{"title":"作为大数据的数字档案","authors":"L. Martinez-Uribe","doi":"10.1080/08898480.2017.1418116","DOIUrl":null,"url":null,"abstract":"ABSTRACT Digital archives contribute to Big data. Combining social network analysis, coincidence analysis, data reduction, and visual analytics leads to better characterize topics over time, publishers’ main themes and best authors of all times, according to the British newspaper The Guardian and from the 3 million records of the British National Bibliography.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"26 1","pages":"69 - 79"},"PeriodicalIF":1.4000,"publicationDate":"2018-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2017.1418116","citationCount":"2","resultStr":"{\"title\":\"Digital archives as Big data\",\"authors\":\"L. Martinez-Uribe\",\"doi\":\"10.1080/08898480.2017.1418116\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Digital archives contribute to Big data. Combining social network analysis, coincidence analysis, data reduction, and visual analytics leads to better characterize topics over time, publishers’ main themes and best authors of all times, according to the British newspaper The Guardian and from the 3 million records of the British National Bibliography.\",\"PeriodicalId\":49859,\"journal\":{\"name\":\"Mathematical Population Studies\",\"volume\":\"26 1\",\"pages\":\"69 - 79\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2018-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/08898480.2017.1418116\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical Population Studies\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.1080/08898480.2017.1418116\",\"RegionNum\":3,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"DEMOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Population Studies","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1080/08898480.2017.1418116","RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"DEMOGRAPHY","Score":null,"Total":0}
ABSTRACT Digital archives contribute to Big data. Combining social network analysis, coincidence analysis, data reduction, and visual analytics leads to better characterize topics over time, publishers’ main themes and best authors of all times, according to the British newspaper The Guardian and from the 3 million records of the British National Bibliography.
期刊介绍：
Mathematical Population Studies publishes carefully selected research papers in the mathematical and statistical study of populations. The journal is strongly interdisciplinary and invites contributions by mathematicians, demographers, (bio)statisticians, sociologists, economists, biologists, epidemiologists, actuaries, geographers, and others who are interested in the mathematical formulation of population-related questions.
The scope covers both theoretical and empirical work. Manuscripts should be sent to Manuscript central for review. The editor-in-chief has final say on the suitability for publication.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
