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Integrating user feedback with open data quality models
Author(s) -
Weber Nicholas,
Yan An
Publication year - 2017
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2017.14505401172
Subject(s) - open data , computer science , open government , data quality , quality (philosophy) , coding (social sciences) , data science , world wide web , scheme (mathematics) , data mining , engineering , metric (unit) , philosophy , operations management , epistemology , mathematical analysis , statistics , mathematics
User feedback is critical to improving the quality of open data. However, most open data publishers gather only anecdotal evidence about user experiences. This unstructured and informal commentary is, consequently, difficult to translate into actionable steps towards improving data quality. Drawing on user comments collected from Data.Gov ‐ an open data portal providing access to thousands of datasets published by city, state, and federal government agencies in the USA – we inductively develop a classification of reported data quality issues. This poster presents preliminary findings from applying this coding scheme to all issues that users filed on Data.gov in 2015 and 2016. We suggest that our classification scheme can help open data publishers collect structured, actionable information to improve data quality.