The NAPDC: Stakeholder Input and Strategic Directions
Bibliographic record
Abstract
In 2021 the United States Department of Agriculture launched a national program called the Open Data Framework (ODF). The ODF program is designed to `build a framework needed to create a neutral and secure data repository and cooperative where producers, universities and nonprofit entities can store and share data in ways that will foster agricultural innovation and will support technological progress, production efficiencies, and environmental stewardship’ [1]. A multidisciplinary team of faculty based at the University of Nebraska-Lincoln is leading the ODF through an effort named the National Agricultural Producers Data Cooperative (NAPDC) [2]. The NAPDC has assembled a cross-disciplinary group of participants representing multiple agroecosystems and organizations to explore producer data needs including physical and cyberinfrastructure, education and training, and policy. These needs are being addressed through funding opportunities made available through the NAPDC for pilot projects conducted by partner institutions including land grant institutions and non-profit organizations.In May 2023 the NAPDC held an inaugural All-Hands Conference at the Nebraska Innovation Campus. The conference featured presentations by national leaders in agricultural data, panel1 discussions of stakeholders across agricultural sectors, and reports from ongoing pilot projects. Over 70 individuals from the US and Canada participated in person. Participants engaged in directed visioning exercises designed to elicit strategic directions for neutral framework development. Further information about the conference, including access to recorded presentations, can be found at the NAPDC website [2].This report provides a summary of the strategic priorities identified at the conference, listed by topic (with brief introductions), and recommendations for the future of the framework.
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.
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 | 0.063 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".