Making science public: a review of journalists' use of Open Access research.

Science journalists are uniquely positioned to increase the societal impact of open research outputs by contextualizing and communicating findings in ways that highlight their relevance and implications for non-specialist audiences. Yet, it is unclear to what degree journalists use open research outputs, such as open access publications or preprints, in their reporting; what factors motivate or constrain this use; and how the recent surge in openly available research seen during the COVID-19 pandemic has affected this. This article examines these questions through a review of relevant literature published from 2018 onwards-particularly literature relating to the COVID-19 pandemic-as well as seminal articles outside the search dates. We find that research that explicitly examines journalists' engagement with open access publications or preprints is scarce, with existing literature mostly addressing the topic tangentially or as a secondary concern, rather than a primary focus. Still, the limited body of evidence points to several factors that may hamper journalists' use of these outputs and thus warrant further exploration. These include an overreliance on traditional criteria for evaluating scientific quality; concerns about the trustworthiness of open research outputs; and challenges using and verifying the findings. We also find that, while the COVID-19 pandemic encouraged journalists to explore open research outputs such as preprints, the extent to which these explorations will become established journalistic practices remains unclear. Furthermore, we note that current research is overwhelmingly authored and focused on the Global North, and the United States specifically. We conclude with recommendations for future research that attend to issues of equity and diversity, and more explicitly examine the intersections of open access and science journalism.

What is critical for human-centered AI at work? - Toward an interdisciplinary theory.

Human-centered artificial intelligence (HCAI) has gained momentum in the scientific discourse but still lacks clarity. In particular, disciplinary differences regarding the scope of HCAI have become apparent and were criticized, calling for a systematic mapping of conceptualizations-especially with regard to the work context. This article compares how human factors and ergonomics (HFE), psychology, human-computer interaction (HCI), information science, and adult education view HCAI and discusses their normative, theoretical, and methodological approaches toward HCAI, as well as the implications for research and practice. It will be argued that an interdisciplinary approach is critical for developing, transferring, and implementing HCAI at work. Additionally, it will be shown that the presented disciplines are well-suited for conceptualizing HCAI and bringing it into practice since they are united in one aspect: they all place the human being in the center of their theory and research. Many critical aspects for successful HCAI, as well as minimum fields of action, were further identified, such as human capability and controllability (HFE perspective), autonomy and trust (psychology and HCI perspective), learning and teaching designs across target groups (adult education perspective), as much as information behavior and information literacy (information science perspective). As such, the article lays the ground for a theory of human-centered interdisciplinary AI, i.e., the Synergistic Human-AI Symbiosis Theory (SHAST), whose conceptual framework and founding pillars will be introduced.

Motivations, concerns and selection biases when posting preprints: A survey of bioRxiv authors.

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.

Research data explored: an extended analysis of citations and altmetrics.

In this study, we explore the citedness of research data, its distribution over time and its relation to the availability of a digital object identifier (DOI) in the Thomson Reuters database Data Citation Index (DCI). We investigate if cited research data "impacts" the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media platforms. Three tools are used to collect altmetrics scores, namely PlumX, ImpactStory, and Altmetric.com, and the corresponding results are compared. We found that out of the three altmetrics tools, PlumX has the best coverage. Our experiments revealed that research data remain mostly uncited (about 85 %), although there has been an increase in citing data sets published since 2008. The percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories are responsible for research data with DOIs and two or more citations. The number of cited research data with altmetrics "foot-prints" is even lower (4-9 %) but shows a higher coverage of research data from the last decade. In our study, we also found no correlation between the number of citations and the total number of altmetrics scores. Yet, certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and also receive higher altmetrics scores. Additionally, we performed citation and altmetric analyses of all research data published between 2011 and 2013 in four different disciplines covered by the DCI. In general, these results correspond very well with the ones obtained for research data cited at least twice and also show low numbers in citations and in altmetrics. Finally, we observed that there are disciplinary differences in the availability and extent of altmetrics scores.

Astrophysicists' conversational connections on Twitter.

Because Twitter and other social media are increasingly used for analyses based on altmetrics, this research sought to understand what contexts, affordance use, and social activities influence the tweeting behavior of astrophysicists. Thus, the presented study has been guided by three research questions that consider the influence of astrophysicists' activities (i.e., publishing and tweeting frequency) and of their tweet construction and affordance use (i.e. use of hashtags, language, and emotions) on the conversational connections they have on Twitter. We found that astrophysicists communicate with a variety of user types (e.g. colleagues, science communicators, other researchers, and educators) and that in the ego networks of the astrophysicists clear groups consisting of users with different professional roles can be distinguished. Interestingly, the analysis of noun phrases and hashtags showed that when the astrophysicists address the different groups of very different professional composition they use very similar terminology, but that they do not talk to each other (i.e. mentioning other user names in tweets). The results also showed that in those areas of the ego networks that tweeted more the sentiment of the tweets tended to be closer to neutral, connecting frequent tweeting with information sharing activities rather than conversations or expressing opinions.