Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This document contains the Data Management Plan (DMP) for the INTAROS project. The DMP describes how new datasets collected or generated by partners in the project, are managed according to guidelines for FAIR data management in Horizon 2020. This includes the procedures for planning and conducting data management within the project, i.e., the data governance framework. 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 (NERSC) and the leaders of the data generating work-packages in the project (FMI, IOPAN, NORDECO, TDUE, IMR). Partners generating data are responsible for making their collections available in line with the recommendations of the DMP. The Data Management Theme Leader (NERSC) and data centre partners (AWI, CNRS, FMI, IMR, IFREMER, ONC, RADI, RIHMI-WDC) provide support with data publication in open data repositories. Close collaboration between data managers and data providers has been key to implementing sound data management in the project. INTAROS is pan-Arctic in scope and collects <em>in situ</em> 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, 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. 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”), datasets collected in WP 3 (“Enhancement of multidisciplinary in situ observing systems”) and WP 4 (“Enhance community-based observing programs for participatory research and capacity-building”), as well as model products and derived datasets from WP6 (“Applications of iAOS towards Stakeholders”). The published datasets have been registered in the INTAROS Data Catalogue, available at https://catalog-intaros.nersc.no/. This data catalogue is updated as new datasets are prepared 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. Some widely used open-source tools and libraries that can help scientists generate metadata and data in standard formats are described. Use of servers that support the OPeNDAP standard protocol is recommended to facilitate data extraction from distributed sources. INTAROS, together with the Useful Arctic Knowledge (UAK) project has organised several user meetings and one research school to build competence in data management within the INTAROS consortium. Training material from these and other events with INTAROS contribution is made publicly available on the INTAROS web site http://intaros.eu/. Major changes since the previous release of the DMP (Hamre et al., 2019) include: Revised the list of recommended metadata and data formats (section 2) Updated the list of datasets collected or generated (sections 3-6) Updated list of recommended data repositories for long-term storage of data (section 8) Added a template for planning data management for a field experiment or a monitoring programme (Appendix D) Added a template for metadata for timeseries data collected by ocean moorings (Appendix E)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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 it