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Supporting cross-domain system-level environmental and earth science

Vermeulen, Alex LU orcid ; Glaves, Helen ; Pouliquen, Sylvie and Kokkinaki, Alexandra (2020) In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12003. p.3-16
Abstract

Answering the key challenges for society due to environmental issues like climate change, pollution and loss of biodiversity, and making the right decisions to tackle these in a cost-efficient and sustainable way requires scientific understanding of the Earth System. This scientific knowledge can then be used to inform the general public and policymakers. Scientific understanding starts with having available the right data, often in the form of observations. Research Infrastructures (RIs) exist to perform these observations in the required quality and to make the data available to first of all the researchers. In the current Big Data era, the increasing challenge is to provide the data in an interoperable and machine-readable and... (More)

Answering the key challenges for society due to environmental issues like climate change, pollution and loss of biodiversity, and making the right decisions to tackle these in a cost-efficient and sustainable way requires scientific understanding of the Earth System. This scientific knowledge can then be used to inform the general public and policymakers. Scientific understanding starts with having available the right data, often in the form of observations. Research Infrastructures (RIs) exist to perform these observations in the required quality and to make the data available to first of all the researchers. In the current Big Data era, the increasing challenge is to provide the data in an interoperable and machine-readable and understandable form. The European RIs on environment formed a project cluster called ENVRI that tackles these issues. In this chapter, we introduce the societal relevance of the environmental data produced by the RIs and discuss the issues at hand in providing the relevant data according to the so-called FAIR principles.

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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Data management, Environmental and earth science, FAIR, Research Infrastructure, Societal challenges
host publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume
12003
pages
14 pages
publisher
Springer Gabler
external identifiers
  • scopus:85089619627
ISSN
1611-3349
0302-9743
DOI
10.1007/978-3-030-52829-4_1
language
English
LU publication?
yes
id
000ccb36-a630-44a6-a197-749505c8c206
date added to LUP
2020-08-28 14:41:31
date last changed
2025-04-04 15:21:09
@inbook{000ccb36-a630-44a6-a197-749505c8c206,
  abstract     = {{<p>Answering the key challenges for society due to environmental issues like climate change, pollution and loss of biodiversity, and making the right decisions to tackle these in a cost-efficient and sustainable way requires scientific understanding of the Earth System. This scientific knowledge can then be used to inform the general public and policymakers. Scientific understanding starts with having available the right data, often in the form of observations. Research Infrastructures (RIs) exist to perform these observations in the required quality and to make the data available to first of all the researchers. In the current Big Data era, the increasing challenge is to provide the data in an interoperable and machine-readable and understandable form. The European RIs on environment formed a project cluster called ENVRI that tackles these issues. In this chapter, we introduce the societal relevance of the environmental data produced by the RIs and discuss the issues at hand in providing the relevant data according to the so-called FAIR principles.</p>}},
  author       = {{Vermeulen, Alex and Glaves, Helen and Pouliquen, Sylvie and Kokkinaki, Alexandra}},
  booktitle    = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
  issn         = {{1611-3349}},
  keywords     = {{Data management; Environmental and earth science; FAIR; Research Infrastructure; Societal challenges}},
  language     = {{eng}},
  pages        = {{3--16}},
  publisher    = {{Springer Gabler}},
  series       = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
  title        = {{Supporting cross-domain system-level environmental and earth science}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-52829-4_1}},
  doi          = {{10.1007/978-3-030-52829-4_1}},
  volume       = {{12003}},
  year         = {{2020}},
}