skills/43-wentorai-research-plugins/skills/literature/metadata/datacite-api/SKILL.md
Resolve dataset DOIs and query research data metadata via DataCite
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research datacite-apiInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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DataCite is a leading global DOI registration agency focused on research data. While CrossRef primarily handles DOIs for publications, DataCite specializes in assigning persistent identifiers to datasets, software, samples, instruments, and other research outputs. DataCite has registered over 50 million DOIs from thousands of data repositories worldwide.
The DataCite REST API provides access to metadata for all DataCite DOIs. It is essential for researchers and developers working with research data discovery, data citation, FAIR (Findable, Accessible, Interoperable, Reusable) data practices, and repository integration. The metadata follows the DataCite Metadata Schema, which is designed specifically for describing research data and includes fields for resource types, funding references, geolocation, and related identifiers.
The API is free, open, and requires no authentication. It returns JSON responses following the JSON:API specification, with robust filtering, faceting, and pagination support.
No authentication required. The DataCite API is fully open and free to use. No API key, registration, or email is needed. For write operations (DOI registration and metadata updates), authentication via DataCite member credentials is required, but read-only access is completely open.
GET https://api.datacite.org/doiscurl "https://api.datacite.org/dois?query=climate+change+dataset&resource-type-id=dataset&page[size]=10&sort=-created"
data array containing DOI records. Each record has attributes with doi, titles, creators, publisher, publicationYear, resourceType, descriptions, subjects, dates, relatedIdentifiers, fundingReferences, and geoLocations.GET https://api.datacite.org/dois/{doi}curl "https://api.datacite.org/dois/10.5281/zenodo.3678171"
GET https://api.datacite.org/providerscurl "https://api.datacite.org/providers?query=CERN&page[size]=5"
name, displayName, region, memberType, website, and associated repositories and DOI prefixes.GET https://api.datacite.org/clientscurl "https://api.datacite.org/clients?query=zenodo&page[size]=5"
No published rate limits. DataCite does not enforce strict API quotas for read access. However, the service is operated by a nonprofit organization, so users should implement reasonable request pacing. For large-scale data mining, use the DataCite OAI-PMH endpoint or the public data file available at https://datafiles.datacite.org. Sustained high-volume requests may be throttled without notice.
Find published datasets related to a research area:
curl -s "https://api.datacite.org/dois?query=CRISPR+genome+editing&resource-type-id=dataset&page[size]=5&sort=-created" | jq '.data[] | {doi: .attributes.doi, title: .attributes.titles[0].title, year: .attributes.publicationYear, publisher: .attributes.publisher}'
Search for research software registered with DataCite:
curl -s "https://api.datacite.org/dois?query=python+machine+learning&resource-type-id=software&page[size]=10" | jq '.data[] | {doi: .attributes.doi, title: .attributes.titles[0].title, year: .attributes.publicationYear}'
Use related identifiers to find papers associated with a dataset:
curl -s "https://api.datacite.org/dois/10.5281/zenodo.3678171" | jq '.data.attributes.relatedIdentifiers[] | select(.relationType == "IsSupplementTo" or .relationType == "IsReferencedBy") | {type: .relationType, id: .relatedIdentifier}'
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.