
Institutional repositories (IRs) are ideal venues for storing research products such as postprint manuscripts and data. However, IRs are generally under-utilized due to the significant time and administrative burdens placed on authors, IR facilitators, and institutional stakeholders. This project seeks to alleviate these burdens by integrating Cornell's IR, eCommons, built in DSpace, with ReCiter, an authorship prediction algorithm that links authors with papers and contains authoritative institutional data about Weill Cornell faculty and their output. To identify which manuscripts can be uploaded into eCommons, journal embargo policy information has been added to the ReCiter database. This allows us to identify applicable papers based on author status and position, as well as what the embargo policy is for each record. Automated/semi-automated processes using Microsoft Power Automate and Airtable have been built to notify authors, collect manuscripts, fill out bulk deposition forms, and generate documentation required for uploading and hosting in eCommons.
machine learning, neural network, institutional repositories, bibliometrics, OR2025, automation
machine learning, neural network, institutional repositories, bibliometrics, OR2025, automation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
