Implementation of Metadata Quality Metrics and Application on Public Government Data

Reiche, Konrad Johannes; Höfig, Edzard

doi:10.1109/compsacw.2013.32

Cited by 44 publications

(41 citation statements)

References 3 publications

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“…In our theory, this happens due to poor metadata that do not provide useful guidance. Although we could not test this cause-effect relationship, we believe that it is safe and reasonable to map the code "Lack of Metadata" with Understandability, given that also in the literature metadata are considered fundamental for the right comprehension of the dataset (see Reiche & Hofig (2013)). In addition, "Lack of Metadata" is also mapped to compliance due to the existence of a standard for metadata in Open Government Data sets.…”

Section: Quality Characteristics and Metrics Definition: Methodsmentioning

confidence: 97%

Open data quality measurement framework: Definition and application to Open Government Data

Vetrò

Canova

Torchiano

et al. 2016

Government Information Quarterly

286

210

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The diffusion of Open Government Data (OGD) in recent years kept a very fast pace. However, evidence from practitioners shows that disclosing data without proper quality control may jeopardize datasets reuse and negatively affect civic participation. Current approaches to the problem in literature lack of a comprehensive theoretical framework. Moreover, most of the evaluations concentrate on open data platforms, rather than on datasets. In this work, we address these two limitations and set up a framework of indicators to measure the quality of Open Government Data on a series of data quality dimensions at most granular level of measurement. We validated the evaluation framework by applying it to compare two cases of Italian OGD datasets: an internationally recognized good example of OGD, with centralized disclosure and extensive data quality controls, and samples of OGD from decentralized data disclosure (municipalities level), with no possibility of extensive quality controls as in the former case, hence with supposed lower quality. Starting from measurements based on the quality framework, we were able to verify the difference in quality: the measures showed a few common acquired good practices and weaknesses, and a set of discriminating factors that pertain to the type of datasets and the overall approach. On the basis of this evaluation, we also provided technical and policy guidelines to overcome the weaknesses observed in the decentralized release policy, addressing specific quality aspects.

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Section: Quality Characteristics and Metrics Definition: Methodsmentioning

confidence: 97%

Open data quality measurement framework: Definition and application to Open Government Data

Vetrò

Canova

Torchiano

et al. 2016

Government Information Quarterly

286

210

View full text Add to dashboard Cite

show abstract

“…Having metadata as part of a published dataset is the first step in putting a dataset on the open data map (thus encouraging discoverability [50]), as it is generally the first access point for consumers who wish to use the published data. Metadata ensures that it complies with best practices by making it self-descriptive [29, §5.5].…”

Section: 'O'penness In the Linked Open Data Cloudmentioning

confidence: 99%

“…A visualisation of the LOD Cloud against the overall quality values is available at http://jerdeb.github.io/lodqa/ overall_vis.html50 This is the only query the Luzzu framework does on the endpoint, until all results are retrieved.…”

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confidence: 99%

Evaluating the quality of the LOD cloud: An empirical investigation

Debattista

Lange

Auer

et al. 2018

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The increasing adoption of the Linked Data principles brought with it an unprecedented dimension to the Web, transforming the traditional Web of Documents to a vibrant information ecosystem, also known as the Web of Data. This transformation, however, does not come without any pain points. Similar to the Web of Documents, the Web of Data is heterogenous in terms of the various domains it covers. The diversity of the Web of Data is also reflected in its quality. Data quality impacts the fitness for use of the data for the application at hand, and choosing the right dataset is often a challenge for data consumers. In this quantitative empirical survey, we analyse 130 datasets (≈ 3.7 billion quads), extracted from the latest Linked Open Data Cloud using 27 Linked Data quality metrics, and provide insights into the current quality conformance. Furthermore, we publish the quality metadata for each assessed dataset as Linked Data, using the Dataset Quality Vocabulary (daQ). This metadata is then used by data consumers to search and filter possible datasets based on different quality criteria. Thereafter, based on our empirical study, we present an aggregated view of the Linked Data quality in general. Finally, using the results obtained from the quality assessment empirical study, we use the Principal Component Analysis (PCA) test in order to identify the key quality indicators that can give us sufficient information about a dataset's quality.

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“…In the context of the public sector, open data creates opportunities for stakeholders such as private sector organizations and non-profit organizations to discover new insights and generate new products and services from the data. Hence, open data is often understood as the only non-privacy-restricted and non-confidential data which is produced with public money and is made available without any restrictions on its usage and distribution [1,10,11].…”

Section: Researchers and Practitioners Have Understood And Defined Opmentioning

confidence: 99%

The Managerial Dimension of Open Data Success: Focusing on the Open Data Initiatives in Korean Local Governments

Kim

Eom

2019

Sustainability

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Open government data (open data) initiatives have been at the forefront of the strategy to make more transparent, responsive, and accountable government, and thereby lead to open innovation across the public and private sector. Governments around the world often understand that open data is disclosing their data to the public as much as possible and that open data success is the result of a data and technology-related endeavor rather than the result of organizational, institutional, and environmental attributes. According to the resource-based theory, however, managerial capability to mobilize tangible and intangible resources and deploy them in adequate places or processes under the leadership of capable leaders during the information technology (IT) project is a core factor leading to organizational performance such as open data success. In this vein, this study aims to analyze managerial factors as drivers and challenges of open data success from the resource-based theory. Findings illustrate that managerial factors are the driving forces that often boost or hinder open data success when institutional, socio-economic, and demographic factors are controlled. Discussion illustrates theoretical and practical implications for the managerial factors as drivers and challenges of open data success in terms of the comparison between technological determinism and the socio-technical perspective.

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Implementation of Metadata Quality Metrics and Application on Public Government Data

Cited by 44 publications

References 3 publications

Open data quality measurement framework: Definition and application to Open Government Data

Open data quality measurement framework: Definition and application to Open Government Data

Evaluating the quality of the LOD cloud: An empirical investigation

The Managerial Dimension of Open Data Success: Focusing on the Open Data Initiatives in Korean Local Governments

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