Deliverable 1.6 Data Governance Framework
Bibliographic record
Abstract
This document contains a description of the Data Governance Framework for the INTAROS project, with an updated version of the Data Management Plan (DMP). The Data Governance Framework defines the procedures for how data management is carried out in the project, including the planning, conducting and monitoring the preparation and distribution of data collections. The DMP describes how new datasets collected or generated by partners in the project, will be managed according to guidelines for FAIR data management in Horizon 2020. Data governance in INTAROS is pragmatic and geared towards supporting partners in preparing and publishing their data collections. The planning and monitoring activities are carried out by the Data Management Theme Leader and the leaders of the four data generating work-packages in the project. Partners generating data are responsible for making their collections available in<br> line with the recommendations of the DMP. The Data Management Theme Leader, data centre partners (AWI, CNRS, FMI, IMR, IFREMER, ONC, RADI, RIHMI-WDC) and the leader of WP5 (“Data integration and management”) (Terradue) are responsible for providing support with technical aspects of data publication and distribution. INTAROS is pan-Arctic in scope and collect in situ observations, extract parameters from satellite data and model projections in several regions and across multiple spheres (themes). The focus areas of INTAROS include Coastal Greenland, North of Svalbard, Fram Strait, the Eurasian Basin, and (5) selected sites in Siberia, Finland, Canada and Alaska. Within these areas,<br> INTAROS partners are collecting new observations and generating high-level data products from different spheres: (1) Atmosphere, (2) Ocean, (3) Sea ice, (4) Marine ecosystems, (5) Terrestrial, (6) Glaciology, (7) Natural hazards, (8) Community-based monitoring. This makes datasets collected or generated within INTAROS relevant for a number of research projects as well as for infrastructures such as EMODNET and GEOSS. Datasets collected or generated within these spheres by the time of writing are summarised in this document, based on the deliverables from WP 2 (“Exploitation of existing observing systems”), new datasets collected in WP 3 (“Enhancement of multidisciplinary in situ observing systems”) and WP 4 (“Enhance community-based observing programs for participatory research<br> and capacity-building”), as well as upcoming model products and derived datasets from WP6 (“Applications of iAOS towards Stakeholders”). Datasets prepared for distribution in WP 2, 3 and 4 have also been registered in the INTAROS Data Catalogue, available at https://catalog-intaros.nersc.no/. This data catalogue will be updated with new datasets collected or generated during the remainder of the INTAROS project. The DMP recommends standards for metadata and data standards that INTAROS partners should prepare their datasets in, to make it easier for other scientists and stakeholders to reuse the data. Open source tools can help scientists generate metadata and data in standard formats, such as Rosetta, GDAL (Geospatial Data Abstraction Library), NetCDF utilities, and widely used programming languages, such as Python, MATLAB and R, offer libraries that can be used to write customised format converter tools. A dataset prepared in NetCDF format can be made publicly available using data publishing tools like the Thredds Data Server (TDS). INTAROS,<br> together with the Useful Arctic Knowledge (UAK) project has organised several user meetings and one research schools, to build competence in data management within the INTAROS consortium. Additional competence building activities are planned in INTAROS; the training material developed will be made publicly available.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.013 | 0.020 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.078 |
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".