Looking for data

Friedrich, Tanja

doi:10.18452/22173

Cited by 6 publications

(6 citation statements)

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“…They understand data retrieval as 'a complex socio-technical process', given the importance of personal networks, literature, and collaborations in the process. This study suggests the significance of personal networks, literature and collaborations in data discovery, which is also supported by the findings in Friedrich (2020), Marée (2016, Yoon (2014), andKrämer et al (2021).…”

Section: Related Worksupporting

confidence: 86%

“…Comparing the results of the ranking to the needs of scientists, as identified in user studies, we found that personal networks, literature, and collaborations are seen as quite important (Friedrich 2020;Marée 2016;Yoon 2014;Krämer et al 2021), while in the user studies, the corresponding clusters were not highly prioritised. This is of course, due to the different perspectives of these two groups of people.…”

Section: Discussionmentioning

confidence: 93%

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What are Researchers’ Needs in Data Discovery? Analysis and Ranking of a Large-Scale Collection of Crowdsourced Use Cases

Mathiak

Juty

Bardi

et al. 2023

Data Science Journal

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Data discovery is important to facilitate data re-use. In order to help frame the development and improvement of data discovery tools, we collected a list of requirements and users' wishes. This paper presents the analysis of these 101 use cases to examine data discovery requirements; these cases were collected between 2019 and 2020. We categorized the information across 12 'topics' and eight types of users. While the availability of metadata was an expected topic of importance, users were also keen on receiving more information on data citation and a better overview of their field. We conducted and analysed a survey among data infrastructure specialists in a first attempt at ranking the requirements. Between these data professionals, these rankings were very different, excepting the availability of metadata and data quality assessment.

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Section: Related Worksupporting

confidence: 86%

Section: Discussionmentioning

confidence: 93%

What are Researchers’ Needs in Data Discovery? Analysis and Ranking of a Large-Scale Collection of Crowdsourced Use Cases

Mathiak

Juty

Bardi

et al. 2023

Data Science Journal

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“…An online repository has to take all its different user groups into consideration, as they have different goals, requirements, problems, and techniques for problem solving. Experienced social scientists require well-documented data of topical fit, high quality, and comparability (Friedrich 2020). Novice researchers are heavily influenced by more experienced social science researchers when it comes to discovering, evaluating, and justifying their reuse of others' data (Faniel et al 2012).…”

Section: Four Key Challenges For Sharing Data In the Social Sciences ...mentioning

confidence: 99%

Five Suggestions Towards User-Centred Data Repositories in the Social Sciences

Kruithof,

Vanroelen,

Van den Borre

2024

Data Science Journal

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Data repositories for the social sciences are facing some discipline-specific challenges. This essay provides an overview of the four key challenges. In addition, five suggestions are made to strengthen domain-specific online data repositories in the social sciences, supported by good practices. Using a user-centred approach, these suggestions aim to further open up the social sciences data landscape. The essay first addresses insights regarding the sharing of quantitative and qualitative data in light of the specific needs and issues regarding these two types of data. Thereafter, we suggest (i) 'data labs' for novice data re-users, (ii) advocate for the repository as an online meeting place for researchers, (iii&iv) argue for two concrete metadata approaches for quantitative and qualitative data separately, (v) provide ideas for handling epistemological challenges of data sharing, and (vi) introduce the data repository as a possible contact medium for research participants.

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“…A comprehensive literature review on dataset retrieval is provided in Gregory et al (2019) focusing on dataset retrieval practices in different disciplines. Research in this area covers, for instance, the analysis of information-seeking behaviour during dataset retrieval through observations Krämer et al (2021), questionnaires and interviews (Kern and Mathiak, 2015;Friedrich, 2020), and transactionlog studies (Kacprzak et al, 2017;Carevic et al, 2020). In Kern and Mathiak (2015), the authors investigated the requirements that users have for a dataset retrieval system.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Recently, numerous studies have been conducted to further identify the characteristics of dataset retrieval. These studies include the observation of data retrieval practices (Krämer et al, 2021), interviews and online questionnaires (Kern and Mathiak, 2015;Friedrich, 2020) and transaction log analysis (Kacprzak et al, 2017;Carevic et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Retrievability in an integrated retrieval system: an extended study

2023

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Retrievability measures the influence a retrieval system has on the access to information in a given collection of items. This measure can help in making an evaluation of the search system based on which insights can be drawn. In this paper, we investigate the retrievability in an integrated search system consisting of items from various categories, particularly focussing on datasets, publications and variables in a real-life digital library. The traditional metrics, that is, the Lorenz curve and Gini coefficient, are employed to visualise the diversity in retrievability scores of the three retrievable document types (specifically datasets, publications, and variables). Our results show a significant popularity bias with certain items being retrieved more often than others. Particularly, it has been shown that certain datasets are more likely to be retrieved than other datasets in the same category. In contrast, the retrievability scores of items from the variable or publication category are more evenly distributed. We have observed that the distribution of document retrievability is more diverse for datasets as compared to publications and variables.

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Looking for data

Cited by 6 publications

References 0 publications

What are Researchers’ Needs in Data Discovery? Analysis and Ranking of a Large-Scale Collection of Crowdsourced Use Cases

What are Researchers’ Needs in Data Discovery? Analysis and Ranking of a Large-Scale Collection of Crowdsourced Use Cases

Five Suggestions Towards User-Centred Data Repositories in the Social Sciences

Retrievability in an integrated retrieval system: an extended study

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