Big Data Initiative on Resources Management

Dear Colleagues,

Early twenty-first century has witnessed depletion in many of the world’s natural resources, along with increasing waste due to the expansion of human economic activities in an unsustainable way. Sustainable management of resources involves maintaining the balance between resource’s source (i.e., the provision of materials for human use) and sink (i.e., absorption of waste products from human activity) functions in such a way that its quality and availability remain stable overtime. The study of resource management has attracted the broad attention of scholars, with the application of different types of data, such as scientific data from dedicated lab or field trials, to explore specific effects or hypotheses regarding system flows or functions, data in soil, forest, fisheries, freshwater, food, waste, etc. databases based on a mix of observation, climatic data (current/projected), Earth observation data, commercial (e.g., socio-environmental field trial data collected by companies), public statistical data, crowdsourced data, socio-cultural dynamics data, etc.

However, given that most of the natural systems are characterized by ecological complexity, achieving a desirable goal of sustainable resource management would require a good understanding about linkages and interactions of different processes and data within the system in a systematic way. Single source or small scale of data has limitations in explaining such complexities comprehensively. On the contrary, big data can provide holistic understanding of such systems and facilitate achieving sustainable natural resource management. Application of big data has facilitated making decisions with better accuracy based on solid data evidence rather than intuition. Nonetheless, facing the sheer amount of data and greater complexity creating value from big data still remains a primary challenge for businesses and scholars.

This Special Issue of Resources is exploring and examining the role of big data in resources management. In particular, we are looking for:

• Empirical research based articles that examine the interdisciplinary contribution of big data for managing biotic and/or abiotic resources.
• Conceptual paper that provides a comprehensive review and/or theoretical frameworks and methodology for conducting big data research in managing different kinds of resources.
• Case studies of the application of big data or multiple types of data in studies of resources management from a single- or cross-country perspective.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are 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. Resources is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). 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.

• big data assessment tools
• big data infrastructure
• big data analytics
• Hadoop
• sustainability
• natural resource management
• waste management
• four V’s
• open data.