Enabling better aggregation and discovery of cultural heritage content for Europeana and its partner institutions. Master's thesis oral defence
Bibliographic record
Abstract
Presentation carried out during a <strong>master's thesis oral defence</strong> at the Haute école de gestion de Genève on 28 August 2020. The recording is available here: https://vimeo.com/453003769 Abstract of the master's thesis: Europeana, a non-profit foundation launched in 2008, aims to improve access to Europe’s digital cultural heritage through its open data platform which aggregates metadata and links to digital surrogates held by over 3700 providers. The data comes both directly from cultural heritage institutions (libraries, archives, museums) as well as through intermediary aggregators. Europeana’s current operating model leverages the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and the Europeana Data Model (EDM) for data import through Metis, Europeana's ingestion and aggregation service. <br> However, OAI-PMH is an outdated technology, is not web-centric, which presents high maintenance implications, in particular for smaller institutions. Consequently, Europeana seeks to find alternative aggregation mechanisms that could complement or supersede it over the long term, and which could also bring further potential benefits. <br> The research scope of this master’s thesis is to extend on previous aggregation experiments that Europeana successfully carried out with various technologies, such as aggregation based on Linked Open Data (LOD) datasets or through the International Image Interoperability Framework (IIIF) APIs. <br> The literature review first focuses on metadata standards and the aggregation landscape in the cultural heritage domain, and then provides an extensive overview of Web-based technologies with respect to two essential components enabling aggregation: data transfer and synchronisation as well as data modelling and representation. <br> Three key results were obtained. First, the participation in the Europeana Common Culture project resulted in the documentation revision of the LOD-aggregator, a generic toolset for harvesting and transforming LOD. Second, 52 respondents completed an online survey to gauge the awareness, interest, and use of technologies other than OAI-PMH for (meta)data aggregation. Third, an assessment of potential aggregation pilots was carried out considering the 23 organisations who expressed interest in follow-up experiments on the basis of the available data and existing implementations. In the allotted time, one pilot was attempted using Sitemaps and Schema.org. <br> In order to encourage the adoption of new aggregation mechanisms, a list of proposed suggestions was then established. All of these recommendations were aligned with the Europeana Strategy 2020-2025 and directed towards one or several of the key roles of the aggregation workflow (data provider, aggregator, Europeana). <br> Even if a shift in Europena’s operating model would require extensive human and technical resources, the effort is clearly worthwhile as technologies presented in this dissertation are indeed well-suited for data enrichment and for keeping an easy update of data. The transition from OAI-PMH will also be facilitated by the integration of such mechanisms within the Metis Sandbox, Europeana's new ad-hoc system where contributors will be able to test their data sources before ingestion into Metis. Ultimately, this shift is also expected to lead to a better digital transformation and discoverability of digital cultural heritage objects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| 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".