A beginner's guide to data stewardship and data sharing

The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary

The sharing and documentation of cardiovascular research data are essential for efficient use and reuse of data, thereby aiding scientific transparency, accelerating the progress of cardiovascular research and healthcare, and contributing to the reproducibility of research results. However, challenges remain. This position paper, written on behalf of and approved by the German Cardiac Society and German Centre for Cardiovascular Research, summarizes our current understanding of the challenges in cardiovascular research data management (RDM). These challenges include lack of time, awareness, incentives, and funding for implementing effective RDM; lack of standardization in RDM processes; a need to better identify meaningful and actionable data among the increasing volume and complexity of data being acquired; and a lack of understanding of the legal aspects of data sharing. While several tools exist to increase the degree to which data are findable, accessible, interoperable, and reusable (FAIR), more work is needed to lower the threshold for effective RDM not just in cardiovascular research but in all biomedical research, with data sharing and reuse being factored in at every stage of the scientific process. A culture of open science with FAIR research data should be fostered through education and training of early-career and established research professionals. Ultimately, FAIR RDM requires permanent, long-term effort at all levels. If outcomes can be shown to be superior and to promote better (and better value) science, modern RDM will make a positive difference to cardiovascular science and practice. The full position paper is available in the supplementary materials.

Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing

The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.

Predicting 30-days Readmission Risk for COPD Patients Care through a Federated Machine Learning Architecture on FAIR Data: Development and Validation Study (Preprint)

Background

Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers.

Objective

The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD).

Methods

The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies.

Results

Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases.

Conclusions

Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles.

Discoverability of information on clinical trial data-sharing platforms

OBJECTIVE

This study was intended to (1) provide clinical trial data-sharing platform designers with insight into users' experiences when attempting to evaluate and access datasets, (2) spark conversations about improving the transparency and discoverability of clinical trial data, and (3) provide a partial view of the current information-sharing landscape for clinical trials.

METHODS

We evaluated preview information provided for 10 datasets in each of 7 clinical trial data-sharing platforms between February and April 2019. Specifically, we evaluated the platforms in terms of the extent to which we found (1) preview information about the dataset, (2) trial information on ClinicalTrials.gov and other external websites, and (3) evidence of the existence of trial protocols and data dictionaries.

RESULTS

All seven platforms provided data previews. Three platforms provided information on data file format (e.g., CSV, SAS file). Three allowed batch downloads of datasets (i.e., downloading multiple datasets with a single request), whereas four required separate requests for each dataset. All but one platform linked to ClinicalTrials.gov records, but only one platform had ClinicalTrails.gov records that linked back to the platform. Three platforms consistently linked to external websites and primary publications. Four platforms provided evidence of the presence of a protocol, and six platforms provided evidence of the presence of data dictionaries.

CONCLUSIONS

More work is needed to improve the discoverability, transparency, and utility of information on clinical trial data-sharing platforms. Increasing the amount of dataset preview information available to users could considerably improve the discoverability and utility of clinical trial data.

Physiotherapy interventions for the treatment of spasticity in people with spinal cord injury: a systematic review

STUDY DESIGN

Systematic review.

OBJECTIVE

To determine the effectiveness of physiotherapy interventions for the treatment of spasticity in people with spinal cord injuries.

SETTING

Not applicable.

METHODS

A comprehensive search was undertaken to identify all randomised controlled trials of physiotherapy interventions that included an assessor-reported (objective) or participant-reported (subjective) measure of spasticity. Only trials that provided a physiotherapy intervention on more than one occasion were included. The susceptibility to bias of each trial was rated on the PEDro scale. Data were extracted to derive mean between-group differences (95% CI) for each trial.

RESULTS

Twenty-eight trials were identified but only 17 provided useable data. Seven trials compared a physiotherapy intervention to no intervention (or a sham intervention) and 10 trials compared one physiotherapy intervention to another physiotherapy intervention. The median (IQR) PEDro score of the 17 trials was 6/10 (6-8). The most commonly used assessor- and participant-reported measures of spasticity were the Ashworth scale and Spinal Cord Injury Spasticity Evaluation Tool, respectively. Only one trial demonstrated a treatment effect. This trial compared continuous passive motion of the ankle to no treatment on the Ashworth scale. The remaining 16 trials were either inconclusive or indicated that the treatment was ineffective for reducing spasticity.

CONCLUSIONS

There is no high-quality evidence to indicate that physiotherapy interventions decrease spasticity but this may reflect a lack of research on the topic. Future trials should focus on participant-reported measures of spasticity that distinguish between the immediate, short-term and long-term effects of any physiotherapy intervention.

Data sharing during COVID-19 pandemic: what to take away

INTRODUCTION

In light of the viral outbreak of SARS-CoV-2 that monopolized the focus of the scientific community and general public alike for the past 6 months, one of the greatest contributors in the battle against this pandemic was the international sharing of information. Whether regarding the viral genome, incubation periods, method of transmission, symptoms, dangerous behaviors, age groups at risk, all information was valuable, all data was shared as soon as possible.

AREAS COVERED

Considering that the most severely impacted group of patients are already suffering from other conditions, accessing the impact that metabolic associated fatty liver disease (MAFLD), obesity, and diabetes has on patients by sharing information between different healthcare facilities is of vital importance. However, the value behind open information sharing would remain significant even without a viral outbreak and should there be a more efficient infrastructure in place, the global exchange of data can become more practical and less arduous.

EXPERT OPINION

Since the sharing of data by individual researchers is often motivated by personal benefits, this observed international collaboration is conditional at best, and the widespread misinformation during this pandemic could be an indication of a certain lack of consensus within the scientific community itself.

Towards an Integrated Framework to Measure Smart City Readiness: The Case of Iranian Cities

This paper introduces an indicator system to measure and assess smart city readiness. Analyzing smart city initiatives in Iran as case studies, the theoretical framework we present reflects on how cities explore the possibility of becoming smart, and prepare themselves to begin implementing the transition towards becoming a smart city. This theoretical framework is then applied to four Iranian cities aspiring to become smart and that already possess credible smart city brands. The findings reveal that the most significant difficulty in Iran is associated with the political context. The changing urban governance model is the most important factor in Iranian smart cities’ readiness. Utilization of open data policies and data sharing, as well as making reforms in government structures are all considered a sine qua non to gain momentum. Based on the results of our empirical analysis a Theory of Change is developed to address the cities’ technological, socio-economic, and political readiness vis-à-vis the desired transition. The framework for measuring smart city readiness and the Theory of Change provide practical guidelines to developing systematic roadmaps for developing and implementing smart city policies.

Classifying Pathways for Smart City Development: Comparing Design, Governance and Implementation in Amsterdam, Barcelona, Dubai, and Abu Dhabi

The emergence of the Internet of Things (IoT) as the new paradigm of Information and Communication Technology (ICT) and rapid changes in technology and urban needs urge cities around the world towards formulating smart city policies. Nevertheless, policy makers, city planners, and practitioners appear to have quite different expectations from what smart cities can offer them. This has led to the emergence of different types of smart cities and pathways of development. This paper aims to answer the research question: When comparing a selection of smart city projects, can we classify pathways for their implementation? We do this by using a cross-case research design of four cities to explore commonalities and differences in development patterns. An input-output (IO) model of smart city development is used to retrieve which design variables are at play and lead to which output. The four cases pertain to the following smart city projects: Smart Dubai, Masdar City, Barcelona Smart City, and Amsterdam Smart City. Our analysis shows that Amsterdam is based on a business-driven approach that puts innovation at its core; for Masdar, technological optimism is the main essence of the pathway; social inclusion is the focus of Barcelona Smart City; and visionary ambitious leadership is the main driver for Smart Dubai. Based on these insights, a classification for smart city development pathways is established. The results of the present study are useful to academic researchers, smart city practitioners, and policy makers.

Data sharing practices in randomized trials of addiction interventions

INTRODUCTION

Transparent, open scientific research practices aim to improve the validity and reproducibility of research findings. A key component of open science is the public sharing of data and metadata that constitute the basis for research findings.

METHODS

We conducted a 6 year cross-sectional investigation of the rates and methods of data sharing in 15 high-impact addiction journals that publish clinical trials. We extracted trial characteristics and whether the trial data were shared publicly in any form. We conducted a sensitivity analysis of only trials with public funding sources.

RESULTS

In the included journals, zero (0/394, 0.0%) RCTs shared their data publicly. The large majority (315/394, 79.9%) of included trials received funding from public sources. Eight journals had data sharing policies and published 299 of the included trials (75.9%).

CONCLUSION

Our finding has significant implications for the addiction research community. These implications are broad, ranging from possibly slowed scientific advancement to noncompliance with obligations to the public whose tax dollars funded a large majority of the included RCTs. To improve the rates of data sharing, we recommend studying incentive systems, while simultaneously working to cultivate a data sharing system that emphasizes scientific, rather than author, accuracy.

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

BACKGROUND

Funding agencies and research journals are increasingly demanding that researchers share their data in public repositories. Despite these requirements, researchers still withhold data, refuse to share, and deposit data that lacks annotation. We conducted a meta-synthesis to examine the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data.

METHODS

We searched the published and unpublished literature for studies on data sharing by researchers in academic institutions. Two independent reviewers screened citations and abstracts, then full-text articles. Data abstraction was performed independently by two investigators. The abstracted data was read and reread in order to generate codes. Key concepts were identified and thematic analysis was used for data synthesis.

RESULTS

We reviewed 2005 records and included 45 studies along with 3 companion reports. The studies were published between 2003 and 2018 and most were conducted in North America (60%) or Europe (17%). The four major themes that emerged were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Researchers lack time, resources, and skills to effectively share their data in public repositories. Data quality is affected by this, along with subjective decisions around what is considered to be worth sharing. Deficits in infrastructure also impede the availability of research data. Incentives for sharing data are lacking.

CONCLUSION

Researchers lack skills to share data in a manner that is efficient and effective. Improved infrastructure support would allow them to make data available quickly and seamlessly. The lack of incentives for sharing research data with regards to academic appointment, promotion, recognition, and rewards need to be addressed.