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Together they advance new thinking about digital environments.   Contributors: Wendy Warren Austin, Edinboro U; Jim Bizzocchi, Simon Fraser U; Collin Gifford Brooke, Syracuse U; Paul Cesarini, Bowling Green State U; Veronique Chance, U of London; Johanna Drucker, U of Virginia; Jenny Edbauer, Penn State U; Robert A. Emmons Jr., Rutgers U; Johndan Johnson-Eilola, Clarkson U; Richard Kahn, UCLA; Douglas Kellner, UCLA; Karla Saari Kitalong, U of Central Florida; Steve Mann, U of Toronto; Lev Manovich, U of California, San Diego; Adrian Miles, RMIT U; Jason Nolan, Ryerson U; Julian Oliver; Mark Paterson, U of the West of England, Bristol; Isabel Pedersen, Ryerson U; Michael Pennell, U of Rhode Island; Joanna Castner Post, U of Central Arkansas; Teri Rueb, Rhode Island School of Design; James J. Sosnoski; Lance State, Fordham U; Jason Swarts, North Carolina State U; Barry Wellman, U of Toronto; Sean D. Williams, Clemson U; Jeremy Yuille, RMIT U.  Byron Hawk is assistant professor of English at George Mason University.David M. Rieder is assistant professor of English at North Carolina State University.Ollie Oviedo is associate professor of English at Eastern New Mexico University.", "bookTitle": "Small Tech: The Culture of Digital Tools", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "24 Jan. 2008", "publisher": "University of Minnesota Press", "place": "Minneapolis", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "", "ISBN": "978-0-8166-4978-5", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Amazon", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2021-04-12T10:50:52Z", "dateModified": "2021-04-12T10:55:38Z" } }, { "key": "I6273K3Z", "version": 172, "library": { "type": "group", "id": 401870, "name": "Data Visualisation", "links": { "alternate": { "href": "https://www.zotero.org/groups/data_visualisation", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/401870/items/I6273K3Z", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/data_visualisation/items/I6273K3Z", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/401870/items/JVUXCHA3", "type": "application/json" } }, "meta": { "createdByUser": { "id": 30496, "username": "jwyg", "name": "Jonathan W. 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Y. Gray", "links": { "alternate": { "href": "https://www.zotero.org/jwyg", "type": "text/html" } } }, "creatorSummary": "Jacomy et al.", "parsedDate": "2014-06-10", "numChildren": 1 }, "data": { "key": "B477VV92", "version": 165, "itemType": "journalArticle", "title": "ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software", "creators": [ { "creatorType": "author", "firstName": "Mathieu", "lastName": "Jacomy" }, { "creatorType": "author", "firstName": "Tommaso", "lastName": "Venturini" }, { "creatorType": "author", "firstName": "Sebastien", "lastName": "Heymann" }, { "creatorType": "author", "firstName": "Mathieu", "lastName": "Bastian" } ], "abstractNote": "Gephi is a network visualization software used in various disciplines (social network analysis, biology, genomics…). One of its key features is the ability to display the spatialization process, aiming at transforming the network into a map, and ForceAtlas2 is its default layout algorithm. The latter is developed by the Gephi team as an all-around solution to Gephi users’ typical networks (scale-free, 10 to 10,000 nodes). We present here for the first time its functioning and settings. ForceAtlas2 is a force-directed layout close to other algorithms used for network spatialization. We do not claim a theoretical advance but an attempt to integrate different techniques such as the Barnes Hut simulation, degree-dependent repulsive force, and local and global adaptive temperatures. It is designed for the Gephi user experience (it is a continuous algorithm), and we explain which constraints it implies. The algorithm benefits from much feedback and is developed in order to provide many possibilities through its settings. We lay out its complete functioning for the users who need a precise understanding of its behaviour, from the formulas to graphic illustration of the result. We propose a benchmark for our compromise between performance and quality. We also explain why we integrated its various features and discuss our design choices.", "publicationTitle": "PLOS ONE", "publisher": "", "place": "", "date": "Jun 10, 2014", "volume": "9", "issue": "6", "section": "", "partNumber": "", "partTitle": "", "pages": "e98679", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "PLOS ONE", "DOI": "10.1371/journal.pone.0098679", "citationKey": "", "url": "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098679", "accessDate": "2018-01-14T17:34:24Z", "PMID": "", "PMCID": "", "ISSN": "1932-6203", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "PLoS Journals", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Convergent evolution", "type": 1 }, { "tag": "Gravitation", "type": 1 }, { "tag": "Network analysis", "type": 1 }, { "tag": "Protein interaction networks", "type": 1 }, { "tag": "Scale-free networks", "type": 1 }, { "tag": "Simulated annealing", "type": 1 }, { "tag": "Social networks", "type": 1 } ], "collections": [ "AE3UCVVS" ], "relations": {}, "dateAdded": "2018-05-11T18:13:04Z", "dateModified": "2018-05-11T18:13:04Z" } }, { "key": "GJCGN94F", "version": 165, "library": { "type": "group", "id": 401870, "name": "Data Visualisation", "links": { "alternate": { "href": "https://www.zotero.org/groups/data_visualisation", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/401870/items/GJCGN94F", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/data_visualisation/items/GJCGN94F", "type": "text/html" } }, "meta": { "createdByUser": { "id": 30496, "username": "jwyg", "name": "Jonathan W. 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Y. Gray", "links": { "alternate": { "href": "https://www.zotero.org/jwyg", "type": "text/html" } } }, "creatorSummary": "Venturini et al.", "parsedDate": "2018", "numChildren": 2 }, "data": { "key": "RE2NSU34", "version": 165, "itemType": "bookSection", "title": "Visual Network Exploration for Data Journalists", "creators": [ { "creatorType": "author", "firstName": "Tommaso", "lastName": "Venturini" }, { "creatorType": "author", "firstName": "Mathieu", "lastName": "Jacomy" }, { "creatorType": "author", "firstName": "Liliana", "lastName": "Bounegru" }, { "creatorType": "author", "firstName": "Jonathan", "lastName": "Gray" }, { "creatorType": "editor", "firstName": "Scott", "lastName": "Eldridge II" }, { "creatorType": "editor", "firstName": "Bob", "lastName": "Franklin" } ], "abstractNote": "Networks are classic but under-acknowledged figures of journalistic storytelling. Who is connected to whom and by which means? Which organizations receive support from which others? What resources or information circulate through which channels and which intermediaries enable and regulate their flows? These are all customary stories and lines of inquiry in journalism and they all have to do with networks. Additionally, the recent spread of digital media has increasingly confronted journalists with information coming not only in the traditional form of statistic tables, but also of relational databases. Yet, journalists have so far made little use of the analytical resources offered by networks. To address this problem in this chapter we examine how “visual network exploration” may be brought to bear in the context of data journalism in order to explore, narrate and make sense of large and complex relational datasets. We borrow the more familiar vocabulary of geographical maps to show how key graphical variables such as position, size and hue can be used to interpret and characterise graph structures and properties. We illustrate this technique by taking as a starting point a recent example from journalism, namely a catalogue of French information sources compiled by Le Monde’s The Decodex. We establish that good visual exploration of networks is an iterative process where practices to demarcate categories and territories are entangled and mutually constitutive. To enrich investigation we suggest ways in which the insights of the visual exploration of networks can be supplemented with simple calculations and statistics of distributions of nodes and links across the network. We conclude with reflection on the knowledge-making capacities of this technique and how these compare to the insights and instruments that journalists have used in the Decodex project – suggesting that visual network exploration is a fertile area for further exploration and collaborations between data journalists and digital researchers.", "bookTitle": "The Routledge Handbook to Developments in Digital Journalism Studies", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2018", "publisher": "Routledge", "place": "Abingdon, UK", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "", "ISBN": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "2018-01-20T14:18:08Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "papers.ssrn.com", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "data journalism" }, { "tag": "networks" }, { "tag": "visual network analysis" }, { "tag": "visual network exploration" } ], "collections": [ "AE3UCVVS" ], "relations": {}, "dateAdded": "2018-05-11T18:12:34Z", "dateModified": "2018-05-11T18:12:34Z" } }, { "key": "F8GA5LBE", "version": 169, "library": { "type": "group", "id": 401870, "name": "Data Visualisation", "links": { "alternate": { "href": "https://www.zotero.org/groups/data_visualisation", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/401870/items/F8GA5LBE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/data_visualisation/items/F8GA5LBE", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/401870/items/HJFCP2EY", "type": "application/json" } }, "meta": { "createdByUser": { "id": 30496, "username": "jwyg", "name": "Jonathan W. 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Y. Gray", "links": { "alternate": { "href": "https://www.zotero.org/jwyg", "type": "text/html" } } }, "creatorSummary": "Bounegru et al.", "parsedDate": "2017-07-03", "numChildren": 1 }, "data": { "key": "HJFCP2EY", "version": 164, "itemType": "journalArticle", "title": "Narrating Networks", "creators": [ { "creatorType": "author", "firstName": "Liliana", "lastName": "Bounegru" }, { "creatorType": "author", "firstName": "Tommaso", "lastName": "Venturini" }, { "creatorType": "author", "firstName": "Jonathan", "lastName": "Gray" }, { "creatorType": "author", "firstName": "Mathieu", "lastName": "Jacomy" } ], "abstractNote": "Networks have become the de facto diagram of the Big Data age (try searching Google Images for [big data AND visualisation] and see). The concept of networks has become central to many fields of human inquiry and is said to revolutionise everything from medicine to markets to military intelligence. While the mathematical and analytical capabilities of networks have been extensively studied over the years, in this article we argue that the storytelling affordances of networks have been comparatively neglected. In order to address this we use multimodal analysis to examine the stories that networks evoke in a series of journalism articles. We develop a protocol by means of which narrative meanings can be construed from network imagery and the context in which it is embedded, and discuss five different kinds of narrative readings of networks, illustrated with analyses of examples from journalism. 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We draw on an empirical study to show a wide range of emotional engagements with diverse aspects of data and their visualisation, and so demonstrate the importance of emotions as vital components of making sense of data. We nuance the argument that regimes of datafication, in which numbers, metrics and statistics dominate, are characterised by a renewed faith in objectivity and rationality, arguing that in datafied times, it is not only numbers but also the feeling of numbers that is important. 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