http://precedings.nature.com/tags/data%20sharing Recently posted documents tagged with 'data sharing' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3647.1 Ready access to data is a key concern in both basic research and problem-solving in the biological sciences, as the scale and scope of the questions that researchers ask expand, and as global problems demand data collected from around the world. With a grant from the National Science Foundation, from 2004 through 2009, the Ecological Society of America (ESA) has led a series of five workshops on data sharing, to help the ecology, evolution, and organismal biology communities find common ground on how to make data more readily discoverable and accessible in their own disciplines. The most recent of these focused in the development of incentives for data sharing, both at the individual and organizational level. This presentation will summarize the workshop recommendations, with a focus on preservation, curation, and access to data; access to analytical and visualization tools; and the need to make data archiving simple and routine. The roles of funders and publishers of research are also key and will be highlighted. Background/Question/MethodsReady access to data is a key concern in both basic research and problem-solving in the biological sciences, as the scale and scope of the questions that researchers ask expand, and as global problems demand data collected from around the world. With a grant from the National Science Foundation, from 2004 through 2009, the Ecological Society of America (ESA) has led a series of five workshops on data sharing, to help the ecology, evolution, and organismal biology communities find common ground on how to make data more readily discoverable and accessible in their own disciplines. The most recent of these focused in the development of incentives for data sharing, both at the individual and organizational level. Results/ConclusionsThis presentation will summarize the workshop recommendations, with a focus on preservation, curation, and access to data; access to analytical and visualization tools; and the need to make data archiving simple and routine. The roles of funders and publishers of research are also key and will be highlighted. http://dx.doi.org/10.1038/npre.2009.3647.1 Wed, 19 Aug 2009 09:23:08 UTC Development of Incentives for Data Sharing in Ecology, Evolution, and Organismal Biology doi:10.1038/npre.2009.3647.1 2009-08-19 Clifford S. Duke Nature Precedings 2009-08-19T09:23:08Z Presentation Ecology Evolutionary Biology http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3138.1 caArray is an open-source, open development, web and programmatically accessible array data management system developed at National Cancer Institute. It was developed to support the exchange of array data across the Cancer Biomedical Informatics Grid (caBIG™), a collaborative information network that connect scientists and practitioners through a shareable and interoperable infrastructure to share data and knowledge. caArray adopts a federated model of local installations, in which data deposited are shareable across caBIG™. Comprehensive in annotation yet easy to use has always been a challenge to any data repository system. To alleviate this difficulty, caArray accepts data upload using the MAGE-TAB, a spreadsheet-based format for annotating and communicating microarray data in a MIAME-compliant fashion (http://www.mged.org/mage-tab). MAGE-TAB is built on community standards – MAGE, MIAME, and Ontology. The components and work flow of MAGE-TAB files are organized in such a way which is already familiar to bench scientists and thus minimize the time and frustration of reorganizing their data before submission. The MAGE-TAB files are also structured to be machine readable so that they can be easily parsed into database. Users can control public access to experiment- and sample-level data and can create collaboration groups to support data exchange among a defined set of partners. All data submitted to caArray at NCI will go through strict curation by a group of scientists against these standards to make sure that the data are correctly annotated using proper controlled vocabulary terms and all required information are provided. Two of mostly used ontology sources are MGED ontology (http://mged.sourceforge.net/ontologies/MGEDontology.php) and NCI thesaurus (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). The purpose of data curation is to ensure easy comparison of results from different labs and unambiguous report of results. Data will also undergo automatic validation process before parsed into database, in which minimum information requirement and data consistency with the array designs are checked. Files with error found during validation are flagged with error message. Curators will re-examine those files and make necessary corrections before re-load the files. The iteration repeats until files are validated successfully. Data are then imported into the system and ready for access through the portal or through API. Interested parties are encouraged to review the installation package, documentation, and source code available from http://caarray.nci.nih.gov. http://dx.doi.org/10.1038/npre.2009.3138.1 Wed, 22 Apr 2009 21:17:19 UTC Data submission and curation for caArray, a standard based microarray data repository system doi:10.1038/npre.2009.3138.1 2009-04-22 X Bian Nature Precedings 2009-04-22T21:17:19Z Poster Cancer Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2176.1 Presented as part of the session titled “Sharing scientific data: who benefits?” at ESOF (The Euroscience Open Forum) 2008.Session abstract: Digital datasets—text-based, numeric, audio, video or image-based—form the output of all scientific disciplines. How are these data being made available for sharing? What quality control mechanisms are in place? What kinds of naming conventions, tags, and metadata are in use and how effective are they at helping to manage open data? Who is storing, archiving and curating open data and at which levels? And how is the production and sharing of open data assessed: what processes are in place for crediting scientists for making their raw data openly accessible for sharing and use? How much can and should data publication replace traditional forms of publication of research findings? http://dx.doi.org/10.1038/npre.2008.2176.1 Tue, 19 Aug 2008 19:56:47 UTC What are the ethical and social responsibilities of scientists? doi:10.1038/npre.2008.2176.1 2008-08-19 Philip Campbell Nature Precedings 2008-08-19T19:56:47Z Presentation Genetics & Genomics Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1721/version/2 Many policies and projects now encourage investigators to share their raw research data with other scientists. Unfortunately, it is difficult to measure the effectiveness of these initiatives because data can be shared in such a variety of mechanisms and locations. We propose a novel approach to find shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles. Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision. A simpler version of our classifier achieved higher recall (86%), though lower precision (49%). We believe our results demonstrate the feasibility of this approach and hope to inspire further study of dataset retrieval techniques and policy evaluation. http://precedings.nature.com/documents/1721/version/2 Mon, 04 Aug 2008 20:32:00 UTC Identifying Data Sharing in Biomedical Literature hdl:10101/npre.2008.1721.2 2008-08-04 Heather Piwowar Nature Precedings 2008-08-04T20:32:00Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2083.1 Molecular biology data are subject to terms of use that vary widely between databases and curating institutions. This research presents a taxonomy of contractual and technical restrictions applicable to databases in life science. It builds upon research led by Science Commons demonstrating why open data and the freedom to integrate facilitate innovation and how this openness can be achieved. The taxonomy describes technical and legal restrictions applicable to life science databases, and its metadata have been used to assess terms of use of databases hosted by Life Science Resource Name (LSRN) Schema. While a few public domain policies are standardized, most terms of use are not harmonized, difficult to understand and impose controls that prevent others from effectively reusing data. Identifying a small number of restrictions allows one to quickly appreciate which databases are open. A checklist for data openness is proposed in order to assist database curators who wish to make their data more open to make sure they do so. http://dx.doi.org/10.1038/npre.2008.2083.1 Fri, 18 Jul 2008 13:51:08 UTC Check Your Data Freedom: A Taxonomy to Assess Life Science Database Openness doi:10.1038/npre.2008.2083.1 2008-07-18 Melanie Dulong de Rosnay Nature Precedings 2008-07-18T13:51:08Z Manuscript Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1721/version/1 Many policies and projects now encourage investigators to share their raw research data with other scientists. Unfortunately, it is difficult to measure the effectiveness of these initiatives because data can be shared in such a variety of mechanisms and locations. We propose a novel approach to finding shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles. Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision. A simpler version of our classifier achieved higher recall (86%), though lower precision (49%). We believe our results demonstrate the feasibility of this approach and hope to inspire further study of dataset retrieval techniques and policy evaluation. http://precedings.nature.com/documents/1721/version/1 Tue, 25 Mar 2008 21:14:35 UTC Identifying Data Sharing in Biomedical Literature hdl:10101/npre.2008.1721.1 2008-03-25 Heather Piwowar Nature Precedings 2008-03-25T21:14:35Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1720/version/1 This module represents the formalized opinion of the authors and the CARMEN consortium, which identifies the minimum information required to report the use of electrophysiology in a neuroscience study, for submission to the CARMEN system (www.carmen.org.uk). http://precedings.nature.com/documents/1720/version/1 Tue, 25 Mar 2008 18:21:55 UTC Minimum Information about a Neuroscience Investigation (MINI) Electrophysiology hdl:10101/npre.2008.1720.1 2008-03-25 Frank Gibson Nature Precedings 2008-03-25T18:21:55Z Manuscript Neuroscience Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1700/version/1 Background: Sharing data is a tenet of science, yet commonplace in only a few subdisciplines. Recognizing that a data sharing culture is unlikely to be achieved without policy guidance, some funders and journals have begun to request and require that investigators share their primary datasets with other researchers. The purpose of this study is to understand the current state of data sharing policies within journals, the features of journals which are associated with the strength of their data sharing policies, and whether the strength of data sharing policies impact the observed prevalence of data sharing. Methods: We investigated these relationships with respect to gene expression microarray data in the journals that most often publish studies about this type of data. We measured data sharing prevalence as the proportion of papers with submission links from NCBI’s Gene Expression Omnibus (GEO) database. We conducted univariate and linear multivariate regressions to understand the relationship between the strength of data sharing policy and journal impact factor, journal subdiscipline, journal publisher (academic societies vs. commercial), and publishing model (open vs. closed access).Results: Of the 70 journal policies, 18 (26%) made no mention of sharing publication-related data within their Instruction to Author statements. Of the 42 (60%) policies with a data sharing policy applicable to microarrays, we classified 18 (26% of 70) as moderately strong and 24 (34% of 70) as strong.Existence of a data sharing policy was associated with the type of journal publisher: half of all commercial publishers had a policy compared to 82% of journals published by academic society. All four of the open-access journals had a data sharing policy. Policy strength was associated with impact factor: the journals with no data sharing policy, a weak policy, and a strong policy had respective median impact factors of 3.6, 4.5, and 6.0. Policy strength was positively associated with measured data sharing submission into the GEO database: the journals with no data sharing policy, a weak policy, and a strong policy had median data sharing prevalence of 11%, 19%, and 29% respectively.Conclusion: This review and analysis begins to quantify the relationship between journal policies and data sharing outcomes and thereby contributes to assessing the incentives and initiatives designed to facilitate widespread, responsible, effective data sharing. http://precedings.nature.com/documents/1700/version/1 Thu, 20 Mar 2008 21:00:20 UTC A review of journal policies for sharing research data hdl:10101/npre.2008.1700.1 2008-03-20 Heather A. Piwowar Nature Precedings 2008-03-20T21:00:20Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/