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Research Data: The Future of FAIR White paper

2021· article· en· W3211155229 on OpenAlex
Varsha Khodiyar, Heidi Laine, David O'Brien, Raul Rodriguez‐Esteban, Yasemin TÃ ⁄ rkyilmaz-van der Velden, Grace Baynes, Matthew Brack, Anne Cambon‐Thomsen, David L. Carr, Elisa Carrus, Louise Chisholm, Claudia Civai, María Cruz, Rebecca Grant, Elizabeth Newbold, Fátima L. S. Nunes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
FundersChina Scholarship CouncilUniversity of Cape TownRural Development AdministrationTechnische Universiteit DelftInternational Development Research Centre
KeywordsWhite (mutation)White paperResearch dataData scienceComputer sciencePolitical scienceLawData curation

Abstract

fetched live from OpenAlex

2021 marks five years since the publication of the FAIR data principles. With a wide range of opinions and commentary, this white paper looks at the real-world impact of FAIR, and considers what will be next for research data and open science.<br>Find out about research data at Springer Nature: https://go.sn.pub/research-data

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.018
Open science0.0080.012
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0300.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.328
GPT teacher head0.442
Teacher spread0.114 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it