Call for papers: Special Issue on Data and Information Services for Interdisciplinary Research and Applications in Earth Science
Making heterogeneous Earth science data easily accessible in an integrated environment, such as online visualization and analysis services without needing to download data and software, is essential to broaden user communities. This effort is especially important to the interdisciplinary research and application communities, providing data and information to end-users with different backgrounds. To date, accessing interdisciplinary datasets is still a challenge to researchers and application users, as often reported in the literature and scientific meetings.
The complexity of interdisciplinary research and applications is driven by scientific, technical, and cultural challenges. From problem definition to disseminating results to diverse audiences, the role of cross-discipline collaborations is increasingly important in a changing world. To this end, adopting FAIR principles (Findable, Accessible, Interoperable, Reusable) is a key step towards sustainable and relevant solutions. This concept encapsulates much of what this thematic number seeks to address.
The development of stable data services and repositories, with persistent access methods is pivotal to ensure findability and the development of solutions downstream. Research projects, both interdisciplinary and otherwise, play an important role in this process when supported by sustainable and open data management plans. In a rapidly expanding science data service ecosystem, ‘black-box’ processors and poorly documented datasets hinder the penetration of the products across domains and publics. As such, the development of robust, well documented, and validated methodologies is paramount to ensure interoperability and reliable reusability for the full exploitation of the potential offered by cross-domain datasets.
Inter-disciplinary research and applications are critical in today’s complex world, where societal challenges break through the traditional boundaries of disciplines and scales.
However, it still faces specific challenges, created by the unique association of diverse science and user communities, which are not always easy to bring together, define and characterize. As such, the development of flexible visualization and dissemination platforms capable of meeting the needs and requirements of these users is needed.
At present, different standards exist in Earth science communities, creating important obstacles to inter-disciplinary research and applications, such as different data formats, structures, technical jargons, etc. Earth science communities need to work together and develop common standards for data products and software to overcome these difficulties.
This thematic volume seeks innovative works, with an important focus on methodological issues, describing Earth science data and information service activities for interdisciplinary research and applications. These include (1) existing tools or data services, ongoing work/project/tool development across the complete data processing, dissemination, and uptake cycle;
(2) the challenges and barriers encountered, lessons learned, experiences, and suggestions with existing tools or services, and ideas/concepts for future data and information services;
(3) emerging solutions for Earth science data analysis and distribution including machine- and deep-learning, cloud data services and applications; or (4) natural language processing (NLP) for findable and accessible datasets for training purposes, service development, and implementation. Addressing the specific needs and challenges facing the big-data and machine-learning community and how these connect to science, education, and societal challenges.
Specific topics of interest include, but are not limited to:
Large Scale Climate Data Analytics;
Open Government and Open Science: more than just repositories;
Ecological monitoring from the local to the global scale;
Ecosystem and geo services;
Alert services using remote sensing, weather forecasts, and observations (including citizen-science);
Education. How successful storytelling needs inter-disciplinary data and information;
Disaster response, mitigation, and recovery;
Data pre-processing (remote sensing) in the cloud;
Science data services and platform development;
a) First submission: January 2020
b) Submission deadline: September 15, 2020
c) Completion of review and revision process: February 2021
Vasco M. Mantas, Univeristy of Coimbra, vasco.mantas @dct.uc.pt
Zhong Liu, George Mason University Fairfax / NASA GSFC GES DISC (CSISS, George Mason University), email@example.com
Jennifer C. Wei, NASA Goddard Space Flight Center, firstname.lastname@example.org
Menglin Jin, University of Maryland – College Park, email@example.com