IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v5y2020i1p5-d304128.html
   My bibliography  Save this article

Overcoming Data Scarcity in Earth Science

Author

Listed:
  • Angela Gorgoglione

    (Department of Fluid Mechanics and Environmental Engineering (IMFIA), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

  • Alberto Castro

    (Department of Computer Science (InCo), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

  • Christian Chreties

    (Department of Fluid Mechanics and Environmental Engineering (IMFIA), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

  • Lorena Etcheverry

    (Department of Computer Science (InCo), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

Abstract

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.

Suggested Citation

  • Angela Gorgoglione & Alberto Castro & Christian Chreties & Lorena Etcheverry, 2020. "Overcoming Data Scarcity in Earth Science," Data, MDPI, vol. 5(1), pages 1-5, January.
  • Handle: RePEc:gam:jdataj:v:5:y:2020:i:1:p:5-:d:304128
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/5/1/5/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/5/1/5/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Angela Gorgoglione & Andrea Gioia & Vito Iacobellis, 2019. "A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    2. Tutz, Gerhard & Ramzan, Shahla, 2015. "Improved methods for the imputation of missing data by nearest neighbor methods," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 84-99.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marco Delle Rose & Paolo Martano, 2023. "Datasets of Groundwater Level and Surface Water Budget in a Central Mediterranean Site (21 June 2017–1 October 2022)," Data, MDPI, vol. 8(2), pages 1-12, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sadaf Kabir & Leily Farrokhvar, 2022. "Non-linear missing data imputation for healthcare data via index-aware autoencoders," Health Care Management Science, Springer, vol. 25(3), pages 484-497, September.
    2. Angela Gorgoglione & Javier Gregorio & Agustín Ríos & Jimena Alonso & Christian Chreties & Mónica Fossati, 2020. "Influence of Land Use/Land Cover on Surface-Water Quality of Santa Lucía River, Uruguay," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    3. Marlene A. Perez-Villalpando & Kelly J. Gurubel Tun & Carlos A. Arellano-Muro & Fernando Fausto, 2021. "Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering," Energies, MDPI, vol. 14(21), pages 1-18, November.
    4. Faisal Shahla & Tutz Gerhard, 2017. "Missing value imputation for gene expression data by tailored nearest neighbors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 95-106, April.
    5. Angela Gorgoglione & Alberto Castro & Vito Iacobellis & Andrea Gioia, 2021. "A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    6. Julia Calderón Cendejas & Lucía Madrid Ramírez & Jorge Ramírez Zierold & Julio Díaz Valenzuela & Martín Merino Ibarra & Santiago Morató Sánchez de Tagle & Alejandro Chino Téllez, 2021. "Evaluation of the Impacts of Land Use in Water Quality and the Role of Nature-Based Solutions: A Citizen Science-Based Study," Sustainability, MDPI, vol. 13(19), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:5:y:2020:i:1:p:5-:d:304128. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.