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Sharing Detailed Research Data Is Associated with Increased Citation Rate

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PLoS ONE

Public Library of Science

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      Abstract

      BackgroundSharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available.Principal FindingsWe examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression.SignificanceThis correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

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      Most cited references 40

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      Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

      The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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        Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

        Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.
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          ONCOMINE: a cancer microarray database and integrated data-mining platform.

          DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.
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            Author and article information

            Affiliations
            Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
            University of Ioannina School of Medicine, Greece
            Author notes
            * To whom correspondence should be addressed. E-mail: hpiwowar@ 123456cbmi.pitt.edu

            Conceived and designed the experiments: HP. Performed the experiments: HP. Analyzed the data: HP. Wrote the paper: HP. Other: Reviewed the data analysis and interpretation, reviewed the paper: RD Discussed the study motivation and scope, reviewed the paper: DF.

            Contributors
            Role: Academic Editor
            Journal
            PLoS ONE
            plos
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2007
            21 March 2007
            : 2
            : 3
            1817752
            17375194
            06-PONE-RA-00481R1
            10.1371/journal.pone.0000308
            (Academic Editor)
            Piwowar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Counts
            Pages: 5
            Categories
            Research Article
            Science Policy
            Genetics and Genomics/Cancer Genetics
            Genetics and Genomics/Gene Expression
            Molecular Biology/Bioinformatics
            Evidence-Based Healthcare/Statistical Methodologies and Health Informatics

            Uncategorized

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