DataOne: Supporting Data Discovery and Access through Social and Technical Infrastructure.pdf
Notice bibliographique
Résumé
Addressing grand challenge questions requires exploration at broad spatial, geographic and temporal scales, facilitated through easy access to distributed, heterogeneous data. DataONE is an interoperable, federated network of data repositories providing open, persistent, robust, and secure access to well-described and easily discovered data about life and the environment. Over the last ten years of development, both technical and social capacity building has been critical in creating an infrastructure that meets the current and future needs of the community. Informed by working group research, community engagement, and usability evaluation, DataONE has developed a comprehensive search and discovery platform exposing over 1.2M data files; tools and services that support research reproducibility, transparency and credit; and data management training and resources to enhance data literacy. Through these and aligned activities, DataONE has improved interoperability across a broad coalition of data repositories and enhanced data practices across a diverse community of researchers, data managers, and data librarians. DataONE is a community-governed network built in partnership with existing data repositories supporting distinct and diverse communities. As DataONE continues to grow from a funded project into a sustained program, this networked, user-driven approach continues to inform infrastructure development, feature design and prioritization, maximizing the value and impact of research data in an increasingly complex, diversified data discovery and use landscape. ABOUT THE AUTHOR(S) Amber Budden is the Director of Learning and Outreach at the National Center for Ecological Analysis and Synthesis where she leads the NCEAS Learning Hub and short course activities. She is an open science facilitator, community manager and data literacy trainer and serves as a co-lead on several projects, including DataONE, a community-networked infrastructure supporting Earth and environmental scientists in their data management, preservation, search and discovery needs. An advocate for open and transparent science, Amber previously conducted research on article publication practices before working in the open data landscape. In her current roles, Amber supports the community in using open science infrastructure and leads training and outreach activities focused on best practices for data management. Amber has a PhD in behavioral ecology and has conducted postdoctoral research on avian sexual selection and life-histories at the University of California Berkeley in addition to bibliometrics research at NCEAS. Amber has held teaching positions at the University of Toronto and York University in Canada and she has worked in outreach and publications within the non-profit sector. She is currently a principal investigator on several cyberinfrastructure awards including DataONE the Arctic Data Centre and the Permafrost Discovery Gateway; is Chair of the ESIP Data Stewardship Committee; Member of the Make Data Count team; Advisory Board member for the Center for Scientific Collaboration and Community Engagement; and was a board member of the National Postdoctoral Association. Amber holds a PhD in Behavioral Ecology from the University of Wales, a Joint Honors BSc in Psychology and Zoology from the University of Bristol and qualification in youth and community work.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,010 | 0,015 |
| Science ouverte | 0,005 | 0,021 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,145 | 0,080 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».