files are large, and some repositories impose size limits or require the use of specialized upload tools. Researchers should deposit both raw and processed data whenever the repository supports it, and they should document the relationship between the two levels of data. The decision about which data levels to deposit should be guided by the anticipated reuse scenarios. If the dataset is likely to be used for meta-analyses or method development, the raw data is essential. If the dataset is primarily a reference resource for a specific biological condition, the processed results may be sufficient for most users. The repository submission should clearly describe what is included at each level. ### Metadata Depth The level of metadata detail affects both the effort required for submission and the reuse value of the dataset. Minimal metadata that satisfies the repository requirements may be sufficient for a dataset that is unlikely to be reused, but it creates barriers for datasets with broad relevance. The effort invested in metadata should scale with the anticipated reuse value. A dataset from a well-characterized cell line with standard conditions requires less annotation than a dataset from a rare clinical cohort with complex sample processing. The de.NBI experience with FAIR implementation demonstrates that the challenges and requirements for adhering to the FAIR principles in a large distributed bioinformatics infrastructure include the heterogeneity of wet lab technologies, data, metadata, software, computational workflows, and the levels of implementation and monitoring of FAIR principles within different bioinformatics sub-disciplines [8]. Researchers should be aware that metadata standards continue to evolve and that their submissions should anticipate future integration needs. ## Observations and Measurements for FAIR Compliance ### Assessing Current Data Management Practices Laboratories can assess their current FAIR compliance by conducting a self-audit of recently completed projects. The audit should examine whether each dataset has a persistent identifier, whether the metadata is complete enough for independent interpretation, whether the files are in standard formats, and whether the access conditions are documented. The de.NBI experience with FAIR self-assessment demonstrates that evaluating the FAIR status of services and datasets reveals the challenges and requirements for adhering to the principles in a distributed bioinformatics infrastructure [8]. The audit results identify specific gaps that can be addressed in future projects. The self-audit should be conducted systematically, with a checklist that covers each FAIR principle. For each dataset, the auditor should record whether the dataset is findable through a searchable resource, whether the access protocol is documented, whether the file formats are interoperable, and whether the metadata supports reuse. The results should be compiled into a laboratory-level assessment that identifies common failure patterns. ### Measuring Data Reuse The success of FAIR implementation can be measured through data reuse indicators. Repository platforms typically track dataset downloads and citations. A dataset that receives downloads from researchers outside the originating laboratory has demonstrated findability and accessibility. A dataset that is cited in publications by other groups has demonstrated reusability. These metrics provide feedback on whether the metadata and format decisions were appropriate, and they can guide improvements to future submissions. The open source omics data movement has demonstrated the power of repurposing deposited data to answer translationally relevant questions [9]. The increasing number of new research findings and publications that stem from open source data provides evidence that FAIR-compliant deposition creates value beyond the originating laboratory [9]. Researchers should track whether their deposited datasets contribute to this broader research ecosystem. ### Tracking Metadata Completeness Laboratories should track the proportion of submitted datasets that include complete metadata for all required fields. Incomplete submissions indicate gaps in the laboratory's data management workflow, often stemming from metadata that was not recorded during the experiment. Tracking this metric over time reveals whether process improvements are effective. The tracking system should record the date of each submission, the repository used, the completeness score for each metadata category, and any issues encountered during the submission process. This record provides the evidence needed to identify systemic problems and to justify investments in improved data management practices. ## Records and Documentation Requirements ### Data Management Plans A data management plan documents how data will be handled throughout the project lifecycle. The plan should specify the types of data that will be produced, the formats that will be used, the metadata standards that will be applied, the repository where data will be deposited, and the timeline for deposition. Funding agencies increasingly require data management plans as part of grant applications, and the National Institutes of Health Data Management and Sharing Policy has made such planning a condition of funding. The plan should be treated as a living document that is updated when the experimental design changes. The de.NBI experience shows that a broad service landscape and an excellent network of experts are a strong basis for the development of useful research data management plans [8]. Researchers should leverage available expertise when developing their plans, including institutional data management support services and bioinformatics training resources. ### Submission Records The repository submission record includes the accession number, the submission date, the file inventory, and the metadata provided. This record should be retained in the laboratory's documentation system alongside the experimental records. The submission record provides the evidence that data sharing obligations were met, and it enables verification that the deposited files match the published results. The submission record should also include any correspondence with the repository about the submission, including requests for additional information or corrections. This correspondence can be valuable if questions arise later about the content or timing of the deposition. ### Analysis Documentation The analysis documentation should include the software versions, the parameter settings, and the workflow steps. Version control systems such as Git provide a mechanism for tracking changes to analysis scripts, and the Carpentries lessons provide foundational training in Git and other computing skills that support reproducible analysis [6]. The documentation should be sufficient for a colleague to reproduce the analysis without oral explanation. The Bioconductor project provides official package, workflow, installation, and reproducible genomic-analysis documentation that can serve as a model for analysis documentation [3]. Researchers should adopt similar standards for their own analysis documentation, including version numbers for all software components and parameter files for all analysis steps. ## Quality Controls for FAIR Data Submission ### File Validation Before submission, validate that all files are readable and complete. Open each converted file in a second software tool to confirm that the conversion was successful. Check that the file sizes are consistent with the expected data volume and that no files are truncated. Repository submission systems often perform automated validation, but the submitting researcher should perform independent checks before upload. The validation process should include checks for file naming consistency, directory structure, and file format compliance. The file inventory should be compared against the experimental records to confirm that all expected files are present and that no extraneous files are included. ### Metadata Review Review the metadata for completeness and accuracy before submission. Verify that sample names match the experimental records, that the organism and tissue annotations are correct, and that the instrument settings are accurately described. A second researcher should review the metadata to catch errors that the original annotator may have overlooked. The metadata review should be conducted using the repository's submission checklist as a guide. Each required field should be checked for completeness and accuracy. The review should also assess whether the metadata would be sufficient for a researcher outside the laboratory to interpret the dataset without additional information. ### Identifier Verification After submission, verify that the assigned accession number resolves correctly and that the dataset can be retrieved through the repository's public interface. Test the retrieval process using a different computer or network to confirm that access is not blocked by local configuration issues. The identifier verification should be repeated after the dataset is made public and again after any updates to the repository record. The accession number should be tested in the context of the publication to confirm that the link works as readers would use it. ## Common Failure Patterns in FAIR Implementation ### Delayed Deposition The most common failure is postponing data deposition until after the manuscript is accepted or the project ends. This delay creates pressure to submit quickly, which leads to incomplete metadata and poorly organized files. The solution is to deposit data at the same time as the manuscript submission, or earlier, so that the accession number can be included in the manuscript and the submission can be completed without time pressure. The delay pattern is often driven by the perception that data deposition is a final administrative step instead of an integral part of the research process. Researchers should reframe deposition as a core research activity that requires the same planning and quality controls as any other experimental step. ### Incomplete Metadata Submissions that include spectral files but omit sample descriptions, instrument settings, or search parameters have limited reuse value. This failure often occurs because the metadata was never recorded during the experiment and cannot be reconstructed later. The solution is to use structured metadata templates during data acquisition so that the information is captured at the source. The incomplete metadata pattern is particularly damaging because it cannot be corrected after the fact. The original experimenter may remember some details, but the complete record of instrument settings and sample handling is rarely recoverable from memory. The investment in structured metadata capture during the experiment is the only reliable prevention. ### Proprietary Format Dependence Datasets deposited only in proprietary instrument formats are difficult for other researchers to use because they require the same vendor software to open. This failure is addressed by converting to open formats such as mzML before deposition. The conversion should be documented so that users know the provenance of the standard-format files. The proprietary format pattern persists because conversion adds time to the data management workflow and because some researchers are unaware of the interoperability barriers created by vendor-specific formats. The conversion step should be built into the standard operating procedure for data handling. ### Missing Analysis Parameters Depositing raw data without documenting the analysis workflow prevents reproduction of the reported results. A reviewer cannot determine how the identified proteins were obtained from the raw spectra without the search engine, database, and parameter information. The solution is to include the analysis documentation in the repository submission or to link to a version-controlled workflow. The missing analysis parameters pattern is often combined with incomplete metadata, because both stem from inadequate documentation practices. The analysis documentation should be prepared alongside the analysis itself, not reconstructed at the time of deposition. ### Unlinked Publications Datasets that are deposited but not cited in the associated publication are difficult to discover because the connection between the paper and the data is missing. This failure is addressed by including the accession number in the manuscript and updating the repository record with the publication citation. The unlinked publication pattern undermines the findability of the dataset even when the deposition itself is complete. The bidirectional link between the paper and the data is essential for both discovery routes, and it should be established as early as possible in the publication process. ## Limitations of FAIR Implementation ### Technical Infrastructure Constraints Some laboratories lack the computational infrastructure to convert large raw files to standard formats or to manage the data volume required for repository submission. The conversion and upload processes can be time-consuming, particularly for datasets from high-throughput instruments. These constraints should be addressed in the data management plan, with dedicated time and resources allocated for data preparation. The infrastructure constraints are often most acute in laboratories that generate large datasets but have limited bioinformatics support. The European Bioinformatics Institute provides training pathways and data-resource training that can help researchers build the skills needed to address these constraints [2], and the Galaxy Training Network provides accessible workflow training that can reduce the computational burden of data processing [4]. ### Metadata Standard Gaps Proteomics metadata standards continue to evolve, and some experimental details lack standardized vocabularies. Researchers may need to use free-text fields for information that does not fit existing controlled vocabularies. The absence of a standard term does not excuse omitting the information, but it does limit the interoperability of that particular metadata element. The metadata standard gaps are a known challenge in the bioinformatics community. The de.NBI experience documents the challenges and requirements for adhering to the FAIR principles in a large distributed bioinformatics infrastructure, including the heterogeneity of data, metadata, software, and computational workflows [8]. Researchers should contribute to the development of improved metadata standards by reporting gaps to the relevant standards organizations. ### Repository Sustainability The long-term availability of deposited data depends on the sustainability of the repository infrastructure. Researchers should choose established repositories with stable funding and clear data retention policies. The National Center for Biotechnology Information and the European Bioinformatics Institute operate major data resources with long-standing commitments to data preservation [1][2], and their training materials help researchers understand how to use these resources effectively. The repository sustainability concern is particularly relevant for large datasets that require significant storage infrastructure. Researchers should verify that their chosen repository has a clear preservation mandate and a sustainable funding model before depositing irreplaceable data. ### Human Data Restrictions Proteomics data derived from human subjects may be subject to consent restrictions that limit data sharing. The consent form may not have authorized deposition in a public repository, or the data may contain information that could identify individuals. These restrictions must be respected even when they conflict with FAIR goals. The FAIR principles accommodate restricted access, but the restrictions must be documented and the access procedure must be functional. The human data restrictions require careful navigation of the tension between data sharing and privacy protection. The sharing of biological data has a crucial role in the advancement of biological science, but the specific suggestions for sharing different kinds of biological data recognize that some data cannot be made openly accessible [7]. Researchers should document their access decisions and the rationale behind them. ## Safety and Regulatory Context ### Data Privacy Obligations Proteomics data from human samples may contain information that is subject to privacy regulations. Even though mass spectrometry data does not directly contain genetic sequences, the peptide identifications can be linked to protein expression patterns that may be sensitive. Researchers must ensure that their data sharing plans comply with applicable privacy laws and with the terms of their institutional review board approvals. The data management plan should address how privacy obligations will be met while maximizing the FAIR characteristics of the data. The privacy obligations should be assessed at the project planning stage, before any data is acquired. The consent form language should be reviewed to determine what data sharing is authorized, and the data management plan should reflect those restrictions. If the consent form does not authorize data sharing, the researcher should consider whether additional consent can be obtained or whether the data can be shared in a de-identified form. ### Funding Agency Requirements Funding agencies increasingly require data sharing as a condition of support. The National Institutes of Health Data Management and Sharing Policy requires that funded research data be shared in a manner that is consistent with the FAIR principles. Researchers should review their funding agreements to understand the specific data sharing obligations that apply to their projects. The Cancer Research Data Commons was developed in part to support the NIH Data Management and Sharing Policy by providing infrastructure for sharing NCI-funded study data [10]. The funding agency requirements should be incorporated into the data management plan at the grant application stage. The plan should identify the repository, the timeline for deposition, and the access conditions that will apply. The plan should also address how the data will be preserved beyond the grant period. ### Institutional Policies Many institutions have data management policies that govern the retention, sharing, and disposal of research data. These policies may specify minimum retention periods, approved repositories, or review procedures for data sharing requests. Researchers should be familiar with their institutional policies and should ensure that their FAIR implementation is consistent with those requirements. The institutional policies should be reviewed in conjunction with the funding agency requirements and the privacy obligations. In some cases, the institutional policies may be more restrictive than the funding agency requirements, and the researcher must comply with the more restrictive standard. ## Professional Escalation Criteria ### When to Seek Informatics Support Researchers should escalate to bioinformatics support when they encounter technical barriers to FAIR implementation. This includes difficulty converting proprietary files to standard formats, uncertainty about which repository is appropriate for a particular data type, or problems with the repository submission system. The European Bioinformatics Institute provides training pathways and data-resource training that can help researchers build the skills needed to resolve these issues independently [2], but complex technical problems may require consultation with dedicated bioinformatics staff. The escalation should occur early in the data management process, not after the submission deadline has passed. Researchers should identify the available bioinformatics support resources at their institution and establish a relationship with those resources before they are needed. ### When to Consult Legal or Compliance Experts Researchers should escalate to legal or compliance experts when data sharing raises questions about privacy obligations, consent restrictions, or intellectual property. This includes situations where the consent form language is ambiguous about data sharing, where the data includes information from vulnerable populations, or where the research is subject to industry partnership agreements that restrict data release. The compliance review should occur before deposition, not after a data sharing conflict has arisen. The legal and compliance consultation should be documented, and the advice received should be incorporated into the data management plan. The documentation provides evidence that the researcher acted in good faith when making data sharing decisions. ### When to Revise the Data Management Plan The data management plan should be revised when the experimental design changes in ways that affect data types, volumes, or sharing restrictions. This includes adding a new assay type, beginning work with human samples, or collaborating with a group that has different data management requirements. The revision should be documented, and the updated plan should be shared with all project team members. The revision trigger should be defined in the original data management plan. Common triggers include changes to the experimental design, changes to the funding agreement, changes to the institutional policy, or the addition of new collaborators. The revision process should be lightweight enough to be practical but formal enough to be documented. ## Frequently Asked Questions ### What is the difference between FAIR data and open data? FAIR data is data that is findable, accessible, interoperable, and reusable according to the FAIR principles. Open data is data that anyone can access and use without restrictions. The two concepts overlap but are not identical. A dataset can be FAIR while remaining under controlled access, provided that the access conditions are clearly documented and the retrieval mechanism works. A dataset can be openly accessible while failing FAIR principles if it lacks metadata, uses proprietary formats, or has no persistent identifier. For proteomics, the practical implication is that researchers should aim for FAIR compliance even when privacy or consent restrictions prevent fully open sharing. ### Which file formats should I use for FAIR proteomics data deposition? The primary standard formats for proteomics data are mzML for raw mass spectrometry data and mzIdentML for peptide and protein identification results. These open formats can be read by multiple software tools, which makes the data interoperable. The original proprietary instrument files should be retained locally, and the conversion process should be documented with the software version and parameters used. Some repositories also accept additional formats for quantification results, but the mzML and mzIdentML files provide the core interoperable representation of the data. ### How do I choose the right repository for my proteomics data? The choice of repository depends on the data type and the anticipated reuse audience. Proteomics-specific repositories that participate in the ProteomeXchange consortium are appropriate for raw mass spectrometry data and identification results. These repositories enforce proteomics metadata standards and index the data alongside comparable datasets. If the study includes additional data types such as genomics or imaging, those components may need to be deposited in separate specialized repositories. The repository record should include links to related datasets in other repositories so that the complete study can be assembled from its parts. ### What metadata is essential for a reusable proteomics dataset? Essential metadata includes the biological sample description, organism, tissue or cell type, sample preparation method, digestion protocol, fractionation strategy, instrument model and settings, acquisition mode, search engine and version, protein database and version, search parameters, and false discovery rate threshold. The metadata should be recorded in a structured format during the experiment, not reconstructed after the fact. Repository submission forms provide templates for the required fields, but the quality of the annotations depends on the completeness of the laboratory records. ### How long does it take to prepare a proteomics dataset for FAIR deposition? The time required depends on the dataset size, the completeness of the existing metadata, and the researcher's familiarity with the repository submission process. The conversion of raw files to mzML can take several hours for large datasets. Metadata preparation and submission can take an additional day or more. The total effort is significantly reduced when metadata is recorded during the experiment and when the data management plan identifies the target repository at the project outset. The effort should be budgeted in the project timeline instead of treated as an afterthought at the publication stage. ### Can I make my proteomics data FAIR if it contains human clinical information? Yes, FAIR compliance is possible for human data, but the implementation differs from fully open datasets. The data should be deposited in a repository that supports controlled access, and the access conditions should be clearly documented. The metadata should describe the data without revealing protected information. The repository record should explain the access request procedure so that qualified researchers can obtain the data. The key is that the data is discoverable and the access process is functional, even if the data itself is not openly downloadable. ### What should I do if my repository submission is rejected? Repository submissions can be rejected for incomplete metadata, file format problems, or missing required documentation. The rejection message typically identifies the specific issues that need to be addressed. Researchers should correct the identified problems and resubmit. If the rejection is due to a technical issue with file conversion or upload, the researcher should seek assistance from the repository help desk or from institutional bioinformatics support. The rejection should not be interpreted as a reason to abandon deposition, because the underlying data still needs to be shared. ### How do I cite proteomics data that I have deposited in a repository? The repository assigns a persistent identifier, typically an accession number, to the deposited dataset. This accession number should be cited in the associated publication, usually in the data availability statement and in the methods section where the data is described. The repository record should also include the publication citation once the article is available, creating a bidirectional link. Some repositories provide a suggested citation format for the dataset, which should be used when the dataset is referenced in other publications. ## Related Bioinformatics Guides - [FAIR Data Principles for AI: Making Your Training Data Reusable](/knowledge/bioinformatics/fair-data-principles-for-ai-making-your-training-data-reusable) - [FAIR Data Maturity Model: A Practical Assessment Framework for Bioinformatics Workflows](/knowledge/bioinformatics/fair-data-maturity-model-a-practical-assessment-framework-for-bioinformatics-workflows) - [FAIR Data Principles in the EU: Compliance and Implementation](/knowledge/bioinformatics/fair-data-principles-in-the-eu-compliance-and-implementation) - [FAIR Data Principles and Metadata: Enhancing Discoverability and Reuse](/knowledge/bioinformatics/fair-data-principles-and-metadata-enhancing-discoverability-and-reuse) - [Olink Proteomics: A Practical Guide to Panel Selection and Data Interpretation](/knowledge/bioinformatics/olink-proteomics-a-practical-guide-to-panel-selection-and-data-interpretation) ## References and Further Reading - [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/). National Center for Biotechnology Information. - [EMBL-EBI Training](https://www.ebi.ac.uk/training). European Bioinformatics Institute. - [Bioconductor](https://bioconductor.org/). Bioconductor Project. - [Galaxy Training Network](https://training.galaxyproject.org/). Galaxy Project. - [nf-core Documentation](https://nf-co.re/docs). nf-core. - [The Carpentries Lessons](https://carpentries.org/lessons). The Carpentries. - [Sharing biological data: why, when, and how.](https://pubmed.ncbi.nlm.nih.gov/33843054). FEBS letters, 2021. - [Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.](https://pubmed.ncbi.nlm.nih.gov/33589928). Briefings in bioinformatics, 2021. - [Exploiting open source omics data to advance pancreas research.](https://pubmed.ncbi.nlm.nih.gov/38524857). Journal of pancreatology, 2024. - [NCI Cancer Research Data Commons: Resources to Share Key Cancer Data.](https://pubmed.ncbi.nlm.nih.gov/38488507). Cancer research, 2024. - [Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center.](https://pubmed.ncbi.nlm.nih.gov/29917015). Scientific data, 2018. > This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.