LIBER 2021 Session #3: Working with Software & Data
Bibliographic record
Abstract
These are the slides from the LIBER 2021 Session Working with Software & Data. This session will be chaired by Birgit Schmidt, University of Göttingen State and University Library, Germany Data citation for the Humanities and Social Sciences: a special case?, Barbara McGillivray, University of Cambridge & The Alan Turing Institute, United Kingdom; Nicolas Larrousse, TGIR Huma-Num, France; Daan Broeder, CLARIN ERIC & KNAW Humanities Cluster, the Netherlands The Curtin Open Knowledge Initiative: sharing data on scholarly research performance, Katie Wilson, Cameron Neylon, Lucy Montgomery, Richard Hosking, Chun-Kai {Karl} Huang, Rebecca N. Handcock, Alkim Ozaygen, Aniek Roelofs, Curtin University, Australia Recognising the value of software: how libraries can help the adoption of software citation, Neil Philippe Chue Hong, University of Edinburgh, United Kingdom, Jez Cope, The British Library, United Kingdom, Patricia Herterich, University of Edinburgh, United Kingdom, Daniel S. Katz, University of Illinois at Urbana-Champaign, United States, Simon Worthington, TIB - German National Library of Science and Technology, Germany The first presentation by Barbara McGillivray, Nicolas Larrousse and Daan Broeder discusses how Data Citation and Data Publication can play a key role in a synergetic relationship between libraries and researchers and have the potential to shape new ways to conduct research in the Social Sciences and Humanities (SSH). The presentation will further discuss how to establish a dialogue within the library community to address the intersection between Data Citation, Data Publication and Open Knowledge, exploring issues to do with how data and data publications can be made available and easily searchable in library catalogues, how librarians can act as data champions training students and researchers in best practices, and how data collections can be best curated to address the needs of SSH researchers. The second presentation by Katie Wilson discusses the Curtin Open Knowledge Initiative (COKI). This innovative research project explores and shares publicly available data, analysis, insights, and software code to expand understanding of institutional scholarly research performance and progress towards becoming Open Knowledge institutions. Building on a critique of limited bibliometric measures and underlying assumptions used by global university rankings, COKI has aggregated trillions of data points from multiple publicly available sources on more than 100 million outputs for more than 20.000 institutions. This presentation deliberates on the work of the COKI project and how collaboration with libraries can enhance institutional understanding of Open Research production, performance, and options to enric/h the implementation of Open Knowledge institutions. Finally, the third presentation by Neil P. Chue Hong seeks to discuss the current status of software citation in the researcher and publishing communities by summarising how libraries can build on the available guidance and by showcasing existing efforts. It will also give insights into how research libraries can collaborate with research software engineering groups and research computing groups at their institutions, to provide broader support for Open Research, FAIR research objects, reproducibility, and software preservation.
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.
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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.732 | 0.654 |
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".