Overcoming data scarcity in earth science.
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 g...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Uruguay |
| Institución: | Universidad de la República |
| Repositorio: | COLIBRI |
| Idioma: | inglés |
| OAI Identifier: | oai:colibri.udelar.edu.uy:20.500.12008/27059 |
| Acceso en línea: | https://hdl.handle.net/20.500.12008/27059 |
| Access Level: | acceso abierto |
| Palabra clave: | Earth-science data Data scarcity Missing data Data quality Data imputation Statistical methods Machine learning Environmental modeling Environmental observations |
| Sumario: | 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. |
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