Open Data and Robust & Reliable GIScience

Dear Colleagues,

With the growing capability of generating, collecting, and storing individuals’ digital footprints and the emerging open culture, big data of various kinds are flooding everywhere.  Geospatial data are an important component of open data unfolding right in front of our eyes. GIS research is shifting towards analyzing ever-increasing amounts of large-scale, diverse data in an interdisciplinary, collaborative, and timely manner, towards enhancing the robustness and reliability of research. Open GIS should embrace eight dimensions related to data, software, hardware, standards, research, publication, funding, and education facilitated by web-based tools and the growing influence of the open culture.

In line with the spirit of crowdsourcing and citizen science, “robust and reliable” GIScience refers to GIScience research that is reproducible, replicable, and generalizable. Data should be legally and technically open to the scientific community, industry, and the public to use and republish. In other words, data should be provided in open machine-readable formats and readily located, along with the relevant metadata evaluating the reliability and quality of the data to promote increased data use and facilitated credibility determination. The open data initiatives encourage peer production, interactivity, and user-generated innovation, which has stimulated the sharing and distribution of information across communities and disciplines. Transparency and participation through data integration and dissemination across domains and boundaries will facilitate collaboration among researchers, private sectors, and civilian society. Robust and reliable research is the foundation of all scientific development and progress, which depends critically on the ability of researchers to build on prior work.

This Special Issue will provide a forum on addressing theoretical, methodological, and empirical frontiers in Robust and Reliable GIScience. In particular, we encourage (but are not limited to) the following topics:

• Data fusion
• Data mining
• Methodological development to improve the robustness/reliability of GIScience research, especially in the context of reproducibility, replication, and generalizability
• Multi-scale modeling of open data
• Open data movement
• Open data privacy
• Open data theories
• The extent of, causes of, or remedies for GIScience research that is neither replicable, reproducible, nor generalizable

Dr. Daniel Z. Sui
Dr. Xinyue Ye
Dr. Jamal Jokar Arsanjani
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 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.