The SUMup collaborative database: Surface mass balance, subsurface temperature and density measurements from the Greenland and Antarctic ice sheets (2025 release)
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
The SUMup database is a compilation of surface mass balance (SMB), subsurface temperature and density measurements from the Greenland and Antarctic ice sheets. This 2025 release contains 7 795 894 data points: 2 738 170 SMB measurements, 2 866 260 density measurements and 2 191 464 subsurface temperature measurements. This is respectively +160 505 and +292 056 observations of density and temperature compared to the 2024 release. Despite many additions of SMB data, there are 119 086 fewer SMB measurements than in 2024 due to the removal of some radar data with quality issues. Note that the accumulated SMB is given, not accumulation rates. The data files are provided in both CSV and NetCDF format and contain, for each measurement: latitude, longitude, elevation, timestamp, method, reference of the data source (as bibtex key and as short/long string) and, when applicable, the name of the measurement group it belongs to (e.g. core name for SMB, profile name for density, station name for temperature). Data users are asked to cite all the original data sources that are being used and a bibtex bibliography is provided to facilitate this. Issues about this release as well as suggestions of datasets to be added in next releases can be done on a dedicated user forum: https://github.com/SUMup-database/SUMup-data-suggestion/issues. We also provide example scripts to use the SUMup 2025 files (https://github.com/SUMup-database/SUMup-example-scripts) as well as the compilation scripts used to build the database (https://github.com/SUMup-database/SUMup-2025-compilation-scripts). SUMup is a community effort and help to compile and curate the database is welcome.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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