An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries

• URL: http://arxiv.org/abs/2403.08451v1
• Date: Wed, 13 Mar 2024 12:06:42 GMT
• Title: An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries
• Authors: Fillip Molodtsov, Anastasija Nikiforova
• Abstract summary: This study explores the critical role of open government data (OGD) portals in fostering transparency and collaboration between diverse stakeholders. Recognizing the challenges of usability, communication with diverse populations, and strategic value creation, this paper develops an integrated framework for evaluating OGD portal effectiveness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study explores the critical role of open government data (OGD) portals in fostering transparency and collaboration between diverse stakeholders. Recognizing the challenges of usability, communication with diverse populations, and strategic value creation, this paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. The framework is validated by applying it to 33 national portals across European Union and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings. Notably, the study unveils the competitive and innovative nature of GCC OGD portals, pinpointing specific improvement areas such as multilingual support and data understandability. The findings underscore the growing trend of exposing data quality metrics and advocate for enhanced two-way communication channels between users and portal representatives. Overall, the study contributes to accelerating the development of user-friendly, collaborative, and sustainable OGD portals while addressing gaps identified in previous research.

Related papers

• Automating the Identification of High-Value Datasets in Open Government Data Portals [0.0]
  High-Value datasets (HVDs) play a crucial role in the broader Open Government Data (OGD) movement. Identifying HVDs on OGD portals presents a resource-intensive and complex challenge due to the nuanced nature of data value. Our proposal aims to automate the identification of HVDs on OGD portals using a quantitative approach based on a detailed analysis of user interest.
  arXiv  Detail & Related papers  (2024-06-15T07:54:37Z)
• From an Integrated Usability Framework to Lessons on Usability and Performance of Open Government Data Portals: A Comparative Study of European Union and Gulf Cooperation Council Countries [0.0]
  This study proposes an integrated usability framework for evaluating Open Government Data (OGD) portals. The framework is developed and applied to 33 OGD portals from the European Union (EU) and Gulf Cooperation Council (GCC) countries.
  arXiv  Detail & Related papers  (2024-06-13T03:05:36Z)
• Bridging the Gap: Unravelling Local Government Data Sharing Barriers in Estonia and Beyond [0.0]
  Open Government Data (OGD) plays a crucial role in transforming smart cities into sustainable and intelligent entities. This study explores barriers preventing Estonian municipalities from sharing OGD.
  arXiv  Detail & Related papers  (2024-06-12T17:51:47Z)
• Collaborative Active Learning in Conditional Trust Environment [1.3846014191157405]
  We investigate collaborative active learning, a paradigm in which multiple collaborators explore a new domain by leveraging their combined machine learning capabilities without disclosing their existing data and models. This collaboration offers several advantages: (a) it addresses privacy and security concerns by eliminating the need for direct model and data disclosure; (b) it enables the use of different data sources and insights without direct data exchange; and (c) it promotes cost-effectiveness and resource efficiency through shared labeling costs.
  arXiv  Detail & Related papers  (2024-03-27T10:40:27Z)
• Unveiling Diversity: Empowering OSS Project Leaders with Community Diversity and Turnover Dashboards [51.67585198094836]
  CommunityTapestry is a dynamic real-time community dashboard. It presents key diversity and turnover signals that we identified from the literature. It helped project leaders identify areas of improvement and gave them actionable information.
  arXiv  Detail & Related papers  (2023-12-13T22:12:57Z)
• Open-Set Domain Adaptation with Visual-Language Foundation Models [51.49854335102149]
  Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge from a source domain to a target domain with unlabeled data. Open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase.
  arXiv  Detail & Related papers  (2023-07-30T11:38:46Z)
• Towards High-Value Datasets determination for data-driven development: a systematic literature review [0.0]
  'High-value dataset' (HVD) recognized as a key trend in the Open Data Directive area in 2022. There is no standardized approach to assist chief data officers in this.
  arXiv  Detail & Related papers  (2023-05-17T14:22:02Z)
• Assessing Scientific Contributions in Data Sharing Spaces [64.16762375635842]
  This paper introduces the SCIENCE-index, a blockchain-based metric measuring a researcher's scientific contributions. To incentivize researchers to share their data, the SCIENCE-index is augmented to include a data-sharing parameter. Our model is evaluated by comparing the distribution of its output for geographically diverse researchers to that of the h-index.
  arXiv  Detail & Related papers  (2023-03-18T19:17:47Z)
• Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy [86.61829236732744]
  This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity. The goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server. We study the tradeoff between collaborative relaying and privacy leakage due to the additional data sharing among nodes.
  arXiv  Detail & Related papers  (2023-02-28T19:17:03Z)
• A Comprehensive Survey on Source-free Domain Adaptation [69.17622123344327]
  The research of Source-Free Domain Adaptation (SFDA) has drawn growing attention in recent years. We provide a comprehensive survey of recent advances in SFDA and organize them into a unified categorization scheme. We compare the results of more than 30 representative SFDA methods on three popular classification benchmarks.
  arXiv  Detail & Related papers  (2023-02-23T06:32:09Z)
• Data Governance in the Age of Large-Scale Data-Driven Language Technology [79.92626780294258]
  This work proposes an approach to global language data governance that attempts to organize data management amongst stakeholders, values, and rights. The framework we present is a multi-party international governance structure focused on language data, and incorporating technical and organizational tools needed to support its work.
  arXiv  Detail & Related papers  (2022-05-04T00:44:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.