Open Life Science: Empowering early career researchers to become open science leaders
Bibliographic record
Abstract
Motivation: As scientists, we are provided training and guidance in how to conduct research in the lab, design algorithms, analyse data and publish them. However, scientists are rarely expected to apply important skills such as open science principles for tooling and road mapping their projects, planning reproducible workflows, involving others in their work, and leading an inclusive community. Modern bioinformatics communities stand in the interface of computational and biological research. This interdisciplinary position requires us to develop collaborative projects by implementing such “open by design” principles in our research projects systematically -- skills that aren’t necessarily taught at university or graduate school level. About the project: Open Life Science (OLS) is a volunteer-driven training and mentoring program aimed at empowering early career researchers and potential academic leaders to become open science ambassadors. Participants join OLS with a proposal to work on an open science project and attend a series of one-on-one mentoring calls over 16 weeks, alternating with full cohort calls that provide training on specific open science and leadership skills. OLS’s work is underpinned by a community of over 50 mentors and expert guest speakers. Cohort calls cover a broad spectrum of topics relevant to leading an open project, ranging from open science topics, community building, project and contribution management of GitHub repositories, and caring both for yourself and others in your community. Calls are designed to be interactive and engaging, utilising a mix of Zoom’s break-out room features to facilitate group discussion, collaborative document editing, and guest speakers from academia and industry giving short talks. The program is modelled on the exact principles we teach, and hence, all materials, including syllabus, call notes, and slides, are shared under the CC-BY licence. Cohort calls are recorded and shared openly on YouTube. Third-party organisations and individuals are encouraged to fork, remix and re-use materials. Overview of the first round: OLS’s first cohort (OLS-1), known as “Open Seeds”, was conducted from January 2020 until May 2020 with 29 project leaders working on 20 projects. Project leaders came from around the world, including the Netherlands, Spain, Norway, Japan, India, Nepal, Thailand, Kenya, Brazil, Russia, Canada, the United Kingdom, and the United States. At the end of the program, the project leaders graduate by presenting their work, share their mentorship experience and discuss their future plans on publicly live-streamed video calls. In this talk, we will report important observations and outcomes from running the first cohort of our mentoring and training program. At the time of writing, OLS-1 is in final stages of wrap-up and graduation, and we aim to open applications for OLS-2 in May 2020. We will also welcome new mentors and experts, including the project leaders from OLS-1, who will be encouraged to return to join the mentor and expert teams for OLS-2.
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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.039 | 0.027 |
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".