Open Life Science: Empowering early career researchers to become open science leaders
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,008 |
| Communication savante | 0,016 | 0,014 |
| Science ouverte | 0,004 | 0,038 |
| Intégrité de la recherche | 0,006 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,027 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».