icepyx: Community and Software for the Open Science Journey
Bibliographic record
Abstract
Abstract Open science accelerates our ability to collaborate and produce quality, inclusive scientific results, making it essential for addressing the challenges of our time. Despite its collaborative nature and outcomes, any one person's path to doing science openly can be highly nonlinear and individualized. Many researchers are supportive of open science ideals like inclusivity, sharing data, collaborating on code, and reducing duplication of effort while increasing reproducibility. Yet these same researchers may lack the safe spaces, support, and technical training required to fully explore what it means to conduct their science openly and collaboratively. Most open-source software communities are truly welcoming, inclusive, and highly supportive of learning and growth, but it can still be intimidating for scientists to enter or remain in these technical spaces. Smaller communities and software packages that provide discipline-, instrument- and/or sensor-specific tooling can fill a critical gap by offering a more intimate space for facilitating open science practices, particularly among researchers who may have minimal formal software development training to accompany their domain expertise. The icepyx (pronounced ice-picks) community and Python software library aims to (1) provide technical software solutions to address shared challenges in NASA’s ICESat-2 data access and analysis pipeline; and (2) create a community and space for people to learn how to collaborate on software and foster the open sharing and co-working that readily takes place during in-person workshops but can be difficult to emulate in a virtual space. By providing a supportive, peer-led space for contributors to practice open, shared development throughout their project timelines, icepyx facilitates the long-term skill building and collaboration required to achieve the ideals of open science that make science accessible, time-efficient, and open for all. More Information Slides for a presentation (talk) in American Geophysical Union (AGU) 2024 Annual Meeting session U31C: Open Science Recognition Prize.
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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.034 |
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".