{"created":"2023-03-31T02:24:03.852449+00:00","id":5358,"links":{},"metadata":{"_buckets":{"deposit":"ec38ac62-a993-4545-9ff9-45737c741b30"},"_deposit":{"id":"5358","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"5358"},"status":"published"},"_oai":{"id":"oai:nied-repo.bosai.go.jp:00005358","sets":[]},"author_link":[],"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-07-01"},"bibliographicIssueNumber":"4","bibliographicPageEnd":"2377","bibliographicPageStart":"2368","bibliographicVolumeNumber":"91","bibliographic_titles":[{"bibliographic_title":"Seismological Research Letters","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"<title>Abstract</title>\n SeisIO for the Julia language is a new geophysical data framework that combines the intuitive syntax of a high-level language with performance comparable to FORTRAN or C. Benchmark comparisons against recent versions of popular programs for seismic data download and analysis demonstrate significant improvements in file read speed and orders-of-magnitude improvements in memory overhead. Because the Julia language natively supports parallel computing with an intuitive syntax, we benchmark test parallel download and processing of multiweek segments of contiguous data from two sets of 10 broadband seismic stations, and find that SeisIO outperforms two popular Python-based tools for data downloads. The current capabilities of SeisIO include file read support for several geophysical data formats, online data access using a variety of services, and optimized versions of several common data processing operations. Tutorial notebooks and extensive documentation are available to improve the user experience. As an accessible example of performant scientific computing for the next generation of researchers, SeisIO offers ease of use and rapid learning without sacrificing computational efficiency.","subitem_description_language":"en","subitem_description_type":"Other"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Seismological Society of America (SSA)","subitem_publisher_language":"en"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1785/0220190295"}}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1938-2057","subitem_source_identifier_type":"EISSN"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Joshua P. Jones","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Kurama Okubo","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Tim Clements","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Marine A. Denolle","creatorNameLang":"en"}]}]},"item_title":"SeisIO: A Fast, Efficient Geophysical Data Architecture for the Julia Language","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"SeisIO: A Fast, Efficient Geophysical Data Architecture for the Julia Language","subitem_title_language":"en"}]},"item_type_id":"40001","owner":"1","path":["1670839190650"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-03-30"},"publish_date":"2023-03-30","publish_status":"0","recid":"5358","relation_version_is_last":true,"title":["SeisIO: A Fast, Efficient Geophysical Data Architecture for the Julia Language"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-03-31T02:24:05.919224+00:00"}