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Biomedical research reporting guidelines provide a framework by which journal editors and the researchers who conduct studies can ensure that the reported research is both complete and transparent. With more than 16 different guidelines for the 11 major study types of medical and health research, authors need to be familiar with journal reporting standards. To assess the current endorsements of reporting guidelines for biomedical and health research, this study examined the instructions for authors (IFAs) of 559 biomedical journals by 11 prominent publishers that publish original research or systematic reviews/meta-analyses. Data from the above original sources were cleaned and restructured, and analyzed in a database and text miner. Each journal's instructions or information for authors were examined to code if any of five prominent reporting guidelines were mentioned and what form the guideline adherence demonstration took. Seventeen journals published the reporting guidelines. Four of the five reporting guidelines listed journals as endorsers. For journals with open peer review reports, a sample of journals and peer reviews was analyzed for mention of adherence to reporting guidelines. The endorsement of research guidelines by publishers and their associated journals is inconsistent for some publishers, with only a small number of journals endorsing relevant guidelines. Based on the analysis of open peer reviews, there is evidence that some reviewers check the adherence to the endorsed reporting guidelines. Currently, there is no universal endorsement of reporting guidelines by publishers nor ways of demonstrating adherence to guidelines. Journals may not directly inform authors of their guideline endorsements, making it more difficult for authors to adhere to endorsed guidelines. Suggestions derived from the findings are provided for authors, journals, and reporting guidelines to ensure increased adequate use of endorsed reporting guidelines. [ABSTRACT FROM AUTHOR]

Journals that adopt open peer review (OPR), where review reports of published articles are publicly available, provide an opportunity to study both review content characteristics and quantitative aspects of the overall review process. This study investigates two areas relevant to the quality assessment of manuscript reviews. First, do journal policies for reviewers to identify themselves influence how reviewers evaluate the merits of a manuscript based on the relative frequency of hedging terms and research-related terms appearing in their reviews? Second, is there an association between the number of reviews/reviewers and the manuscript's research impact once published as measured by citations? We selected reviews for articles published in 17 OPR journals from 2017 to 2018 to examine the incidence of reviewers' uses of hedging terms and research-related terms. The results suggest that there was little difference in the relative use of hedging term usage regardless of whether reviewers were required to identify themselves or if this was optional, indicating that the use of hedging in review contents was not influenced by journal requirements for reviewers to identify themselves. There was a larger difference observed for research-related terminology. We compared the total number of reviews for a manuscript, rounds of revisions, and the number of reviewers with the number of Web of Science citations the article received since publication. The findings reveal that scrutiny by more reviewers or conducting more reviews or rounds of review do not result in more impactful papers for most of the journals studied. Implications for peer review practice are discussed. [ABSTRACT FROM AUTHOR]

Social media platforms, search engine transaction logs, Web portal transaction logs, large text corpora, and other data sources offer users a variety of invaluable data sources. Data generated based on social media platforms and other data sources have become important sources of big data for researchers in information science. Big data provide not only challenges but also opportunities for information science. Emerging big data trends inevitably have an impact on research methods in information science. The authors of this paper discuss the impact of big data on research methods in information science. This paper addresses these challenges and opportunities through the lens of research methods, ranging from data processing, to sampling, to information visualization, to temporal analysis, to sentiment analysis, to correlation, to cause-effect relationship, to data accessibility, to data privacy, and data ethics issues. The discussions on related aspects of research methods provoke a healthy reflection and debate for the information science research community, which can help researchers in the field produce sound research designs for big data-oriented research studies and assist information practitioners in solving problems they face in the big data age. Research methods are fundamental and essential for any research studies. Big data analysis has a natural relationship with research methods. The paper discusses an emerging and important research topic big data from the unique angle of research methods. [ABSTRACT FROM AUTHOR]