Assessing needs and developing solutions
Notice bibliographique
Résumé
Welcome to the second issue of IASSIST Quarterly for 2024, IQ 48(2). It was wonderful to meet so many old and new colleagues at the Best IASSIST Ever in Halifax. It was really inspiring to learn about all the great work that is being done by members of this community. For those of you who presented, please consider turning your conference presentation or poster into a paper and submitting it to IQ. This will allow you to share your expertise with a wider audience. If you were not able to attend the conference, you may have missed the announcement about the winner of the IASSIST Conference Paper Competition. This year’s winner is the paper “How are we FAIR-ing? Creating a FAIR Self-Assessment Checklist for Data Repositories” by Lauren Phegley and Lynda Kellam. In the paper the authors describe a project in which a data repository’s staff wanted to gauge how well they were enabling FAIR principles. A small team from Penn Libraries found that much of the literature about FAIR was from the perspective of data creators, so they developed a FAIR Principles Self-Assessment Tool for repository teams. We look forward to publishing this paper in a future issue. We would like to take this opportunity to encourage you to look ahead to submitting your papers for next year’s paper competition. In addition to bragging rights, the award incudes free registration for the first author to the following year’s IASSIST conference. The four papers included in this issue of IQ introduce tools developed in several institutions, representing a wide geographic diversity, to assess and resolve operational challenges. In the article titled ”Research analysis: A World Data System and Canadian CoreTrustSeal Cohort needs assessment,” Lee, Gonzalez, Payne and Goins describe how they designed a method to identify the needs and challenges faced by members of the World Data System (WDS) and Canadian CoreTrustSeal Pilot. They also describe the assessment tool they developed and the overarching challenges and goals identified through the usage of this tool. Based on their findings, they provide recommendations on how best to assist the WDS members and the cohort of Canadian data repositories. Constanzo and Cooper, in their article ”Developing institutional research data management strategies in Canada: Setting the foundation for stronger partnerships and collaborations,” describe national surveys developed by Research Intelligence Expert Group (RIEG) to gauge institutions’ readiness for developing an institutional RDM strategy required by the Government of Canada’s Tri-Agency. The first survey was conducted in 2019 and a follow-up survey in 2022 in order to assess the progress of institutions in creating their institutional strategies and identifying additional challenges. The authors and report the findings and recommendations from their study and share their survey instruments. In ”Enhancing FAIR compliance: A controlled vocabulary for mapping social sciences survey variables,” authors Bach and Klas introduce the GESIS Controlled Vocabulary (CV) for Variables in Social Sciences Research Data. This CV is designed to enhance semantic interoperability across various organizations and systems, and facilitates harmonization across different study waves. This endeavor aligns with the FAIR data principles, and aims to foster a more integrated and accessible research landscape. Obasola and Usman in their article ”Digitising old Yoruba newspapers at Kenneth Dike Library,” describe in detail the digitisation of a collection of old Yoruba newspapers stored at Kenneth Dike Library in Ibadan, Nigeria. The project was undertaken in order to preserve this historical and delicate material, which includes rich details of local history. In addition to providing a detailed workflow, the authors share lessons learned. We hope you enjoy reading and wish you a productive summer. Ofira Schwartz and Michele Hayslett, June 2024
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 | 0,035 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,019 | 0,029 |
| Science ouverte | 0,004 | 0,016 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,019 |
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 ».