Understanding data search as a socio-technical practice

Gregory, Kathleen; Cousijn, Helena; Groth, Paul; Scharnhorst, Andrea; Wyatt, Sally

doi:10.1177/0165551519837182

Cited by 52 publications

(60 citation statements)

References 42 publications

(191 reference statements)

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“…Given how colleagues figure prominently as sources of information for web-based species occurrence information systems, the human dimensions and social networks (not just electronic but human networks in general) of professionals should figure more prominently in outreach and communication strategies aimed at improving discoverability of web-based species occurrence information systems. A review of data retrieval practices across scientific disciplines found that data retrieval is a process that involves not only the user and the information system, but rather includes the user's social networks and personal exchanges as the user navigates finding, obtaining and evaluating data for his or her use [45]. Our results support this broader view of data retrieval as a process that involves social aspects when using data from web-based species occurrence information systems.…”

Section: Plos Onesupporting

confidence: 70%

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Use of web-based species occurrence information systems by academics and government professionals

2020

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Web-based information systems designed to increase access to species occurrence data for use in research and natural resource decision-making have become more prevalent over the past few decades. The effectiveness of these systems depends on their usability and extent of use by their intended audiences. We conducted an online survey of academics and government professionals in the United States to compare their species occurrence data needs and their perceptions and use of web-based species occurrence information systems. Our results indicate that although views and perceptions held by academics and government professionals about the importance, usefulness, and ease of use of these information systems tend to be similar, there were differences in their use of species occurrence data and web-based species occurrence information systems. The baseline information obtained in this study will help inform future directions for improvements in species occurrence information systems.

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Section: Plos Onesupporting

confidence: 70%

“…Our definition of web included not only web-based species occurrence information systems but also search engines and other websites. The use of search engines [28,45,109] and other…”

Section: Sources Of Data and Informationmentioning

confidence: 99%

Use of web-based species occurrence information systems by academics and government professionals

2020

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“…For selected disciplines, similarities in how users search for research data are identified. In [11], the same group of authors investigate the search for research data from a socio-technical perspective by combining results from the literature with conducted interviews.…”

Section: Plos Onementioning

confidence: 99%

Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study

2020

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Open research data practices are a relatively new, thus still evolving part of scientific work, and their usage varies strongly within different scientific domains. In the literature, the investigation of open research data practices covers the whole range of big empirical studies covering multiple scientific domains to smaller, in depth studies analysing a single field of research. Despite the richness of literature on this topic, there is still a lack of knowledge on the (open) research data awareness and practices in materials science and engineering. While most current studies focus only on some aspects of open research data practices, we aim for a comprehensive understanding of all practices with respect to the considered scientific domain. Hence this study aims at 1) drawing the whole picture of search, reuse and sharing of research data 2) while focusing on materials science and engineering. The chosen approach allows to explore the connections between different aspects of open research data practices, e.g. between data sharing and data search. In depth interviews with 13 researchers in this field were conducted, transcribed verbatim, coded and analysed using content analysis. The main findings characterised research data in materials science and engineering as extremely diverse, often generated for a very specific research focus and needing a precise description of the data and the complete generation process for possible reuse. Results on research data search and reuse showed that the interviewees intended to reuse data but were mostly unfamiliar with (yet interested in) modern methods as dataset search engines, data journals or searching public repositories. Current research data sharing is not open, but bilaterally and usually encouraged by supervisors or employers. Project funding does affect data sharing in two ways: some researchers argue to share their data openly due to their funding agency's policy, while others face legal restrictions for sharing as their projects are partly funded by industry. The time needed for a precise description of the data and their generation process is named as biggest obstacle for data sharing. From these findings, a precise set of actions is derived suitable to support Open Data, involving training for researchers and introducing rewards for data sharing on the level of universities and funding bodies.

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“…The data scientists need understand the domain context. They not only have to negotiate the access to data [44] but also negotiate the definition and scope of their tasks. On the other hand, the engineers need to instruct the data scientists and interpret their results.…”

Section: Skills Of a Data Scientist -The Myth Of The Data Science Unimentioning

confidence: 99%

Data Fairy in Engineering Land: The Magic of Data Analysis as a Sociotechnical Process in Engineering Companies

Eckert

Isaksson

Eckert

et al. 2020

Journal of Mechanical Design

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In the era of digitalization, manufacturing companies expect their growing access to data to lead to improvements and innovations. Manufacturing engineers will have to collaborate with data scientists to analyze the ever-increasing volume of data. This process of adopting data science techniques into an engineering organization is a sociotechnical process fraught with challenges. This article uses a participant observation case study to investigate and discuss the sociotechnical nature of the adoption data science technology into an engineering organization. In the case study, a young data scientist/statistician interacted with experienced production engineers in a global automotive organization to mutual satisfaction. However, the case study highlights the mis-aligned expectations between engineers and data scientists and knowledge in what is necessary to successfully benefit from manufacturing process data. The results reveal that the engineers had an initially romantic and idealistic view on how data scientists can bring value out of dispersed and complex information residing in the multisite manufacturing organization’s datasets in a “magic” way. Conversely, the data scientist had not enough engineering and contextual understanding to ask the right questions. The case reveals important shortcomings in the sociotechnical processes that undergo changes as digitalization is brought into mature engineering organizations and points to a lack of knowledge on multiple levels of the data analysis process and the ethical implications this could have.

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Understanding data search as a socio-technical practice

Cited by 52 publications

References 42 publications

Use of web-based species occurrence information systems by academics and government professionals

Use of web-based species occurrence information systems by academics and government professionals

Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study

Data Fairy in Engineering Land: The Magic of Data Analysis as a Sociotechnical Process in Engineering Companies

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