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Based on discussions and hands-on experiments conducted with the participants of a HERMES Bring Your Own Data Labs workshop hosted in Mainz in February 2025, this paper shares four key recommendations for (AI-supported) pre-and post-ATR (automatic text recognition) in historical research. The code used during the workshop and important observations are also published in the following GitHub repository:Barget, Monika (2025). Automated text recognition in historical research. [Github repository]. https://monikabarget.github.io/atr-historical-research/ The repository is work-in-progress and will be updated further.
citations 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 |