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This license allows First Monday to publish a manuscript in a given issue. Authors have a choice of: 1. Dedicating the article to the public domain. This allows anyone to make any use of the article at any time, including commercial use. A good way to do this is to use the Creative Commons Public Domain Dedication Web form; see http://creativecommons.org/license/publicdomain-2?lang=en . 2. Retaining some rights while allowing some use. For example, authors may decide to disallow commercial use without permission. Authors may also decide whether to allow users to make modifications (e.g. translations, adaptations) without permission. A good way to make these choices is to use a Creative Commons license. * Go to http://creativecommons.org/license/ . * Choose and select a license. * What to do next — you can then e–mail the license html code to yourself. Do this, and then forward that e–mail to First Monday’s editors. 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India", "links": { "alternate": { "href": "https://www.zotero.org/groups/open_science_-_india", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/566911/items/F5FWBG5D", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/open_science_-_india/items/F5FWBG5D", "type": "text/html" } }, "meta": { "createdByUser": { "id": 3032656, "username": "rishikark", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/rishikark", "type": "text/html" } } }, "creatorSummary": "Kostkova et al.", "parsedDate": "2016", "numChildren": 0 }, "data": { "key": "F5FWBG5D", "version": 234, "itemType": "journalArticle", "title": "Who Owns the Data? Open Data for Healthcare", "creators": [ { "creatorType": "author", "firstName": "Patty", "lastName": "Kostkova" }, { "creatorType": "author", "firstName": "Helen", "lastName": "Brewer" }, { "creatorType": "author", "firstName": "Simon", "lastName": "de Lusignan" }, { "creatorType": "author", "firstName": "Edward", "lastName": "Fottrell" }, { "creatorType": "author", "firstName": "Ben", "lastName": "Goldacre" }, { "creatorType": "author", "firstName": "Graham", "lastName": "Hart" }, { "creatorType": "author", "firstName": "Phil", "lastName": "Koczan" }, { "creatorType": "author", "firstName": "Peter", "lastName": "Knight" }, { "creatorType": "author", "firstName": "Corinne", "lastName": "Marsolier" }, { "creatorType": "author", "firstName": "Rachel A.", "lastName": "McKendry" }, { "creatorType": "author", "firstName": "Emma", "lastName": "Ross" }, { "creatorType": "author", "firstName": "Angela", "lastName": "Sasse" }, { "creatorType": "author", "firstName": "Ralph", "lastName": "Sullivan" }, { "creatorType": "author", "firstName": "Sarah", "lastName": "Chaytor" }, { "creatorType": "author", "firstName": "Olivia", "lastName": "Stevenson" }, { "creatorType": "author", "firstName": "Raquel", "lastName": "Velho" }, { "creatorType": "author", "firstName": "John", "lastName": "Tooke" } ], "abstractNote": "Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such open data can shed light on the causes of disease and effects of treatment, including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual’s characteristics, known as personalized or “stratified medicine.” Developments, such as crowdsourcing, participatory surveillance, and individuals pledging to become “data donors” and the “quantified self” movement (where citizens share data through mobile device-connected technologies), have great potential to contribute to our knowledge of disease, improving diagnostics, and delivery of ­healthcare and treatment. There is not only a great potential but also major concerns over privacy, confidentiality, and control of data about individuals once it is shared. Issues, such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, are becoming increasingly scrutinized at national and international levels. This can be seen in the recent backlash over the proposed implementation of care.data, which enables individuals’ NHS data to be linked, retained, and shared for other uses, such as research and, more controversially, with businesses for commercial exploitation. By way of contrast, through increasing popularity of social media, GPS-enabled mobile apps and tracking/wearable devices, the IT industry and MedTech giants are pursuing new projects without clear public and policy discussion about ownership and responsibility for user-generated data. In the absence of transparent regulation, this paper addresses the opportunities of Big Data in healthcare together with issues of responsibility and accountability. It also aims to pave the way for public policy to support a balanced agenda that safeguards personal information while enabling the use of data to improve public health.", "publicationTitle": "Digital Health", "publisher": "", "place": "", "date": "2016", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "7", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Front. 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Discovering, integrating and analyzing massive amounts of heterogeneous data are central to ecology as researchers address complex questions at scales from the gene to the biosphere. Ecoinformatics offers tools and approaches for managing ecological data and transforming the data into information and knowledge. Here, we review the state-of-the-art and recent advances in ecoinformatics that can benefit ecologists and environmental scientists as they tackle increasingly challenging questions that require voluminous amounts of data across disciplines and scales of space and time. 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