Rates and predictors of data and code sharing in the medical and health sciences: A systematic review with meta-analysis of individual participant data

Abstract

Objectives: Many meta-research studies have investigated rates and predictors of data and code sharing in medicine. However, most of these studies have been narrow in scope and modest in size. We aimed to synthesise the findings of this body of research to provide an accurate picture of how common data and code sharing is, how this frequency has changed over time, and what factors are associated with sharing. Design: Systematic review with meta-analysis of individual participant data (IPD) from meta-research studies. Data sources: Ovid MEDLINE, Ovid Embase, MetaArXiv, medRxiv, and bioRxiv were searched from inception to July 1st, 2021. Eligibility criteria: Studies that investigated data or code sharing across a sample of scientific articles presenting original medical and health research. Data extraction and synthesis: Two authors independently screened records, assessed risk of bias, and extracted summary data from study reports. IPD were requested from authors when not publicly available. Key outcomes of interest were the prevalence of statements that declared data or code were publicly available, or "available on request" (declared availability), and the success rates of retrieving these products (actual availability). The associations between data and code availability and several factors (e.g., journal policy, data type, study design, research subjects) were also examined. A two-stage approach to IPD meta-analysis was performed, with proportions and risk ratios pooled using the Hartung-Knapp-Sidik-Jonkman method for random-effects meta-analysis. Three-level random-effects meta-regressions were also performed to evaluate the influence of publication year on sharing rate. Results: 105 meta-research studies examining 2,121,580 articles across 31 specialties were included in the review. Eligible studies examined a median of 195 primary articles (IQR: 113-475), with a median publication year of 2015 (IQR: 2012-2018). Only eight studies (8%) were classified as low risk of bias. Useable IPD were assembled for 100 studies (2,121,197 articles), of which 94 datasets passed independent reproducibility checks. Meta-analyses revealed declared and actual public data availability rates of 8% (95% CI: 5-11%, 95% PI: 0-30%, k=27, o=700,054) and 2% (95% CI: 1-3%, 95% PI: 0-11%, k=25, o=11,873) respectively since 2016. Meta-regression indicated that only declared data sharing rates have increased significantly over time. For public code sharing, both declared and actual availability rates were estimated to be less than 0.5% since 2016, and neither demonstrated any meaningful increases over time. Only 33% of authors (95% CI: 5-69%, k=3, o=429) were estimated to comply with mandatory data sharing policies of journals. Conclusion: Code sharing remains persistently low across medicine and health research. In contrast, declarations of data sharing are also low, but they are increasing. However, they do not always correspond to the actual sharing of data. Mandatory data sharing policies of journals may also not be as effective as expected, and may vary in effectiveness according to data type - a finding that may be informative for policymakers when designing policies and allocating resources to audit compliance.

Longitudinal studies of child mental disorders in the general population: A systematic review of study characteristics

“…Some studies included an observational assessment of the child, EMA assessment data, such as written or electronic diaries, app-based data, or physiological sensors, or interviewer assessments of the child, their family, or environment. There is an increasing expectation for researchers to share the data they report on (Mello et al, 2013) although sharing also raises concerns around informed consent, data management, data dissemination, and validation of research contributions (Alter & Vardigan, 2015) and has logistical and cost implications (Hamilton et al, 2023). Many studies did not report on data accessibility and of those that did, the majority reported restricted access-mainly due to data privacy-or required application and/or payment for data access.…”

What do Australians affected by cancer think about oncology researchers sharing research data: a cross-sectional survey

“…Less attention has been paid to what the key contributors to, and ultimate consumers of medical research -patients -think of these practices [11,12]. Furthermore, research on this topic has focussed on certain data types (e.g., omics data [13,14]), study designs for which public sharing of data is not routine (e.g., clinical trials [12,15,16]) or specific cancer types (e.g., breast cancer [4]). Therefore, this study aims to contextualise and build upon previous research into public opinions on data sharing by engaging Australians affected by cancer and characterising their views on the sharing of deidentified research data with third parties, including the public.…”