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Record W3209393613 · doi:10.5281/zenodo.3946100

ZÁSADY zajištení FAIRové správy a využitelnosti dat

2020· article· en· W3209393613 on OpenAlex
Hella Hollander, Frank Uiterwaal, Femmy Admiraal, Thorsten Trippel Clarin, Sara Di Giorgio, Emiliano Degl’Innocenti, Roberta Giacomi, V. Gilissen, Vanessa Hannesschläger, Mark Hedges, Klaus Illmayer, Adeline Joffres, Emilie Kraaikamp, Antonio Davide Madonna, Lene Offersgaard, Marie Puren, Paola Ronzino, Maurizio Senesi, Claus Spiecker, Michael Svendsen, H. W. Tjalsma, Marnix van Berchum, Eld Zierau

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

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Fiscal Studies
Canadian institutionsPrairie Improvement NetworkCanarie
FundersEuropean Commission
KeywordsComputer science

Abstract

fetched live from OpenAlex

A comprehensive set of Guidelines to FAIRify data management and make data reusable is focusing on the topic of common policies. This compact guide offers twenty guidelines to align the efforts of data producers, data archivists and data users in humanities and social sciences to make research data as reusable as possible based upon the FAIR Principles. Each guideline has recommendations for both researchers and archives as it is recognised that different priorities may apply to each case. The guidelines result from the work of over fifty PARTHENOS, ARIADNEplus and SEADDA project members. They were responsible for investigating commonalities in the implementation of policies and strategies for research data management and used results from desk research, questionnaires and interviews with selected experts to gather around one hundred current data management policies (including guides for preferred formats, data review policies and best practices, both formal as well as tacit). Other versions of the guidelines are available in the following languages: <em><strong>English</strong></em>: "PARTHENOS Guidelines to FAIRify data management and make data reusable" (https://doi.org/10.5281/zenodo.3368858) <strong><em>French</em></strong><em>:</em><strong> </strong>"PARTHENOS Recommandations pour FAIRiser vos données" (https://doi.org/10.5281/zenodo.3463521) <em><strong>German</strong>:</em><strong> </strong>"PARTHENOS Leitfaden zur "FAIRifizierung" des Datenmanagements und der Ermöglichung der Nachnutzung von Daten" (https://doi.org/10.5281/zenodo.3363078) <strong><em>Greek:</em></strong> "PARTHENOS Οδηγίες για την εφαρμογή των αρχών FAIR στη διαχείριση και επανάχρηση δεδομένων" (https://doi.org/10.5281/zenodo.3363386) <strong><em>Hungarian:</em></strong> "PARTHENOS A tudományos adatok újrafelhasználhatóságának és FAIR kezelésének irányelveii" (https://doi.org/10.5281/zenodo.3363355) <strong><em>Italian:</em></strong> "PARTHENOS Linee guida per l’applicazione dei principi FAIR alla gestione e al riuso dei dati" (https://doi.org/10.5281/zenodo.3363243) <strong><em>Turkish</em></strong>: "Veri Yönetimi ve verinin yeniden kullanımı için FAIR Prensipleri Rehberi" (https://doi.org/10.5281/zenodo.3937149) <em><strong>Portuguese</strong></em>: "Diretrizes para aplicação dos princípios FAIR à gestão e reutilização de dados" (https://doi.org/10.5281/zenodo.3937183) <pre> </pre>

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.060

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 head0.092
GPT teacher head0.211
Teacher spread0.119 · 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