DINA: Open Source and Open Services - A modern approach for natural history collection management systems and research
Bibliographic record
Abstract
The DINA Consortium (“DIgital information system for NAtural history data”, https://dina-project.net ) was formed in order to provide a framework for like-minded large natural history collection-holding institutions to collaborate through a distributed Open Source development model to produce a flexible and sustainable collection management system. Target collections include zoological, botanical, mycological, geological and paleontological collections, living collections, biodiversity inventories, observation records, and molecular data. DINA is funded by the participating member institutions. DINA Core Members are organizations or individuals who commit at least one half-time equivalent of resources to the development of the consortium goals, at least half of which should be available for code development. The DINA system is architected as a loosely-coupled set of several web-based modules. The conceptual basis for this modular ecosystem is a compilation of comprehensive guidelines for Web application programming interfaces (APIs) to guarantee the interoperability of its components. Thus, all DINA components can be modified or even replaced by other components without crashing the rest of the system as long as they are DINA compliant. Furthermore, the modularity enables the institutions to host only the components they need. DINA focuses on an Open Source software philosophy and on community-driven open development, so the contributors share their development resources and expertise outside of their own institutions. One of the overarching reasons to develop a new collection management system is the need to better model complex relationships between collection objects (typically specimens), research data and associated workflows. We will present the enhancements provided by the approach of the DINA system focussing on the flexibility to plug in compliant components and accommodate additional (meta-)data and specimen related research data with the help of a generic data module. Furthermore, we will discuss challenges in the governance of the development activities such as organizing the distributed code development of the core modules, the code review process and the choice of the software stack. These organizational challenges will be overcome with the help of a revised Memorandum of Understanding.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.018 |
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".