Author credit taxonomy support differs sharply across manuscript submission systems: some platforms capture each contributor’s CRediT role as a structured, exportable data field, while others still reduce it to a single free-text paragraph that cannot be parsed by machines. For institutions, publishers and developers evaluating submission infrastructure, that distinction determines whether contributor-role data can flow into JATS XML, Crossref deposits and ORCID records — or whether it dead-ends as unstructured prose on a PDF.
The author credit taxonomy in question is CRediT (Contributor Roles Taxonomy), a controlled vocabulary of 14 categories used to describe individual contributions to a research output. CASRAI originated CRediT in 2014, following a 2012 workshop convened by Harvard University and the Wellcome Trust with input from the ICMJE and publishers including Cell Press; the taxonomy is now stewarded by NISO as ANSI/NISO Z39.104-2022. This article compares how ScholarOne, Editorial Manager, OJS and newer submission platforms implement that taxonomy at the point of manuscript intake.
- What is the CRediT taxonomy, and why does the data model matter?
- Structured fields vs free text: the distinction that matters
- Which manuscript systems support CRediT as structured metadata?
- How structured CRediT data reaches JATS, Crossref and ORCID
- What to check before choosing or configuring a system
- Frequently asked questions
- Implications for institutions and publishers
What is the CRediT taxonomy, and why does the data model matter?
CRediT is a 14-role controlled vocabulary for describing the specific contributions each named author made to a scholarly output, rather than relying on author order or vague acknowledgements. It was designed to be machine-readable from the outset: each role is a discrete, defined category that can, in principle, be tagged, exported and queried rather than buried in a sentence.
That “in principle” is the crux of this article. A system can offer CRediT in name — a page that says “describe contributions using CRediT” — while storing the result as one text block. Or it can capture role selection as structured, per-author data. The two approaches look identical to a human reader and completely different to any system trying to reuse the data.
Structured fields vs free text: the distinction that matters
Under a structured implementation, the system presents each of the 14 CRediT roles as a selectable field (typically a checkbox or dropdown per co-author), often with a degree-of-contribution qualifier (“lead”, “equal”, “supporting”). The result is stored as discrete, per-author, per-role records that can be exported programmatically.
Under a free-text implementation, the system asks the corresponding author to write an “Author Contributions” statement, sometimes with the 14 role names listed as a prompt. The output is prose: it reads correctly on the published page but cannot be parsed reliably without natural-language processing, and cannot be validated against the controlled vocabulary at entry.
This is the single biggest configuration variable buyers should test for, because the same underlying platform can be deployed either way:
- A structured field validates role names against the controlled vocabulary and rejects free-form entries.
- A structured field allows more than one role per author and more than one author per role.
- A structured field can be exported to XML without re-keying or manual extraction.
- A free-text field can drift from the taxonomy’s exact role names over time, breaking downstream matching.
Which manuscript submission systems support CRediT as structured metadata?
Support for structured CRediT capture is uneven and, for the two dominant commercial systems, largely a function of publisher configuration rather than a fixed platform feature. The table below summarises the current landscape.
| System | CRediT capture model | Structured export | Buyer note |
|---|---|---|---|
| ScholarOne (Clarivate) | Configurable per journal — per-author role checkboxes, or a single free-text contributions field | Achievable via JATS mapping when configured as structured fields | Ask the editorial office how the instance is configured; the platform supports both models |
| Editorial Manager (Aries Systems) | Configurable per journal — a dedicated CRediT role-selection step for co-authors, where enabled | Achievable via JATS/Crossref mapping once the structured step is switched on | Structured capture is not universal across every Editorial Manager journal |
| OJS (Public Knowledge Project) | Native structured role assignment via the PKP “CRediT Roles” plugin, available from OJS 3.3 onward | Role data included in OJS metadata export | Open-source and self-hosted; the plugin is purpose-built for CRediT, but must be enabled per journal |
| F1000Research and similar open-research platforms | CRediT-style author-role fields built into the core submission form | Varies by platform; several publish structured contribution statements alongside the article | Newer platforms increasingly treat CRediT as a native field rather than a retrofit |
The practical implication: two journals on the same ScholarOne or Editorial Manager instance can have different CRediT data quality, purely because one editorial office enabled structured role capture and the other did not. This is the gap generic “what is CRediT” explainers do not cover — and the one that matters most to institutions harvesting contribution data at scale.
How structured CRediT data reaches JATS, Crossref and ORCID
Structured CRediT capture only pays off downstream if the submission system exports it in a format that JATS-compliant production systems and Crossref’s deposit schema can consume. JATS4R (JATS for Reuse) publishes a tagging recommendation for encoding contributor roles inside the JATS XML <role> element, referencing the CRediT vocabulary so that role names are unambiguous across publishers. Crossref’s metadata schema likewise supports contributor role information within deposited XML records, which is how role data becomes queryable through Crossref’s own metadata search rather than existing only inside the published PDF.
ORCID adds a further layer: authors can have specific roles asserted against their ORCID record, giving a portable, verifiable claim to a specific contribution rather than a generic “authorship” credit. None of this works if the originating manuscript system stored the contribution as free text — there is no reliable path from a paragraph of prose back to a controlled-vocabulary role tag.
What to check before choosing or configuring a system
For research offices, publishers and developers evaluating or configuring a submission system, the following checklist separates genuine structured support from a CRediT-branded free-text box:
- Does the submission form present the 14 CRediT roles as selectable fields, not a text prompt?
- Can more than one role be assigned to a single co-author, and more than one co-author to a single role?
- Is a degree-of-contribution qualifier (lead/equal/supporting) captured alongside the role?
- Does the system export role data in JATS XML or an equivalent structured format, rather than only rendering it as prose on the typeset article?
- Is the exported role data mapped to Crossref’s contributor metadata so it is discoverable outside the publisher’s own site?
- Does the workflow validate entries against the current CRediT role list, preventing drift from the controlled vocabulary?
Any system can fail several of these checks while still advertising “CRediT support” on its marketing page, because CRediT compliance and structured-data compliance are configured separately.
Frequently asked questions
What is the CRediT taxonomy?
CRediT (Contributor Roles Taxonomy) is a standardised, 14-role controlled vocabulary used to describe individual author contributions to a research output. It replaces vague authorship statements with discrete, named categories such as Investigation, Methodology and Supervision, submitted alongside the manuscript.
What are the 14 roles in the CRediT taxonomy?
The 14 roles are Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, and Writing – review and editing. Each is independently assignable to any number of contributors.
What does “Investigation” mean in the CRediT taxonomy?
Investigation covers conducting the research process itself — specifically performing experiments or collecting data and evidence. It is distinct from Methodology (designing the approach) and Formal analysis (applying statistical or computational techniques to the resulting data).
Implications for institutions and publishers
Research offices building contribution-tracking dashboards, funders assessing author-level input, and publishers demonstrating transparent attribution all depend on the same upstream condition: structured capture at the point of submission. Retrofitting structured data from published free-text statements is expensive and unreliable at scale.
The near-term trend favours structure. OJS has built a native CRediT plugin rather than treating role capture as an afterthought, and newer open-research platforms are designing CRediT fields into submission forms from launch. Commercial systems remain configurable rather than structured-by-default, so responsibility currently sits with the editorial office, not the vendor, to switch on genuine structured capture. Institutions evaluating submission infrastructure should treat “structured CRediT export” as a specific, testable procurement requirement — not an assumption that follows from a vendor’s marketing claim of CRediT support.