LEADER 05165nam 22006975 450 001 9910254835903321 005 20200630142226.0 010 $a3-319-59186-X 024 7 $a10.1007/978-3-319-59186-5 035 $a(CKB)4340000000223229 035 $a(DE-He213)978-3-319-59186-5 035 $a(MiAaPQ)EBC5164444 035 $a(PPN)221253246 035 $a(EXLCZ)994340000000223229 100 $a20171128d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig Data Factories $eCollaborative Approaches /$fedited by Sorin Adam Matei, Nicolas Jullien, Sean P. Goggins 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (VI, 141 p. 18 illus., 14 illus. in color.) 225 1 $aComputational Social Sciences,$x2509-9574 311 $a3-319-59185-1 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aChapter1. Introduction -- Part 1: Theoretical Principles and Approaches to Data Factories --  Chapter2. Accessibility and Flexibility: Two Organizing Principles for Big Data Collaboration -- Chapter3. The Open Community Data Exchange: Advancing Data Sharing and Discovery in Open Online Community Science -- Part 2: Theoretical principles and ideas for designing and deploying data factory approaches -- Chapter4. Levels of Trace Data for Social and Behavioral Science Research -- Chapter5. The 10 Adoption Drivers of Open Source Software that Enables e-Research in Data Factories for Open Innovations -- Chapter6. Aligning online social collaboration data around social order: theoretical considerations and measures -- Part 3: Approaches in action through case studies of data based research, best practice scenarios, or educational briefs -- Chapter7. Lessons learned from a decade of FLOSS data collection -- Chapter8. Teaching Students How (NOT) to Lie, Manipulate, and Mislead with Information Visualizations -- Chapter9. Democratizing Data Science: The Community Data Science Workshops and Classes. 330 $aThe book proposes a systematic approach to big data collection, documentation and development of analytic procedures that foster collaboration on a large scale. This approach, designated as ?data factoring? emphasizes the need to think of each individual dataset developed by an individual project as part of a broader data ecosystem, easily accessible and exploitable by parties not directly involved with data collection and documentation. Furthermore, data factoring uses and encourages pre-analytic operations that add value to big data sets, especially recombining and repurposing. The book proposes a research-development agenda that can undergird an ideal data factory approach. Several programmatic chapters discuss specialized issues involved in data factoring (documentation, meta-data specification, building flexible, yet comprehensive data ontologies, usability issues involved in collaborative tools, etc.). The book also presents case studies for data factoring and processing that can lead to building better scientific collaboration and data sharing strategies and tools. Finally, the book presents the teaching utility of data factoring and the ethical and privacy concerns related to it. Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com. 410 0$aComputational Social Sciences,$x2509-9574 606 $aData mining 606 $aBig data 606 $aBioinformatics 606 $aApplication software 606 $aResearch?Moral and ethical aspects 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aBioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15001 606 $aComputer Appl. in Social and Behavioral Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/I23028 606 $aResearch Ethics$3https://scigraph.springernature.com/ontologies/product-market-codes/E14040 615 0$aData mining. 615 0$aBig data. 615 0$aBioinformatics. 615 0$aApplication software. 615 0$aResearch?Moral and ethical aspects. 615 14$aData Mining and Knowledge Discovery. 615 24$aBig Data/Analytics. 615 24$aBioinformatics. 615 24$aComputer Appl. in Social and Behavioral Sciences. 615 24$aResearch Ethics. 676 $a005.7 702 $aMatei$b Sorin Adam$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJullien$b Nicolas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGoggins$b Sean P$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254835903321 996 $aBig Data Factories$92545046 997 $aUNINA