ZÁSADY zajištení FAIRové správy a využitelnosti dat
Bibliographic record
Abstract
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>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.073 | 0.038 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".