Open Data User Needs: A Preliminary Synthesis

Degbelo, Auriol

doi:10.1145/3366424.3386586

Cited by 7 publications

(9 citation statements)

References 34 publications

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“…However, as we observed in Quarati et al [62], data quality assessment tasks frequently involve providing judgements on some quality dimensions not generally measurable through a procedure alone, but that require qualitative assertions of their importance for a given scenario. For instance, contextual factors concerning users’ needs [69], competencies and skills [45] can affect their approach to OGD resources, and hence the resulting number of views and downloads. As discussed in section ‘Usage metrics’, our data sets’ usage analysis is based exclusively on direct portal users that we measure with the two metrics Views and Downloads.…”

Section: Discussionmentioning

confidence: 99%

Open Government Data: Usage trends and metadata quality

Quarati

2021

Journal of Information Science

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Open Government Data (OGD) have the potential to support social and economic progress. However, this potential can be frustrated if these data remain unused. Although the literature suggests that OGD data sets’ metadata quality is one of the main factors affecting their use, to the best of our knowledge, no quantitative study provided evidence of this relationship. Considering about 400,000 data sets of 28 national, municipal and international OGD portals, we have programmatically analysed their usage, their metadata quality and the relationship between the two. Our analysis has highlighted three main findings. First, regardless of their size, the software platform adopted, and their administrative and territorial coverage, most OGD data sets are underutilised. Second, OGD portals pay varying attention to the quality of their data sets’ metadata. Third, we did not find clear evidence that data sets’ usage is positively correlated to better metadata publishing practices. Finally, we have considered other factors, such as data sets’ category, and some demographic characteristics of the OGD portals, and analysed their relationship with data sets’ usage, obtaining partially affirmative answers.

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Section: Discussionmentioning

confidence: 99%

Open Government Data: Usage trends and metadata quality

Quarati

2021

Journal of Information Science

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“…Gregory et al (2020a) moreover show that data needed for a research task can be diverse data from different sources of various types. Degbelo (2020) formulate 27 open data user needs as a synthesis of current findings from recent literature focusing on smart city data. They structure the statements to 10 categories.…”

Section: Related Workmentioning

confidence: 99%

“…They structure the statements to 10 categories. One of the categories is called serendipitous resource discovery (SRD) which involves user questions such as “Are there datasets that I never thought of, that could also be relevant to my tasks?.” Degbelo (2020) emphasize the need of cross-linking to other datasets related to the dataset being looked at by the user.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of missing information about data is inherently related to the big data phenomenon and is generally discussed as the problem of data findability by Zezula (2015). In their studies, Gregory et al (2020a), Koesten (2018) and Degbelo (2020) show that users typically need to search for more than a single, isolated dataset. Typically, the users wish to find multiple datasets similar to each other in some way, and this is where the pure metadata-based search methods come up short.…”

Section: Introductionmentioning

confidence: 99%

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Modular framework for similarity-based dataset discovery using external knowledge

Nečaský

Škoda

Bernhauer

et al. 2022

DTA

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PurposeSemantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.Design/methodology/approachIn this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.FindingsThe study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.Originality/valueTo the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

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“…• creation of smart city living labs, by harnessing the use of information technologies and knowledge infrastructure-making sense of vast quantities of data and large volumes of information, to deliver high-quality services and infrastructures, alongside with innovative urban management (Arman, Abbas, & Hurriyati, 2015;Degbelo, 2020;Moustaka et al, 2019;Nam & Pardo, 2011;Ruhlandt, 2018).…”

Section: Smart Cities Backgroundmentioning

confidence: 99%