DataOne: Supporting Data Discovery and Access through Social and Technical Infrastructure.pdf
Bibliographic record
Abstract
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.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.145 | 0.080 |
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".