scholarly journals International Transfers of Health Research Data Following Schrems II: A Problem in Need of a Solution

2020 ◽  
Author(s):  
Dara Hallinan ◽  
Alexander Bernier ◽  
Anne Cambon-Thomsen ◽  
Francis P. Crawley ◽  
Diana Dimitrova ◽  
...  
Keyword(s):  
Health Research ◽  
Research Data ◽  
International Transfers

scholarly journals Challenges arising when seeking broad consent for health research data sharing: a qualitative study of perspectives in Thailand

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Phaik Yeong Cheah ◽  
Nattapat Jatupornpimol ◽  
Borimas Hanboonkunupakarn ◽  
Napat Khirikoekkong ◽  
Podjanee Jittamala ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Data Sharing ◽  
Health Research ◽  
Research Data ◽  
Broad Consent

scholarly journals Facilitating Harmonized Data Quality Assessments. A Data Quality Framework for Observational Health Research Data Collections With Software Implementations in R

2020 ◽  
Author(s):  
Carsten Schmidt ◽  
Stephan Struckmann ◽  
Cornelia Enzenbach ◽  
Achim Reineke ◽  
Jürgen Stausberg ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Indicators ◽  
Health Research ◽  
Research Data ◽  
Formal Concept ◽  
Software Implementations ◽  
Quality Framework ◽  
Data Collections ◽  
Quality Assessments ◽  
Data Quality Indicators

Abstract Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).Results The data quality framework comprises 34 data quality indicators. These target three aspects of data quality: compliance with pre-specified structural and technical requirements (Integrity), presence of data values (completeness), and error in the data values (correctness). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses.

scholarly journals Research Stakeholders’ Views on Benefits and Challenges for Public Health Research Data Sharing in Kenya: The Importance of Trust and Social Relations

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0135545 ◽  
Author(s):  
Irene Jao ◽  
Francis Kombe ◽  
Salim Mwalukore ◽  
Susan Bull ◽  
Michael Parker ◽  
...  
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Social Relations ◽  
Health Research ◽  
Public Health Research ◽  
Research Data

scholarly journals The Mutual Enablement of Research Data and Care

2021 ◽  
Author(s):  
Francisca Nordfalk
Keyword(s):  
Public Health ◽  
Health Research ◽  
Population Data ◽  
Public Health Research ◽  
Research Data ◽  
Ethnographic Approach ◽  
Explicit Consent ◽  
Research Population ◽  
Professional Care ◽  
Research Resources

Public health research depends on access to population data. This article is a study of the practices and the work enabling data collection for public health research. In Denmark, a blood sample is taken from practically every single newborn baby through a national screening programme. These samples can be combined with other health data and used for research purposes without explicit consent from those giving the samples. With an ethnographic approach, I study the practices, the work and the workers of the Danish NDBS samples, and explore how newborn babies come to serve as an important national research resource. From these studies, I argue that the making of national research resources in this way is ‘mutual enablement’ of research data and care. The work of both health professionals and researchers mutually enables professional care and opportunities for collection of samples and data for research. It is through this mutual enablement of research data and care that newborn babies become a national research population.

scholarly journals Rates and predictors of data and code sharing in the medical and health sciences: Protocol for a systematic review and individual participant data meta-analysis.

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 491
Author(s):  
Daniel G. Hamilton ◽  
Hannah Fraser ◽  
Fiona Fidler ◽  
Steve McDonald ◽  
Anisa Rowhani-Farid ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Research ◽  
Science Studies ◽  
Research Output ◽  
Meta Analysis ◽  
Research Data ◽  
Health Science ◽  
Data Availability ◽  
Code Sharing ◽  
Public Sharing

Numerous studies have demonstrated low but increasing rates of data and code sharing within medical and health research disciplines. However, it remains unclear how commonly data and code are shared across all fields of medical and health research, as well as whether sharing rates are positively associated with implementation of progressive policies by publishers and funders, or growing expectations from the medical and health research community at large. Therefore this systematic review aims to synthesise the findings of medical and health science studies that have empirically investigated the prevalence of data or code sharing, or both. Objectives include the investigation of: (i) the prevalence of public sharing of research data and code alongside published articles (including preprints), (ii) the prevalence of private sharing of research data and code in response to reasonable requests, and (iii) factors associated with the sharing of either research output (e.g., the year published, the publisher’s policy on sharing, the presence of a data or code availability statement). It is hoped that the results will provide some insight into how often research data and code are shared publicly and privately, how this has changed over time, and how effective some measures such as the institution of data sharing policies and data availability statements have been in motivating researchers to share their underlying data and code.

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