Reading Faculty’s Research Publications Helps to Determine Which Professors to Target for Data Services
Bibliographic record
Abstract
Objective – The research project examined university faculty’s publications in order to find professors with previous data experiences. The professors could then be approached with an offer of the library’s data services. Design – Bibliographic study. Setting – Department of Crop Sciences in the College of Agricultural, Consumer, and Environmental Sciences at the University of Illinois at Urbana-Champaign. Subjects – A total of 62 assistant, associate, and full professors. Methods – The author searched Web of Science and faculty web pages to find each of the subjects’ two most recent research or review articles. Altogether, 124 articles were read to check whether data sources were used and shared. Data sources were defined as sources other than traditional citations to literature for information or ideas, such as data repositories, supplementary files, and weather stations. Data sharing was defined as publicly sharing data beyond that published in the journal article, such as providing supplementary files with the article or submitting data sets to a disciplinary repository (p. 205). Main Results – Thirty of the 124 articles, which were written by 20 different professors, referred to additional data that was made openly accessible. The analysis of the articles uncovered a variety of data experiences, such as faculty who utilized repository data, published supplementary files, submitted their own data to repositories, or posted data on their university’s website. These 20 faculty members were contacted and asked for a meeting “to discuss their data sharing thoughts and experiences and to ask whether they [saw] a role for the library in facilitating data sharing” (p. 206). The author received a positive response from seven of the faculty members and had a successful meeting with each of them. Conclusion – A bibliographic study can be employed to select which professors to target for data services. While this method is time-consuming, it allows librarians to gather rich data about faculty research that will help them to create customized, relevant messages to professors about the library’s data services. It also allows them to become more knowledgeable about data practices and resources in a particular discipline.
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 | 0.041 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.037 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.040 |
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".