Bibliographic record
Abstract
Introduction 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. Big data The vast volume, variety and complexity of digital data available on the web has resulted in the emergence of what is called ‘big data’. Digital libraries, search engines, social media sites, cloudbased computing infrastructures, as well as virtual collaboratories, e-science, e-humanities and e-social-science projects produce massive volumes of data that call for proper management and preservation planning approaches and strategies in order to provide users with effective and efficient data access. Many terms used in the literature refer to, or are associated with, the phenomenon of big data, including ‘digital data’, ‘research data’, ‘linked data’, ‘open data’, ‘web of data’ and ‘data repositories’ (Borgman, 2012; Hodson, 2012; Lyon, 2007; National Science Foundation, 2012). The availability and discourse of these data types presents new research and development opportunities as well as challenges. To provide a coherent and contextualized understanding of big data, one approach would be to place big data in the context of digital libraries, as the latter have been well researched and share a number of similarities with big data.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".