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      F1000Research: Tics welcomes you to 21st century biomedical publishing

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          Abstract

          Tics are repeated, usually suppressible movements or vocalizations. They are the defining features of tic disorders including Tourette syndrome, but many people have them for shorter durations at some point in childhood. This editorial marks the beginning of the F1000Research: Tics specialty section, an effort to provide a single portal to modern research on tics and tic disorders. Publications in F1000Research: Tics benefit from F1000Research’s novel approach to publishing, in which articles can be published within days of submission. Peer review happens after publication and is fully open. When the submitted article or a revision is approved, it is promptly submitted to repositories including NIH’s PubMed Central. In addition to research articles and reviews, F1000Research: Tics will publish study protocols, clinical practice articles, case reports, and data notes. The home page will also provide links to expert recommendations of articles that have appeared elsewhere, and to relevant posters from scientific meetings (http://f1000.com/posters/). F1000Research’s approach is enabled by the capabilities of internet publication, including space to publish the full results of a study rather than just a few graphs selected from the data. Publishing methodologically sound studies without requiring subjective editorial judgments of novelty or broad appeal brings numerous advantages, including minimizing publication bias and shining the light of openness on peer review. To celebrate the launch of the Tics section, F1000Research is offering discounted article processing charges for manuscripts submitted by March 1st 2015. I have had good experiences publishing in F1000Research, and look forward to seeing a wide range of tic-related manuscripts submitted.

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          Most cited references15

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          Data reuse and the open data citation advantage

          Background. Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the “citation benefit”. Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results. Here, we look at citation rates while controlling for many known citation predictors and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations than similar studies for which the data was not made available. Date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic were included as covariates. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 2004 and 2005, at about 30%. Authors published most papers using their own datasets within two years of their first publication on the dataset, whereas data reuse papers published by third-party investigators continued to accumulate for at least six years. To study patterns of data reuse directly, we compiled 9,724 instances of third party data reuse via mention of GEO or ArrayExpress accession numbers in the full text of papers. The level of third-party data use was high: for 100 datasets deposited in year 0, we estimated that 40 papers in PubMed reused a dataset by year 2, 100 by year 4, and more than 150 data reuse papers had been published by year 5. Data reuse was distributed across a broad base of datasets: a very conservative estimate found that 20% of the datasets deposited between 2003 and 2007 had been reused at least once by third parties. Conclusion. After accounting for other factors affecting citation rate, we find a robust citation benefit from open data, although a smaller one than previously reported. We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data. Other factors that may also contribute to the citation benefit are considered. We further conclude that, at least for gene expression microarray data, a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.
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            Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results

            Background The widespread reluctance to share published research data is often hypothesized to be due to the authors' fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically. Methods and Findings We related the reluctance to share research data for reanalysis to 1148 statistically significant results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on statistical significance. Conclusions Our findings on the basis of psychological papers suggest that statistical results are particularly hard to verify when reanalysis is more likely to lead to contrasting conclusions. This highlights the importance of establishing mandatory data archiving policies.
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              Deep impact: unintended consequences of journal rank

              Most researchers acknowledge an intrinsic hierarchy in the scholarly journals (“journal rank”) that they submit their work to, and adjust not only their submission but also their reading strategies accordingly. On the other hand, much has been written about the negative effects of institutionalizing journal rank as an impact measure. So far, contributions to the debate concerning the limitations of journal rank as a scientific impact assessment tool have either lacked data, or relied on only a few studies. In this review, we present the most recent and pertinent data on the consequences of our current scholarly communication system with respect to various measures of scientific quality (such as utility/citations, methodological soundness, expert ratings or retractions). These data corroborate previous hypotheses: using journal rank as an assessment tool is bad scientific practice. Moreover, the data lead us to argue that any journal rank (not only the currently-favored Impact Factor) would have this negative impact. Therefore, we suggest that abandoning journals altogether, in favor of a library-based scholarly communication system, will ultimately be necessary. This new system will use modern information technology to vastly improve the filter, sort and discovery functions of the current journal system.
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                Author and article information

                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000Research (London, UK )
                2046-1402
                12 November 2014
                2014
                : 3
                : 272
                Affiliations
                [1 ]Departments of Psychiatry, Neurology, Radiology, and Anatomy & Neurobiology, Washington University School of Medicine, St. Louis, MO, USA
                Author notes

                Competing interests: No competing interests were disclosed. Dr. Black is a (volunteer) member of the F1000Research Advisory Board.

                Article
                10.12688/f1000research.5664.1
                4288402
                25580234
                f676e6ce-3d0c-4189-bfff-7d86d06f8eeb
                Copyright: © 2014 Black KJ

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 November 2014
                Funding
                Funded by: National Institutes of Health
                Award ID: K24 MH087913
                This work was funded by National Institutes of Health (NIH) grant K24 MH087913. The content is solely the responsibility of the author and does not necessarily represent the official view of NIH.
                NIH had no role in the decision to publish or preparation of the manuscript.
                Categories
                Editorial
                Articles
                Motor Systems
                Movement Disorders

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