Accessing government statistical information

Marchionini, Gary; Haas, Stephanie W.; Zhang, J.; Elsas, Jonathan L.

doi:10.1109/mc.2005.393

Cited by 11 publications

(17 citation statements)

References 4 publications

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“…Currently dataset search is mainly performed over metadata, so the users understanding of what the dataset contains before download is limited by the quality, comprehensiveness and nature of metadata. A number of frameworks or SERP designs have been proposed as research prototypes for data search and exploration, such as TableLens ( [114], DataLens [94], the relation browser [97] for sensemaking with statistical data, or summarization approaches of aggregate query answers in databases [136]. Navigational structures can support the cognitive representation of information [118] and we see a large space to explore interfaces that allow more complex interaction with datasets such as sophisticated querying [69] (e.g.…”

Section: Results Presentationmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

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Section: Results Presentationmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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“…Sensemaking in secondary data use has been subject of studies in several domains, e.g. science [20,69] and open government data [16,43].…”

Section: Collaborative Sensemakingmentioning

confidence: 99%

Collaborative Practices with Structured Data

Koesten

Kacprzak

Tennison

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Collaborative work with data is increasingly common and spans a broad range of activities -from creating or analysing data in a team, to sharing it with others, to reusing someone else's data in a new context. In this paper, we explore collaboration practices around structured data and how they are supported by current technology. We present the results of an interview study with twenty data practitioners, from which we derive four high-level user needs for tool support. We compare them against the capabilities of twenty systems that are commonly associated with data activities, including data publishing software, wikis, web-based collaboration tools, and online community platforms. Our findings suggest that data-centric collaborative work would benefit from: structured documentation of data and its lifecycle; advanced affordances for conversations among collaborators; better change control; and custom data access. The findings help us formalise practices around data teamwork, and build a better understanding how people's motivations and barriers when working with structured data. CCS CONCEPTS• Human-centered computing → Collaborative and social computing systems and tools; • Information systems → Collaborative and social computing systems and tools; Data exchange.

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“…In particular, Borning, Friedman, Davis, and Lin [1] identified the goal of making information "ready-to-hand", that is, easy to access in the course of interacting with UrbanSim, both as an end in itself and also in support of transparency. Prior work on the GovStat project [13], an effort to help people find and understand statistical information provided by the United States federal government, faced similar problems with presenting metadata and explaining unfamiliar abstractions.…”

Section: Value Sensitive Design Of Indicatorsmentioning

confidence: 99%

“…Where it is necessary for transparency to use specialized terms such as "grid cell," comprehensibility is enhanced by using ready-to-hand FAQs and visual explanations. This approach is similar to the GovStat project's Statistical Interactive Glossary [13].…”

Section: Comprehensibility and Transparencymentioning

confidence: 99%

Engaging and Informing Citizens with Household Indicators

Davis

2008

Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008)

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Urban simulation systems can be a powerful tool for helping to understand the complex, long-term consequences of urban planning decisions. Simulation results are summarized and reported using indicators: aggregate measures such as population density or total minutes of vehicle delay. To citizens without expertise in urban planning, such indicators may be abstract and unfamiliar; they are difficult to compare to experiences of the city today. Household Indicators address this gap by presenting a personalized, household-level view on simulation results through a web-based interface. This paper discusses the design of a Household Indicators tool, employing the Value Sensitive Design theory and methodology.

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Accessing government statistical information

Cited by 11 publications

References 4 publications

Dataset search: a survey

Dataset search: a survey

Collaborative Practices with Structured Data

Engaging and Informing Citizens with Household Indicators

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