Developments in Polar Data Management 2006 – 2019 and Beyond: standardization and community-building in support of enhanced interoperability
Bibliographic record
Abstract
A consortium of polar data coordinating bodies has recently hosted a number of useful workshops and events to foster collaboration between individuals, institutions, projects and organizations. These events have built on polar data coordination efforts including progress made during the International Polar Year, focused workshops in 2016, 17, and 18, and three Polar Data Forum meetings (2013,15,19). These and other activities have identified a need for continued community development and detailed technical collaboration in order to advance Polar Data Management. Technical activity has centred on achieving federated search through the exchange of standardised, well formatted discovery metadata. This is an important first step towards an interconnected polar data system and important gaps and mitigation have been identified at the levels of standardisation, exchange protocols, and eventually semantic annotation of datasets. These activities have been and will continue to be organized by a group of coordination bodies including the IASC-SAON Arctic Data Committee, the Southern Ocean Observing System, Standing Committee on Antarctic Data Management, GEO Cold Regions Initiative, Polar View, Arctic Portal, ELOKA, Canadian Consortium on Arctic Data Interoperability, U.S. Inter-agency Arctic Research Policy Committee Arctic Data Sub-Team, and the WMO Global Cryosphere Watch. As a contribution to these international efforts, in January 2020, the European Union Horizon 2020 project CAPARDUS was established as a coordination and support action with the objective to establish a comprehensive framework for development, understanding and implementation of Arctic standards with focus on environmental topics and related data. The framework will integrate standards used by communities active in the Arctic and polar regions including research and services, Indigenous and local communities, commercial operators and governance bodies. Development of standards is important for many technologies and services (e.g. federated search) that can bring broad social and economic benefits within and beyond the Arctic region. In this presentation we first provide a synthesis of more than a decade and a half of activity and development in polar data management and interoperable data sharing. Results from this analysis reveal two primary areas of successful developments: i) social and organizational including data policy, building working relationships, and funding cyberinfrastructure ; ii) technical developments in federated search, semantic interoperability, and use of web services. Patterns, advancements and development gaps are identified and discussed. Secondly, we present an overview of the first quarter of activity under the CAPARDUS project, including a preliminary model aimed and enhancing appropriate levels of standardization in the polar data 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 | 0.116 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".