HeFDI Code School - Code Competencies for sustainable research software
Bibliographic record
Abstract
HeFDI Code School - Code Competencies for sustainable research software The HeFDI Code School has been tested since 2023 to meet the high demand for competence development in programming scientific software. The Code School supports researchers, especially young scientists, in the development of high-quality research software - a central building block for reproducible science. The program has so far been carried out with changing partners such as SURESOFT /TU Braunschweig, NFDI4ING, NFDI4EARTH and the Hessian Competence Center for High Performance Computing (HKHLR). Cooperation with proven experts on the one hand and with NFDI consortia on the other is particularly important to ensure the effective dissemination of the offering and, at the same time, to utilize existing skills and resources as efficiently as possible. Demand remains high: the online courses in particular reach well over 100 participants per course in a very short space of time. The high demand for the basic track, which has only been offered once so far, shows that it would be desirable to set up a graduated program that systematically guides those interested from basic skills through an intermediate level to “Advanced” and “Expert” with the best support. The unbroken high demand underlines the fact that such a format is also needed throughout Germany. However, many questions remain unanswered, such as the need for a finely tuned overall program in terms of content, long-term cooperation with consortia and/or other initiatives, as well as certificates or other proof of performance and the verification of skills development. On the one hand, the poster aims to present the experiences and the program to date. On the other hand, it is intended to get into conversation with other interested parties in order to explore future collaborative perspectives and synergy opportunities.
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.
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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".