Talking datasets – Understanding data sensemaking behaviours

Koesten, Laura; Gregory, Kathleen; Groth, Paul; Simperl, Elena

doi:10.1016/j.ijhcs.2020.102562

Cited by 51 publications

(20 citation statements)

References 40 publications

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“…In Koesten et al., 37 the authors describe the information structures needed for both qualitative and quantitative data reuse among researchers, highlighting that, while there is a lot of overlap, there are also certain aspects of the study design worth detailing for specific methods, due to their high impact on results. This includes, for instance, information about whether a survey question is required or not, because, for the former, participants are more likely to select a random answer to be able to continue, also mentioned by Koesten et al.…”

Section: Dataset Reuse: the View Of The Literaturementioning

confidence: 99%

“…This includes, for instance, information about whether a survey question is required or not, because, for the former, participants are more likely to select a random answer to be able to continue, also mentioned by Koesten et al. 37 …”

Section: Dataset Reuse: the View Of The Literaturementioning

confidence: 99%

“…Discussions of data quality often include how well an awareness of uncertainty attached to the dataset is communicated, 33 , 37 as well as the negotiation of potential biases or the meaning of missing values. All these aspects often require the user to access additional information, and remain challenging.…”

Section: Dataset Reuse: the View Of The Literaturementioning

confidence: 99%

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Dataset Reuse: Toward Translating Principles to Practice

et al. 2020

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Section: Dataset Reuse: the View Of The Literaturementioning

confidence: 99%

Section: Dataset Reuse: the View Of The Literaturementioning

confidence: 99%

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Dataset Reuse: Toward Translating Principles to Practice

et al. 2020

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“…Despite the variety and richness of the literature on the topic, only a limited number of studies address how and when researchers reuse data they obtain from other researchers (Curty et al, 2017;Gregory et al, 2019b;Koesten et al, 2019;Borst & Limani, 2020), and even fewer studies address how such data recycling practices could potentially contribute to sustainability (Jetzek et al, 2019;Turki et al, 2019).…”

Section: ) Literature Reviewmentioning

confidence: 99%

Research Data Recycling through Open Sharing and Reuse: A Case Study of Sustainable Digital Good Consumption in the Sharing Economy

Niankara¹

2020

Preprint

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In order to meet the needs of an increasingly complex research landscape, researchers engage in “collaborative prosumption” through open data sharing and reuse. Although significant gains have been achieved in this regards because of growing requirements from funding agencies, governments and journals, the question of how reuse of openly available data for new research contribute to sustainability is yet to be appropriately addressed in the literature. Therefore, relying on a three stage stratified clustered random sampling of the Journal of Applied Econometrics data archive (JAEDA), the present research provides a case study of the value of research data recycling for sustainable research and economic development. More specifically our analysis show that reformatting from wide to long format, openly shared equity price index data on eleven European countries’ extracted from JAEDA, and augmented with country level geospatial Meta data, provides a new basis for interesting descriptive analytics and spatio-temporal econometric modeling and inference. Given the ever-increasing volume of openly available research data, our study provides a first-hand insight on open data reuse, which should benefit all stakeholders in the research community, as they seek sustainable solutions for scientific productivity and progress.

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“…We define data sensemaking as detecting trends or differences in sets of data without the use of formal analytical tools [4,23,24]. We draw evidence from perception [25] and numerical cognition [26] to suggest that data sensemaking relies on the same perceptual mechanisms that allow people to quickly aggregate and summarize complex information from the environment [27].…”

Section: Introductionmentioning

confidence: 99%

A Model of Scientific Data Reasoning

Masnick

Morris

2022

Education Sciences

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Data reasoning is an essential component of scientific reasoning, as a component of evidence evaluation. In this paper, we outline a model of scientific data reasoning that describes how data sensemaking underlies data reasoning. Data sensemaking, a relatively automatic process rooted in perceptual mechanisms that summarize large quantities of information in the environment, begins early in development, and is refined with experience, knowledge, and improved strategy use. Summarizing data highlights set properties such as central tendency and variability, and these properties are used to draw inferences from data. However, both data sensemaking and data reasoning are subject to cognitive biases or heuristics that can lead to flawed conclusions. The tools of scientific reasoning, including external representations, scientific hypothesis testing, and drawing probabilistic conclusions, can help reduce the likelihood of such flaws and help improve data reasoning. Although data sensemaking and data reasoning are not supplanted by scientific data reasoning, scientific reasoning skills can be leveraged to improve learning about science and reasoning with data.

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Talking datasets – Understanding data sensemaking behaviours

Cited by 51 publications

References 40 publications

Dataset Reuse: Toward Translating Principles to Practice

Dataset Reuse: Toward Translating Principles to Practice

Research Data Recycling through Open Sharing and Reuse: A Case Study of Sustainable Digital Good Consumption in the Sharing Economy

A Model of Scientific Data Reasoning

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