About this Research Topic
The United Nation’s (UN) 2030 Agenda for Sustainable Development has defined 17 sustainable development goals (SDGs) with 174 specific targets for human society. To achieve these SDGs, integrated action on social, environmental and economic challenges are required. Compared with traditional intradisciplinary research norms, convergent, systems, and interdisciplinary approaches are urgently needed to guide the design and adoption of these integrated actions. The big data revolution is regarded as one of the most important keys to facilitate action planning, designing, and implementation, and ultimately to support achieving the SDGs. Big data is now being generated at every second and from earth to space using different new technologies. Analyzing those big data can reveal the history, pinpoint current status, and predict the future of our dynamic planet and human society. Advanced big data cyberinfrastructures can enable faster and smarter action design and societal responses to complex challenges towards the SDGs.
The goal of this article collection is to report recent achievements in harnessing big data to facilitate the SDGs. Any submissions related to recent advances in big data theories, data collection techniques, analysis algorithms, intelligent systems, knowledge discoveries, and big data ethics are welcome. We are particularly interested in submissions using big data to solve real-world challenges related to one or multiple SDGs. Studies that conduct integrated analysis of physical and socioeconomic aspects of this planet are highly encouraged. The spatial and temporal domains of the submissions could vary across multiple scales, which are largely determined by the specific challenges the submissions are addressing. We also encourage submissions resulting from interdisciplinary collaborations on using big data to address challenges related with the SDGs.
Specifically, we are looking forward to contributions related to one or more of the following topics which are broadly related with the SDGs:
(1) Developing new techniques to collect big data, including but not limited to multi-platform remote sensing (proximal, airborne, and satellite), crowdsourcing, information and communications technology (ICT), wireless sensor network (WSN) and internet of things (IoT);
(2) Developing new analysis algorithms (machine learning and deep learning) to fully harness the power of big data for the SDGs;
(3) Developing more accessible and intelligent data management and analysis platforms and cyberinfrastructures to support the SDGs;
(4) Fusing multiple types of big data to holistically improve the data quality and use easiness;
(5) Using big data to study ecosystem service, global climate change, and planetary health;
(6) Using big data to improve the resilience and efficiency of crop and food production;
(7) Using big data to conduct socioeconomic predictions;
(8) Revealing new emerged phenomena and knowledge of both physical and socioeconomic aspects of our planet;
(9) Generating new insights and protocols to resolve challenges in big data ethics;
(10) Promoting big data education, especially in less developed regions;
(11) Building new partnerships and alliances related to big data for sustainable development.
This article collection welcomes diverse article types, including Original Research, Reviews, and Perspective Papers. Upon consultation with the Editors, we may also include Hypothesis & Theory papers, Technology Reports, Mini Reviews, Code, Data Report, General Commentaries, and other article types.
Keywords: sustainable development goals, Big Data, action planning, cyberinfrastructure, socioeconomic challenges, multi-platform remote sensing, machine learning, deep learning, global climate change, ecosystems, SDG's
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.