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Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.

Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.

This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

Currently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight against this pandemic, this study developed a framework for assessing open government health data at the dataset level, providing a tool to evaluate current open government health data's quality and usability COVID-19.

Based on the review of the existing quality evaluation methods of open government data, the evaluation metrics and their weights were determined by 15 experts in health through the Delphi method and analytic hierarchy process. The authors tested the framework's applicability using open government health data related to COVID-19 in the US, EU and China.

The results of the test capture the quality difference of the current open government health data. At present, the open government health data in the US, EU and China lacks the necessary metadata. Besides, the number, richness of content and timeliness of open datasets need to be improved.

Unlike the existing open government data quality measurement, this study proposes a more targeted open government data quality evaluation framework that measures open government health data quality on a range of data quality dimensions with a fine-grained measurement approach. This provides a tool for accurate assessment of public health data for correct decision-making and assessment during a pandemic.

The purpose of this paper is to develop data quality (DQ) assessment model based on content analysis and metadata analysis.

A literature review of DQ assessment models has been conducted. A study of DQ key performances (KPIs) has been done. Finally, the proposed model has been developed and applied in a case study.

The results of this study shows that the metadata data have important information about DQ in a database and can be used to assess DQ to provide decision support for decision makers.

There is a lot of DQ assessment in the literature; however, metadata are not considered in these models. The model developed in this study is based on metadata in addition to the content analysis, to find a quantitative DQ assessment.

Data quality metadata (DQM) is a set of quality measurements associated with the data. Prior research in data quality has shown that DQM improves decision performance. The same research has also shown that DQM overloads the cognitive capacity of decision-makers. Visualization is a proven technique to reduce cognitive overload in decision-making. This paper aims to describe a prototype decision support system with a visual interface and examine its efficacy in reducing cognitive overload in the context of decision-making with DQM.

The authors describe the salient features of the prototype and following the design science paradigm, this paper evaluates its usefulness using an experimental setting.

The authors find that the interface not only reduced perceived mental demand but also improved decision performance despite added task complexity due to the presence of DQM.

A drawback of this study is the sample size. With a sample size of 51, the power of the model to draw conclusions is weakened.

In today’s decision environments, decision-makers deal with extraordinary volumes of data the quality of which is unknown or not determinable with any certainty. The interface and its evaluation offer insights into the design of decision support systems that reduce the complexity of the data and facilitate the integration of DQM into the decision tasks.

To the best of my knowledge, this is the only research to build and evaluate a decision-support prototype for structured decision-making with DQM.

The purpose of this paper is to employ a case study of the Omeka content management system to demonstrate how the adoption and implementation of a metadata standard (in this case, Dublin Core) can result in contrasting rhetorical arguments regarding metadata utility, quality, and reliability. In the Omeka example, the author illustrate a conceptual disconnect in how two metadata stakeholders – standards creators and standards users – operationalize metadata quality. For standards creators such as the Dublin Core community, metadata quality involves implementing a standard properly, according to established usage principles; in contrast, for standards users like Omeka, metadata quality involves mere adoption of the standard, with little consideration of proper usage and accompanying principles.

The paper uses an approach based on rhetorical criticism. The paper aims to establish whether Omeka’s given ends (the position that Omeka claims to take regarding Dublin Core) align with Omeka’s guiding ends (Omeka’s actual argument regarding Dublin Core). To make this assessment, the paper examines both textual evidence (what Omeka says) and material-discursive evidence (what Omeka does).

The evidence shows that, while Omeka appears to argue that adopting the Dublin Core is an integral part of Omeka’s mission, the platform’s lack of support for Dublin Core implementation makes an opposing argument. Ultimately, Omeka argues that the appearance of adopting a standard is more important than its careful implementation.

This study contributes to our understanding of how metadata standards are understood and used in practice. The misalignment between Omeka’s position and the goals of the Dublin Core community suggests that Omeka, and some portion of its users, do not value metadata interoperability and aggregation in the same way that the Dublin Core community does. This indicates that, although certain values regarding standards adoption may be pervasive in the metadata community, these values are not equally shared amongst all stakeholders in a digital library ecosystem. The way that standards creators (Dublin Core) understand what it means to “adopt a standard” is different from the way that standards users (Omeka) understand what it means to “adopt a standard.”

Satellite images can be a powerful source of data for analyses of conflict dynamics and social movements, but sociology has been slow to develop methods and metadata standards for transforming those images into data. We ask: How can satellite images become useful data? What are the key methodological and ethical considerations for incorporating high-resolution satellite images into conflict research? Why are metadata important in this work? We begin with a review of recent developments in satellite-based social scientific work on conflict, then discuss the technical and epistemological issues raised by machine processing of satellite information into user-ready images. We argue that high-resolution images can be useful analytical tools provided they are used with full awareness of their ethical and technical parameters. To support our analysis, we draw on two novel studies of satellite data research practices during the Syrian war. We conclude with a discussion of specific methodological procedures tried and tested in our ongoing work.

Open government data (OGD) has emerged as a radical paradigm shift and endeavor among government administrations across the world mainly due to its promises of transparency, accountability, public-private collaboration, civic participation, social innovation and data-driven value creation. Complexity, cross-cutting nature, diversity of data sets, interoperability and quality issues usually hamper unlocking the full potential value of data. To tackle these challenges, this paper aims to provide a novel solution using a top-down approach.

In this paper, the authors propose a systematic ontology-based approach combined with a novel architecture and its corresponding processes enabling organizations to carry out all the steps in the OGD value chain. In addition, an OGD Platform including a portal (www.iranopendata.ir) and a data management system (www.ogdms.iranopendata.ir) are developed to showcase the proposed solution.

The efficiency and the applicability of the solution are evaluated by a real-life use case on energy consumption of the buildings of the city of Tehran, Iran. Finally, a comparison was made with existing solutions, and the results show the proposed approach is able to address the existing gaps in the literature.

The results imply that modeling and designing the data model, as well as exploiting an ontology-based approach are critical pillars to create rich, relevant and well-described OGD data sets. Moreover, clarity on processes, roles and responsibilities are the key factors influencing the quality of the published data services. Thus, to the best of the knowledge, this is the first study that exploits and considers an ontology-based approach in a top-down manner to create OGD data sets.

An issue of increased interest in metadata research concerns finding ways to store, in the metadata of an information resource, data regarding the resource's quality. The purpose of this paper is to present a metadata schema that facilitates representation and storage of data related to the quality of an e‐commerce resource, the e‐commerce evaluation metadata (ECEM) schema.

A study of quality approaches that can be applied for the evaluation of e‐commerce resources is provided. The ECEM schema structure and elements are described. To demonstrate how ECEM can be used, two indicative examples are given: describing an e‐commerce quality approach, and storing quality evaluation results. A discussion about the validation and implementation of ECEM is also provided.

It has been demonstrated that ECEM can be effectively used to describe e‐commerce quality approaches. ECEM also facilitates the structured representation and storage of quality evaluation results. It is recommended that ECEM metadata be encoded using the eXtensible markup language (XML); thus, a corresponding XML schema has been produced.

Metadata about resources' quality has been developed ad hoc, according to the needs of each particular application domain, and cannot be applied in other contexts. E‐commerce is one application domain where no such contributions currently exist. ECEM is a step towards the reusable and interoperable storage of quality information in metadata. It is expected to facilitate a large number of potential applications, such as quality‐oriented search of e‐commerce resources and reusable storage of evaluation results.