News from the GGOS DOI Working Group - Presentation slides
Bibliographic record
Abstract
The “GGOS Working Group on Digital Object Identifiers (DOIs) for Geodetic Data Sets” is entering its third year of regular meetings and discussions to develop best practices, recommendations and advocate for improved global coordination for using DOI to geodetic data and products. The group was established by the International Association of Geodesy’s (IAG) Global Geodetic Observing System (GGOS) and includes international representatives of IAG Services and geodetic data centres and associated members. Data publications with digital object identifiers (DOI) are best practice for FAIR sharing data. They are fully citable in scholarly literature and many journals require the data underlying a publication to be available. Initial metrics for data citation allows data providers to demonstrate the value of the data collected by institutes and individual scientists. This possibility to get credit for providing data products and running data services has been identified in the group as key requirement for the motivation to implement DOIs to geodetic data. Our group activities include the collection of data products and discussions on already existing and planned DOI activities for IAG services and geodetic data centres, including for recent projects, like FAIR GNSS. Whenever possible, we recommend that DOIs shall be included in standard data formats (e.g. Rinex) and cited when using the data. This presentation will give an update of the group activities. GGOS DOI Working Group: Detlef Angermann (TU Munich, Germany); Yehuda Bock (UCDC, US); Sylvain Bonvalot (GET, France); Roelf Botha (SARAO, South Africa); Markus Bradke (GFZ, Germany); Elizabeth Bradshaw (NOC, UK); Carine Bruyninx (ROB, Belgium); Daniela Carrion (Politecnico Milan, Italy); Glenda Coetzer (SARAO, South Africa); Kirsten Elger (GFZ, Germany, chair); Pierre Fridez (CODE/AIUB, Switzerland); Elmas Sinem Ince (GFZ, Germany); Philippe Lamothe (Geodetic Survey Canada); Anna Miglio (ROB), Vicente Navarro (ESA); Carey Noll (CDDIS/NASA, US); Mirko Reguzzoni (Politecnico Milan, Italy); Jim Riley (UNAVCO, US); Dan Roman (NGS, US); Laurent Soudarin (CLS, France); Daniela Thaller (BKG, Germany); Yusuke Yokota (GGOS Japan); Godfred Amponsah (NGS, US); Sandra Blevins (CDDIS/NASA, US); Francine Coloma (NOAA, US), Allison Craddock (JPL/NASA, US); Michael Craymer (Canadian Geodetic Networks, Canada); Theresa Damiani (NOAA, US), John Galetzka (NOAA, US), Ryan Hippenstiel (NOAA, US), Patrick Michael (CDDIS/NASA, US); Basara Miyahara (GGOS, Japan); Mike Pearlman (Harvard Smithsonian – Center for Astrophysics, US); Nacho Romero (ESA); Ira Sellars (NOAA, US) Christian Schwatke (TU Munich, Germany); Martin Sehnal (GGOS, BEV, Austria); Lori Tyahla (CDDIS/NASA, US)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".