18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Data visualization, bar naked: A free tool for creating interactive graphics

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Although bar graphs are designed for categorical data, they are routinely used to present continuous data in studies that have small sample sizes. This presentation is problematic, as many data distributions can lead to the same bar graph, and the actual data may suggest different conclusions from the summary statistics. To address this problem, many journals have implemented new policies that require authors to show the data distribution. This paper introduces a free, web-based tool for creating an interactive alternative to the bar graph ( http://statistika.mfub.bg.ac.rs/interactive-dotplot/). This tool allows authors with no programming expertise to create customized interactive graphics, including univariate scatterplots, box plots, and violin plots, for comparing values of a continuous variable across different study groups. Individual data points may be overlaid on the graphs. Additional features facilitate visualization of subgroups or clusters of non-independent data. A second tool enables authors to create interactive graphics from data obtained with repeated independent experiments ( http://statistika.mfub.bg.ac.rs/interactive-repeated-experiments-dotplot/). These tools are designed to encourage exploration and critical evaluation of the data behind the summary statistics and may be valuable for promoting transparency, reproducibility, and open science in basic biomedical research.

          Related collections

          Most cited references 21

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm

          Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Public Availability of Published Research Data in High-Impact Journals

            Background There is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature. Methods and Results We reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available. Conclusion A substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis?

               Stanley Lazic (2010)
              Background Pseudoreplication occurs when observations are not statistically independent, but treated as if they are. This can occur when there are multiple observations on the same subjects, when samples are nested or hierarchically organised, or when measurements are correlated in time or space. Analysis of such data without taking these dependencies into account can lead to meaningless results, and examples can easily be found in the neuroscience literature. Results A single issue of Nature Neuroscience provided a number of examples and is used as a case study to highlight how pseudoreplication arises in neuroscientific studies, why the analyses in these papers are incorrect, and appropriate analytical methods are provided. 12% of papers had pseudoreplication and a further 36% were suspected of having pseudoreplication, but it was not possible to determine for certain because insufficient information was provided. Conclusions Pseudoreplication can undermine the conclusions of a statistical analysis, and it would be easier to detect if the sample size, degrees of freedom, the test statistic, and precise p-values are reported. This information should be a requirement for all publications.
                Bookmark

                Author and article information

                Journal
                J Biol Chem
                J. Biol. Chem
                jbc
                jbc
                JBC
                The Journal of Biological Chemistry
                American Society for Biochemistry and Molecular Biology (11200 Rockville Pike, Suite 302, Rockville, MD 20852-3110, U.S.A. )
                0021-9258
                1083-351X
                15 December 2017
                3 October 2017
                3 October 2017
                : 292
                : 50
                : 20592-20598
                Affiliations
                From the []Division of Nephrology and Hypertension and
                the []Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota 55905 and
                the [§ ]Department of Medical Statistics and Informatics, Medical Faculty, University of Belgrade, Belgrade 11000, Serbia
                Author notes
                [2 ] Supported by Office of Research on Women's Health Grant BIRCWH K12HD065987 and American Heart Association Grant 16GRNT30950002. To whom correspondence should be addressed: Division of Nephrology and Hypertension, Mayo Clinic, 200 First St. SW, RO-HA-06-675-11, Rochester, MN 55905. Tel.: 507-266-2844; Fax: 507-266-7891; E-mail: weissgerber.tracey@ 123456mayo.edu .
                [1]

                Both authors contributed equally to this work.

                [3]

                Senior co-authors.

                Edited by Roger J. Colbran

                Article
                RA117.000147
                10.1074/jbc.RA117.000147
                5733595
                28974579
                c649ac6f-36b6-4ad0-963e-538e2d5fb760
                © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

                Author's Choice—Final version free via Creative Commons CC-BY license.

                Product
                Funding
                Funded by: HHS NIH Office of Research on Women's Health (ORWH) , open-funder-registry 10.13039/100000124;
                Award ID: K12HD065987
                Funded by: HHS NIH National Center for Advancing Translational Sciences (NCATS) , open-funder-registry 10.13039/100006108;
                Award ID: UL1 TR000135
                Funded by: American Heart Association (AHA) , open-funder-registry 10.13039/100000968;
                Award ID: 16GRNT30950002
                Categories
                Methods and Resources

                Biochemistry

                bar graphs, data visualization, interactive graphics, open science, transparency

                Comments

                Comment on this article