Free and Open-Source Automated Open Access Preprint Harvesting
Bibliographic record
Abstract
Universities are attempting to ensure that all of their research is publicly accessible because of funding mandates. Many universities have established campus open access (OA) repositories but are struggling with how to upload millions of manuscripts under numerous license agreements while also linking metadata to make them discoverable. To do this manually requires around 15 minutes per manuscript from an experienced librarian. The time and cost to do this campus-wide is prohibitive. To radically reduce the time and costs of this process and to harvest all past work, this article reports on the development and testing of a free and open source (FOSS) JavaScript-based application, aperta-accessum, which does the following: 1) harvests names and emails from a department’s faculty webpage; 2) identifies scholars’ Open Researcher and Contributor IDentifiers (ORCID iDs); 3) obtains digital object identifiers (DOIs) of publications for each scholar; 4) checks for existing copies in an institution’s OA repository; 5) identifies the legal opportunities to provide OA versions of all of the articles not already in the OA repository; 6) sends authors emails requesting a simple upload of author manuscripts; and 7) adds link-harvested metadata from DOIs with uploaded preprints into a bepress repository; the code can be modified for additional repositories. The results of this study show that, in the administrative time needed to make a single document OA manually, aperta-accessum can process approximately five entire departments worth of peer-reviewed articles. Following best practices discussed, it is clear that this open-source OA harvester enables institutional library’s stewardship of OA knowledge on a mass scale for radically reduced costs.
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 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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.114 | 0.338 |
| Open science | 0.030 | 0.082 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".