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Abstract Evaluation of researchers’ output is vital for hiring committees and funding bodies, and it is usually measured via their scientific productivity, citations, or a combined metric such as the h-index. Assessing young researchers is more critical because it takes a while to get citations and increment of h-index. Hence, predicting the h-index can help to discover the researchers’ scientific impact. In addition, identifying the influential factors to predict the scientific impact is helpful for researchers and their organizations seeking solutions to improve it. This study investigates the effect of the author, paper/venue-specific features on the future h-index. For this purpose, we used a machine learning approach to predict the h-index and feature analysis techniques to advance the understanding of feature impact. Utilizing the bibliometric data in Scopus, we defined and extracted two main groups of features. The first relates to prior scientific impact, and we name it ‘prior impact-based features’ and includes the number of publications, received citations, and h-index. The second group is ‘non-prior impact-based features’ and contains the features related to author, co-authorship, paper, and venue characteristics. We explored their importance in predicting researchers’ h-index in three career phases. Also, we examined the temporal dimension of predicting performance for different feature categories to find out which features are more reliable for long- and short-term prediction. We referred to the gender of the authors to examine the role of this author’s characteristics in the prediction task. Our findings showed that gender has a very slight effect in predicting the h-index. Although the results demonstrate better performance for the models containing prior impact-based features for all researchers’ groups in the near future, we found that non-prior impact-based features are more robust predictors for younger scholars in the long term. Also, prior impact-based features lose their power to predict more than other features in the long term.

Since 2013, the usage of preprints as a means of sharing research in biology has rapidly grown, in particular via the preprint server bioRxiv. Recent studies have found that journal articles that were previously posted to bioRxiv received a higher number of citations or mentions/shares on other online platforms compared to articles in the same journals that were not posted. However, the exact causal mechanism for this effect has not been established, and may in part be related to authors' biases in the selection of articles that are chosen to be posted as preprints. We aimed to investigate this mechanism by conducting a mixed-methods survey of 1,444 authors of bioRxiv preprints, to investigate the reasons that they post or do not post certain articles as preprints, and to make comparisons between articles they choose to post and not post as preprints. We find that authors are most strongly motivated to post preprints to increase awareness of their work and increase the speed of its dissemination; conversely, the strongest reasons for not posting preprints centre around a lack of awareness of preprints and reluctance to publicly post work that has not undergone a peer review process. We additionally find evidence that authors do not consider quality, novelty or significance when posting or not posting research as preprints, however, authors retain an expectation that articles they post as preprints will receive more citations or be shared more widely online than articles not posted.

An increasing number of scientific publications are created in open and transparent peer review models: a submission is published first, and then reviewers are invited, or a submission is reviewed in a closed environment but then these reviews are published with the final article, or combinations of these. Reasons for open peer review include giving better credit to reviewers, and enabling readers to better appraise the quality of a publication. In most cases, the full, unstructured text of an open review is published next to the full, unstructured text of the article reviewed. This approach prevents human readers from getting a quick impression of the quality of parts of an article, and it does not easily support secondary exploitation, e.g., for scientometrics on reviews. While document formats have been proposed for publishing structured articles including reviews, integrated tool support for entire open peer review workflows resulting in such documents is still scarce. We present AR-Annotator, the Automatic Article and Review Annotator which employs a semantic information model of an article and its reviews, using semantic markup and unique identifiers for all entities of interest. The fine-grained article structure is not only exposed to authors and reviewers but also preserved in the published version. We publish articles and their reviews in a Linked Data representation and thus maximise their reusability by third party applications. We demonstrate this reusability by running quality-related queries against the structured representation of articles and their reviews.