CASRAI Dictionary

Category: Perspectives

Opinion, argument, and field-shaping commentary on research-administration standards.

  • Responsible Research Assessment: Navigating DORA and CoARA Commitments

    Introduction

    For decades, academic hiring, promotion, and funding decisions have heavily relied on simplistic, journal-level quantitative metrics, primarily the Journal Impact Factor (JIF) and h-index. This over-reliance has created perverse incentives, encouraging quantity over quality, scientific conformity over breakthrough risk-taking, and hyper-competitiveness that devalues collaborative and open science.

    The San Francisco Declaration on Research Assessment (DORA)

    Drafted in 2012, the San Francisco Declaration on Research Assessment (DORA) was a milestone in challenging this metric-centric evaluation culture. Its primary recommendation is simple yet revolutionary: do not use journal-based metrics, such as Journal Impact Factors, as a surrogate measure of the quality of individual research articles to assess an individual scientist’s contributions.

    The Coalition for Advancing Research Assessment (CoARA)

    Building on DORA, the Coalition for Advancing Research Assessment (CoARA), launched in 2022, represents a systematic global coalition to reform evaluation systems. CoARA establishes a shared direction based on ten commitments, emphasizing qualitative judgment with peer-review at its core, supported by the responsible use of quantitative indicators, and respecting the diversity of research outputs.

    Practical Institutional Pathways for Reform

    Transitioning to responsible evaluation requires concrete policy changes within universities. This includes: 1. Adopting Narrative CV formats (like the Resume for Researchers) where academics describe their contributions contextually. 2. Training review panels on the limitations of bibliometrics. 3. Rewarding open science, data curation, public outreach, and teaching contributions alongside publications.

    Key Comparison Matrix

    Evaluation Model Key Characteristics Main Advantages Hurdles to Adoption
    Metric-Centric Heavy reliance on JIF, h-index, and citation counts. Quick, low administrative overhead, seemingly objective. Encourages citation gaming, devalues non-article outputs.
    Responsible/Holistic Peer review, qualitative CVs, and targeted responsible metrics. Recognizes diverse contributions, rewards open science. Higher panel review time, requires cultural shift.

    Actionable Checklist for Responsible Assessment

    • Incorporate DORA principles explicitly into university promotion and tenure guidelines.
    • Introduce narrative-style CV templates for internal institutional grant proposals.
    • Explicitly prohibit the mention of Journal Impact Factors in job descriptions and promotion files.
    • Provide bibliometric education sessions for hiring and promotion committees.
    • Define metrics policies that reward software, dataset publications, and mentoring activities.

  • Beyond the h-index: Modern Altmetrics and the Shift Toward Holistic Research Evaluation

    The Limitations of the Traditional Citation Economy

    For over two decades, the h-index—proposed by physicist Jorge Hirsch—has served as the dominant quantitative proxy for academic productivity and impact. Defined as the number of papers (h) that have received at least h citations, this single metric has heavily influenced university hiring, grant distributions, and promotion decisions. However, relying on the h-index has introduced severe systemic distortions. It penalizes early-career researchers, favors disciplines with high citation rates, ignores the contribution of non-first authors, and completely overlooks non-traditional research outputs like software, clinical datasets, and policy briefs.

    This perspective article examines the limitations of the h-index, introduces the rise of alternative metrics (altmetrics), and explores the global shift toward holistic research evaluation.

    Analyzing the Distortions of Legacy Metrics

    While quantitative metrics are easy to track, they present several systemic flaws:

    Metric Core Vulnerability Systemic Distortion / Bias
    h-index Strictly cumulative; dependent on career length. Discriminates against early-career scholars and researchers with career breaks (e.g., maternity leave).
    Journal Impact Factor (JIF) Measures journal-level citations, not article-level quality. Encourages editors to reject specialized, high-quality papers in favor of trendy, highly cited reviews.
    Total Citations Subject to citation manipulation and ‘citation cartels’. Overvalues older, established papers and ignores the downstream real-world impact on policy or industry.

    The Rise of Altmetrics: Capturing Real-World Impact

    To capture the immediate, real-world reach of research, alternative metrics (altmetrics) have emerged. Rather than waiting years for journal citations to accumulate, altmetrics track real-time engagement across diverse digital platforms:

    • Policy Document Mentions: How often is a study cited in official whitepapers by organizations like the WHO, World Bank, or the EU Commission? This represents a direct measure of societal impact.
    • Clinical Guideline Inclusions: For medical trials, inclusion in clinical guidelines directly translates lab discoveries into patient care improvements.
    • Software and Code Reuse: Tracking downloads, forks, and stars on platforms like GitHub or Zenodo validates the utility of research software.
    • Public Engagement and Media: Mentions in reputable news outlets and academic blogs highlight the broader public relevance of scientific discoveries.

    Global Policy Reform: DORA, CoARA, and Narrative CVs

    The transition to holistic evaluation is supported by major international policy agreements and standard bodies:

    1. San Francisco Declaration on Research Assessment (DORA)

    DORA mandates that institutions do not use journal-based metrics—such as Journal Impact Factors—as a surrogate measure of the quality of individual research articles in funding, hiring, or promotion decisions.

    2. Coalition for Advancing Research Assessment (CoARA)

    CoARA represents a global coalition of universities and funding agencies committed to reforming research assessment. CoARA principles state that evaluations must be based primarily on qualitative judgment, supported by the responsible use of quantitative indicators.

    3. The Rise of Narrative CVs

    Funding agencies like the UKRI (using the Resume for Research and Innovation, or R4RI) and the NIH (via SciENcv) are replacing long publication lists with structured Narrative CVs. Researchers are asked to describe their contributions in plain-text narratives, explaining the impact of their work across four key categories: contribution to knowledge, development of others, contribution to the research community, and societal impact.

    Conclusion: Designing Fairer Futures

    The academic community is slowly but surely moving past the simplistic reliance on the h-index and Journal Impact Factors. Designing a fairer, more accurate evaluation system requires a combination of robust qualitative peer review, responsible altmetrics tracking, and the complete embrace of narrative cvs. By valuing diverse contributions, universities can incentivize open science, celebrate collaborative teamwork, and ensure scholarly work directly benefits human society.

  • AI agents and autonomous research: attribution and accountability

    For most of the history of science, the tools of research — however sophisticated — did the bidding of the people using them. A telescope or a statistical package extended human capability but did not decide what to investigate. That assumption is now being tested. AI agents capable of a degree of autonomy are beginning to appear in research: systems that can generate hypotheses, design experiments, and in some cases run them through automated laboratory equipment, iterating with limited human intervention. Autonomous experimentation of this kind raises a question scholarship was never built to answer: when an AI system materially contributes to a discovery, how should that contribution be attributed, and who is accountable for it? This article examines those questions, drawing on the AI and ML research-outputs domain of the CASRAI Dictionary.

    What autonomous research looks like

    The systems in question share a common feature: they make consequential choices in the research process rather than merely executing instructions. An AI agent might propose which compounds to test next, design the sequence of experiments, control the apparatus that performs them, and analyse the results to decide what to try next — a loop that can continue with the human supervisor stepping in only occasionally. The appeal is obvious: such systems can explore vast spaces of possibility far faster than people, accelerating discovery from materials science to drug development. But the autonomy that makes them powerful is what unsettles the established account of who does research and who answers for it. The agent is no longer just a tool; it is participating in the intellectual work. That shift forces the questions of attribution and accountability.

    Why an AI cannot be an author

    The clearest and most settled point in this debate is also the most important: an AI system cannot be an author of a research work. This is not technophobia or an arbitrary rule; it follows directly from what authorship means. Authorship carries accountability. An author is someone who can take responsibility for the integrity of the work, vouch for its honesty, defend it when questioned, and be answerable if it proves flawed or fraudulent. An AI system can do none of these things; it cannot be held responsible or called to account. The major editorial and integrity bodies have converged firmly on this position: AI tools, however capable, cannot meet the criteria for authorship, because the defining quality of an author — answerability — is one a machine cannot possess. The principles of authorship rest on responsibility, and responsibility is irreducibly human.

    Accountability stays with people

    If the AI cannot be accountable, accountability does not vanish — it remains with the humans involved. The researchers who deploy an autonomous system, decide to use its outputs, design the study it operates within and interpret and publish the results are responsible for that work, including for the AI’s contributions to it. This has a sharp consequence: a researcher cannot disclaim responsibility for an error or fabrication by pointing to the machine. If an AI agent generates a flawed hypothesis and a researcher publishes it as sound, the failure is the researcher’s, because the duty to verify and stand behind the work was theirs. Far from diluting human responsibility, autonomous systems concentrate it: the more capable the tool, the more important the human judgement about whether and how to trust it. Autonomy in the tool does not mean autonomy from accountability for the people.

    Disclosure and the provenance of AI contributions

    If an AI agent cannot be credited as an author but did genuinely contribute, the honest course is to describe what it did transparently. This is a matter of disclosure and provenance rather than authorship. A research report should be clear about the role an autonomous system played — which hypotheses it generated, which experiments it designed, which analyses it performed — so readers can understand how the work was produced and judge it accordingly. Recording the provenance of AI contributions serves several ends at once:

    • Transparency. Readers and reviewers can see where machine judgement entered the work and weigh it appropriately.
    • Reproducibility. Knowing which system was used, and how, is part of being able to reproduce the result.
    • Accountability. Clear provenance makes plain which choices were the system’s and which the researchers’, keeping responsibility traceable.

    Disclosure does not credit the machine; it documents it — an entirely different and appropriate act.

    The limits of CRediT

    It is natural to ask whether a contribution taxonomy could simply add the AI as a contributor. Here it is worth being precise about what the CRediT taxonomy is for. CRediT describes the contributions of people to a research work; it is a vocabulary for human roles, anchored in the assumption that contributors are accountable agents. An autonomous system is not a contributor in that sense, because it cannot bear the responsibility contributorship implies. The right place for AI involvement is therefore not the contributor list but the methods and disclosure sections, where its use can be described as part of how the work was done. What CRediT continues to do well is capture the human contributions around the AI — the conceptualisation, methodology, investigation and interpretation that remain human even when a machine assists. The taxonomy’s limits here are not a defect; they reflect the correct distinction between a tool that is used and a person who is answerable.

    A consistent vocabulary for a changing landscape

    As autonomous systems become more common, describing their involvement consistently — what was used, for what, and where human responsibility sat — will matter increasingly across journals and institutions. That consistency is what the CASRAI Dictionary works towards: a shared vocabulary so a statement about how AI contributed to a piece of research, and who is accountable for it, is understood the same way wherever it is recorded. AI agents may transform the pace of discovery; the durable principles — that authorship means accountability, that responsibility stays with people, and that AI contributions are disclosed rather than credited — are what keep research trustworthy as the tools grow more powerful.

  • Reforming research culture: institutional change beyond the metrics debate

    Much of the conversation about responsible research assessment has, understandably, focused on metrics: the over-reliance on journal impact factors, the misuse of citation counts, the distorting effect of ranking people by where they publish rather than by what they contribute. These are real problems, and reforming how research is measured is genuinely important. But there is a risk in framing the whole challenge as a debate about metrics, because it can make the task look smaller than it is. Replacing one set of numbers with another, or adding a narrative section to an application form, does not by itself change the culture of research — the web of incentives, behaviours, relationships and rewards that actually shapes how people do their work. This article looks at the broader project of reforming research culture, drawing on the responsible assessment domain of the CASRAI Dictionary.

    Why culture, not just metrics

    Research culture is the environment in which research happens: how people are hired, promoted and rewarded; whether collaboration, mentorship and openness are valued or merely tolerated; whether the pressure to produce flashy results crowds out the slow, careful, reproducible work that good science depends on. Metrics are part of this culture, but only part. A system can adopt enlightened assessment criteria on paper while still, in practice, rewarding the same narrow behaviours, because the underlying incentives, expectations and norms have not shifted. Genuine reform means attending to the whole environment, not just the measurement layer on top of it. The metrics debate is the visible tip; the culture is the larger mass beneath.

    Wellcome and the research-culture agenda

    One of the organisations that has done most to widen this conversation is Wellcome, whose work on research culture has drawn attention to the lived experience of researchers and the pressures that shape it. Wellcome’s research-culture programme has highlighted that the environment in which research is conducted — the competitiveness, the precarity of careers, the toll on wellbeing — is itself a determinant of research quality and integrity. The insight is that you cannot reliably get good, honest, careful research out of a culture that rewards the opposite. By framing research culture as worthy of serious attention in its own right, this work has moved the conversation beyond the technicalities of assessment towards the human realities that assessment exists to serve.

    The Hong Kong Principles

    If the goal is to reward the behaviours that make research trustworthy, then assessment needs to be aligned with research integrity — and this is precisely what the Hong Kong Principles for assessing researchers set out to do. The Hong Kong Principles propose that researchers should be assessed in ways that recognise and reward trustworthy research practices: responsible research conduct, transparent reporting that includes the full record rather than only positive results, open science, a diversity of contributions and roles, and the activities that build and sustain the research community. Their distinctive contribution is to connect assessment directly to integrity: instead of asking only “how productive or highly cited is this researcher?”, they ask “does this researcher do their work in a trustworthy, open and responsible way?” This reframes assessment as a lever for better behaviour, not merely a measurement exercise — if institutions reward the practices that make research reliable, they get more of them.

    CoARA and institutional commitment

    Principles need vehicles for action, and one of the most significant is the Coalition for Advancing Research Assessment (CoARA). CoARA brings together organisations that commit to reforming research assessment, and crucially it asks them to make concrete commitments and to develop action plans for change within their own institutions. This institutional dimension is what distinguishes durable reform from good intentions. It is one thing for an individual to believe assessment should be broader and more responsible; it is another for a university or funder to commit publicly, develop a plan, and hold itself accountable for changing its own practices. By moving reform from the level of individual conviction to the level of institutional commitment, CoARA helps ensure that cultural change is embedded in how organisations actually operate, rather than remaining an aspiration that never reaches the committee rooms where decisions are made.

    Recognising diverse contributions and reproducible work

    A recurring theme across all of these efforts is the recognition of a broader range of contributions and the valuing of careful, reproducible practice. Several strands matter:

    • Diverse contributions. Research depends on far more than first-author papers — on data and software, on mentorship, on peer review, on technical and supporting work, on building shared resources. A reformed culture finds ways to recognise these.
    • Reproducibility. Valuing rigorous, transparent, reproducible work — rather than only novel or eye-catching results — is central to a healthier culture, because reproducibility is the foundation of reliable knowledge.
    • Openness. Rewarding open practices — open data, open methods, open access — aligns incentives with the kind of transparent research the community says it wants.

    From assessment to culture, and back

    Assessment and culture are bound together. How we assess researchers signals what we value, and what we value shapes how people behave. The structured description of contributions plays a role here: when a person’s full range of contributions can be recorded and recognised — through frameworks such as the CRediT taxonomy and its set of contribution roles — it becomes possible to value more than the narrow signals that metrics capture. But the description is a means, not an end. The end is a research culture in which good, honest, open, careful, collaborative work is genuinely rewarded, and in which the people who do it can build sustainable careers.

    A shared vocabulary for a shared reform

    Reforming culture across many institutions requires a common language for what is being recognised and valued. Contribution types, assessment criteria and the elements of a researcher’s record must be described consistently, or reform in one place cannot be understood or built upon elsewhere. That consistency is what the CASRAI Dictionary supports: a shared vocabulary for describing the contributions and activities that a reformed culture seeks to reward. The metrics debate opened the door; the larger work — the one Wellcome, the Hong Kong Principles and CoARA are pursuing — is changing the culture the metrics were only ever a symptom of.

  • Mentorship as a CRediT role: pro and con

    The CRediT Supervision role is broad. The role definition reads: Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team. The role bundles mentorship into Supervision, which leaves the question: should mentorship be a CRediT role of its own? This post lays out the arguments on both sides and proposes a mid-path.

    The case for

    Three arguments for a dedicated Mentorship role.

    First, visibility. Mentorship is a substantial intellectual and time-consuming activity that current CRediT-style contributorship statements largely render invisible. A senior researcher who mentored an early-career colleague through the discovery, the writing, and the navigation of peer review has contributed significantly to the paper; the current taxonomy captures this only through the catch-all Supervision role, which is also used for project oversight that is quite different in character.

    Second, career-stage equity. Mentorship contribution is most often delivered by mid-career and senior researchers to early-career ones, and is most often invisibilised in the way it currently is. Making it a CRediT role would help correct the under-recognition of mid-career mentorship work in promotion and tenure decisions. The mentorship and career stages domain at CASRAI tracks the assessment-side implications.

    Third, distinction from supervision. Project supervision (the senior researcher with PI responsibility) and mentorship (the senior or peer researcher who guided a junior contributor’s development through the work) are different activities. Bundling them into one role loses the distinction. A paper where the PI did the supervision and a separate mid-career colleague did the mentorship has a contributorship structure that current CRediT cannot express cleanly.

    The case against

    Three arguments against.

    First, taxonomic stability. CRediT has held to 14 roles deliberately. Each addition raises cognitive load and risks the taxonomy becoming unusable through over-specification. Liz Allen and the original CRediT designers have consistently argued that the taxonomy gains value from being small enough to use; adding a Mentorship role pushes against this.

    Second, boundary problems. What distinguishes mentorship from supervision, from teaching, from co-authorship, from collaboration? The lines are real but fuzzy. A senior colleague who reviewed the draft and suggested major revisions is doing Writing – review & editing; the same colleague who guided the junior author through how to think about the discovery is doing mentorship; in practice the activities overlap. A role that requires reviewers to distinguish them may produce noise more than signal.

    Third, recognition versus contribution. CRediT is a contributorship taxonomy, describing what people did on the paper. Mentorship is broader than per-paper contribution; it is a sustained relationship that spans many papers and many years. Capturing per-paper mentorship in CRediT may be the wrong instrument; a separate mentorship-recognition mechanism (in narrative CVs, in promotion dossiers, in institutional mentorship programmes) may fit better.

    A proposed mid-path

    We propose a mid-path that addresses the visibility and equity concerns without expanding the CRediT role count.

    First, clarify the Supervision definition. The current definition bundles mentorship with project leadership. The bundling could be unbundled within the existing role through definitional refinement: the role description could be revised to explicitly recognise mentorship as a sub-activity within Supervision, with guidance on when each is being discharged. This is a low-cost intervention that does not require a new role.

    Second, add a structured qualifier for Supervision. The existing degree-of-contribution qualifier already provides lead/equal/supporting. A sub-qualifier indicating whether the Supervision was project-oriented, mentorship-oriented, or both, would add the granularity without adding a role. This is a small schema change with substantial value.

    Third, build the recognition layer outside CRediT. The narrative-CV format, mentorship-specific recognition programmes, and institutional career-development frameworks should carry mentorship recognition at a sustained-relationship granularity that CRediT cannot. The mentorship recognition that early-career researchers most value is not the per-paper notation; it is the cumulative recognition of mentorship across a career. The CASRAI institutional mentorship guide walks through the recognition options.

    What CRediT v2026.3 should do

    Our recommendation for the v2026.3 revision discussion: do not add a Mentorship role; do refine the Supervision definition to recognise mentorship explicitly; do add a sub-qualifier capturing the project/mentorship/both distinction; do coordinate with the narrative-CV and institutional-recognition communities to ensure that the cumulative mentorship recognition picture is captured outside CRediT.

    This is the position we lean toward, with the explicit acknowledgment that reasonable people disagree. The discussion at the December 2025 CRediT stewardship meeting was substantive; the community consultation through 2026 will be the place to settle it. The CASRAI CRediT governance page tracks the consultation process and welcomes input from the broader community.

    A broader observation

    The mentorship question is one instance of a broader pattern. CRediT, as a per-paper contributorship taxonomy, captures certain things well and certain things less well. The work that spans papers (sustained mentorship, leadership of a community, contribution to standards, infrastructure stewardship) does not fit naturally into a per-paper taxonomy. The right response is not to expand CRediT to cover everything but to build complementary recognition mechanisms for what CRediT does not capture.

    This is the argument running through the responsible-assessment community, the narrative-CV adoption push, and the CoARA reform agenda. CRediT is part of the picture, not the whole picture. A senior researcher’s contribution profile is captured by CRediT statements on their papers, by their narrative CV, by their teaching record, by their mentorship record, by their service to the community. The integrated picture is the goal; CRediT is one component.

    Practical recommendations

    Three for institutions. First, capture mentorship in your institutional records and recognition systems; do not wait for it to be a CRediT role. Second, train promotion-and-tenure committees to read mentorship contribution explicitly when reviewing dossiers. Third, support narrative-CV formats that surface mentorship.

    Three for researchers. First, claim your mentorship contribution in narrative CVs and professional records; do not depend on per-paper CRediT to capture it. Second, in CRediT statements, use Supervision appropriately and consider noting the mentorship dimension in the prose contribution statement that accompanies the structured CRediT. Third, contribute to the CRediT consultation if you have a view on the question.

    Three for the CRediT stewardship community. First, run the v2026.3 consultation transparently and document the outcomes. Second, coordinate with the responsible-assessment community on the broader recognition picture. Third, treat the question of taxonomic expansion as a serious one with substantive trade-offs, not as a routine update.

    Related dictionary entries

  • CRediT for AI-generated content: where the line is

    The ICMJE 2023 position is settled: artificial-intelligence systems cannot be authors. The follow-on question that journals and authors continue to negotiate is how to represent, in a contributorship statement, the human work that goes into producing AI-assisted content. When a co-author prompts an LLM to draft a section, verifies the output, edits it, and stands behind it, which CRediT role describes their contribution? This post proposes a working line.

    The shape of the question

    Three scenarios make the question concrete.

    Scenario one: an author uses an LLM to polish prose in a draft they wrote. The intellectual content is theirs; the language is partly the model’s. The CRediT role is straightforwardly Writing – original draft for the author.

    Scenario two: an author uses an LLM to draft a first version of a section, which they then heavily revise. The first draft is the model’s; the final draft is the author’s, but the model substantively shaped what the final draft says. The CRediT role is still Writing – original draft for the author, but the contribution is meaningfully different from scenario one.

    Scenario three: an author uses an LLM to propose a study design, which they then refine. The intellectual content of the methodology was partly the model’s. The CRediT role is Methodology for the author, but again the contribution is meaningfully different from the unaided version.

    In all three, the human author is the role-holder; the model is not a co-author. What is different across the scenarios is the magnitude and the character of the human contribution. CRediT, as currently constituted, does not distinguish these.

    The working line

    Our proposed line is the verification-and-responsibility threshold. A human contributor who has substantively verified the AI-generated content, taken responsibility for it, and is prepared to defend it in correspondence or post-publication discussion is properly credited with the relevant CRediT role. The role describes what they contributed to the paper, which includes verification work even if the first-draft work was the model’s.

    The line shifts where the human contribution is insubstantial — a contributor who pasted a prompt, accepted the output without verification, and added their name to the paper has not discharged the role and should not be credited. This is the same line that has always applied to non-AI cases (a co-author who did not contribute should not be credited; gift authorship is a well-recognised failure mode).

    The line is therefore not about AI use per se; it is about whether the human contribution clears the substantive-contribution threshold. AI use does not displace the threshold; it changes what discharging the role looks like in practice.

    Disclosure runs parallel

    The disclosure of AI use is a separate question, addressed via publisher-mandated AI disclosure declarations. The disclosure says what tools were used and for what; the CRediT statement says who contributed what to the paper. The two run parallel and are both required by most major publishers in 2026. The CASRAI AI disclosure for authors guide walks through the publisher-by-publisher requirements.

    Implications for specific roles

    Writing – original draft

    The most common case. A human author whose draft was AI-assisted is properly credited with Writing – original draft if they verified the content, took responsibility for it, and produced the version that is the paper. The disclosure declaration says the AI was used; the CRediT statement names the human as the writer-of-record.

    Methodology and Formal analysis

    More delicate. If an AI-assisted statistical-discovery tool proposed a method or an analytic approach, the human contributor’s role is partly verification (was the proposal sound?) and partly extension (refining the proposal into the actual method). The CRediT role is still Methodology and/or Formal analysis for the human, but the verification dimension is foregrounded. If the human did not verify — accepted the AI proposal without independent assessment — the contribution is weaker and may not clear the role threshold.

    Investigation

    A subtle case. AI-assisted data extraction (e.g., from imaging, from medical records, from text corpora) involves a human contribution that runs from setup through verification to interpretation. Investigation includes the data-gathering activity; an AI-assisted version still has a human Investigation lead, who is responsible for the setup, the verification of extracted data, and the handling of errors.

    Validation

    Perhaps the most directly affected. Where AI tools are used for cross-checking, sensitivity analyses, or reproduction of results, the human Validation contributor is responsible for setting up the validation, interpreting its results, and acting on discrepancies. The AI does the mechanics; the human does the judgement.

    Visualization

    AI-assisted figure generation is increasingly common. The human Visualization contributor is responsible for the figure-design decisions, for verifying that the AI-generated figure accurately represents the data, and for the final version that appears in the paper. Where the AI generated an image that the human did not substantively verify, the threshold may not be cleared.

    The role-as-recognition trap

    A failure mode to flag explicitly. The temptation, when AI did most of the actual production work, is to inflate the human contributor’s role assignment to compensate. “The AI wrote the draft, but I prompted it, so I should still be Lead on Writing – original draft.” This is a misreading. The CRediT role is a description of contribution; if the human contribution was “prompted and accepted”, that is a smaller contribution than “drafted, verified, revised, took responsibility.” Calling both “Lead” obscures the difference.

    The remedy is the degree-of-contribution qualifier. A human contributor whose AI-assisted contribution was substantial may be Lead; one whose contribution was lighter may be Supporting. The qualifier discipline forces an honest assessment of magnitude.

    Where this leaves the AI-assistance-role question

    We have argued elsewhere that a 15th CRediT role explicitly for AI assistance is worth considering. The argument from this post is partly orthogonal: the existing 14 roles can accommodate AI-assisted work if the verification-and-responsibility threshold is honoured and the qualifier is used honestly. The case for a 15th role rests on whether the structured disclosure-of-AI-use is better placed inside the contributorship statement or outside it. Reasonable people disagree; we lean toward keeping AI disclosure parallel to CRediT rather than inside it, with attention to the verification-and-responsibility line.

    Practical recommendations

    Three for authors. First, treat AI assistance as a tool, not a substitute. Verify, edit, and take responsibility for what appears in the paper. Second, assign CRediT roles based on what you contributed including verification, not based on what the AI produced. Third, disclose AI use in the publisher-mandated declaration; the disclosure runs parallel to CRediT, not inside it.

    Three for editors. First, treat the verification-and-responsibility threshold as the operating standard for AI-assisted contributorship. Second, require both the CRediT statement and the AI-use disclosure at submission. Third, where a contributorship statement looks like it may reflect AI-assistance role inflation, ask the standard editorial question: what did this contributor actually do?

    Three for the broader system. First, harmonise AI-disclosure formats across publishers (work the NISO and COPE community has begun). Second, maintain the contributorship-versus-disclosure separation; do not collapse them. Third, evaluate the case for a 15th CRediT role on its merits, including the costs of taxonomic expansion.

    Related dictionary entries

  • Plan S and Diamond OA: where the open-access conversation is going

    Seven years after Plan S was announced and five years after its 2021 implementation deadline, the open-access conversation in 2026 looks meaningfully different from what its architects expected. The APC-plus-Read-and-Publish trajectory that dominated 2019-2023 is now competing with a much louder Diamond OA movement, with Subscribe-to-Open playing a quiet but important supporting role, and with sharp critique of the inequity that APC-based OA has reinforced. This post walks through the current state of the conversation and what we expect to see settle by 2027.

    Plan S compliance in 2026

    Plan S, the policy framework launched by cOAlition S in 2018, required that recipients of cOAlition S funder grants make their resulting publications immediately open access under a CC BY licence, via one of three routes: publication in a fully OA journal, publication in a hybrid journal under a transformative agreement, or self-archiving in a repository (the green route) without embargo and with retained rights.

    By 2026 the picture is mixed but not in the directions originally feared. Compliance among cOAlition S funder grantees is high (above 90% in the most recent monitoring report) but the modal route is no longer transformative agreements; it is rights-retention deposit. The Rights Retention Strategy, in which authors apply a CC BY licence to the accepted manuscript regardless of journal policy, has been quietly successful. By 2026 most major publishers have either explicitly accommodated RRS or stopped fighting it.

    The transformative-agreements track did not transform the publishing economy as cOAlition S had hoped. Read-and-Publish deals at the consortium and country level moved a lot of money from subscriptions to APCs but did not significantly change the underlying cost or the publisher consolidation. The funder-led price transparency requirement (Plan S has required publishers to disclose service-based pricing for some years now) has produced data but not yet pressure.

    The Diamond OA inflection

    The biggest shift in 2024-2025 was the move of significant funder attention from APC-based Gold OA to Diamond OA: journals that charge neither authors nor readers, funded instead by institutions, learned societies, libraries, and consortia. The 2023 Action Plan for Diamond Open Access from Science Europe, cOAlition S, OPERAS, and the French ANR, followed by the 2024 launch of the Diamond Open Access Capacity Centre, materially changed the funding landscape for community-led journals.

    By 2026 the visible result is a wave of new and renewed Diamond OA journals, particularly in the humanities and social sciences where APCs have always sat uncomfortably with the discipline’s economics. The OPERAS DOAB and DOAJ now flag Diamond OA journals explicitly. The 2024 Plan Diamond joint declaration committed signatory funders and institutions to channelling a defined fraction of OA-related spending into Diamond OA infrastructure.

    The structural challenge for Diamond OA remains sustainability. A journal funded by a single consortium is one budget cycle away from disappearing. The current direction is to pool funding across consortia and to professionalise the support layer (production, hosting, copy-editing, indexing) rather than reinventing it per journal.

    Subscribe-to-Open

    Subscribe-to-Open deserves more attention than it receives. The S2O model, pioneered by Annual Reviews and now adopted by EDP Sciences, Berghahn, and a growing list of others, asks the existing subscribers to continue paying their subscriptions; if enough do, the journal flips to open access for that year. If the threshold is not met, the journal stays subscription. The mechanism preserves a sustainable revenue model while flipping content to OA without APCs.

    S2O has been remarkably durable. Annual Reviews has hit the threshold every year of the programme; Berghahn flipped over thirty journals to S2O across humanities and social sciences. The model is constrained: it works for journals with a substantial existing institutional subscriber base, less well for new journals or for those without an institutional market. But where it fits, it works, and it sidesteps the APC-inequity problem entirely.

    Read-and-Publish agreements: the messy middle

    Read-and-Publish (also called Transformative Agreements) bundle subscription access and APC publishing into a single contract between a library consortium and a publisher. They peaked in deal-volume around 2022-2023 and have plateaued since.

    The criticisms have sharpened. R&P deals concentrated OA publishing capacity in well-resourced consortia (Germany’s Project DEAL, the UK’s JISC deals, the Dutch and Swedish consortia). Authors at unsupported institutions, particularly outside the wealthy world, faced full APCs while their better-resourced peers published OA “for free” under their consortium’s deal. The result was an inequity that the OA movement explicitly set out to remove and instead repackaged.

    cOAlition S’s commissioned review in 2023 concluded that transformative agreements had not produced the cost decrease that would justify their continued central role and recommended a transition away from them. By 2026 several major consortia are renegotiating R&P deals into a hybrid of capped APC pools, fee waivers for unaffiliated authors, and Diamond OA investments. The direction of travel is clear; the speed is slow.

    The equity reframing

    The phrase bibliodiversity entered the open-access conversation in 2018 via the Jussieu Call and has steadily gained traction since. It captures something the Plan S framing missed: openness alone does not address the dominance of English-language, Global-North-headquartered, APC-funded publishing. A genuinely equitable scholarly communication system needs multiple languages, multiple regional infrastructures, and multiple economic models, not just open access to the existing system.

    The 2024 UNESCO Recommendation on Open Science, and the 2024-2025 work by the cOAlition S successor strategy group on “equity in scholarly communication,” both center bibliodiversity. The practical translation is that funders are increasingly willing to count regional-language publication, Diamond OA, and community-led infrastructure as legitimate research outputs, not as second-tier venues.

    The push intersects with responsible assessment: as long as assessment privileges high-impact English-language journals, no amount of OA policy will rebalance global publishing. DORA, CoARA, and the Hong Kong Principles all argue for that broader reform, but the mechanics of changing institutional promotion and tenure committees lag the policy by years.

    Where the global South is going

    SciELO (Latin America), AJOL (Africa), J-STAGE (Japan), and similar regional infrastructures are the underacknowledged backbone of global Diamond OA. They have operated for decades on a model that flagship Plan S signatories are belatedly endorsing. The 2024-2025 conversation has shifted from “how do we bring Global South authors to Global North journals” to “how do we recognise and resource the regional infrastructures that already publish them.” cOAlition S has begun direct funding agreements with several regional infrastructures.

    The peer-review and quality-assurance question that historically dogged regional infrastructures has not gone away, but it has changed shape. AJOL and SciELO have invested heavily in DOAJ-aligned editorial practice; the data show their journals’ peer-review rigour comparable to similarly-scoped Global North journals. The reputational gap that remains is mostly a function of bibliometric assessment patterns, not editorial quality.

    What’s coming in 2026-2027

    Three things to watch. First, the second wave of Plan S: cOAlition S has signalled that its next-phase strategy (drafted through 2025, expected in mid-2026) will pivot toward Diamond OA, equity, and the de-prioritisation of transformative agreements. Second, institutional re-investment: as R&P deals expire, libraries are increasingly redirecting their formerly subscription budgets into open infrastructure (Diamond OA, preprint servers, institutional repositories). The MIT Framework and similar institutional principles are influential here. Third, cross-funder coordination: the gap between cOAlition S funders and the major North American funders (NIH, NSF, the Tri-Agencies in Canada) has narrowed; OSTP’s 2022 memo and its 2026 implementation are pushing the US system in a similar direction, though through different mechanisms.

    For authors, the practical advice is unchanged: deposit your accepted manuscript in your institutional repository under a CC BY licence using the rights-retention model; choose Diamond OA where it exists and serves your community; choose Gold OA in journals with transparent pricing where it does not; resist the assumption that the journal-impact-factor ladder is the path to a sustainable career.

    Related dictionary entries

    References

    cOAlition S, Plan S Principles and Implementation Guidance (2018, revised 2020). Science Europe, cOAlition S, OPERAS, ANR, Action Plan for Diamond Open Access (2023). Jussieu Call for Open Science and Bibliodiversity (2018). UNESCO, Recommendation on Open Science (2021, with 2024 implementation report). Suber, Open Access (MIT Press, revised 2024).

  • The case for narrative CVs beyond UKRI

    The UK Research and Innovation Resume for Research and Innovation (R4RI), launched in pilot in 2019 and now the default CV format across UKRI’s seven research councils, has accumulated enough operational experience to draw lessons. The narrative-CV approach has spread internationally: the Dutch Research Council, the Swiss National Science Foundation, the Royal Society’s CV format, parts of the EU’s Horizon Europe evaluation, and several US private funders have adopted variants. Where it has not spread is the major US federal funders. This post argues the case for broader adoption with reference to the UKRI experience.

    What R4RI is

    R4RI replaces the traditional publication-list CV with a structured narrative covering four modules: how the researcher has contributed to the generation of new ideas, tools, methodologies, or knowledge; how they have contributed to the development of others; how they have contributed to the wider research community; how they have contributed to broader research and innovation users and audiences, and the wider environment. Each module has a 250-word limit and the researcher provides specific evidence.

    What R4RI explicitly does not ask for: lengthy publication lists, journal impact factors, h-indexes, citation counts, exhaustive grant histories. The Resume for Research and Innovation can include publications but as evidence of contribution, not as a metric.

    Five years of operational experience

    UKRI’s commissioned evaluations of R4RI (published 2022, 2024) and the broader literature on narrative-CV use have produced a reasonably clear picture.

    First, reviewer time. The early concern was that narrative CVs would take longer to review than conventional ones. The evaluation data show modest increases for first-time reviewers, settling to comparable or shorter review times once reviewers were familiar with the format. Reviewers report that R4RI gives them a clearer picture of the applicant’s actual contribution.

    Second, applicant time. Writing an R4RI takes longer than updating a publication list, and applicants without writing support are at a disadvantage. The equity implication is real: a researcher with access to research-administration support to help draft R4RI does better than one without. UKRI has invested in writing-support resources and several institutions have built internal capacity.

    Third, career-stage equity. Narrative CVs perform better for early-career researchers whose publication record is short but whose contribution is significant; they perform better for researchers with non-traditional career paths; they perform better for researchers in disciplines where high-impact publication is not the norm. They perform less well for researchers with very strong conventional records who feel the narrative format does not adequately recognise their publications. On balance the evaluation suggests narrative CVs reduce systemic bias against under-represented career patterns.

    Fourth, inter-rater reliability. The concern that narrative CVs would produce more variable assessment than metric-based CVs has been partially borne out: inter-rater reliability is somewhat lower for R4RI than for conventional CVs. This is in part a feature, not a bug — different reviewers genuinely weight different contributions differently, and the narrative CV surfaces those judgements. UKRI has responded with reviewer-training resources and structured rubrics.

    Why other funders should adopt narrative CVs

    Four reasons.

    First, narrative CVs operationalise DORA and CoARA commitments in a concrete way. A funder that has signed DORA but continues to use publication-metric CVs is operating in contradiction with its commitment; a funder that adopts a narrative CV format is operationalising it. The CASRAI responsible assessment domain tracks the gap between policy and practice across major funders.

    Second, narrative CVs make CRediT more useful. A CV that reports CRediT roles for the applicant’s recent papers gives the reviewer specific information about contribution; a publication list without CRediT gives only the byline order. The integration is operationally simple: narrative CVs cite specific contributions, CRediT statements describe what the contribution was.

    Third, narrative CVs reduce the metric-feedback loop. The pernicious cycle in which researchers chase high-impact-factor publications because funders weight them and funders weight them because researchers seek them is one of the system pathologies that responsible-assessment reform aims to break. A narrative CV format breaks the funder side of the loop, which gives researchers permission to optimise differently.

    Fourth, narrative CVs encode the broader range of contributions that modern research actually requires. Software, datasets, public engagement, peer review, mentorship, leadership of community-led infrastructure, contributions to open standards — none of these show up well in a conventional CV. They show up in a narrative CV. The CASRAI institutional responsible-assessment guide includes a checklist of contribution types that narrative formats can capture.

    The objections, addressed

    Three objections recur and deserve direct responses.

    Narrative CVs are subjective and unscientific. The metric-based CV is also subjective: someone decided which metrics to weight, what the weighting should be, and what counts as success. Narrative CVs make the subjectivity explicit and reviewable; metric-based CVs hide it behind a number.

    Narrative CVs disadvantage non-English-speakers. The concern is real and the mitigation is to allow CVs in the applicant’s working language with translation support funded by the funder. UKRI does not currently allow non-English R4RIs because UKRI operates in English; an international funder with multilingual operations would need to.

    Narrative CVs are too long for high-volume review. The 250-word-per-module limit and the four-module structure produce a CV that is no longer than a conventional 5-page academic CV; in many cases shorter. The objection is empirically wrong as stated.

    Practical recommendations

    For funders considering adoption, the practical steps are: pilot with one or two grant streams; train reviewers with worked examples; develop a structured rubric for scoring; provide writing-support resources for applicants; commit to an evaluation at year three; iterate the format based on the evaluation. UKRI’s experience suggests this approach yields a usable format within three years.

    For institutions supporting applicants, the practical steps are: build internal capacity to support narrative-CV drafting; offer it equitably across career stages and disciplines; treat it as part of the research-administration support package, not as an exceptional service for senior researchers only.

    For applicants, the practical advice is to start drafting in a narrative format now, even where the funder does not require it. The discipline of articulating contribution rather than enumerating publications produces a richer self-understanding of one’s own research and is useful for promotion, tenure, and personal career planning regardless of funder requirements.

    The trajectory

    We expect narrative-CV adoption to accelerate through 2026-2028. The CoARA commitment to reforming research assessment, combined with the maturity of the R4RI model, gives funders a credible template to adopt. The remaining holdouts are the major US federal funders (NIH and NSF), whose biosketch and current-and-pending-support formats are partial moves toward narrative but retain substantial metric content. The next round of US-funder review-criteria revision will be the test.

    Related dictionary entries

  • Three CRediT misuses we see in submitted papers

    CASRAI’s editorial network includes journal editors who handle CRediT statements daily, and we periodically aggregate the patterns of misuse they see. Three failures recur across disciplines, journal sizes, and submission systems. None are scandalous; all are correctable with attention. This post catalogues them with concrete examples and the editorial responses that work.

    Failure one: role inflation

    Role inflation is the most common CRediT failure by a wide margin. It is the practice of assigning every author every role, or near-every role, regardless of what they actually did. A typical inflated statement reads like a litany: Author A: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Funding acquisition, Project administration. Author B: Conceptualization, Methodology, Investigation, Data curation, Writing – review & editing. Author C: Conceptualization, Methodology, Writing – review & editing. Every author is conceptualisation-positive; every author methodology-positive; every author writing-positive.

    The pattern is recognisable and almost always wrong. Five authors did not all conceive the study. Five authors did not all design the method. Five authors did not all write the original draft. Role inflation reflects a misunderstanding of what CRediT is for: it treats the role assignment as a credit allocation (the more roles you have, the more credit you get), when CRediT is a description of contribution. As Liz Allen and the original CRediT designers were explicit, the taxonomy is meant to record what each contributor actually did, not to maximise their visible role count.

    The editorial fix

    Editors increasingly push back at submission. The Lancet‘s convention of requiring each author to write a prose contribution statement in their own words is unusually effective; it forces a moment of reflection on what the author actually did. Several other journals have adopted variations. The CASRAI CRediT authors guide includes a role-assignment worksheet that asks each author to write a one-sentence justification per role before the statement is finalised; the discipline of writing the justification surfaces most cases of role inflation before submission.

    Where inflation has already made it into a submission, the editorial response is to ask the corresponding author to revise. The framing that works is methodological: “We use CRediT to describe what each contributor actually did. Please review the role assignments and confirm that each role corresponds to a substantive contribution by that author.” This is rarely contentious; in our experience the corresponding author tightens the statement on review.

    Failure two: byline order substituting for qualifiers

    The degree-of-contribution qualifier was added to NISO Z39.104 specifically to resolve byline-order disputes. A paper with three co-first-authors should mark them all as Equal on the roles they share; a paper with a clear lead on one role and supporting contributors on others should use Lead and Supporting accordingly. The qualifier is structurally what byline order has long tried to encode implicitly.

    The misuse we see is statements that ignore the qualifier and rely on byline order or footnotes to communicate contribution magnitude. A typical example: a paper with five authors and a footnote saying “authors 1 and 2 contributed equally” but a CRediT statement that assigns roles without qualifiers, leaving the reader to infer what “equally” means across the roles. Is author 1’s Investigation equal to author 2’s Investigation? Is author 1’s Formal analysis equal to author 2’s Formal analysis? The footnote does not say; the unqualified CRediT statement does not say.

    The editorial fix

    Adopt the qualifier explicitly. If two authors contributed equally to a role, mark both Equal on that role. If one author was the lead and others supported, mark Lead and Supporting. Footnotes about equal contribution become redundant; the structured statement carries the information.

    For journals, the editorial implementation is to require the qualifier in the submission system. The CRediT JATS specification supports the qualifier via the specific-use attribute; submission systems should expose this and require it. A few publishers have already moved here; we expect most to follow through 2026.

    Failure three: missing writing roles

    Every paper has someone who wrote the first draft. If a CRediT statement omits Writing – original draft, the editor will ask. This is the third recurring failure: statements that distribute Methodology, Investigation, Formal analysis, and Supervision but leave Writing – original draft unassigned.

    The pattern usually reflects a real ambiguity. In a paper with three co-equal authors who jointly drafted, who gets Writing – original draft? The answer is all three, marked Equal. In a paper where a postdoc drafted under supervision and a senior author heavily revised, who gets which writing role? Almost always: postdoc gets Writing – original draft (lead); senior author gets Writing – review & editing (lead). In a paper where a paid medical writer drafted, the medical writer is typically not an author per ICMJE — they are acknowledged separately — and the authors who substantively shaped the draft get Writing – original draft as appropriate.

    The editorial fix

    Editors should treat “who wrote the first draft” as a required question at submission. The BMJ asks this explicitly. The CASRAI worksheet asks it. If the statement does not name a Writing – original draft contributor, the editor’s standard response is a one-line query: “Please indicate which author or authors discharged the Writing – original draft role; the role is currently absent from the CRediT statement.” In our editor network this query gets a fast, accurate response and the role is added before review proceeds.

    Three lesser failures worth a paragraph each

    Beyond the big three, three lesser failures are worth noting. First, conflating Methodology and Formal analysis: the role definitions distinguish these (Methodology is the study design; Formal analysis is the statistical or analytical work on the resulting data) and assigning both to the same person without distinction loses information. Second, assigning Software to anyone who touched a computer: Software is meaningful programming work, not opening Excel; if the contributor wrote no code, did not script the analysis, did not configure REDCap, they probably did not discharge the Software role. Third, missing Funding acquisition: someone wrote the grant. If the CRediT statement does not name a Funding acquisition contributor and the paper is grant-funded, the role is missing.

    What CASRAI recommends

    Four practical recommendations. First, use the role-assignment worksheet at the drafting stage, not at submission; it catches most misuse early. Second, require the degree-of-contribution qualifier in your journal submission system. Third, treat missing Writing – original draft as a default editorial query. Fourth, when in doubt about role inflation, ask each author to write a one-sentence justification per role; the discipline reveals the over-assignment naturally.

    For the broader system, the most useful intervention is journal submission system support. Adoption at the policy level is now widespread, but the per-submission UX varies enormously. A submission system that prompts for qualifiers, validates that every role has a contributor, and asks per-author confirmation of role assignment catches most failures before they reach editorial review. We expect this UX to converge through 2026 as publishers update their Editorial Manager and ScholarOne configurations.

    Related dictionary entries

  • Data papers, software papers, and the limits of CRediT

    The 14 roles of CRediT were designed against the model of a conventional research article reporting empirical work: a study with a hypothesis, a method, data, analysis, and a written argument. Data papers and software papers fit this model awkwardly. A data paper describes a dataset; a software paper describes a piece of software. The intellectual contribution is the artefact itself, not the prose around it. The CRediT roles, applied to these papers, produce statements that are technically valid but substantively misleading. This post catalogues the friction and suggests where the taxonomy could be extended.

    What a data paper actually is

    A data paper, as the genre has developed in venues like Scientific Data, Earth System Science Data, GigaScience, and the data-paper streams of disciplinary journals, is a peer-reviewed description of a dataset: its provenance, its collection method, its quality, its access conditions, and its potential reuse. The dataset itself lives in a repository with its own DOI; the data paper provides the citable, peer-reviewed scholarly record that the dataset exists, that it was collected with rigour, and that it is fit for reuse.

    The intellectual labour behind a data paper is mostly not in the paper. It is in the years of fieldwork or instrument operation that produced the data, the protocols that ensured comparability across collection events, the curation work that turned raw observations into a structured deposit, the documentation that lets a stranger understand what the data mean. The paper is a summary record of that work.

    Where CRediT falls short for data papers

    Three friction points. First, Investigation and Data curation bear most of the load and they are not differentiated finely enough. A field ecologist who spent years collecting samples, a lab technician who processed them, a data manager who normalised the schema, and a metadata specialist who wrote the documentation are all plausibly Investigation or Data curation; the roles do not distinguish them. The result is that two papers with very different actual contributorship patterns can have identical-looking CRediT statements.

    Second, Resources overlaps with Investigation in a confusing way. A data paper describing a long-term ecological observatory has a Resources contribution (the observatory itself) that is distinct from the per-sample Investigation. CRediT does not currently cleanly separate “provided the infrastructure that produced the data” from “provided the samples that went into the data.”

    Third, Writing – original draft is often the smallest contribution, not the largest, and assigning it Lead can misrepresent the contribution structure. The person who wrote the paper is often a relatively junior team member, not the senior person whose intellectual contribution was the protocol and the multi-year campaign.

    Software papers and the JOSS model

    Software papers, exemplified by the Journal of Open Source Software (JOSS), face an analogous problem from a different direction. A JOSS paper is short — often under 1,000 words — and is paired with a peer-reviewed software repository. The intellectual contribution is the software: its design, its implementation, its tests, its documentation, its maintenance over time. The paper is a stub.

    JOSS itself uses CRediT for its papers and has done so since 2020. The community has converged on a set of mappings:

    • Conceptualization covers software design and architectural decisions.
    • Software covers implementation. This is the central role for most JOSS contributors.
    • Validation covers testing, both unit tests and validation against reference implementations.
    • Methodology covers the algorithmic content, where the software implements a non-trivial method.
    • Writing – original draft covers the paper itself. The README, the developer documentation, and the user docs are also writing work, but they are not the JOSS paper.
    • Supervision covers project leadership; Project administration covers maintenance and coordination.

    The friction in this mapping is that the Software role is overloaded. It conflates the initial implementation, ongoing maintenance, bug-fixing, refactoring, and tooling. A contributor who implemented the core algorithm and a contributor who maintains the CI/CD pipeline both get “Software” with no further distinction. For long-lived software with many contributors over years, the role assignment ends up giving everyone Software (lead/equal/supporting) and the differentiation lives in the GitHub commit history, not in CRediT.

    The FAIR4RS angle

    The FAIR4RS Principles for research software, finalised in 2022, set out what FAIR means for software: findable, accessible, interoperable, reusable. They explicitly acknowledge that software citation needs a richer model than data citation, because software has versions, dependencies, and ongoing development that data typically does not.

    FAIR4RS implies, though does not directly require, a richer contributorship taxonomy for software. The Software Citation Implementation Working Group has been chewing on this for several years. Their working position is that CRediT remains the right vocabulary for software paper contributorship, but that the software repository itself should carry its own contributor metadata using a complementary scheme — typically CITATION.cff with extended fields — that captures the per-version, per-component contributorship that CRediT cannot.

    The mapping problem

    For data papers and software papers, the operational reality is that two parallel records exist: the paper’s CRediT statement and the dataset or software repository’s contributor metadata. They overlap but do not align cleanly. The dataset DOI and software DOI live in DataCite; the paper DOI lives in Crossref; the relations between them are declared in the metadata but not always reciprocally.

    The CASRAI research outputs domain tracks the mapping conventions in current use. Our recommendation, for now, is that data papers and software papers should publish a CRediT statement covering the paper’s contributorship and should additionally publish a richer contributor metadata file with the dataset or software, using CRediT roles plus the disciplinary-specific extensions that have emerged.

    Possible extensions

    Three extensions would meaningfully improve the situation. First, sub-roles within Software: an extended taxonomy with implementation, testing, documentation, maintenance, and integration as sub-roles would give a software paper a more truthful contributorship statement. This work has been drafted by the FORCE11 software citation working group but not formally proposed as a CRediT extension.

    Second, distinguished Investigation roles for data papers: collection, processing, curation, documentation as sub-roles of Investigation and Data curation would let a data paper describe its contributorship more faithfully. The challenge here is keeping the taxonomy usable; an over-elaborate vocabulary loses adoption.

    Third, artefact-level role assignments: the current CRediT statement applies at the paper level. For a paper that describes a dataset and a software package, it might be more useful to have role assignments at the artefact level (paper, dataset, software each get their own statement) with cross-references. This would require schema work in Crossref, DataCite, and ORCID.

    What to do now

    For authors of data papers, the practical advice is: use CRediT for the paper; deposit a complementary contributors.json with the dataset that captures finer-grained roles; cross-reference the two in the related-identifier blocks. For authors of software papers, use CRediT for the paper and CITATION.cff for the repository, with the CFF carrying the rich per-component contributor data. The CASRAI data and software papers guide has worked examples.

    For the CRediT stewardship group, the recommendation is to prioritise the data-paper and software-paper mapping problem in the v2026.3 revision discussion. The friction is real, the workarounds are working but ugly, and the taxonomy will be strengthened by a thoughtful extension.

    Related dictionary entries