MétaCan
Menu
Cohort builder

4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Research Data Management Practices
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

2,427 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
2,427 works in the cohort · of 4,299,418page 5 of 49

Labels cover 37 of 2,427 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 2,427 of 2,427 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
Transdisciplinary approaches to critical data studies
Ryan Burns, Blake Hawkins, Anna Lauren Hoffmann, Andrew Iliadis, Jim Thatcher
2018· article· en· Proceedings of the Association for Information Science and Technology· Computer Science
machine prediction:candidate · metaresearch+stsconsensus · none
6
citations
affno abstractunlabeled
Research Data Management
Heather Ganshorn, Jennifer Abel
2024· book-chapter· en· Elsevier eBooks· Computer Science
machine prediction:candidate · metaresearchconsensus · none
6
citations
affunlabeled
Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done
Christel Annemieke Romein, Tobias Hodel, Femke Gordijn, Joris van Zundert, Alix Chagué, Milan van Lange +54 more
2022· article· en· Data Archiving and Networked Services (DANS)· Computer Science
machine prediction:candidate · metaresearch+open_scienceconsensus · none
6
citations
affunlabeled
Foreground Science Knowledge and Prospects
Aurélien A. Fraisse, J. C. Brown, Gregory Dobler, Jessie Dotson, B. T. Draine, P. C. Frisch +16 more
2009· preprint· en· AIP conference proceedings· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
FAIRification of biomedical research data
K. Tai, Marcel L. Müller, Ulrich Mansmann, Anna Catharina Vieira Armond, Évelyne Decullier, Anne Le Louarn +4 more
2025· article· en· Journal of Clinical Epidemiology· Computer Science
machine prediction:candidate · metaresearch+open_scienceconsensus · metaresearch
6
citations
affvenueunlabeled
Mendeley Data
Michelle Swab
2016· article· fr· Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada· Computer Science
machine prediction:candidate · open_science+insufficient_payloadconsensus · none
6
citations
venueno affunlabeled
Don't ask too much from data literacy
Nicolas Kayser-Bril
2016· article· en· The Journal of Community Informatics· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundvenueaboutunlabeled
Inter-institutional data-driven education research: consensus values, principles, and recommendations to guide the ethical sharing of administrative education data in the Canadian medical education research context
Lawrence Grierson, Alice Cavanagh, Alaa Youssef, Rachelle Lee-Krueger, Kestrel McNeill, Brenton Button +1 more
2023· article· en· Canadian Medical Education Journal· Computer Science
machine prediction:candidate · metaresearch+open_scienceconsensus · metaresearch
5
citations

How this was built: Screen · Findings · About