RESUMO
New academic knowledge in journal articles is partly built on peer reviewed research already published in journals or books. Academics can also draw from non-academic sources, such as the websites of organisations that publish credible information. This article investigates trends in the academic citing of this type of grey literature for 17 health, media, statistics, and large international organisations, with a focus on Covid-19. The results show substantial and steadily increasing numbers of citations to all 17 sites, with larger increases from 2019 to 2020. In 2020, Covid-19 citations to these websites were particularly common for news organisations, the WHO, and the UK Office for National Statistics, apparently for up-to-date information in the rapidly changing circumstances of the pandemic. Except for the UN, the most cited URLs of each organisation were not traditional report-like grey literature but were other types, such as news stories, data, statistics, and general guidance. The Covid-19 citations to most of these websites originated primarily from medical research, commonly for coronavirus data and statistics. Other fields extensively cited some of the non-health websites, as illustrated by social science (including psychology) studies often citing UNESCO. The results confirm that grey literature from major websites has become even more important within academia during the pandemic, providing up-to-date information from credible sources despite a lack of academic peer review. Researchers, reviewers, and editors should accept that it is reasonable to cite this information, when relevant, and evaluators should value academic work that supports these non-academic outputs. Supplementary Information: The online version of this article (10.1007/s11192-022-04398-3) contains supplementary material, which is available to authorized users.
RESUMO
Grey literature encompasses documents not published in academic journals or books. Some grey literature has substantial societal importance, such as medical guidelines, government analyses and pressure group reports. Academic research cited in such documents may therefore have had indirect societal impact, such as in policy making, clinical practice or legislation. Identifying citations to academic research from grey literature may therefore help assess its societal impacts. This is difficult, however, due to the variety of document and referencing formats used in grey literature, even from a single organisation. In response, this study introduces and tests a semi-automatic method to match academic journal articles with unstandardised grey literature cited references. For this, the metadata (lead author last name, title, year) of 2.45 million UK Russell Group university outputs was matched against a 100-document sample of UK government grey literature to assess the accuracy of 21 matching heuristics. The optimal method (lead author last name and title in either order, maximum of 200 characters apart) is sufficiently accurate and scalable to make the task of matching research outputs to grey literature references feasible. The method was then applied to 3347 government publications, showing approximately 23% of UK government grey literature in this study contained at least one reference to UK Russell Group university output, and of this grey literature, an average of 3.79 references were present per document. The applied method also shows that economics and environmental science academic research is most cited between 2010 and 2018. Supplementary Information: The online version contains supplementary material available at 10.1007/s11192-022-04408-4.
RESUMO
The h-index is an indicator of the scientific impact of an academic publishing career. Its hybrid publishing/citation nature and inherent bias against younger researchers, women, people in low resourced countries, and those not prioritizing publishing arguably give it little value for most formal and informal research evaluations. Nevertheless, it is well-known by academics, used in some promotion decisions, and is prominent in bibliometric databases, such as Google Scholar. In the context of this apparent conflict, it is important to understand researchers' attitudes towards the h-index. This article used public tweets in English to analyse how scholars discuss the h-index in public: is it mentioned, are tweets about it positive or negative, and has interest decreased since its shortcomings were exposed? The January 2021 Twitter Academic Research initiative was harnessed to download all English tweets mentioning the h-index from the 2006 start of Twitter until the end of 2020. The results showed a constantly increasing number of tweets. Whilst the most popular tweets unapologetically used the h-index as an indicator of research performance, 28.5% of tweets were critical of its simplistic nature and others joked about it (8%). The results suggest that interest in the h-index is still increasing online despite scientists willing to evaluate the h-index in public tending to be critical. Nevertheless, in limited situations it may be effective at succinctly conveying the message that a researcher has had a successful publishing career.
RESUMO
Primary data collected during a research study is often shared and may be reused for new studies. To assess the extent of data sharing in favourable circumstances and whether data sharing checks can be automated, this article investigates summary statistics from primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 314 primary human GWAS papers. Of these, only 13% reported the location of a complete set of GWAS summary data, increasing from 3% in 2010 to 23% in 2017. Whilst information about whether data was shared was typically located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong data sharing norms. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.