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Record W3114046631 · doi:10.5703/1288284317185

The Time Has Come… To Talk About Why Research Data Management Isn’t Easy

2020· article· en· W3114046631 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsMaturity (psychological)Sample (material)Public relationsComputer scienceAcademic libraryResearch dataKnowledge managementData scienceWorld Wide WebLibrary sciencePolitical scienceBusinessData curation

Abstract

fetched live from OpenAlex

For the last decade, academic libraries have talked with each other and with potential partners about their roles in helping to manage research data and their plans to expand or initiate research data services (RDS). Libraries have the capacity to provide these services, but the range and maturity of research data services from libraries vary considerably. In summer 2019, our team surveyed a sample of academic libraries of all sizes who are members of the Association of College and Research Libraries (ACRL) to find out about their current RDS and plans for the future. This study is a follow-up to surveys of this same group in 2012 and 2015. Our findings include the types of RDS currently being offered in academic libraries, the barriers that hinder RDS implementation, and staff capacity for creating RDS.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.306
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0140.017
Open science0.0230.036
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.006

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 head0.397
GPT teacher head0.442
Teacher spread0.046 · 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

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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