Comparing METS and OAI-ORE for Encapsulating Scientific Data Products: A Protein Crystallography Case Study
Bibliographic record
Abstract
This paper describes the set of eResearch services developed by the eResearch Lab within the University of Queensland (UQ) for the Structural Genomics (SG) Group at UQ. The aim of these services is to enable collaborative teams of protein crystallographers in the SG group to track their experiments and to manage the plethora and diversity of data that they generate through distributed high-throughput approaches and complex scientific workflows. More specifically we describe: the secure Web-based laboratory information management system (TIMTAM) and the X-ray diffraction image archive (DIMER) used to monitor experiments and record data captured prior to structure determination and the publication of a new crystal structure in public repositories such as the Protein Data bank (PDB). We also describe the services that we have developed to relate the different products generated at each stage in the protein crystallography pipeline through OAI-ORE compound objects. We conclude by comparing the OAI-ORE approach for publishing and sharing related scientific outcomes with the METS-based approach employed by other scientific laboratories.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".