{"id":1258,"date":"2020-12-11T21:05:00","date_gmt":"2020-12-11T12:05:00","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/?p=1258"},"modified":"2022-08-27T09:47:36","modified_gmt":"2022-08-27T00:47:36","slug":"20201211_4027","status":"publish","type":"event","link":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/event\/20201211_4027\/","title":{"rendered":"\uff1cCiNet \u30e1\u30f3\u30d0\u30fc\u3092\u5bfe\u8c61\u306bon-line \u958b\u50ac\uff1e\u9752\u6728\u4fca\u592a\u90ce\uff1a\u201fData and code sharing for advanced analysis of large-scale fMRI data\u201d"},"content":{"rendered":"\n<p>202012\u670811\u65e5\u3000\u3000Friday Lunch Seminar<br>12:15&nbsp;\u301c&nbsp;13:00<br>CiNet \u30e1\u30f3\u30d0\u30fc\u306e\u307f\u3092\u5bfe\u8c61\u306b On-line\u3067\u958b\u50ac\u3044\u305f\u3057\u307e\u3059\u3002\u4e8b\u524d\u7533\u3057\u8fbc\u307f\u8981\u6f14\u984c\uff1aData and code sharing for advanced analysis of large-scale fMRI data<\/p>\n\n\n\n<p>\u4eac\u90fd\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u5b66\u7814\u7a76\u79d1<br>\u7279\u5b9a\u7814\u7a76\u54e1\u3000\u9752\u6728 \u4fca\u592a\u90ce<\/p>\n\n\n\n<p>\u62c5\u5f53PI :\u00a0<a href=\"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/people\/2014455\/\">\u548c\u7530 \u5145\u53f2<\/a><\/p>\n\n\n\n<p>Abstract:<br>Sharing data and analysis code is crucial to enhance transparency in scientific research including neuroscience. In addition, efficient data and code management in laboratories facilitates computational neuroscience research exploiting large amounts of data and advanced analysis methods. In this presentation, I introduce the practices in my laboratory (Yukiyasu Kamitani\u2019s lab at Kyoto University and ATR) to manage and share large-scale fMRI data and machine\/deep learning-based analysis code. fMRI data are organized in the standard data structure (BIDS; Poldrack et al., 2013), preprocessed with automated pipeline (fmriprep; Esteban et al., 2019), and formed in our in-house data format (BData) that provides simple interface to the data. Code for typical analyses (e.g., DNN feature decoding [Horikawa &amp; Kamitani, 2017] and image reconstruction [Shen et al., 2019]) are standardized and shared in the laboratory so that laboratory members can quickly scale the analyses to new data. We developed libraries to support handling of shared data and scripting of analysis code. The data and code are shared with public audiences via sharing platforms for neuroimaging data, (OpenNeuro; Gorgolewski et al., 2017), general scientific data (figshare), and source code (GitHub). These practices achieved efficient sharing, preprocessing, and analysis of the data within the laboratory members as well as active use of our data by researchers outside the laboratory.<\/p>\n","protected":false},"featured_media":0,"template":"","acf":[],"_links":{"self":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/event\/1258"}],"collection":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/event"}],"about":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/types\/event"}],"wp:attachment":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/media?parent=1258"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}