Managing linked open data across discovery systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter examines and explores linked open data in the context of the current digital data landscape, drawing on recent developments associated with digital data: big data, research data, open data and web of data. A specific goal of this chapter is to draw attention to the importance of the ways in which linked open data can provide libraries with opportunities to enhance the findability of their data and information resources, and to support seamless and unified access in heterogeneous content repositories, such as digital libraries and integrated discovery systems. The first part of the chapter addresses the key concepts of big data, research data, the Semantic Web and open data. The second part of the chapter focuses on the definition and importance of linked data and its current applications in various settings. Specific examples of libraries and major projects associated with using and implementing linked open data are briefly reviewed. BIBFRAME is reviewed as a popular framework to support the transformation of library data into linked open data. An overview of publishing linked data is presented, along with a reference to useful resources for publishing, browsing and linking linked open data tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it