FAIR SCI Ahead: The Evolution of the Open Data Commons for Pre-Clinical Spinal Cord Injury Research
Bibliographic record
Abstract
Over the last 5 years, multiple stakeholders in the field of spinal cord injury (SCI) research have initiated efforts to promote publications standards and enable sharing of experimental data. In 2016, the National Institutes of Health/National Institute of Neurological Disorders and Stroke hosted representatives from the SCI community to streamline these efforts and discuss the future of data sharing in the field according to the FAIR (Findable, Accessible, Interoperable and Reusable) data stewardship principles. As a next step, a multi-stakeholder group hosted a 2017 symposium in Washington, DC entitled "FAIR SCI Ahead: the Evolution of the Open Data Commons for Spinal Cord Injury research." The goal of this meeting was to receive feedback from the community regarding infrastructure, policies, and organization of a community-governed Open Data Commons (ODC) for pre-clinical SCI research. Here, we summarize the policy outcomes of this meeting and report on progress implementing these policies in the form of a digital ecosystem: the Open Data Commons for Spinal Cord Injury (ODC-SCI.org). ODC-SCI enables data management, harmonization, and controlled sharing of data in a manner consistent with the well-established norms of scholarly publication. Specifically, ODC-SCI is organized around virtual "laboratories" with the ability to share data within each of three distinct data-sharing spaces: within the laboratory, across verified laboratories, or publicly under a creative commons license (CC-BY 4.0) with a digital object identifier that enables data citation. The ODC-SCI implements FAIR data sharing and enables pooled data-driven discovery while crediting the generators of valuable SCI data.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.013 | 0.007 |
| Research integrity | 0.001 | 0.020 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".