{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T14:21:19Z","timestamp":1790518879450,"version":"4.1.0"},"reference-count":15,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response\u2019s complexity in any environmental system to any input\/change (natural and human-induced). In such a case, before getting engaged with new expensive studies to gather and analyze additional data, it is reasonable first to understand what enhancement in estimates of system performance would result if all the available data could be well exploited. The purpose of this Special Issue, \u201cOvercoming Data Scarcity in Earth Science\u201d in the Data journal, is to draw attention to the body of knowledge that leads at improving the capacity of exploiting the available data to better represent, understand, predict, and manage the behavior of environmental systems at meaningful space-time scales. This Special Issue contains six publications (three research articles, one review, and two data descriptors) covering a wide range of environmental fields: geophysics, meteorology\/climatology, ecology, water quality, and hydrology.<\/jats:p>","DOI":"10.3390\/data5010005","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T04:43:03Z","timestamp":1578026583000},"page":"5","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Overcoming Data Scarcity in Earth Science"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2476-2339","authenticated-orcid":false,"given":"Angela","family":"Gorgoglione","sequence":"first","affiliation":[{"name":"Department of Fluid Mechanics and Environmental Engineering (IMFIA), School of Engineering, Universidad de la Rep\u00fablica, Montevideo 11300, Uruguay"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9174-398X","authenticated-orcid":false,"given":"Alberto","family":"Castro","sequence":"additional","affiliation":[{"name":"Department of Computer Science (InCo), School of Engineering, Universidad de la Rep\u00fablica, Montevideo 11300, Uruguay"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Chreties","sequence":"additional","affiliation":[{"name":"Department of Fluid Mechanics and Environmental Engineering (IMFIA), School of Engineering, Universidad de la Rep\u00fablica, Montevideo 11300, Uruguay"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8121-8076","authenticated-orcid":false,"given":"Lorena","family":"Etcheverry","sequence":"additional","affiliation":[{"name":"Department of Computer Science (InCo), School of Engineering, Universidad de la Rep\u00fablica, Montevideo 11300, Uruguay"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chaulya, S.K., and Prasad, G.M. (2016). Chapter 7\u2014Application of cloud computing technology in mining industry. Sensing and Monitoring Technologies for Mines and Hazardous Areas, Elsevier.","DOI":"10.1016\/B978-0-12-803194-0.00007-6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.envsoft.2018.09.022","article-title":"Uncertainty in the parameterization of sediment build-up and wash-off processes in the simulation of water quality in urban areas","volume":"111","author":"Gorgoglione","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9327614","DOI":"10.1155\/2016\/9327614","article-title":"A rationale for pollutograph evaluation in ungauged areas, using daily rainfall patterns: Case studies of the Apulian region in Southern Italy","volume":"2016","author":"Gorgoglione","year":"2016","journal-title":"Appl. Environ. 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Analysis of Incomplete Multivariate Data, CRC Press."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1016\/j.atmosenv.2004.02.026","article-title":"Methods for imputation of missing values in air quality data sets","volume":"38","author":"Junninen","year":"2004","journal-title":"Atmosph. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.csda.2015.04.009","article-title":"Improved methods for the imputation of missing data by nearest neighbor methods","volume":"90","author":"Tutz","year":"2015","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Abraham, S., Huynh, C., and Vu, H. (2020). Classification of soils into hydrologic groups using machine learning. Data, 5.","DOI":"10.3390\/data5010002"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zavareh, M., and Maggioni, V. (2018). Application of rough set theory to water quality analysis: A case study. 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System for collecting, processing, visualization, and storage of the MT-Monitoring data. Data, 4.","DOI":"10.3390\/data4030099"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/1\/5\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:03:29Z","timestamp":1760364209000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/1\/5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,1]]},"references-count":15,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["data5010005"],"URL":"https:\/\/doi.org\/10.3390\/data5010005","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,1]]}}}