Open data in Nepal: a webometric network analysis

Acharya, Srijana; Park, Han Woo

doi:10.1007/s11135-016-0379-1

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…The limitations that this study faces are similar to those Acharya and Park (2017) face. Firstly, from a total of 71 possible websites, we manually the ones we deemed more important and more relevant to our study and used them as seed sites.…”

Section: Discussionmentioning

confidence: 94%

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Misinformation and Its Stakeholders in Europe: A Web-Based Analysis

Koulas

Anthopoulos

Grammenou

et al. 2021

Lecture Notes in Networks and Systems

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The rise of the internet and computational power in recent years allowed for the exponential growth of misinformation phenomena. An issue that was a non-issue a decade ago, became a challenge for societal cohesion. The emergence of this new threat has led many stakeholders, especially in Europe, to act in order to tackle this phenomenon. This paper provides in its first part a literature review on misinformation in Europe, and in its second part a webometrics analysis on the identified key stakeholders. In the results we discuss who those stakeholders are, what actions do they perform to limit misinformation and whether those actions have an impact.

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Section: Discussionmentioning

confidence: 94%

“…Last but not least, one of the significant studies regarding webometrics is "Open Data in Nepal: A Webometric Analysis", which measures the impact of Open Data in the Nepalese cyber domain. (Acharya & Park, 2017). Acharya and Park's study serves as a guide for this study.…”

Section: Webometrics Related Studiesmentioning

confidence: 99%

Misinformation and Its Stakeholders in Europe: A Web-Based Analysis

Koulas

Anthopoulos

Grammenou

et al. 2021

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

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“…Data feedback is a way to obtain a consensus among stakeholders, including the community. The data providers examine the user feedback about data and again publish modified data after incorporating the data users' feedback [98,173]. The PAs can gather a vast amount of all stakeholders' viewpoints, as evidence-based information, on public data [58,120].…”

Section: Storage Phase Key Functionsmentioning

confidence: 99%

DaLiF: a data lifecycle framework for data-driven governments

2021

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The public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

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