D5.1 Report on data management recommendations and guidelines
Notice bibliographique
Résumé
The 4CH project aims to prepare the establishment of an international Competence Centre (CC) on the digitisation, preservation and valorisation of cultural heritage objects. In this 4CH competence centre digital data will play an important role, especially 3D models of cultural heritage objects. This report covers data management aspects related to 4CH. Data management concerns the handling and organisation of data throughout its life cycle, from creation to storage and reuse. This report constitutes the results of Work Package 5 “Data Management” of the 4CH project. This deliverable aims to provide recommendations and guidance specific to 4CH users and the digital cultural heritage assets they produce, use, and manage. Broad groups of users are defined and analysed with respect to their data management needs, building on work done in Work Package 1 of the 4CH project. An extensive list of user needs and related issues is presented that can be broadly classified as needs for standards, services and knowledge. This user oriented approach is taken forward in this deliverable taking into account that the formulated recommendations are clustered around three user groups: practitioners, policy makers and managers. The FAIR principles (Findable, Accessible, Interoperable, Reusable) are very important building blocks of data management policies, practices and services. Data Management Plans (DMP) are a prominent instrument to formulate and apply the FAIR principles.Several tools and services exist that support the creation of a DMP. Aspects are, among others, the application of persistent identifiers (PIDs), standard file formats and metadata schemas, as well as the role of trustworthy repositories that facilitate the durable storage and access of data. Numerous tools and services are available to assist in creating a DMP. Key considerations include the implementation of PIDs, adherence to standard file formats and metadata schemas, and the involvement of reliable repositories that enable the long-term storage and retrieval of data. Examples of the application of the FAIR principles for the 4CH target group concern the recommendation to use preferred file formats, specific metadata schemas, and controlled vocabularies and thesauri. The analysis of European-level policies and guidance documents on cultural heritage digitisation focused keenly on significant initiatives that impact data management, such as the European Strategy for Data, the European Open Science Cloud (EOSC), and European Data Spaces. This examination aimed to construct a foundational framework to inform the 4CH recommendations. Additionally, the research explored the primary means, including institutions and funding, by which Member States implement these directives nationally. This was further enriched by an in-depth look at Italy and Romania—countries represented by 4CH partners—highlighting the necessity for the future Competence Centre (CC) to recognize and accommodate the diverse national landscapes within the EU. Based on the user needs classification, the standards and services to implement the FAIR principles and the (inter)national data management strategies, policies and initiatives recommendations can be formulated.
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 | 0,105 | 0,185 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,016 | 0,015 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,023 | 0,016 |
| Science ouverte | 0,014 | 0,012 |
| Intégrité de la recherche | 0,016 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,101 | 0,172 |
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 ».