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      Open data products-A framework for creating valuable analysis ready data

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          Abstract

          This paper develops the notion of “open data product”. We define an open data product as the open result of the processes through which a variety of data (open and not) are turned into accessible information through a service, infrastructure, analytics or a combination of all of them, where each step of development is designed to promote open principles. Open data products are born out of a (data) need and add value beyond simply publishing existing datasets. We argue that the process of adding value should adhere to the principles of open (geographic) data science, ensuring openness, transparency and reproducibility. We also contend that outreach, in the form of active communication and dissemination through dashboards, software and publication are key to engage end-users and ensure societal impact. Open data products have major benefits. First, they enable insights from highly sensitive, controlled and/or secure data which may not be accessible otherwise. Second, they can expand the use of commercial and administrative data for the public good leveraging on their high temporal frequency and geographic granularity. We also contend that there is a compelling need for open data products as we experience the current data revolution. New, emerging data sources are unprecedented in temporal frequency and geographical resolution, but they are large, unstructured, fragmented and often hard to access due to privacy and confidentiality concerns. By transforming raw (open or “closed”) data into ready to use open data products, new dimensions of human geographical processes can be captured and analysed, as we illustrate with existing examples. We conclude by arguing that several parallels exist between the role that open source software played in enabling research on spatial analysis in the 90 s and early 2000s, and the opportunities that open data products offer to unlock the potential of new forms of (geo-)data.

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          Welcome to the Tidyverse

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            GeoDa: An Introduction to Spatial Data Analysis

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              Reproducible research in computational science.

              Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
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                Author and article information

                Contributors
                D.Arribas-Bel@liverpool.ac.uk
                Journal
                J Geogr Syst
                J Geogr Syst
                Journal of Geographical Systems
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1435-5930
                1435-5949
                20 October 2021
                20 October 2021
                : 1-18
                Affiliations
                GRID grid.10025.36, ISNI 0000 0004 1936 8470, Geographic Data Science Lab, Department of Geography and Planning, , University of Liverpool, ; Roxby Building, 74, Bedford St S., Liverpool, L69 7ZT UK
                Article
                363
                10.1007/s10109-021-00363-5
                8528182
                aef41d05-8382-449f-bee1-b7c295510cfb
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 October 2019
                : 29 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000269, Economic and Social Research Council;
                Award ID: ES/L011840/1
                Categories
                Original Article

                geographic data science,open data,open source,c55,c63,c80
                geographic data science, open data, open source, c55, c63, c80

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