Overcoming data scarcity in earth science.
Resumen:
The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response?s 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, "Overcoming Data Scarcity in Earth Science" 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.
2020 | |
Earth-science data Data scarcity Missing data Data quality Data imputation Statistical methods Machine learning Environmental modeling Environmental observations |
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Inglés | |
Universidad de la República | |
COLIBRI | |
https://hdl.handle.net/20.500.12008/27059 | |
Acceso abierto | |
Licencia Creative Commons Atribución (CC - By 4.0) |
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---|---|
author | Gorgoglione, Angela |
author2 | Castro, Alberto Chreties, Christian Etcheverry, Lorena |
author2_role | author author author |
author_facet | Gorgoglione, Angela Castro, Alberto Chreties, Christian Etcheverry, Lorena |
author_role | author |
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collection | COLIBRI |
dc.contributor.filiacion.none.fl_str_mv | Gorgoglione Angela, Universidad de la República (Uruguay). Facultad de Ingeniería. Castro Alberto, Universidad de la República (Uruguay). Facultad de Ingeniería. Chreties Christian, Universidad de la República (Uruguay). Facultad de Ingeniería. Etcheverry Lorena, Universidad de la República (Uruguay). Facultad de Ingeniería. |
dc.creator.none.fl_str_mv | Gorgoglione, Angela Castro, Alberto Chreties, Christian Etcheverry, Lorena |
dc.date.accessioned.none.fl_str_mv | 2021-04-13T15:31:45Z |
dc.date.available.none.fl_str_mv | 2021-04-13T15:31:45Z |
dc.date.issued.none.fl_str_mv | 2020 |
dc.description.abstract.none.fl_txt_mv | The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response?s 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, "Overcoming Data Scarcity in Earth Science" 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. |
dc.format.extent.es.fl_str_mv | 5 p. |
dc.format.mimetype.es.fl_str_mv | application/pdf |
dc.identifier.citation.es.fl_str_mv | Gorgoglione, A., Castro, A., Chreties, C. y otros. "Overcoming data scarcity in earth science". Data. [en línea]. 2020, vol. 5, no 1, pp. 1-5. DOI: 10.3390/data5010005 |
dc.identifier.doi.none.fl_str_mv | 10.3390/data5010005 |
dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/27059 |
dc.language.iso.none.fl_str_mv | en eng |
dc.publisher.es.fl_str_mv | MDPI |
dc.relation.ispartof.es.fl_str_mv | Data, vol. 5, no 1, pp. 1-5, jan 2020. |
dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
dc.subject.es.fl_str_mv | Earth-science data Data scarcity Missing data Data quality Data imputation Statistical methods Machine learning Environmental modeling Environmental observations |
dc.title.none.fl_str_mv | Overcoming data scarcity in earth science. |
dc.type.es.fl_str_mv | Artículo |
dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
description | The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response?s 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, "Overcoming Data Scarcity in Earth Science" 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. |
eu_rights_str_mv | openAccess |
format | article |
id | COLIBRI_ed1cc8a3024c045e3f3deebfda78811f |
identifier_str_mv | Gorgoglione, A., Castro, A., Chreties, C. y otros. "Overcoming data scarcity in earth science". Data. [en línea]. 2020, vol. 5, no 1, pp. 1-5. DOI: 10.3390/data5010005 10.3390/data5010005 |
instacron_str | Universidad de la República |
institution | Universidad de la República |
instname_str | Universidad de la República |
language | eng |
language_invalid_str_mv | en |
network_acronym_str | COLIBRI |
network_name_str | COLIBRI |
oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/27059 |
publishDate | 2020 |
reponame_str | COLIBRI |
repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
repository.name.fl_str_mv | COLIBRI - Universidad de la República |
repository_id_str | 4771 |
rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
spelling | Gorgoglione Angela, Universidad de la República (Uruguay). Facultad de Ingeniería.Castro Alberto, Universidad de la República (Uruguay). Facultad de Ingeniería.Chreties Christian, Universidad de la República (Uruguay). Facultad de Ingeniería.Etcheverry Lorena, Universidad de la República (Uruguay). Facultad de Ingeniería.2021-04-13T15:31:45Z2021-04-13T15:31:45Z2020Gorgoglione, A., Castro, A., Chreties, C. y otros. "Overcoming data scarcity in earth science". Data. [en línea]. 2020, vol. 5, no 1, pp. 1-5. DOI: 10.3390/data5010005https://hdl.handle.net/20.500.12008/2705910.3390/data5010005The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response?s 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, "Overcoming Data Scarcity in Earth Science" 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.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2021-04-13T04:54:22Z No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) GCCE20.pdf: 193301 bytes, checksum: 891a0535dfa21fdb0b82cd54c37513d3 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2021-04-13T15:30:17Z (GMT) No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) GCCE20.pdf: 193301 bytes, checksum: 891a0535dfa21fdb0b82cd54c37513d3 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@fic.edu.uy) on 2021-04-13T15:31:45Z (GMT). No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) GCCE20.pdf: 193301 bytes, checksum: 891a0535dfa21fdb0b82cd54c37513d3 (MD5) Previous issue date: 20205 p.application/pdfenengMDPIData, vol. 5, no 1, pp. 1-5, jan 2020.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)Earth-science dataData scarcityMissing dataData qualityData imputationStatistical methodsMachine learningEnvironmental modelingEnvironmental observationsOvercoming data scarcity in earth science.Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaGorgoglione, AngelaCastro, AlbertoChreties, ChristianEtcheverry, LorenaLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/27059/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-844http://localhost:8080/xmlui/bitstream/20.500.12008/27059/2/license_urla0ebbeafb9d2ec7cbb19d7137ebc392cMD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
spellingShingle | Overcoming data scarcity in earth science. Gorgoglione, Angela Earth-science data Data scarcity Missing data Data quality Data imputation Statistical methods Machine learning Environmental modeling Environmental observations |
status_str | publishedVersion |
title | Overcoming data scarcity in earth science. |
title_full | Overcoming data scarcity in earth science. |
title_fullStr | Overcoming data scarcity in earth science. |
title_full_unstemmed | Overcoming data scarcity in earth science. |
title_short | Overcoming data scarcity in earth science. |
title_sort | Overcoming data scarcity in earth science. |
topic | Earth-science data Data scarcity Missing data Data quality Data imputation Statistical methods Machine learning Environmental modeling Environmental observations |
url | https://hdl.handle.net/20.500.12008/27059 |