{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:04:56Z","timestamp":1775912696425,"version":"3.50.1"},"reference-count":26,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2023,6,1]]},"DOI":"10.1109\/tbdata.2022.3191749","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T20:29:56Z","timestamp":1658176196000},"page":"773-791","source":"Crossref","is-referenced-by-count":4,"title":["Big Data for the Social Good: The Drought Early-Warning Experience Report"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1230-8961","authenticated-orcid":false,"given":"Damian A.","family":"Tamburri","sequence":"first","affiliation":[{"name":"Data Science, TU\/e - JADS, DA s&#x0027;Hertogenbosch, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent R.","family":"van Mierlo","sequence":"additional","affiliation":[{"name":"Jheronimus Academy of Data Science, Tilburg University, Tilburg, AB, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Willem-Jan","family":"van den Heuvel","sequence":"additional","affiliation":[{"name":"Jheronimus Academy of Data Science, Tilburg University, Tilburg, AB, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","article-title":"POWER release 8.0.1 (with GIS applications) methodology","author":"stackhouse","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1127\/0941-2948\/2006\/0130"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-9437(1985)111:2(113)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.2307\/210739"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2018.09.020"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.4314\/wsa.v45i1.09"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/wat2.1085"},{"key":"ref1","first-page":"104","article-title":"DataOps &#x2013; Towards a definition","author":"ereth","year":"2018","journal-title":"Proc Lernen Wissen Daten Analysen"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3349341.3349371"},{"key":"ref16","first-page":"1","article-title":"An update for the definition of reference evapotranspiration","volume":"43","author":"allen","year":"1994","journal-title":"ICID Bul"},{"key":"ref19","first-page":"785","article-title":"XGBoost: A scalable tree boosting system","author":"guestrin","year":"2016","journal-title":"Proc ACM SIGKDD Int Conf Knowledge Discovery and Data Mining"},{"key":"ref18","article-title":"Validating your machine learning model; going beyond k-fold cross-validation","author":"grootendorst","year":"2019"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2016.05.033"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2017.12.002"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI-D-19-0696.1"},{"key":"ref25","first-page":"1","author":"walter","year":"0","journal-title":"ASCE Standardized Reference Evapotranspiration Equation"},{"key":"ref20","article-title":"Tree boosting with XGBoost; why does XGBoost win &#x201D;Every&#x201D; machine","author":"nielsen","year":"2016"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/5.554205"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IRI49571.2020.00063"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1080\/00431672.1968.9932814"},{"key":"ref7","first-page":"58","article-title":"Meteorologic drought","year":"1965"},{"key":"ref9","first-page":"439","article-title":"Updated world map of the K&#x00F6;ppen-Geiger climate","volume":"4","author":"peel","year":"2007","journal-title":"Hydrol Earth Syst Sci Discussions Eur Geosci Union"},{"key":"ref4","first-page":"1","article-title":"DataOps in manufacturing and utilities industries","volume":"12","author":"sahoo","year":"2019","journal-title":"Int J Appl Inf Syst"},{"key":"ref3","article-title":"Enhancing drought monitoring and early-warning by linking indicators to impacts","author":"hannaford","year":"2015"},{"key":"ref6","article-title":"Guidelines: Land evaluation for Rainfed Agriculture","year":"1983"},{"key":"ref5","author":"thusoo","year":"2017","journal-title":"Creating a Data-Driven Enterprise With DataOps - Insights From Facebook Uber LinkedIn Twitter and eBay"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687317\/10124065\/09832803.pdf?arnumber=9832803","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T17:42:36Z","timestamp":1685382156000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9832803\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":26,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2022.3191749","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}