icepyx as an icebreaker: starting conversations and building competencies in open science
Notice bibliographique
Résumé
AGU 2022 Fall Meeting Presentation (Invited) Abstract: Open science is critical for advancing cryospheric science. Open science improves our ability to conduct adaptive, rigorous research in response to rapidly changing systems, creates community, enhances cooperation, promotes diversity and inclusivity, and reduces computational challenges associated with analyzing increasingly large and complex datasets. Fostering open science practices as the norm requires discussion and compromise, yet initiating and sustaining conversations around this topic can be challenging and is often perceived as sub-par to science objectives. The NASA Transform to Open Science (TOPS) initiative provides training and resources to enable this transition across all NASA missions, aligning well with icepyx’s ongoing work initiated within the cryospheric science community. icepyx is a community and Python software library designed for working with large, complex data products collected on the second Ice, Cloud, and land Elevation Satellite (ICESat-2). As a widely applicable, domain agnostic tool, icepyx provides functionality for data access, visualization, processing, and analysis. Critically, it also serves to facilitate conversations around open science and demonstrates one way that collaborative development is leveraged to build shared tools. An important component of this is not only creating and fostering community, but introducing and building core open science skills in a relevant, applied framework for cryospheric scientists. icepyx also catalyzes cross-disciplinary research by lowering the barrier of entry through widely usable, modular, and extensible tools requiring minimal technical expertise for sustained engagement. This presentation will highlight the people and features behind icepyx and its relevance as a teaching tool at ICESat-2 themed hackweeks and for advancing open science. Plain Language Abstract: The world is changing rapidly, as are the tools we use to study it. This is particularly true for people who study snow and ice. For success, we need everyone to be able to participate and work together. When ideas, methods, and information are shared openly (called open science), anyone can see what is happening and contribute ideas. We won’t end up repeating work that has already been done. This requires learning how to communicate well - even with strangers. It can be scary to enter a new space, but friendly, welcoming, inclusive communities like icepyx are here to show you how! You can think about icepyx like a jigsaw puzzle. Many people know icepyx as a set of software tools (the pieces) for working with satellite data. But behind the code are people (those pieces will not put themselves together!). Each person has a piece of the puzzle. icepyx acts like the puzzle’s edge, providing structure and a shared end goal. The community can solve the puzzle together, piece by piece, to work towards the bigger picture. Our goal is for everyone to be able to contribute and enjoy the experience, answering interesting questions about snow and ice together!
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 | 0,012 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,011 | 0,011 |
| Science ouverte | 0,002 | 0,020 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,069 | 0,023 |
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 ».