Acquiring High Quality Research Data

Hense, Andreas V.; Quadt, Florian

doi:10.1045/january2011-hense

Cited by 4 publications

(6 citation statements)

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“…Data depositors may also use optional features to improve data curation quality from basic to Category II, which includes the use of custom metadata blocks, dataset versioning, and supporting documentation. The use of managed data curation processes, together with the reputation and reliability of a repository, can influence researchers' perception of data quality [24]. Therefore, the extent to which depositors and data curators use optional repository features can be considered an indicator of the overall quality of a dataset.…”

Section: Encourage Use Of Curation Features and Pre-submission Dataset Reviewmentioning

confidence: 99%

Repository Approaches to Improving the Quality of Shared Data and Code

Trisovic

Mika

Boyd

et al. 2021

Data

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Sharing data and code for reuse have become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible. Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets. This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code. The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.

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Section: Encourage Use Of Curation Features and Pre-submission Dataset Reviewmentioning

confidence: 99%

Repository Approaches to Improving the Quality of Shared Data and Code

Trisovic

Mika

Boyd

et al. 2021

Data

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“…Table 1 gives an overview of some of the differences, as presented by Hense & Quadt (2011). While this perspective on data and text publication is rather technical, differences within the publication process itself can be identified as well.…”

Section: Data Publication Compared To Text Publicationmentioning

confidence: 99%

“…In addition to this logistical requirement, the data themselves have to be prepared for being cited and used by other researchers and thus have to be of high scientific Data Science Journal, Volume 11, 9 November 2012 89 and technical quality (Hense & Quadt, 2011). The process to ensure these quality aspects differs from one institute to the next and is hardly systematised and automated.…”

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Atarrabi - A Workflow System for the Publication of Environmental Data

Quadt¹,

Düsterhus²,

Höck³

et al. 2012

Data Sci. J.

Self Cite

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MOTIVATIONIn these days, research is conducted in a highly computer-aided manner, and it has become more and more challenging to select, annotate, store, and publish the data generated in order to enable other people to find and reuse them for further research (Science Staff, 2011). Besides the technical aspects, scientific quality is a very important factor for determining the scientific value of the data. An example of this kind of research is the field of meteorology, where vast amounts of data are produced in observations and simulations. These data have to be stored and published in a way such that they can be uniquely cited in publications and easily retrieved.Technically speaking, persistent identifiers are already in place, e.g., those of the Handle system or Digital Object Identifiers (DOIs). These identifiers consist of a unique, immutable resource name and a landing page that contains or references the actual content. Both are registered in a central and publicly available directory. If the data move to another site, the landing page value can easily be changed to the new location. Persistent identifiers have been used for scientific articles for some time now and have become increasingly popular for citing research data as well (Paskin, 2005 ;Brase, 2004). Just to mention an example, the citation reference to data which have been assigned the DOI "10.1594/WDCC/dphase_mpeps" looks as follows: The Internet address has been set up on the basis of the DOI, and it enables the reader to directly interpret the DOI and to be directed to the current landing page.Since the availability of research data is an important factor for reuse, the data usually have to enter a long-term archive as a prerequisite for being attributed a persistent identifier. In addition to this logistical requirement, the data themselves have to be prepared for being cited and used by other researchers and thus have to be of high scientific Data Science Journal, Volume 11, 9 November 2012 89 and technical quality (Hense & Quadt, 2011). The process to ensure these quality aspects differs from one institute to the next and is hardly systematised and automated.From April 2009 to March 2012, three complementary institutions conducted the joint research project "Publication of Environmental Data" (http://umwelt.wikidora.com). The project was funded by the German Research Foundation (DFG), with the aim of developing a well-defined scientific data publication process (based on research data that had already been stored in a long-term archive); the project was also aimed at developing a software solution that supports and automates this process. The following roles were represented: meteorologists as subject matter experts for the publication of data from observational experiments,  experts for the management of (meta)data and the data publication and DOI registration procedure, and  business process analysts and software developers.In the following, the approaches to and achievements of the research project will be presented in det...

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“…Recent discussions have expanded on the definition of data quality and acquisition, curation and assuring of high-quality data (e.g., Hense & Quadt, 2011;RIN, 2008;Marchionini et al, 2012). Although common definition of quality includes the terms "fitness for use" (e.g., Madnick, et.…”

Section: Introductionmentioning

confidence: 99%

Visible Evidence of Invisible Quality Dimensions and the Role of Data Management

Yoon¹

iConference 2016 Proceedings

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Past research has shown that data reusers are concerned with the issue of data quality and the identified attributes of quality. While data reusers find evidence of the attributes of data quality during their assessment of data for reuse, there may be other dimensions of data quality that reusers are concerned about but that are not always visible to them. This study explores these invisible dimensions of data quality that have been identified by data reusers. The findings of this study indicate that data reusers are concerned with two kinds of invisible characteristics for assessing the data: the efforts put on data, and the ethics behind the data. While these quality dimensions cannot be easily measured at face-level, data reusers find proxy evidence that indicates the presence of these invisibilities. This finding signifies the role of data management that can make these invisible data qualities visible.

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Acquiring High Quality Research Data

Cited by 4 publications

References 0 publications

Repository Approaches to Improving the Quality of Shared Data and Code

Repository Approaches to Improving the Quality of Shared Data and Code

Atarrabi - A Workflow System for the Publication of Environmental Data

Visible Evidence of Invisible Quality Dimensions and the Role of Data Management

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