A method for interoperable knowledge-based data quality assessment

Tute, Erik; Scheffner, Irina; Marschollek, Michael

doi:10.1186/s12911-021-01458-1

Cited by 20 publications

(40 citation statements)

References 32 publications

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“…Although 40, 31, and 31% of respondents indicated an absence of policies that support data quality assessment, interoperability and sharing respectively in their institutions, 52, 44, and 39%, respectively, do not know whether their institution has any of these policies. These are in line with the results of recent studies (e.g., Bian et al, 2020;Tute et al, 2021) who found that data quality dimensions of assessment for accuracy, completeness, conformance and plausibility, interoperability, and reuse are limited in practice across disciplines.…”

Section: Institutional Policies That Support Data Management and Sharingsupporting

confidence: 91%

Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management

Ugochukwu

Phillips

2022

The Plant Phenome Journal

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The application of new technologies in scientific research, particularly automated sensing of plant phenotypic performance, has resulted in a deluge of data and raised the question of how these data can be efficiently managed and shared. Many studies have examined the benefits and constraints of data sharing in different disciplines. We focus on plant phenotyping due to the increasing volume of digital data generated in multi‐disciplinary plant phenotyping research. Data sharing and reuse practices in plant phenotyping research have not been widely explored. Study results show that data sharing in plant phenotyping research occurs mostly through direct personal requests based on trust relationships and technical supplements (appendices) to publications, and researchers are willing to share data if incentives and policies are aligned to overcome the barriers. This paper provides empirical evidence to guide the establishment of incentive systems and policy frameworks that support FAIR (findability, accessibility, interoperability, and reusability) data, promote behavioral change, and enhance data sharing for the advancement of science and innovation by research communities, institutions, policymakers, and funders.

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Section: Institutional Policies That Support Data Management and Sharingsupporting

confidence: 91%

Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management

Ugochukwu

Phillips

2022

The Plant Phenome Journal

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“…Comprehensive organizational structures have been established including data stewards for modeling clinical concepts of a domain, responsible parties for each source system, and a data reviewing board for overall, regular analysis of data sets. The in-house development openCQA [6] makes commonly governed compilations (e.g. for reports or dashboards) of various data quality indicators applicable on HiGHmed's technical architecture.…”

Section: Resultsmentioning

confidence: 99%

Towards Harmonized Data Quality in the Medical Informatics Initiative – Current State and Future Directions

Löbe¹,

Wabo

Sinza

et al. 2022

Studies in Health Technology and Informatics

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Health data from hospital information systems are valuable sources for medical research but have known issues in terms of data quality. In a nationwide data integration project in Germany, health care data from all participating university hospitals are being pooled and refined in local centers. As there is currently no overarching agreement on how to deal with errors and implausibilities, meetings were held to discuss the current status and the need to develop consensual measures at the organizational and technical levels. This paper analyzes the discovered similarities and differences. The result shows that although data quality checks are carried out at all sites, there is a lack of both centrally coordinated data quality indicators and a formalization of plausibility rules as well as a repository for automatic querying of the rules, for example in ETL processes.

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“…We used the open source tool openCQA ([ 11 , 27 ] commit c0a8a784 from 2021-02-19) for DQA. We generated simple MMs based on variables’ datatypes calculating results per patient and per site.…”

Section: Methodsmentioning

confidence: 99%

“…A knowledge base (applicable compilation of MMs for a certain task, cf. [ 11 ]) considering this derived DQA-knowledge could for example include an MM that lists all patients without blood pressure values (Additional file 4 : Appendix D shows an example of such a MM for openCQA).…”

Section: Methodsmentioning

confidence: 99%

“…So far, applied DQA-knowledge is often intangible and based on experts’ personal experience [ 6 , 8 , 10 ]. Our previous work proposed an interoperable knowledge-based approach to DQA to support the application and collaborative governance of formalized task and domain dependent DQA-knowledge [ 11 ]. The work we report on here addresses the challenge of learning such DQA-knowledge out of data.…”

Section: Introductionmentioning

confidence: 99%

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A data driven learning approach for the assessment of data quality

Tute

Ganapathy

Wulff

2021

BMC Med Inform Decis Mak

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Background Data quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues. Objectives To explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task. Methods We started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods’ results and corresponding outcome data (data that indicated the data’s suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without. Results Our self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods’ results. Conclusions Our data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods’ results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task.

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A method for interoperable knowledge-based data quality assessment

Cited by 20 publications

References 32 publications

Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management

Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management

Towards Harmonized Data Quality in the Medical Informatics Initiative – Current State and Future Directions

A data driven learning approach for the assessment of data quality

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