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Agile production of high quality open data

Published: 30 May 2018 Publication History
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  • Abstract

    This paper introduces an Agile Methodology to support the production of Open Data (OD) which can be internally adopted and adapted by Public Agencies (PAs) in order to make data publicly available. The methodology is iterative, incremental, evolutionary, test-driven, and collaborative. The novel idea is that PAs produce open data and, at same time, envision and anticipate possible uses and re-uses by citizens. The opportunity is that PAs will publish use cases along with Open Data as a way to engage citizens. The fit-for-use test is a specific step of the methodology in which PAs conceive possible uses of the dataset by creating a set of relevant visualisations (e.g., charts). This step mitigates some well-known barriers in the field of the OD, such as, that the data could be not accurate, not interesting, or too costly to be re-used [27]. The paper describes a platform named SPOD, based on the above agile methodology, meant to be used by PAs over their Intranet to support the collaborative process to make data publicly available. Moreover, the methodology and the SPOD have specific steps to assist PAs in checking the dataset quality through syntactic, sanity and domain-specific steps implemented through a set of heuristics. Both the methodology and the SPOD have been successfully adopted by the Council of the Campania Region in Italy to produce their open data, this experience has detailed described and reported in the paper.

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    Cited By

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    • (2021)Data Quality Categories with a First Exploration Towards AgilityKnowledge Management in Organizations10.1007/978-3-030-81635-3_35(429-443)Online publication date: 17-Jul-2021
    • (2020)Unconventional Usage of a Web-Based Survey: Gathering Quality Data for Green Awareness in LawmakingWeb, Artificial Intelligence and Network Applications10.1007/978-3-030-44038-1_66(720-729)Online publication date: 31-Mar-2020
    • (2019)A Non-prescriptive Environment to Scaffold High Quality and Privacy-aware Production of Open Data with AIProceedings of the 20th Annual International Conference on Digital Government Research10.1145/3325112.3325230(25-34)Online publication date: 18-Jun-2019
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    cover image ACM Other conferences
    dg.o '18: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age
    May 2018
    889 pages
    ISBN:9781450365260
    DOI:10.1145/3209281
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 30 May 2018

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    Author Tags

    1. open data
    2. open data authoring
    3. open government

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    View all
    • (2021)Data Quality Categories with a First Exploration Towards AgilityKnowledge Management in Organizations10.1007/978-3-030-81635-3_35(429-443)Online publication date: 17-Jul-2021
    • (2020)Unconventional Usage of a Web-Based Survey: Gathering Quality Data for Green Awareness in LawmakingWeb, Artificial Intelligence and Network Applications10.1007/978-3-030-44038-1_66(720-729)Online publication date: 31-Mar-2020
    • (2019)A Non-prescriptive Environment to Scaffold High Quality and Privacy-aware Production of Open Data with AIProceedings of the 20th Annual International Conference on Digital Government Research10.1145/3325112.3325230(25-34)Online publication date: 18-Jun-2019
    • (2019)Syntactical Heuristics for the Open Data Quality Assessment and Their ApplicationsBusiness Information Systems Workshops10.1007/978-3-030-04849-5_51(591-602)Online publication date: 3-Jan-2019

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