SSH Vocabulary Initiative - What Users Want
Notice bibliographique
Résumé
SSHOC will build the Social Sciences and Humanities part of the European Open Science Cloud. One of the SSHOC project’s core objectives is to foster the transition from the current Social Sciences and Humanities landscape to a cloud-based infrastructure that will operate according to the FAIR principles, offering access to research data and related services adapted to the needs of the Social Science and Humanities (SSH) community. Furthermore, the tools, services, repositories and other resources brought in by the project partners or generated during the project will be featured in the SSH Open Marketplace. The SSH European Research Infrastructure Consortia (ERICs) partnering in SSHOC are exploring and enabling collaboration and deeper integration of each other’s infrastructures. One topic that is of relevance for all SSHOC SSH stakeholders is that of managing and using vocabularies. Here we use vocabularies as a general term covering a range of semantic artefacts such as wordlists, taxonomies and thesauri. The SSH vocabularies are essential for a proper description of resources and phenomena and in SSHOC many tasks are concerned with them. In SSHOC, a specific “Vocabulary Initiative” was launched last year to coordinate related vocabulary activities and investigate, inform and exchange expertise on vocabularies and the platforms that are hosting and managing them. Therefore, the proposed workshop will have the following main objectives: To engage the SSH end-user communities present at ICTeSSH in the SSHOC Vocabulary Initiative, to collect their input and feedback on managing vocabularies, and vocabularies as FAIR semantic artefacts. To raise awareness in the SSH research community present at ICTeSSH on finding, understanding and reusing vocabularies via the SSH Open Marketplace. This workshop will consist of several presentations that will the following topics: Vocabularies and their use in the SSH community: Given the breadth of the Social Sciences and Humanities sector, it is of no surprise that researchers are faced not only with a multitude of theoretical and empirical approaches to research but also with an enormous pool of various tools, systems, and resources that are intended to help researchers in their endeavours. Managing Vocabularies: Recently, SSHOC and CLARIN organised a series of info sessions and a workshop to discuss the respective merits of different available vocabulary management platforms. This presentation will give an overview of the platforms, highlighting the differences and their impact on data aggregation, discovery and access. Vocabularies as FAIR semantic artefacts: In the data management landscape, vocabularies and their interrelations have not always been considered as primary data themselves. Nowadays, they are considered an essential part of data processing that should be made FAIR as other research data. There are currently numerous initiatives that register vocabularies to make them ‘findable’ and ‘accessible’ while interoperability can be provided by existing standards. Using Vocabularies in different SSH tools, such as: SSHOC Dataverse, CLARIN metadata component registry and the ADS Vocabulary Matching tool Finding vocabularies via the SSH Open Marketplace: The more established and well-known vocabularies are, the more useful they are because the users are better acquainted with them and thus better understand their structure and the meaning of individual concepts. Moreover, reuse of vocabularies is crucial for achieving semantic interoperability between systems and datasets. One could even argue that controlled vocabularies are only used to their full potential if they are being reused. This is why researchers can find vocabularies as individual semantic artefacts, as items of the Marketplace.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,022 | 0,035 |
| Science ouverte | 0,004 | 0,008 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,005 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».