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Call for papers
Data Journal Special Issue on
"Overcoming Data Scarcity in Earth Science"
http://www.mdpi.com/journal/data/special_issues/Data_Scarcity

Deadline for manuscript submissions: 30 May 2019

Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website.
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Description
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Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios.
These models heavily rely on the data collected by direct field observations. However, a functional and comprehensive dataset of
any environmental variable is hard to collect, mainly because of: i) the high cost of the monitoring campaigns; and ii) the low
reliability in the measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to the equipment location).
The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any
environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies
to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would
result if all the available data could be well exploited.

Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with
missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are
used in different domains to deal with single and multiple imputation problem. More recently, machine learning techniques as
clustering and classification, have been proposed to complete missing data.

This Special Issue on “Overcoming Data Scarcity in Earth Science” of the Journal Data is designed to draw attention to the body
of knowledge that aims at improving the capacity of exploiting the available data to better represent, understand, predict,
and manage the behavior of environmental systems at all practical scales.

Authors are encouraged to submit research articles, reviews, and short communications addressing this theme in this Special Issue.

Keywords
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- Earth science data
- Data scarcity
- Missing data
- Data quality
- Data Imputation
- Statistical Methods
- Machine learning
- Environmental modeling
- Environmental observations


Submission
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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website.
Manuscripts can be submitted until the deadline. All papers will be peer-reviewed.
Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed
together on the special issue website. Research articles, review articles as well as short communications
are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication
elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind
peer-review process. A guide for authors and other relevant information for submission of manuscripts is available
on the Instructions for Authors page. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript.
The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue.
Submitted papers should be well formatted and use good English.
Authors may use MDPI's English editing service prior to publication or during author revisions.



Important Dates
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30 May 2019: Manuscript submission deadline
Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors 19 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in the first six months of 2018).
Special Issue Publication: TBA


Editorial Review Board
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Dr. Angela Gorgoglione
Institute of Fluid Mechanics and Environmental Engineering (IMFIA), Engineering College, Universidad de la República, Uruguay

Dr. Alberto Castro Casales
Computer Science Department, Engineering College, Universidad de la República, Uruguay

Dr. Christian Chreties Ceriani
Institute of Fluid Mechanics and Environmental Engineering (IMFIA), Engineering College, Universidad de la República, Uruguay

Dr. Lorena Etcheverry Venturini
Computer Science Department, Engineering College, Universidad de la República, Uruguay