[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-136127-en":3,"doc-seo-136127-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},136127,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","How to Compare One Million Images - NEH/NSF Digging into Data and Visual Analytics","The text examines how scale transforms humanities and social science research when scholars gain access to massive digitized repositories and when everyday cultural production grows at unprecedented speed. It frames the NEH/NSF Digging into Data questions about what “data” means for language, history, and the arts. Using the Software Studies Initiative at UCSD/Calit2 as context, it proposes a two-part approach: automatic image analysis that generates numerical descriptions, followed by visualizations that organize complete image sets to reveal patterns and relations across very large corpora.","1/40  \nHow to Compare One Million Images?  \nDr. Lev Manovich,  \nProfessor, Visual Arts Department, University of California, San Diego (UCSD) . Director, Software Studies Initiative ([softwarestudies.com](softwarestudies.com)) at California Institute for Telecommunication and Information (Calit2) .  \nDr. Jeremy Douglass,  \nPost‐doctoral Researcher, Software Studies Initiative.  \nTara Zepel,  \nPh.D. candidate, Art History, Theory, & Criticism, UCSD.  \nExploring one million manga pages on the 287 megapixel HIPerSpace (The Highly Interactive Parallelized Display Space) at Calit2, San Diego. HIPerSpace offers 35,840 x 8,000 pixels resolution (287 megapixels) on 31.8 feet wide and 7.5 feet tall display wall made from 70 30‐inch monitors.  \n2/40  \nINTRODUCTION  \nThe description of joint NEH/NSF Digging into Data competition (2009) organized by Office of Digital Humanities at the National Endowment of Humanities (the U.S. federal agency which funds humanities research) opened with these questions: “How does the notion of scale affect humanities and social science research? Now that scholars have access to huge repositories of digitized data—far more than they could read in a lifetime—what does that mean for research?” A year, later, an article in New York Time (November 16, 2010) stated: “The next big idea in language, history and the arts? Data.”  \nWhile digitized archives of historical documents certainly represent a jump in scale in comparison to traditionally small corpora used by humanists, researchers and critics interested in contemporary culture have even a larger challenge. With the notable exception of Google Books, the size of digital historical archives pales in comparison to the quantity of digital media created by contemporary cultural producers and users – designs, motion graphics, web sites, blogs, YouTube videos, Flickr photos, Facebook postings, Twitter messages, and other kinds of professional and participatory media. This quantitative change is as at least as important as the other fundamental effects of the political, technological and social processes that start after the end of the Cold War (for instance, free long‐distance multimedia communication) . In an earlier article I described this in the following way:  \nThe exponential growth of a number of both non‐professional and professional media producers over the last decade has created a fundamentally new cultural situation and a challenge to our normal ways of tracking and studying culture. Hundreds of millions of people are routinely creating and sharing cultural content ‐ blogs, photos, videos, online comments and discussions, etc. At the same time, the rapid growth of professional educational and cultural institutions in many newly globalized countries along with the instant availability of cultural news over the web and ubiquity of media and design software has also dramatically increased the number of culture professionals who participate in global cultural production and discussions. Before, cultural theorists and historians could generate theories and histories based on small data sets (for instance, \"Italian Renaissance,\" \"classical Hollywood cinema,\" “post‐modernism,” etc.) But how can we track\"global digital cultures\", with their billions of cultural objects, and hundreds of millions of contributors? Before you could write about culture by following what was going on in a small number of world capitals and schools. But how can we follow the developments in tens of thousands of cities and educational institutions?(Manovich, Cultural Analytics, 2009) .  \nWhile the availability of large digitized collections of humanities data certainly creates the case for humanists to use computational tools, the rise of social media and globalization of professional culture leave us no other choice. But how can we explore patterns and relations between sets of photographs, designs, or video, which may number in hundreds of thousands, millions, or billions?(By summer","cbCaivenV7hG0THr","https://ap.wps.com/l/cbCaivenV7hG0THr","pdf",4950321,1,40,"English","en",105,"# Introduction\n## Scale and digitized cultural archives\n## Challenges from social media and globalization\n## Software Studies Initiative and the two-part method\n## Applications to large visual datasets","[{\"question\":\"Why does scale matter for humanities research in the Digging into Data framing?\",\"answer\":\"Scale changes how scholars interpret evidence, since vast digitized repositories and media corpora cannot be read or analyzed using traditional small-sample approaches.\"},{\"question\":\"What two-part method does the article describe for analyzing large image collections?\",\"answer\":\"It combines automatic digital image analysis that produces numerical descriptions of visual characteristics with visualizations that organize the entire image set by those dimensions.\"},{\"question\":\"How is the method tested using manga datasets?\",\"answer\":\"In Fall 2009 the initiative downloaded complete runs of hundreds of manga series from a scanlation site to evaluate how the approach performs with larger datasets.\"}]","How to Compare One Million Images - 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