{"created":"2024-10-11T03:28:54.647160+00:00","id":2000192,"links":{},"metadata":{"_buckets":{"deposit":"2b7be6ce-312f-4c5c-9b45-0d03190f6304"},"_deposit":{"created_by":18,"id":"2000192","owner":"18","owners":[18],"pid":{"revision_id":0,"type":"depid","value":"2000192"},"status":"published"},"_oai":{"id":"oai:nies.repo.nii.ac.jp:02000192","sets":["33:41:1704769899814"]},"author_link":[],"control_number":"2000192","item_10010_description_6":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"本データセットには、2030年から2100年までの、日本の842都市を対象とした湿球黒球温度(WBGT)の予測値（時別、4～10月まで）が格納されている。予測は、機械学習手法の1つであるeXtreme Gradient Boostingを用いて、過去の時別WBGTと日別気象変数の関係を学習して構築したモデルを、日本域の将来気候シナリオデータ（NIES2020）の日別値に適用して行った。過去（1980～2014年）期間のデータも参考として提供する。","subitem_description_language":"ja","subitem_description_type":"Other"},{"subitem_description":"This dataset contains predicted values (hourly, from April to October) of wet bulb globe temperature (WBGT) for 842 cities in Japan from 2030 to 2100. The predictions were made by applying a model constructed using eXtreme Gradient Boosting, a machine learning method, to learn the relationship between hourly WBGT and daily meteorological variables in the past, to the daily values of the future climate scenario data for Japan (NIES2020). Data for the past period (1980-2014) is also provided as a reference.","subitem_description_language":"en","subitem_description_type":"Other"},{"subitem_description":"[貢献者]\n　データ収集者：石崎 紀子\n　監督者：肱岡 靖明\n[助成情報]\n　国立環境研究所 気候変動適応研究プログラム\n　環境省・環境再生保全機構 環境研究総合推進費　（JPMEERF20231007,JPMEERF23S21120)","subitem_description_language":"ja","subitem_description_type":"Other"},{"subitem_description":"[Contributors]\n　Data Collector : ISHIZAKI,Noriko \n　Supervisor : HIJIOKA, Yasuaki\n[Fund]\nClimate Change Adaptation Research Program of National Institute for Environmental Studies Environment Research and Technology Development Fund (JPMEERF20231007, JPMEERF23S21120) of the Environmental Restoration and Conservation Agency provided by Ministry of the Environment of Japan","subitem_description_language":"en","subitem_description_type":"Other"}]},"item_10010_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Environmental Data Initiative","subitem_publisher_language":"en"}]},"item_10010_relation_14":{"attribute_name":"item_10010_relation_14","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_language":"en","subitem_relation_name_text":"[Data Package] Future hourly wet-bulb globe temperature dataset for 842 cities in Japan"}],"subitem_relation_type":"isSourceOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.6073/pasta/a34e743f786d30a04aac4ae2e333eb3a","subitem_relation_type_select":"DOI"}}]},"item_10010_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"[データ概要] 日本国内842都市を対象とした時別の湿球黒球温度（WBGT）将来予測データ"}],"subitem_relation_type":"isReferencedBy","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://www.nies.go.jp/doi/10.6073/pasta/a34e743f786d30a04aac4ae2e333eb3a.html","subitem_relation_type_select":"Local"}},{"subitem_relation_name":[{"subitem_relation_name_language":"en","subitem_relation_name_text":"[Data Summary] Future hourly wet-bulb globe temperature dataset for 842 cities in Japan"}],"subitem_relation_type":"isReferencedBy","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://www.nies.go.jp/doi/10.6073/pasta/a34e743f786d30a04aac4ae2e333eb3a-e.html","subitem_relation_type_select":"Local"}}]},"item_10010_relation_18":{"attribute_name":"他の資源との関係","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"石崎紀子, 2021, CMIP6をベースにしたCDFDM手法による日本域バイアス補正気候シナリオデータ (NIES2020)"},{"subitem_relation_name_language":"en","subitem_relation_name_text":"Ishizaki, N. N., 2021, Bias corrected climate scenarios over Japan based on CDFDM method using CMIP6 (NIES2020),"}],"subitem_relation_type":"isDerivedFrom","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.17595/20210501.001","subitem_relation_type_select":"DOI"}},{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"環境省　熱中症予防情報サイト"},{"subitem_relation_name_language":"en","subitem_relation_name_text":"Ministry of the Environment Government of Japan, Heat Illness Prevention Information"}],"subitem_relation_type":"isDerivedFrom","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://www.wbgt.env.go.jp/","subitem_relation_type_select":"Local"}},{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"気象庁 過去の気象データ検索"},{"subitem_relation_name_language":"en","subitem_relation_name_text":"Japan Meteorological Agency, Historical weather data search"}],"subitem_relation_type":"isDerivedFrom","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://www.data.jma.go.jp/obd/stats/etrn/index.php","subitem_relation_type_select":"Local"}},{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"農研機構 メッシュ農業気象データシステム"},{"subitem_relation_name_language":"en","subitem_relation_name_text":"NARO, The Agro-Meteorological Grid Square Data"}],"subitem_relation_type":"isDerivedFrom","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://amu.rd.naro.go.jp/wiki_open/doku.php?id=start2","subitem_relation_type_select":"Local"}}]},"item_10010_subject_21":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"451.35","subitem_subject_language":"ja","subitem_subject_scheme":"NDC"},{"subitem_subject":"451.8","subitem_subject_language":"ja","subitem_subject_scheme":"NDC"},{"subitem_subject":"451.9","subitem_subject_language":"ja","subitem_subject_scheme":"NDC"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大山 剛弘","creatorNameLang":"ja","creatorNameType":"Personal"},{"creatorName":"OYAMA, Takahiro","creatorNameLang":"en","creatorNameType":"Personal"}]},{"creatorNames":[{"creatorName":"高倉 潤也","creatorNameLang":"ja","creatorNameType":"Personal"},{"creatorName":"Jun'ya Takakura","creatorNameLang":"en","creatorNameType":"Personal"}]}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"湿球黒球温度, 都市, 将来予測, 機械学習, 時別, 日本","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"wet-bulb globe temperature, city scale, future projection, machine learning, hourly, Japan","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"item_resource_type","attribute_value_mlt":[{"resourcetype":"simulation data","resourceuri":"http://purl.org/coar/resource_type/W2XT-7017/"}]},"item_title":"日本国内842都市を対象とした時別の湿球黒球温度（WBGT）将来予測データ","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"日本国内842都市を対象とした時別の湿球黒球温度（WBGT）将来予測データ","subitem_title_language":"ja"},{"subitem_title":"Future hourly wet-bulb globe temperature dataset for 842 cities in Japan","subitem_title_language":"en"}]},"item_type_id":"10010","owner":"18","path":["1704769899814"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-08-30"},"publish_date":"2024-08-30","publish_status":"0","recid":"2000192","relation_version_is_last":true,"title":["日本国内842都市を対象とした時別の湿球黒球温度（WBGT）将来予測データ"],"weko_creator_id":"18","weko_shared_id":-1},"updated":"2024-10-11T04:42:37.952414+00:00"}