Enabling better aggregation and discovery of cultural heritage content for Europeana and its partner institutions
Bibliographic record
Abstract
Europeana, a non-profit foundation launched in 2008, aims to improve access to Europe’s digital cultural heritage through itsopen data platform that aggregates metadata and links to digital surrogates held by over 3700 providers. The data comes bothdirectlyfrom 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Data Model (EDM) for data import through Metis, Europeana's ingestion and aggregation service.However, OAI-PMH is an outdated technology,andis not web- centric, which presents high maintenance implications, in particular for smaller institutions. Consequently, Europeana seeks to find alternative aggregation mechanisms that couldcomplement or supersede it over the long-term, and which could also bring further potential benefits.In scope,this master’s thesisseeksto extendthe researchon earlier 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.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 componentsthat enableaggregation: data transfer and synchronisationas well as data modelling and representation.Three key resultswere obtained. First, the participation in the Europeana Common Culture project resulted in the documentation revision of the LOD-aggregator, a generictoolset 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 outconsideringthe23 organisations who expressedinterest infollow-up experimentson the basis ofthe available data and existing implementations. In the allotted time, one pilot was attempted using Sitemaps and Schema.org.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).Even if a shift in Europena’s operating model would require extensive human and technical resources, such an effort is clearly worthwhile as solutions presented in this dissertation are well-suited for data enrichment and for allowing datato be easily updated. 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 discoverability of digital cultural heritage objects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".