NIH-NSF visualization research challenges report summary
Bibliographic record
Abstract
The US National Science Foundation (NSF) convened a panel to report on the potential of visualization as a new technology. The NSF and US National Institutes of Health (NIH) convened the Visualization Research Challenges (VRC) Executive Committee to write a new report. Here, we summarize that new VRC report. We explore the state of the field, examine the potential impact of visualization on areas of national and international importance, and present our findings and recommendations for the future of our growing discipline. Our audience is twofold: the supporters, sponsors, and application users of visualization research on the one hand, and researchers and practitioners in visualization on the other. We direct our discussion toward solving key problems of national interest and helping this work's sponsors to concentrate resources to the greatest effect. Our findings and recommendations reflect information gathered from visualization and applications scientists during two workshops on VRC, as well as input from the larger visualization community.
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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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.029 |
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".