http://precedings.nature.com/tags/sharing%20data Recently posted documents tagged with 'sharing data' 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.2008.2537.1 Tuberculosis is a ravaging disease killing one person in every 1.5 minutes in India alone. Someone in the world is newly infected with TB bacilli every second. Overall, one-third of the world’s population is currently infected with the TB bacillus. Left untreated, each person with active TB disease will infect on average between 10 and 15 people every year. 450 000 new MDR-TB cases are estimated to occur every year. Estimated 5% of TB patients are HIV infected. Although the complete genome sequence of the causative pathogen Mycobacterium tuberculosis was published about a decade ago and many years of painstaking efforts have been invested, we are still far from having a good, fast acting drug and vaccine which confers long lasting protection. Despite increasing investment, led by charities including the Gates Foundation, no novel drugs for TB have entered the market for many years.Novel mechanisms for attracting funding, particularly private sector funding, into this research area are desperately needed. We wish to bring in the power of genomics, computational technologies and participation of young and brilliant talent from Universities and Industrial partners with a strong inclination to apply a concerted effort to address this important scourge. All researchers contribute data on tuberculosis drug targets and active molecules through a copyleft agreement; anyone who is prepared to keep to this may participate. All the data is Clickwrap protected and credit sharing will be based on a novel and flexible micro-attribution system. This system is aimed at providing due credit through an active process. Various levels of investigators shall have appropriate levels of rights, recognition and responsibilities. Another valuable aspect is the partnerships of Industry with belief in Open Source systems and models. We name this concerted effort to tackle this dreaded disease as Open Source Drug Discovery for Tuberculosis. http://dx.doi.org/10.1038/npre.2008.2537.1 Mon, 01 Dec 2008 22:11:37 UTC Open Source Drug Discovery doi:10.1038/npre.2008.2537.1 2008-12-01 Jyoti Yadav Nature Precedings 2008-12-01T22:11:37Z Poster Pharmacology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2007.425.3 Does your research area re-use shared datasets?* Re-using data has many benefits, including research synergy and efficient resource use* Some research areas have tools, communities, and practices which facilitate re-use* Identifying these areas will allow us to learn from them, and apply the lessons to areas which underutilize the sharing and re-purposing of scientific data between investigators Which datasets?This preliminary analysis examines the re-use of microarray gene expression datasets. Thousands of microarray gene expression datasets have been deposited in publicly available databases. Many studies reuse this data, but it is not well understood for what purposes. Here, we examined all publications found in PubMed Central on April 1, 2007 whose full-text contained the phrases “microarray” and “gene expression” to find studies which re-used microarray data. How did we identify re-use?We developed prototype machine-learning classifiers to identify a) studies containing original microarray data (n=900) and b) studies which instead re-used microarray data (n=250). Preprocessing (Python NLTK) extracted manually-selected keyword frequencies from the full-text publications as features for a Support Vector Machine (SVMlite). The classifier was trained and tested on a manually-labeled set of documents (PLoS articles prior to January 2007 containing the word “microarray,” n=200). How did we identify patterns of re-use?We compared the Medical Subject Heading (MeSH) of the two classes to estimate the odds that a specific MeSH term would be used given all studies with original microarray data, compared to the odds of the same term describing studies with re-used data. Terms were truncated to comparable levels in the MeSH hierarchy. ResultsPublications with original vs. re-used microarray data have different distributions of MeSH terms (Figure 1), and occur in different proportions across various journals (Figure 2). Microarray data source (original vs. re-used) did not affect the odds of a study focusing on humans, mice, or invertebrates, whereas publications with re-used data did involve a relatively high proportion of studies involving fungi (odds ratio (OR)=2.4), and a relatively low proportion involving rats, bacteria, viruses, plants, or genetically-altered or inbred animals (OR Trends in odds ratios of MeSH terms for other attributes can be seen in Figure 3. HopeAlthough not all research topics can be addressed by re-using existing data, many can. Identifying areas with frequent re-use can highlight best practices to be used when developing research agendas, tools, standards, repositories, and communities in areas which have yet to receive major benefits from shared data. Future WorkWe plan to refine our tool for identifying studies which re-use data, and continue studying and measuring re-use and reusability. http://dx.doi.org/10.1038/npre.2007.425.3 Wed, 18 Jul 2007 13:26:38 UTC Examining the uses of shared data doi:10.1038/npre.2007.425.3 2007-07-18 Heather A. Piwowar Nature Precedings 2007-07-18T13:26:38Z Poster Bioinformatics http://creativecommons.org/licenses/by/2.5/