Policy and Data-Intensive Scientific Discovery in the Beginning of the 21st Century
Bibliographic record
Abstract
Recent developments in our ability to capture, curate, and analyze data, the field of data-intensive science (DIS), have indeed made these interesting and challenging times for scientific practice as well as policy making in real time. We are confronted with immense datasets that challenge our ability to pool, transfer, analyze, or interpret scientific observations. We have more data available than ever before, yet more questions to be answered as well, and no clear path to answer them. We are excited by the potential for science-based solutions to humankind's problems, yet stymied by the limitations of our current cyberinfrastructure and existing public policies. Importantly, DIS signals a transformation of the hypothesis-driven tradition of science ("first hypothesize, then experiment") to one that is typified by "first experiment, then hypothesize" mode of discovery. Another hallmark of DIS is that it amasses data that are public goods (i.e., creates a "commons") that can further be creatively mined for various applications in different sectors. As such, this calls for a science policy vision that is long term. We herein reflect on how best to approach to policy making at this critical inflection point when DIS applications are being diversified in agriculture, ecology, marine biology, and environmental research internationally. This article outlines the key policy issues and gaps that emerged from the multidisciplinary discussions at the NSF-funded DIS workshop held at the Seattle Children's Research Institute in Seattle, on September 19-20, 2010.
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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 | 0.129 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.030 | 0.064 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.027 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".