{"id":333,"date":"2018-07-16T22:49:46","date_gmt":"2018-07-16T14:49:46","guid":{"rendered":"http:\/\/140.112.114.25\/TADH\/?p=333"},"modified":"2018-07-16T22:49:46","modified_gmt":"2018-07-16T14:49:46","slug":"%e3%80%90%e5%be%b5%e7%a8%bf%e5%be%b5%e4%bb%b6%e3%80%91computational-archival-science-workshop-at-ieee-big-data-2018-call-for-papers","status":"publish","type":"post","link":"https:\/\/tadh.org.tw\/en\/2018\/07\/16\/%e3%80%90%e5%be%b5%e7%a8%bf%e5%be%b5%e4%bb%b6%e3%80%91computational-archival-science-workshop-at-ieee-big-data-2018-call-for-papers\/","title":{"rendered":"\u3010\u5fb5\u7a3f\u5fb5\u4ef6\u3011Computational Archival Science Workshop at IEEE Big Data 2018 \u2013 call for papers"},"content":{"rendered":"

The organizers of the\u00a0Computational\u00a0Archival\u00a0Science\u00a0(CAS)\u00a0Workshop\u00a0at\u00a0IEEE Big Data 2018<\/a>\u00a0have issued a formal call for papers. This is the 3rd\u00a0workshop\u00a0at IEEE Big Data addressing CAS, following on from workshops in\u00a02016<\/a>\u00a0and\u00a02017<\/a>. All papers accepted for the\u00a0workshop\u00a0will be included in the Conference Proceedings published by the IEEE Computer Society Press, made available at the conference, which takes place Dec. 10 \u2013 13, 2018 in Seattle, WA, USA.<\/p>\n

The\u00a0workshop\u00a0will explore the conjunction (and its consequences) of emerging methods and technologies around big data with\u00a0archival\u00a0practice and new forms of analysis and historical, social, scientific, and cultural research engagement with archives. We aim to identify and evaluate current trends, requirements, and potential in these areas, to examine the new questions that they can provoke, and to help determine possible research agendas for the evolution of\u00a0computational\u00a0archival\u00a0science\u00a0in the coming years. At the same time, we will address the questions and concerns scholarship is raising about the interpretation of \u2018big data\u2019 and the uses to which it is put, in particular appraising the challenges of producing quality \u2013 meaning, knowledge and value \u2013 from quantity, tracing data and analytic provenance across complex \u2018big data\u2019 platforms and knowledge production ecosystems, and addressing data privacy issues.<\/p>\n

Important dates:<\/p>\n