Heather A. Piwowar and Todd J. Vision

“ Here, we perform a large multivariate analysis of the citation differential for studies in which gene expression microarray data either was or was not made available in a public repository.
Estimating the citation differential is not enough: crafting good data archiving policy requires an understanding of its origins. How quickly do data reuse citations accrue? Do the additional citations arise due to data reuse – as we might expect – or simply from increased exposure or trust in the original study? How often do data reuse studies attribute data from more than one source? ”

“ Working through the one area where incentives between scientific good and author incentives conflict, – finding weaknesses or faults in published research – may require mandates. Or, instead, the research community may eventually come to associate withheld data with poor quality research, as it does today for findings that are not disclosed in a peer-reviewed paper (Ware, 2008) .
The citation benefit observed in the current study is consistent with data reuse found in this study and the small-scale annotation reported in Rung & Brazma (2013) . ”

“ This finding reinforces the conclusions of an earlier analysis: even modest data reuse can provide an impressive return on investment for science funders (Piwowar, Vision & Whitlock, 2011b) .
Finally, we observed a moderate proportion of datasets being reused by third parties (more than 20% of the datasets deposited between 2003 and 2007) . It is important to recognize that this is likely a gross underestimate. It includes only those instances of reuse that can be recognized through the mention of accession number in PubMed Central. ”

“ Publicly sharing detailed research data – sample attributes, clinical factors, patient outcomes, DNA sequences, raw mRNA microarray measurements – with other researchers allows these valuable resources to contribute far beyond their original analysis. In addition to being used to confirm original results, raw data can be used to explore related or new hypotheses, particularly when combined with other publicly available data sets. Real data is indispensable when investigating and developing study methods, analysis techniques, and software implementations. ”

“ The larger scientific community also benefits: sharing data encourages multiple perspectives, helps to identify errors, discourages fraud, is useful for training new researchers, and increases efficient use of funding and patient population resources by avoiding duplicate data collection.
Making research data publicly available also has challenges and costs. Some costs are borne by society: For example, data archives must be created and maintained. Many costs, however, are borne by the data-collecting investigators: Data must be documented, formatted, and uploaded. ”

“ Importantly, almost all of these mechanisms are aligned with more efficient and effective scientific progress: increased data use, facilitated credibility determination, earlier access, improved discoverability, and a focus on best work through data availability are good for both investigators and the science community as a whole. ”

“ Our understanding of research practice suffers because access to tools and data is so difficult.
Patterns of data reuse To better understand patterns of data reuse, a larger sample of reuse instances was needed than could easily be assembled through manual classification of citation context. To that end, we used a complementary source of information about reuse of the same datasets: direct mention of GEO or ArrayExpress accession numbers within the body of a full-text research article. ”

“ Fewer covariates could be included in these yearly regressions because they included fewer datapoints than the full regression. The yearly regressions included date of publication, journal that published the study, journal impact factor, journal’s open access policy, number of authors of the study, citation score of the institution of the corresponding author, previous number of PubMed Central citations received by the first and last author, whether the topic was cancer, and whether the study used animals. ”

“ Future work Future work could improve on these results by considering and integrating all methods of data use attribution. This holistic effort would include identifying citations to the paper that describes the data collection, mentions of the dataset identifier itself – whether in full text, the references section, or supplementary information – citations to the dataset as a first-class research object, and even mentions of the data collection investigators in acknowledgement sections. ”

“ Building upon this work, we suggest several possible sources for an “Open Data citation benefit”:
Data Reuse. Papers with available datasets can be used in ways that papers without data cannot, and may receive additional citations as a result. Credibility Signalling. The credibility of research findings may be higher for research papers with available data. Such papers may be preferentially chosen as background citations or the foundation of additional research. Increased Visibility. ”

“ To explore which variables to include in these regressions, we investigated correlations between the number of citations and a set of candidate variables. We also calculated correlations amongst all variables to investigate collinearity. We explored a subset of the 124 attributes from Piwowar (2011a) previously shown or suspected to correlate with citation rate. We selected covariates found to have a strong pairwise correlation (positive or negative) with citation rate, using Pearson correlations for numeric variables and polyserial correlations for binary and categorical variables. ”

“ The large number of instances of reuse identified this way allowed us to ask questions about the distribution of reuse over time and across datasets. The results indicate that dataset reuse has been increasing over time (excluding the initial years of GEO and ArrayExpress when few datasets were deposited and reuse appears to have been atypically broad) . ”

“ For example, links from a data repository to a paper may increase the search ranking of the research paper. Early View. When data is made available before a paper is published, some citations may accrue earlier than they would otherwise because of accelerated awareness of the methods, findings, and so on. Selection Bias. Authors may be more likely to publish data for papers they judge to be their best quality work, because they are particularly proud or confident of the results (Wicherts, Bakker & Molenaar, 2011) . ”

“ Packages used included reshape2 (Wickham, 2007) , plyr (Wickham, 2011) , rms (Harrell, 2012) , polycor (Fox, 2010) , ascii (Hajage, 2011) , ggplot2 (Wickham, 2009) , gplots (Warnes et al., 2012) , knitr (Xie, 2012) , and knitcitations (Boettiger, 2013) . ”

“ Repeatability of published microarray gene expression analyses. Nature Genetics 41 (2) : 149–155 DOI 10.1038/ng.295
Mooney H, Newton MP. 2012. The anatomy of a data citation: discovery, reuse, and credit. Journal of Librarianship and Scholarly Communication 1 (1) : eP1035 DOI 10.7710/2162-3309.1035
Pienta AM, Alter GC, Lyle JA. 2010. The enduring value of social science research: the use and reuse of primary research data. The Organisation, Economics and Policy of Scientific Research Workshop http://hdl.handle.net/2027.42/78307
Piwowar HA. 2011a. Who shares? Who doesn’t? ”