6 FAIR data and materials
7 sub-clusters · 82 referencesStudents will learn about FAIR data (and education/research materials) principles that address Findability, Accessibility, Interoperability, and Reusability; engage with reasons to share data, the initiatives designed to increase scientific openness; as well as of possible privacy and security considerations together with anonymization procedures. There are 7 sub-clusters which aim to further parse the learning and teaching process:
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Reasons to share data and materials
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Sharing data and research materials is beneficial for science and society. Open data can enable validation of results, inspire new discoveries through re-use, increase researcher credit (e.g., via data citations), and promote transparency that enhances trust. Key studies have shown that papers with shared data receive more citations and foster broader collaboration.
Revisiting Qualitative Data Reuse
This article evaluates the rapid growth and mainstream acceptance of qualitative data reuse in the United Kingdom, identifying the specific policy and cultural drivers behind this shift. It explores how secondary analysis provides opportunities to gain new methodological insights and substantive findings from existing raw research materials.
The citation advantage of linking publications to research data
This study empirically quantifies the citation premium associated with articles that provide data availability statements and functional links to external data repositories. By analyzing over half a million articles using an automated system, it provides large-scale evidence that transparent data sharing is linked to increased scientific impact across disciplines.
An Agenda for Open Science in Communication
This paper outlines a seven-point agenda for integrating open science practices into communication research to address the discipline's replication crisis. It advocates for specific shifts in research culture, such as the publication of materials and code, to enhance the transparency and generalizability of communication studies.
DuBois, J. M., Strait, M., & Walsh, H. (2018). Is it time to share qualitative research data? Qualitative Psychology, 5(3), 380–393.
https://doi.org/10.1037%2Fqup0000076
Many Labs 2: Investigating Variation in Replicability Across Samples and Settings
This large-scale empirical study examines the replicability of 28 psychological findings across 125 diverse samples from 36 countries to investigate how variation in settings affects results. It provides critical meta-research evidence suggesting that the primary determinant of replication success is the strength of the original effect rather than the specific sample or context.
Investigating Variation in Replicability
This foundational study provides empirical data on the replicability of 13 psychological effects by testing them across 36 independent labs and diverse participant pools. It contributes to the understanding of reproducibility by demonstrating that most of the tested effects were consistently reproducible regardless of whether the research was conducted in a lab, online, or in different geographical locations.
Data: Sharing Is Caring
This resource provides actionable guidance on implementing data management best practices throughout the research lifecycle to facilitate easier data sharing. It emphasizes the importance of formal citations for data to ensure researchers receive professional recognition for the intellectual labor involved in data curation.
Transparency: The Emerging Third Dimension of Open Science and Open Data
This paper proposes a three-dimensional model of open science by incorporating transparency as a distinct axis alongside open access and open data. It provides a conceptual framework that links transparency to research integrity and policy contexts, helping to clarify its role within the broader open science movement.
McKiernan, E. C., Bourne, P. E., Brown, C. T., Buck, S., Kenall, A., Lin, J., McDougall, D., Nosek, B. A., Ram, K., Soderberg, C. K., Spies, J. R., Thaney, K., Updegrove, A., Woo, K. H., & Yarkoni, T. (2016). How open science helps researchers succeed. ELife, 5. CLOCKSS.
https://doi.org/10.7554/eLife.16800
The Reproducibility Crisis in Science: A Statistical Counterattack
This resource identifies the lack of advanced statistical and analytical skills as a primary driver of the reproducibility crisis in science. It calls for a systemic shift in scientific training, arguing that rigorous statistical education is the most effective tool for ensuring research findings are replicable.
Data reuse and the open data citation advantage
This study provides a statistically rigorous analysis of the 'citation advantage' associated with making research datasets publicly available. It contributes high-powered empirical evidence that documents the link between data sharing and increased scholarly impact, while also examining the temporal patterns of data reuse.
Rosenberg, J. M., Borchers, C., Burchfield, M. A., Anderson, D., Stegenga, S. M., & Fischer, C. (2022). Posts About Students on Facebook: A Data Ethics Perspective. Educational Researcher, 51(8), 547–550.
https://doi.org/10.3102/0013189X221120538
The what, why, and how of born-open data
This resource introduces the concept of "born-open" data, which involves automating the archiving of datasets to public repositories at the moment of creation. It provides a practical workflow to reduce the time and effort required for data sharing while ensuring complete transparency from the pilot phase through to final analysis.
Stodden, V. C. (2011). Trust Your Science? Open Your Data and Code. Columbia University.
https://doi.org/10.7916/D8CJ8Q0P
The academic, economic and societal impacts of Open Access: an evidence-based review
This comprehensive review evaluates the academic, economic, and societal consequences of open access, providing a data-driven foundation for ongoing policy debates. It synthesizes empirical findings from a wide range of meta-research to highlight the tangible benefits and potential challenges of transitioning away from paywalled research.
The Value of Openness in Open Science
This paper offers a philosophical caution against uncritical openness, arguing that transparency initiatives can sometimes have unintended detrimental effects on scientific epistemology. It proposes a framework where openness is treated as one governing value among many that must be carefully balanced to preserve the overall integrity of the scientific process.
Open science, data sharing and solidarity: who benefits?
This resource summarizes multidisciplinary discussions regarding the intersection of data sharing, ethics, and solidarity within the life sciences. It specifically addresses the distribution of benefits in open science, questioning how the movement impacts different global populations and who truly gains from the sharing of personal and research data.
Promises and pitfalls of data sharing in qualitative research
This resource provides a balanced overview of the advantages and ethical complexities associated with data sharing in qualitative research. It specifically examines the tension between the push for research transparency and the need to protect sensitive information, outlining the unique risks and responsibilities qualitative researchers must manage when sharing their work.
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Reasons not to share: Privacy and security considerations
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Open sharing of data sometimes poses legitimate privacy and security concerns. These include protecting participant privacy, honoring cultural ownership of data, and security risks. It emphasizes that not all data can or should be open, and ethical frameworks guide decisions in these cases.
A context-consent meta-framework for designing open (qualitative) data studies
This article introduces a context-consent meta-framework designed to assist researchers in the ethical planning and execution of studies involving open qualitative data. Based on interviews with qualitative psychologists, the resource specifically recommends conducting 'archaeologies of context and consent' to evaluate the suitability of datasets for secondary use.
Open Data in Qualitative Research
This resource surveys the evolving landscape of open data in qualitative research, discussing the impact of technological changes and funder mandates on research practices. It contrasts the benefits of transparency and accountability with the unique challenges posed by the non-standardized nature of qualitative data.
Open With Care! Consent, Context, and Co-production in Open Qualitative Research
This resource responds to scholarly critiques of the Netherlands Code of Conduct for Research Integrity, arguing that the existing code remains a justified and functional framework for ethical practice. It contributes to the broader debate on research ethics by clarifying the purpose and limitations of national integrity policies.
Participation in patient support forums may put rare disease patient data at risk of re-identification
This study provides empirical evidence of re-identification risks for rare disease patients who participate in online support forums. It identifies how specific health data patterns and ICD codes act as quasi-identifiers, and proposes practical adjustments to forum guidelines and organizational privacy measures to protect patient anonymity.
Aspects of Data Ethics in a Changing World: Where Are We Now?
This paper surveys the current ethical landscape of data science, emphasizing how the ubiquity of data and advanced extraction tools creates a pressing need for oversight. It specifically examines the tension between the potential for technological progress and the risks of misuse in a data-saturated world.
Applying the ‘CARE Principles for Indigenous Data Governance’ to ecology and biodiversity research
This article provides actionable guidance on integrating the CARE Principles for Indigenous Data Governance into ecology and biodiversity research. It explains how researchers can move beyond standard open data practices to prioritize Indigenous sovereignty, benefit-sharing, and ethical stewardship of biodiversity data.
Jacobs, A. M., Büthe, T., Arjona, A., Arriola, L. R., Bellin, E., Bennett, A., Björkman, L., Bleich, E., Elkins, Z., Fairfield, T., Gaikwad, N., Greitens, S. C., Hawkesworth, M., Herrera, V., Herrera, Y. M., Johnson, K. S., Karakoç, E., Koivu, K., Kreuzer, M., … Yashar, D. J. (2021). The Qualitative Transparency Deliberations: Insights and Implications. Perspectives on Politics, 19(1), 171–208.
https://doi.org/10.1017/S1537592720001164
Involving Research Stakeholders in Developing Policy on Sharing Public Health Research Data in Kenya
This document establishes the formal principles of the Cape Town Statement, providing a policy framework for promoting fairness, equity, and diversity within the global research ecosystem. It serves as a normative guideline for institutions and researchers to align their collaborative practices with ethical standards of equity.
Research Stakeholders’ Views on Benefits and Challenges for Public Health Research Data Sharing in Kenya: The Importance of Trust and Social Relations
This study employs a deliberative qualitative approach to explore how research stakeholders in Kenya perceive the benefits and risks of sharing public health data. It identifies specific stakeholder concerns regarding fairness and interest protection, providing empirical evidence to inform data-sharing policies in low-to-middle income countries.
Open Sharing of Data on Close Relationships and Other Sensitive Social Psychological Topics: Challenges, Tools, and Future Directions
This article details an adversarial collaboration that navigated the complexities of sharing sensitive social psychological data from a speed-dating study. It provides a reflective case study of the ethical decision-making process, highlighting how even shared data can prompt retroactive concerns about participant confidentiality.
A dataset without a code book: ethnography and open science
Drawing on research in sexual violence, this paper argues that the ontological and epistemic foundations of open science are often incompatible with ethnographic practice. It identifies three distinct logics of open data—epistemic, political-economic, and regulatory—to demonstrate how standardized data-sharing expectations can conflict with the nature of knowledge production in ethnography.
Rethinking Data Sharing and Human Participant Protection in Social Science Research: Applications from the Qualitative Realm
This article evaluates how traditional participant protection models designed for quantitative data fail to address the unique privacy requirements of qualitative research. It proposes a rethinking of data-sharing frameworks to better accommodate the specific epistemic and ethical nuances of the qualitative social science realm.
Open Science as Confused: Contradictory and Conflicting Discourses in Open Science Guidance to Researchers
This formal comment advocates for the integration of gender and diversity considerations into researcher assessment frameworks to improve institutional integrity and representation. It specifically argues that reshaping assessment criteria is a necessary step in fostering an inclusive and responsible research environment.
The challenges of open data sharing for qualitative researchers
This resource critiques the application of universal open science mandates to qualitative research, arguing that sharing full datasets for replication is epistemologically and ethically problematic. It highlights how standardized requirements fail to account for methodological differences and the specific risks involved in de-identifying complex qualitative narratives.
Secondary analysis of qualitative data: a valuable method for exploring sensitive issues with an elusive population?
This publication provides a procedural demonstration of how to conduct secondary analysis on qualitative datasets, focusing on the often-undocumented 'how-to' of the research process. It specifically illustrates the value of this methodology for exploring sensitive issues and reaching elusive populations by repurposing existing longitudinal transcripts.
McGrath, C., & Nilsonne, G. (2018). Data sharing in qualitative research: Opportunities and concerns. MedEdPublish, 7(255), 255.
Adversarial reanalysis and the challenge of open data in regulatory science
This paper examines the risks associated with open data mandates in the specific context of environmental regulatory science, where transparency requirements can be used as 'Trojan Horses' to undermine scientific evidence. It distinguishes between replication and reanalysis to highlight how adversarial reanalysis can be weaponized to exclude critical studies from the policy-making process.
Research Participant Views regarding Qualitative Data Sharing
This study explores the under-researched area of participant attitudes toward qualitative data sharing through interviews with individuals involved in sensitive research. It highlights that participants value the potential impact of their data but demand strong protections regarding confidentiality and the prevention of data misuse by secondary users.
#MeToo Online Disclosures: A Survivor-Informed Approach to Open Science Practices and Ethical Use of Social Media Data
This resource proposes a survivor-informed framework for the ethical collection, storage, and sharing of sensitive social media data, specifically focusing on sexual violence disclosures. It addresses the unique ethical challenges of applying open science principles to public digital data that may carry significant safety and privacy risks for the individuals who posted them.
Reflection over compliance: Critiquing mandatory data sharing policies for qualitative research
This resource critiques the 'Mandatory Inclusion of Raw Data' (MIRD) model, arguing that universal data-sharing mandates fail to account for the unique ethical and epistemological challenges of qualitative research. It provides a series of reflective questions to help researchers in health psychology and related fields navigate these policies while protecting participant confidentiality and methodological integrity.
When open data closes the door: A critical examination of the past, present and the potential future for open data guidelines in journals
This paper provides a critical evaluation of how current journal open data guidelines often overlook the specific ethical and theoretical nuances of qualitative research. It uses a content analysis of 261 journals to demonstrate the disconnect between broad open-science mandates and the practical realities of qualitative social psychology.
Ethical aspects of data sharing and research participant protections.
This article provides a comprehensive overview of how open data practices intersect with established ethical frameworks for human subjects research. It explores the reinterpretation of respect, beneficence, and justice in the context of protecting participant privacy while maximizing the value of shared research data.
Authorial and institutional stratification in open access publishing: the case of global health research
This research identifies socioeconomic inequalities in open access publishing by showing how institutional prestige and funding levels correlate with the type of OA model authors can afford. It demonstrates that authors from lower-ranked institutions are disproportionately excluded from gold OA options, potentially exacerbating global disparities in research visibility.
Generating evidence on privacy outcomes to inform privacy risk management: A way forward?
This resource advocates for the development of empirical evidence to inform privacy risk management strategies in data sharing. It proposes a shift toward a more systematic, evidence-based approach to privacy assessments to ensure that data protection measures are effective without being unnecessarily restrictive.
Qualitative Data Sharing: Participant Understanding, Motivation, and Consent
This empirical study investigates how research participants in a sensitive context—abortion reporting—understand and perceive the sharing of their qualitative data. By analyzing participant motivations and responses to consent requests, it provides evidence-based insights into the complexities of obtaining informed consent for data reuse.
Diffusion of ethical governance policy on sharing of biological materials and related data for biomedical research
This study examines the diffusion of ethical norms regarding the sharing of human biological materials by analyzing national policy frameworks in Guinea, Argentina, India, and Malawi. It uses an analytical framework to demonstrate how international expert communities influence the adoption and adaptation of global governance standards at the domestic level.
Enabling Open-Science Initiatives in Clinical Psychology and Psychiatry Without Sacrificing Patients’ Privacy: Current Practices and Future Challenges
This article reviews the unique privacy risks associated with open data sharing in the fields of clinical psychology and psychiatry. It evaluates current governance mechanisms and technical strategies that allow researchers to participate in open science initiatives while maintaining necessary patient confidentiality.
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Licenses and reuse
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Licensing determines how others may access, cite, remix, and redistribute your work. This section orients you to data/code/materials licenses (e.g., CC BY/CC0), data-use agreements, and rights/obligations that shape ethical, legally sound reuse, especially for qualitative and sensitive data
Anon. (n.d.). Can I reuse someone else’s research data? OpenAIRE. https://www.openaire.eu/can-i-reuse-someone-else-research-data
Getting the most from archived qualitative data: epistemological, practical and professional obstacles
This resource surveys the landscape of qualitative secondary analysis, identifying the specific epistemological, professional, and practical hurdles that hinder its wider adoption. It examines the evolving status of qualitative research and the barriers researchers face when attempting to maximize the utility of archived data.
Creative Commons licenses and the non-commercial condition: Implications for the re-use of biodiversity information
This analysis investigates the impact of the Creative Commons 'non-commercial' (NC) license condition on the interoperability and reuse of biodiversity information. It clarifies common misconceptions regarding the legal definition of the NC clause and highlights how its use can inadvertently create barriers to scientific progress.
Data Sharing in Psychology: A Survey on Barriers and Preconditions
This study provides empirical findings from a survey of 600 psychology authors regarding their data sharing habits and perceived obstacles. It highlights specific barriers such as the lack of established disciplinary norms, the effort required for curation, and the preference for sharing data only upon individual request.
Many Labs 2: Investigating Variation in Replicability Across Samples and Settings
This large-scale empirical study examines the replicability of 28 psychological findings across 125 diverse samples from 36 countries to investigate how variation in settings affects results. It provides critical meta-research evidence suggesting that the primary determinant of replication success is the strength of the original effect rather than the specific sample or context.
Investigating Variation in Replicability
This foundational study provides empirical data on the replicability of 13 psychological effects by testing them across 36 independent labs and diverse participant pools. It contributes to the understanding of reproducibility by demonstrating that most of the tested effects were consistently reproducible regardless of whether the research was conducted in a lab, online, or in different geographical locations.
Barriers and facilitators to qualitative data sharing in the United States: A survey of qualitative researchers
This study provides empirical evidence on the current state of qualitative data sharing by surveying 425 US-based researchers about their experiences and concerns. It identifies specific barriers, such as a lack of participant permission and concerns over sensitive data, that contribute to the low prevalence of repository-based sharing.
Stodden, V. (2009). The Legal Framework for Reproducible Scientific Research: Licensing and Copyright. Computing in Science & Engineering, 11(1), 35–40.
https://doi.org/10.1109/MCSE.2009.19
Wicherts, J. M., Borsboom, D., Kats, J., & Molenaar, D. (2006). The poor availability of psychological research data for reanalysis. American Psychologist, 61(7), 726–728.
https://doi.org/10.1037/0003-066X.61.7.726
Whylly, K. E., Karcher, S., & Renbarger, R. (2023, January 25). Data sharing for qualitative research: Webinar and panel. Center for Open Science. https://youtu.be/eWZvmSIfhQY
Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking
This resource provides an extensive checklist of 34 specific researcher degrees of freedom that can lead to p-hacking across various stages of the research process. It serves as a practical tool for psychologists to preemptively identify and minimize opportunistic choices during study planning, data collection, analysis, and reporting.
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Metadata standards
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Reusable research starts with good, machine-actionable metadata. This sub-cluster points to field-tested schemas and “minimum information” checklists so teams can capture provenance, methods, and context consistently across datasets, code, and teaching materials.
Anon. (n.d.). Fair cookbook. FAIR Cookbook. https://faircookbook.elixir-europe.org/content/home.html
Anon. (n.d.). Project metadata. LDbase. https://www.ldbase.org/resources/user-guide/information-to-gather/project-metadata
The FAIR Cookbook - the essential resource for and by FAIR doers
This resource provides a collection of actionable, community-curated "recipes" that translate abstract FAIR principles into concrete technical workflows for data management. It helps researchers and data stewards bridge the gap between high-level policy requirements and the actual technical implementation needed to make research data findable, accessible, interoperable, and reusable.
The FAIR Guiding Principles for scientific data management and stewardship
This seminal paper establishes the FAIR Data Principles as a formal framework for improving the machine-actionability of scholarly data. It provides the foundational criteria meant to guide diverse stakeholders in enhancing the long-term stewardship and reuse of digital research assets across all scientific disciplines.
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Repositories
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Trusted places to deposit datasets, code, and teaching materials so they remain findable, citable, and preserved.
Anon. (2019). Home. Open Science Framework. https://osf.io/
Anon. (n.d.). Figshare - credit for all your research. Figshare.com. https://figshare.com
Anon. (n.d.). LDbase: A Learning & Development Data Repository. www.ldbase.org. https://www.ldbase.org/
Anon. (n.d.). Zenodo - Research. Shared. Zenodo.org. https://zenodo.org
Practical Solutions for Sharing Data and Materials From Psychological Research
This article offers practical workflows and solutions tailored to the specific challenges of sharing complex research materials in psychology, such as sensitive video and audio data. It highlights tools like Databrary.org to demonstrate how researchers can navigate privacy concerns and technical barriers to make their research products more accessible.
Github. (2013). Build software better, together. GitHub. https://github.com
Qualitative Data Repository (n.d.). Guidance and resources. Qualitative Data Repository. https://qdr.syr.edu/guidance
The what, why, and how of born-open data
This resource introduces the concept of "born-open" data, which involves automating the archiving of datasets to public repositories at the moment of creation. It provides a practical workflow to reduce the time and effort required for data sharing while ensuring complete transparency from the pilot phase through to final analysis.
Using OSF to Share Data: A Step-by-Step Guide
This tutorial provides a practical, step-by-step workflow for utilizing the Open Science Framework (OSF) to share research materials, data, and scripts. It specifically addresses the needs of researchers in psychological science by detailing how to establish stable storage and generate unique identifiers for their digital objects.
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Research data management
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Introduces the planning and processes for managing research data through its lifecycle, from organizing files and documenting data (so that you and others can understand it later) to storing it securely and preparing it for sharing or archiving. Good RDM underpins the ability to be FAIR.
Introducing the Qualitative Data Repository's Curation Handbook
This practice-oriented paper presents the Qualitative Data Repository’s Curation Handbook, which outlines formalized procedures for structuring and archiving qualitative and multi-method research. The handbook serves as a guide for both researchers and data curators to ensure that shared data is discoverable, accessible, and meaningful for secondary analysis.
Valid replications require valid methods: Recommendations for best methodological practices with lab experiments.
This resource provides actionable methodological recommendations for conducting lab experiments to ensure they serve as a solid foundation for valid replications. It highlights specific practices in experimental design and implementation that are essential for producing reliable and reproducible findings.
Misidentified biomedical resources: Journal guidelines are not a quick fix
This resource critiques the effectiveness of current journal policies in mitigating the use of misidentified biological materials, such as contaminated cell lines or non-specific antibodies. It argues that simple policy mandates are insufficient to address these deep-seated biomedical research quality issues without more comprehensive institutional interventions.
How Data Curation Enables Epistemically Responsible Reuse of Qualitative Data
This paper presents a conceptual argument for the 'epistemically responsible reuse' of qualitative data, countering common skepticism regarding the ethical and epistemological validity of secondary analysis. It highlights the vital role of meticulous data curation in preserving the necessary context and integrity for meaningful reuse in qualitative inquiry.
Qualitative Data Sharing: Data Repositories and Academic Libraries as Key Partners in Addressing Challenges
This resource outlines the collaborative roles that data repositories and academic libraries can play in overcoming practical hurdles to qualitative data sharing. It specifically addresses how these institutions can partner with researchers to manage informed consent, de-identification, and data access challenges.
Ten Simple Rules for Creating a Good Data Management Plan
This resource provides a practical framework for developing comprehensive Data Management Plans required by research funders. It breaks down the complex data life cycle into ten actionable steps to ensure research data is properly discovered, collected, and preserved.
The what, why, and how of born-open data
This resource introduces the concept of "born-open" data, which involves automating the archiving of datasets to public repositories at the moment of creation. It provides a practical workflow to reduce the time and effort required for data sharing while ensuring complete transparency from the pilot phase through to final analysis.
UK Data Service. (n.d.). Research data management., https://ukdataservice.ac.uk/learning-hub/research-data-management/
De-Identification When Making Data Sets Findable, Accessible, Interoperable and Reusable
This resource offers technical and ethical guidance for the de-identification of research datasets to enable sharing in accordance with FAIR principles. It identifies common pitfalls in the anonymization process and provides strategies to balance data utility with participant privacy protection.
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FAIR principles applied to Education & Training
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FAIR isn’t only for datasets, syllabi, slides, and assignments can be findable, accessible, interoperable, and reusable too. This section offers institutional and practical roadmaps to make FAIR-by-design teaching materials the default.
D2.2 Methodology for FAIR-by-Design Training Materials
This resource outlines a six-stage methodology for creating educational materials that are FAIR by design, using a backward instructional design process. It provides practical checklists and guidance on metadata and granularity to help trainers produce learning resources that are discoverable and reusable across different contexts.
Ten quick tips for navigating intellectual property in FAIR educational resources
This guide provides ten practical tips for educators to navigate the intersection of intellectual property law and FAIR principles when creating Open Educational Resources. It offers actionable advice on copyright and licensing to ensure that materials are both legally compliant and easily shareable.
Kohrs, F. E., Auer, S., Bannach-Brown, A., Fiedler, S., Haven, T. L., Heise, V., Holman, C., Azevedo, F., Bernard, R., Bleier, A., Bössel, N., Cahill, B. P., Castro, L. J., Ehrenhofer, A., Eichel, K., Frank, M., Frick, C., Friese, M., Gärtner, A., … Weissgerber, T. L. (2023). Eleven strategies for making reproducible research and open science training the norm at research institutions. ELife, 12. CLOCKSS.
https://doi.org/10.7554/eLife.89736
Towards FAIRification of learning resources and catalogues—lessons learnt from research communities
This publication synthesizes community-led efforts to apply FAIR principles to educational materials, offering practical rules for enhancing the findability and reusability of learning resources. It highlights lessons learned across different research communities to guide the development of interoperable training catalogues and shared educational infrastructures.
