Towards a Contextual Approach to Data Quality

Abstract

In this commentary, I propose a framework for thinking about data quality in the context of scientific research. I start by analyzing conceptualizations of quality as a property of information, evidence and data and reviewing research in the philosophy of information, the philosophy of science and the philosophy of biomedicine. I identify a push for purpose dependency as one of the main results of this review. On this basis, I present a contextual approach to data quality in scientific research, whereby the quality of a dataset is dependent on the context of use of the dataset as much as the dataset itself. I exemplify the approach by discussing current critiques and debates of scientific quality, thus showcasing how data quality can be approached contextually.

Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets

“…However, dataset quality information is not routinely curated and much less represented in a human-and machinereadable manner, despite the fact that international standards for describing the quality of geographic data have been in place since 2003(e.g., ISO 19157: 2013ISO 19115-1:2014). The lack of adoption of one or more data quality standards may in part reflect the diversity of approaches, availability of resources, technologies, networks, and research questions of investigators (Leonelli 2017), as well as the context for the planned purpose and use of the data (Canali 2020;Illari 2014). Lack of motivation to document quality can be caused by the lack of prescriptiveness of existing standards -documentation of data quality metadata has always been optional in the ISO 19100 series and as of 2014, the ISO 19115-1 standard for metadata does not define a minimum set of discovery metadata, which used to suggest at least one data quality element (the provenance of a dataset).…”

Open science, data sharing and solidarity: who benefits?

“…A federated system enables legal data control to remain within the country from which the data originates and the Global Alliance for Genomics and Health (GA4H) was cited as a good example (GA4H, 2016). What is important is that in the development of standards and governance, a contextual approach is taken, that is responsive to the different contexts in which data takes place, the different repositories in which data is held, the different relationships in which data is exchanged, and the different type of data that is shared (Canali, 2020).…”

Credibility and evidence in the handling of SARS-CoV-2

“…These measures are necessary because the number of cases could increase exponentially due to some scientists have estimated that the basic reproductive number of this virus is around 2 (Lipsitch, 2020). Here, we could find a traditional concept of evidence from evidence-based medicine (EBM) which focuses on quality of data, and is implemented in clinical epidemiology regularly with universal criteria of rationality without considering the institutional and local features (Canali, 2020b). I do not mean that the evidence concept from EBM is not adequate.…”