Charting a Course to Collaboration: The LIbrary Data Services (LIDS) Dataset
Bibliographic record
Abstract
Research data services (RDS) are expanding across college and university libraries. To better understand the current state of RDS in R1 and R2 university research libraries in the United States, how they have evolved since the onset of the COVID-19 pandemic, and who is providing these services, this research project built an interoperable dataset, LIbrary Data Services (LIDS) dataset, to inform RDS development and assessment. The dataset records data service area(s) (e.g., Research Data Management), fifteen data service types (e.g., data management/data curation), and personnel and unit information gathered through website content analyses, alongside Carnegie Classification data. How can the data services community build on LIDS? While the focus for this research project is R1 and R2 university research libraries in the United States, similar studies have examined data services in libraries at other levels of American higher education (Radecki & Springer, 2019; Murray, et al., 2019; Yoon & Schultz, 2017), and academic and research libraries across Canada and the United States (Kouper, et al, 2017; Tenopir, et al., 2019), Europe (Tenopir, et al., 2017; Yu, 2017), Spain (Martin-Melon, et al., 2023), the United Kingdom (Cox & Pinfield, 2014), southern Africa (Chiware, 2020; Chiware & Becker, 2018), and globally (Cox, et al., 2019; Liu, et al., 2020; Reilly, 2012; Si, et al., 2019). How can we work together to change LIDS to I-LIDS, the International LIbrary Data Services dataset? Can we better reflect the post-COVID data services environment in higher education globally? This presentation will share the LIDS dataset with the international community to inform data services collaboration, service development, and assessment, and consider how they might want to expand the dataset to cover their region of the world.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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; both teacher heads agree on what is shown here.
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".