Data Sharing Research Articles

Data management and sharing.

Guided by the FAIR principles (Findable, Accessible, Interoperable, Reusable), responsible data sharing requires well-organized, high-quality datasets. However, researchers often struggle with implementing Data Management and Sharing Plans (DMSPs) due to lack of knowledge on how to do this, time constraints, legal, technical and financial challenges, particularly concerning data ownership and privacy. While patients support data sharing, researchers and funders may hesitate, fearing the loss of intellectual property or competitive advantage. Although some journals and institutions encourage or mandate data sharing, further progress is needed. Additionally, global solutions are vital to ensure equitable participation from low- and middle-income countries. Ultimately, responsible data sharing requires strategic planning, cultural shifts in research, and coordinated efforts from all stakeholders to become standard practice in biomedical research.

Antibiotic treatment versus appendicectomy for acute appendicitis in adults: an individual patient data meta-analysis.

Randomised controlled trials (RCTs) have found antibiotics to be a feasible and safe alternative to appendicectomy in adults with imaging-confirmed acute appendicitis. However, patient inclusion criteria and outcome definitions vary greatly between RCTs. We aimed to compare antibiotics with appendicectomy for the treatment of acute appendicitis using individual patient data and uniform outcome definitions. In this individual patient data meta-analysis, we searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials without language restrictions between database inception and June 6, 2023, for RCTs comparing appendicectomy with antibiotics for the treatment of adults (≥18 years) with imaging-confirmed acute appendicitis. Studies without 1-year follow-up data on complications were excluded, as were patients. Corresponding authors of eligible studies were contacted and invited to share data; individual patient data were merged after validation. One-stage meta-analyses were conducted using a generalised, mixed-effects linear regression model, accounting for clustering of patients within studies. The primary outcome was the complication rate at 1-year follow-up, uniformly harmonised across trials using the Clavien-Dindo classification. Complications were further divided into minor (grade 1-2 or equivalent) and major (grade 3-5 or equivalent) complications. Appendicectomy rate during 1 year was a key secondary outcome but not considered a complication for the antibiotics group. Outcomes were described separately for patients with and without an appendicolith. This study is registered with PROSPERO, CRD42023391676. Of 887 potentially relevant articles, eight were eligible for inclusion, of which six RCTs could provide data for 2101 eligible patients (1050 assigned to antibiotics and 1051 assigned to appendicectomy; 830 [39·5%] women and 1271 [60·5%] men). All studies raised some bias concerns due to absence of blinding. One study was judged to have a high risk of bias due to the exclusion of eligible patients after randomisation, but these patients were eligible for inclusion in our meta-analysis. At 1 year, 57 (5·4%) of 1050 patients randomly assigned to antibiotics had a complication compared with 87 (8·3%) of 1051 patients randomly assigned to appendicectomy (odds ratio [OR] 0·49 [95% CI 0·20 to 1·20]; risk difference -4·5 percentage points [95% CI -11·6 to 2·6]). At 1 year, 1025 (97·5%) patients in the appendicectomy group had undergone appendicectomy compared with 356 (33·9%) patients in the antibiotics group. In patients with an appendicolith at pre-interventional imaging, there were more complications at 1 year among patients who received antibiotic treatment than among those who underwent appendicectomy (29 [15·0%] of 193 patients vs 12 [6·3%] of 190 patients; OR 2·82 [95% CI 1·11 to 7·18]; risk difference 13·2 percentage points [95% CI 2·3 to 24·2]). In the antibiotics group, 94 (48·7%) of 193 patients with an appendicolith underwent appendicectomy within 1 year versus 262 (30·6%) of 857 patients without an appendicolith. This meta-analysis showed that antibiotic treatment in adults with imaging-confirmed acute appendicitis was a safe alternative to surgery and resulted in around two-thirds of patients avoiding appendicectomy during the first year. In patients with an appendicolith, initial antibiotic treatment increased the risk of complications compared with appendicectomy, and around half of these patients assigned to antibiotics underwent step-up appendicectomy within 1 year. These data should be key components in shared decision making. None.

Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C.elegans.

We develop a data harmonization approach for C.elegans volumetric microscopy data, consisting of a standardized format, pre-processing techniques, and human-in-the-loop machine-learning-based analysis tools. Using this approach, we unify a diverse collection of 118 whole-brain neural activity imaging datasets from five labs, storing these and accompanying tools in an online repository WormID (wormid.org). With this repository, we train three existing automated cell-identification algorithms, CPD, StatAtlas, and CRF_ID, to enable accuracy that generalizes across labs, recovering all human-labeled neurons in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. This growing resource of data, code, apps, and tutorials enables users to (1) study neuroanatomical organization and neural activity across diverse experimental paradigms, (2) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (3) share data with the community and comply with data-sharing policies.

Advancing Digital Transformation in Material Science: The Role of Workflows Within the MaterialDigital Initiative

The MaterialDigital initiative represents a major driver toward the digitalization of material science. Next to providing a prototypical infrastructure required for building a shared data space and working on semantic interoperability of data, a core focus area of the Platform MaterialDigital (PMD) is the utilization of workflows to encapsulate data processing and simulation steps in accordance with findable, accessible, interoperable, and reusable principles. In collaboration with the funded projects of the initiative, the workflow working group strives to establish shared standards, enhancing the interoperability and reusability of scientific data processing steps. Central to this effort is the Workflow Store, a pivotal tool for disseminating workflows with the community, facilitating the exchange and replication of scientific methodologies. This article discusses the inherent challenges of adapting workflow concepts, providing the perspective on developing and using workflows in the respective domain of the various funded projects. Additionally, it introduces the Workflow Store's role within the initiative and outlines a future roadmap for the PMD workflow group, aiming to further refine and expand the role of scientific workflows as a means to advance digital transformation and foster collaborative research within material science.

Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework

Flight operations data play a central role in ensuring flight safety, optimizing operations, and driving innovation. However, these data have become a key target for cyber-attacks, and are especially vulnerable to property inference attacks. Aiming at property inference attacks in shared application model training, we proposed FedMeta-CTGAN, a novel approach that leverages federated meta-learning and conditional tabular generative adversarial networks (CTGANs) to protect flight operations data. Motivated by the need for secure data sharing in aviation, as highlighted by the Federal Aviation Administration’s requirement for ADS-B Out equipment on aircraft to create a shared situational awareness environment, our method aims to prevent sensitive information leakage while maintaining model performance. FedMeta-CTGAN exploits the natural privacy-preserving properties of a two-stage update in meta-learning, using real data to train the CTGAN model and synthetic fake data as query data during meta-training. Comprehensive experiments using a real flight operation dataset demonstrate the effectiveness of our proposed method. FedMeta-CTGAN adapts quickly to unbalanced data, achieving a prediction accuracy of 96.33%, while reducing the attacker’s inference AUC score to 0.51 under property inference attacks. Our contribution lies in the development of a secure and efficient data-sharing solution for flight operations data, which has the potential to revolutionize the aviation industry.

DriveGroups: Using Group Perspective for Usable Data Sharing in Research Collaborations

Sharing data with collaborators is a complicated task that is nonetheless fundamental to academic research. We present the results of two studies investigating data sharing within academic scientific collaborations, as well as a system called DriveGroups designed to facilitate data sharing. First, we observed and interviewed 38 academic researchers engaged in collaborative research about their data sharing practices. We found that these researchers struggle to manage access to data, especially when different types of collaborators (e.g., students, co-principal investigators) require different access settings. In response, we built DriveGroups, a Google add-on designed to alleviate participant challenges with access control, and compared its usability to unmodified Google Drive. DriveGroups allows users to manage file access from two separate perspectives: 1) the traditional file perspective and 2) a role-based group perspective, which simplifies the data sharing process. DriveGroups matched or outperformed unmodified Google Drive in terms of usability, access control, and transparency, and will help scientists advance high-impact academic research.

How to: share and reuse data - challenges and solutions from PrIMAVeRa project.

Data sharing accelerates scientific progress and improves evidence quality. Even though journals and funding institutions require investigators to share data, only a small part of studies made their data publicly available upon publication. The procedures necessary to share retrospective data for re-use in secondary data analysis projects can be cumbersome. Predicting the Impact of Monoclonal Antibodies & Vaccines on Antimicrobial Resistance (PrIMAVeRa) is a European research project that aims to develop mathematical models and an epidemiological repository to assess the impact of vaccines and monoclonal antibodies on antimicrobial resistance (AMR). To accomplish the project aim, Work Package 3 was responsible for gathering historical anonymized individual patient datasets. Through a systematic search we have identified 108 eligible studies for data sharing; of which eight have completed all legal requirements and shared their datasets, with data from four infectious syndromes and seven resistant pathogens. The AMR data gathered in PrIMAVeRa project is publicly available in European Clinical Research Alliance on Infectious Disease epidemiology network platform (https://epi-net.eu/primavera/about/anonymized-individual-patient-data/). Challenges and possible solutions in data sharing activities were mapped and discussed: lack of researchers' interest in sharing data, cumbersome ethical and legal requirements, laborious data management procedures, specific requirements for public data access, insufficient training and funding. We expect that experience gained in this project can be useful to improve data sharing; and that the datasets gathered can be used in future AMR projects.

The EU Pollinator Hub: Operationalisation of the EU Bee Partnership Platform for Harmonised Data Collection and Sharing Among Stakeholders on Bees and Pollinators

The EU Pollinator Hub: Operationalisation of the EU Bee Partnership Platform for Harmonised Data Collection and Sharing Among Stakeholders on Bees and Pollinators

Secure Data Sharing and Prediction with Digital Twin and Blockchain in Healthcare

Secure Data Sharing and Prediction with Digital Twin and Blockchain in Healthcare

Seasonal influenza vaccination coverage and the social determinants of influenza vaccination among people over 50 with diabetes in Europe: Analyzing population-based SHARE data for the 2019-2020 and 2021-2022 influenza seasons.

Seasonal influenza vaccination coverage data for adults with chronic medical conditions are very scarce. We aimed to investigate the influenza vaccination rates and explore the common social determinants associated with vaccination in Europe across two influenza seasons. This cross-sectional study used data from the European SHARE survey for the 2019-2020 and 2021-2022 influenza seasons. Participants over 50 diagnosed with diabetes were included, and weighted influenza vaccination rates for both seasons were calculated. Multivariable analyses were used to examine the relationships between vaccination coverage and demographic, physical, social, and healthcare factors over the two seasons. The weighted seasonal influenza vaccination coverage rate was significantly higher in the 2021-2022 season (54%) than in the 2019-2020 season (46%) among people over 50 with diabetes. Factors simultaneously and positively associated with influenza vaccination in both seasons included being aged 65 or older; residing in Western, Northern, or Southern Europe; satisfaction with basic health insurance; having supplementary health insurance; and frequent medical consultations. Despite the increase, the average European influenza vaccination level remains below the EU target of 75%. This study fills an important data gap for the ECDC by providing information on influenza vaccination coverage rates among people over 50 with diabetes. The findings highlight the crucial role of a robust social and healthcare system in promoting vaccination. To improve vaccination rates, the ECDC-funded VENICE network should enhance vaccination knowledge, address socioeconomic disparities by strengthening local programs and funding, and collaborate with various stakeholders to develop regional strategies.

Extended Donor Criteria Outcomes in Adult Congenital Heart Disease Patients Undergoing Heart Transplantation in the United States.

Background: There are very few published outcomes for the use of extended criteria donor (ECD) organs in adult congenital heart disease (ACHD) patients undergoing cardiac transplantation. We investigated outcomes with the use of ECD organs in this population. We hypothesized that ECD organs may be safely used in this population in patients who are younger and carry less vascular and renal disease than the general cardiac transplant population. Methods: United Network of Organ Sharing data from all ACHD heart transplant recipients between October 18, 2018, and September 30, 2023, was analyzed. Patients were grouped by ECD transplantation. The primary outcome of interest was one-year survival. Secondary outcomes included length of stay, posttransplant stroke, retransplantation, kidney transplantation, dialysis, pacemaker implantation, and rejection. Results: Extended criteria donor organs were utilized in 39.1% (200/512) of ACHD heart transplantation cases. There was marked regional variation in the use of ECD organs. Extended criteria donor criteria in this population were solely met by ischemic time ≥4 h in 89.5% (179/200) of cases. Transplantation with ECD donors was associated with lower survival (one-year survival 82.76% vs 90.62%). A multivariate Cox regression analysis suggested a hazard ratio of 1.96 with the use of these organs. Conclusion: Compared with organs accepted under traditional donor criteria, ECD organs appear to compromise short- and long-term survival in ACHD transplant candidates.

Blockchain-based privacy-preserving technology to secure shared data in vehicular communication

Blockchain-based privacy-preserving technology to secure shared data in vehicular communication

Decision of Demand-Responsive Transit Virtual Stops Using Shared Bicycle Data

Decision of Demand-Responsive Transit Virtual Stops Using Shared Bicycle Data

ENHANCING HEALTHCARE VALUE THROUGH INTEROPERABLE E-CARE PLANS: A FRAMEWORK FOR QUALITY IMPROVEMENT

Abstract Patient-centered data is critical for providing high-value care and assessing quality including functional and cognitive status, goals, and outcomes that matter most to people; these are often missing in Electronic Health Records (EHRs). Furthermore, lack of interoperability makes it challenging to share and aggregate data across clinicians and care settings, leading to fragmented and/or duplicative care. E-care plans (eCP) and data standards that focus on goal setting and outcome management are in development, and hold promise to support person- centered care planning across sites and settings of care. E-Care Plans support care coordination and quality improvement and inform future healthcare value-focused policies—such as those related to equitable coverage, quality improvement, measurement, payment, and innovation model planning. Interoperable eCPs could be integrated into clinical care and across the healthcare ecosystem. This integration will enhance care coordination and facilitate seamless information exchange. In this session, Federal officials from the CMS, VA, and AHRQ will explore the benefits of eCPs and discuss the work underway to support the implementation of these plans in clinical care. This presentation will also discuss efforts to develop value sets and FHIR mappings to enable bidirectional information exchange and the concurrent pilot testing of applications with patient data. E-care plans have the potential improve patient safety, reduce burden, support the efficient exchange of patient data such as cognitive function, physical function, and individual goals to support improving person-centered outcomes, advancing health equity, conducting economic analysis, and tailored program development.

Perceptions of Chinese medicine information sharing in electronic health record sharing system (eHRSS) for patients: a mixed-method study

PurposeThe Stage Two Development of eHRSS intends to expand the breadth of shareable data and increase technical capabilities for sharing Chinese Medicine (CM) information. This study aims to investigate patients' perceptions, perceived benefits and difficulties toward the adoption of EC Connect and eHRSS.MethodsAll patients who are eHRSS users or non-eHRSS users were recruited. Several logistic regression models were conducted with descriptive analysis including age, gender, socioeconomic status, and past medical history. In-depth interviews were conducted among Healthcare Recipients (HCRs) of eHRSS whose information about Chinese Medicine is shared to eHRSS.ResultsA total of 884 respondents were analysable in this study. Older age (50–64, compared to 18–29, aOR = 3.02, 95% CI = 1.48–6.17, p = 0.002), participation in eHRSS (aOR = 1.95, 95% CI = 1.17–3.27, p = 0.011) and usage of eHealth app (aOR = 1.87, 95% CI = 1.19–2.95, p = 0.007) were associated with a higher level of acceptability on the expansion of eHRSS sharing scope to CM. In study 2, users agreed that CM information sharing to eHRSS improved patient management while knowledge of CM information exchange was insufficient. The respondents were supportive towards the expansion of sharing scope which could enhance the intention in joining eHRSS.ConclusionsWe found that patients' western medical records should be reviewed to deliver better patient care. For future development of eHRSS in CM, it was important to increase propaganda among the general population and to improve consistency between Western and Chinese medical systems.

WebSEQ: A New Tool for Democratizing Omics Data Sharing.

The relative ease of generation and proliferation of omics datasets has moved considerably faster than the effective dissemination of these data to the scientific community. Despite advancements in making raw data publicly available, many researchers struggle with data analysis and integration. We propose sharing analyzed data through user-friendly platforms to enhance accessibility. Here, we present a free, online tool, for sharing basic omics data in a searchable and user-friendly format. Importantly, it requires no coding or prior computational knowledge to build-only a data spreadsheet. Overall, this tool facilitates the exploration of transcriptomic, proteomic, and metabolomics data, which is crucial for understanding glial diversity and function. This initiative underscores the importance of accessible molecular data in advancing neuroscience research.

Automated redaction of names in adverse event reports using transformer-based neural networks

Abstract Background Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the clinical course of events, differential diagnosis, and patient-reported reflections may often only be conveyed in narrative form. The aim of this study is to develop and evaluate a method for automated redaction of person names in English narrative text on adverse event reports. The target domain for this study was case narratives from the United Kingdom’s Yellow Card scheme, which collects and monitors information on suspected side effects to medicines and vaccines. Methods We finetuned BERT – a transformer-based neural network – for recognising names in case narratives. Training data consisted of newly annotated records from the Yellow Card data and of the i2b2 2014 deidentification challenge. Because the Yellow Card data contained few names, we used predictive models to select narratives for training. Performance was evaluated on a separate set of annotated narratives from the Yellow Card scheme. In-depth review determined whether (parts of) person names missed by the de-identification method could enable re-identification of the individual, and whether de-identification reduced the clinical utility of narratives by collaterally masking relevant information. Results Recall on held-out Yellow Card data was 87% (155/179) at a precision of 55% (155/282) and a false-positive rate of 0.05% (127/ 263,451). Considering tokens longer than three characters separately, recall was 94% (102/108) and precision 58% (102/175). For 13 of the 5,042 narratives in Yellow Card test data (71 with person names), the method failed to flag at least one name token. According to in-depth review, the leaked information could enable direct identification for one narrative and indirect identification for two narratives. Clinically relevant information was removed in less than 1% of the 5,042 processed narratives; 97% of the narratives were completely untouched. Conclusions Automated redaction of names in free-text narratives of adverse event reports can achieve sufficient recall including shorter tokens like patient initials. In-depth review shows that the rare leaks that occur tend not to compromise patient confidentiality. Precision and false positive rates are acceptable with almost all clinically relevant information retained.

AVChain: Trusted Sharing of Autonomous Vehicle Crash Incident Data using Interoperating HyperLedger Fabric Networks and IPFS

Autonomous vehicles (AVs) are gaining in popularity over the years as a viable cab service apps as well as for personal use. However, incidents of crashes involving AVs continue to occur, adversely affecting their prospects for widespread acceptance by both end users and regulatory authorities. While such cases are routinely investigated, in the absence of a human to testify on what caused the crash, one has to rely solely on available data. It is therefore imperative that the data logged by AVs is accessible to the concerned parties in a trustworthy manner. In this paper, we present AVChain - a novel framework for using a permissioned blockchain like HyperLedger Fabric (HLF) to record and share AV data comprised of sensors, actuators, maps, planning algorithms and machine learning models so that the data stays immutable even in the face of cross blaming among involved parties. Since the data volume is extremely large, we appropriately compress and down sample the same before storing in a distributed file system, namely, IPFS (Inter-Planetary File System). The hashes of such IPFS data called Content Ids (CIDs) are committed to the HLF network for making them tamper proof. The HLF ledger can later be queried to obtain the CIDs, which are then further used to retrieve and un-compress the original data from IPFS. Effectiveness and usability of AVChain is demonstrated by generating autonomous vehicle data from CARLA, which is a widely used open source AV simulator. For sharing AV data across organizations like sensor and actuator suppliers, map service providers, machine learning model developers and law enforcement authorities, the Weaver tool has been used to make multiple HLF networks interoperate. We have also developed a web application to demonstrate the working of AVChain. Results of an extensive set of experiments establish the efficacy of our approach.

Availability of Critical Benchmark Experiments for the Pebble Tanker Transportation Model for Nuclear Criticality Safety Validation of TRISO Pebbles

This study addresses the need for comprehensive investigations into TRi-structural ISOtropic (TRISO) fuel pebble transportation validation. In this work, an exploratory model, the pebble tanker(PT), was developed with the aim of facilitating the validation of nuclear criticality safety calculations in the context of industrial-scale transportation of TRISO fuel. The PT model was designed to investigate the availability and applicability of critical benchmark experiments crucial for assessing the transportation of these pebbles. This work incorporated sensitivity/uncertainty (S/U) similarity studies to quantify the applicability of critical benchmark experiments and to address nuclear data uncertainties in the context of TRISO transportation. Two container models were investigated: one for the Hermes-type pebble and one for the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering fuel, containment, and either water or air, to enable a focus on the underlying physics of applications involving TRISO fuel pebbles using the PT model. A crucial aspect under consideration was the capacity of the transport package to hold pebbles while ensuring subcriticality in the flooded state. An approach in the criticality validation process involves assessing the similarity between systems through an integral index parameter evaluation. This involves calculating a correlation coefficient (referred to as ck) based on shared nuclear data–induced uncertainty between a benchmark experiment and the application of the PT model. To facilitate this analysis, the SCALE tools, particularly the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences, were employed for comprehensive studies in neutronics and S/U analysis. Our findings showed that there are sufficient critical experimental benchmarks to perform this validation of the PT model in the most reactive state, i.e. when the tanker is flooded. This paper provides valuable insights into validating a transport package for Generation IV TRISO fuel pebbles.

Research on Service-Oriented Sharing and Computing Framework of Geographic Data for Geographic Modeling and Simulation

Geographic data are the foundation of geographic model construction, and any stage of their acquisition, processing, and analysis may have an impact on the efficiency and quality of geographic modeling and simulation. With the advent of the era of big data, a large number of data resources are generated in the field of geographic information. However, due to the heterogeneity of geographic data and the security of data usage, massive geographic data resources are difficult to fully explore and utilize, resulting in the formation of data islands. This paper proposes a service-oriented geographic data-sharing and computing framework, which provides users with a complete set of geographic data access and application processes (such as data acquisition, processing, configuration, etc.), so as to reduce the difficulty of using data and improve the efficiency of data sharing. The framework mainly consists of three core components: (1) the “Data service container” can publish data resources as data services to provide a consistent data access interface; (2) the “Workspace” provides a series of methods and tools for users to develop data-computing solutions; and (3) the “Data-computing engine” is responsible for performing computing tasks such as data processing and configuration. Finally, a case of runoff simulation using the SWAT model is designed, in which the whole process of data sharing, acquisition, calculation, and application is realized, so as to verify the validity of the proposed framework.