D5.1 Report on data management recommendations and guidelines
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".