Community recommendations for geochemical data, services and analytical capabilities in the 21st century
Bibliographic record
Abstract
The majority of geochemical and cosmochemical research is based upon observations and, in particular, upon the acquisition, processing and interpretation of analytical data from physical samples. The exponential increase in volumes and rates of data acquisition over the last century, combined with advances in instruments, analytical methods and an increasing variety of data types analysed, has necessitated the development of new ways of data curation, access and sharing. Together with novel data processing methods, these changes have enabled new scientific insights and are driving innovation in Earth and Planetary Science research. Yet, as approaches to data-intensive research develop and evolve, new challenges emerge. As large and often global data compilations increasingly form the basis for new research studies, institutional and methodological differences in data reporting are proving to be significant hurdles in synthesising data from multiple sources. Consistent data formats and descriptions as well as appropriate information on data quality are becoming crucial to enabling reproducibility and integration of results and fostering confidence for data reuse. Here, we explore the key challenges faced by the geo- and cosmochemistry community and, by drawing comparisons from other communities, recommend possible approaches to overcome them. The first challenge is bringing together the numerous sub-disciplines within our community. One key factor for this convergence will be gaining endorsement from the international geochemical, cosmochemical and analytical societies and associations, journals and institutions. Increased education and outreach, spearheaded by ambassadors recruited from leading scientists across disciplines, will further contribute to raising awareness, and to uniting and mobilising the community. Appropriate incentives, recognition and credit for good data management as well as an improved, user-oriented technical infrastructure will be essential for achieving a cultural change towards an environment in which the effective use and real-time interchange of large datasets is common-place. Finally, the development of best practices for standardised data reporting and exchange, driven by expert working groups, will be a crucial step towards making geo- and cosmochemical data more Findable, Accessible, Interoperable and Reusable by both humans and machines (FAIR).
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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 | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".