What Do We (Not) Know About Research Software Engineering?
Bibliographic record
Abstract
As recognition of the vital importance of software for contemporary research is increasing, Research Software Engineering (RSE) is emerging as a discipline in its own right. We present an inventory of relevant research questions about RSE as a basis for future research and initiatives to advance the field, highlighting selected literature and initiatives. This work is the outcome of a RSE community workshop held as part of the 2020 International Series of Online Research Software Events (SORSE) which identified and prioritized key questions across three overlapping themes: people, policy and infrastructure. Almost half of the questions focus on the people theme, which addresses issues related to career paths, recognition and motivation; recruitment and retention; skills; and diversity, equity and inclusion. However, the people and policy themes have the same number of prioritized questions. We recommend that different types of stakeholders, such as RSE employers and policy makers, take responsibility for supporting or encouraging answering of these questions by organizations that have an interest. Initiatives such as the International Council of RSE Associations should also be engaged in this work.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.087 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.022 | 0.055 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".