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A longitudinal resource for population neuroscience of school-age children and adolescents in China.Scientific Data Aug 2023During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era...
During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), the first ten-year stage of the lifespan CCNP (2013-2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.
Topics: Humans; Asian People; Brain; China; Data Warehousing; Databases, Factual; Neurosciences
PubMed: 37604823
DOI: 10.1038/s41597-023-02377-8 -
PloS One 2023Clinical, time-dependent, therapeutic and diagnostic data of patients with LUTS are highly complex. To better manage these data for therapists' and researchers' we...
OBJECTIVES
Clinical, time-dependent, therapeutic and diagnostic data of patients with LUTS are highly complex. To better manage these data for therapists' and researchers' we developed the application ShinyLUTS.
MATERIAL AND METHODS
The statistical programming language R and the framework Shiny were used to develop a platform for data entry, monitoring of therapy and scientific data analysis. As part of a use case, ShinyLUTS was evaluated for patients with non-neurogenic LUTS who were receiving Rezum™ therapy.
RESULTS
The final database on patients with LUTS comprised a total of 8.118 time-dependent parameters in 11 data tables. Data entry, monitoring of therapy as well as data retrieval for scientific use, was deemed feasible, intuitive and well accepted.
CONCLUSION
The ShinyLUTs application presented here is suitable for collecting, archiving, and managing complex data on patients with LUTS. Aside from the implementation in a scientific workflow, it is suited for monitoring treatment of patients and functional results over time.
Topics: Humans; Data Management; Software; Programming Languages; Information Storage and Retrieval; Lower Urinary Tract Symptoms
PubMed: 37756331
DOI: 10.1371/journal.pone.0292117 -
Health Research Policy and Systems Jul 2023Digital transformation in healthcare and the growth of health data generation and collection are important challenges for the secondary use of healthcare records in the...
BACKGROUND
Digital transformation in healthcare and the growth of health data generation and collection are important challenges for the secondary use of healthcare records in the health research field. Likewise, due to the ethical and legal constraints for using sensitive data, understanding how health data are managed by dedicated infrastructures called data hubs is essential to facilitating data sharing and reuse.
METHODS
To capture the different data governance behind health data hubs across Europe, a survey focused on analysing the feasibility of linking individual-level data between data collections and the generation of health data governance patterns was carried out. The target audience of this study was national, European, and global data hubs. In total, the designed survey was sent to a representative list of 99 health data hubs in January 2022.
RESULTS
In total, 41 survey responses received until June 2022 were analysed. Stratification methods were performed to cover the different levels of granularity identified in some data hubs' characteristics. Firstly, a general pattern of data governance for data hubs was defined. Afterward, specific profiles were defined, generating specific data governance patterns through the stratifications in terms of the kind of organization (centralized versus decentralized) and role (data controller or data processor) of the health data hub respondents.
CONCLUSIONS
The analysis of the responses from health data hub respondents across Europe provided a list of the most frequent aspects, which concluded with a set of specific best practices on data management and governance, taking into account the constraints of sensitive data. In summary, a data hub should work in a centralized way, providing a Data Processing Agreement and a formal procedure to identify data providers, as well as data quality control, data integrity and anonymization methods.
Topics: Humans; Data Accuracy; Data Collection; Data Management; Europe; Health Facilities
PubMed: 37430347
DOI: 10.1186/s12961-023-01026-1 -
Frontiers in Bioinformatics 2023As biological imaging continues to rapidly advance, it results in increasingly complex image data, necessitating a reevaluation of conventional bioimage analysis methods...
As biological imaging continues to rapidly advance, it results in increasingly complex image data, necessitating a reevaluation of conventional bioimage analysis methods and their accessibility. This perspective underscores our belief that a transition from desktop-based tools to web-based bioimage analysis could unlock immense opportunities for improved accessibility, enhanced collaboration, and streamlined workflows. We outline the potential benefits, such as reduced local computational demands and solutions to common challenges, including software installation issues and limited reproducibility. Furthermore, we explore the present state of web-based tools, hurdles in implementation, and the significance of collective involvement from the scientific community in driving this transition. In acknowledging the potential roadblocks and complexity of data management, we suggest a combined approach of selective prototyping and large-scale workflow application for optimal usage. Embracing web-based bioimage analysis could pave the way for the life sciences community to accelerate biological research, offering a robust platform for a more collaborative, efficient, and democratized science.
PubMed: 37560357
DOI: 10.3389/fbinf.2023.1233748 -
Big Data Oct 2023Organizations have been investing in analytics relying on internal and external data to gain a competitive advantage. However, the legal and regulatory acts imposed...
Organizations have been investing in analytics relying on internal and external data to gain a competitive advantage. However, the legal and regulatory acts imposed nationally and internationally have become a challenge, especially for highly regulated sectors such as health or finance/banking. Data handlers such as Facebook and Amazon have already sustained considerable fines or are under investigation due to violations of data governance. The era of big data has further intensified the challenges of minimizing the risk of data loss by introducing the dimensions of Volume, Velocity, and Variety into confidentiality. Although Volume and Velocity have been extensively researched, Variety, "the ugly duckling" of big data, is often neglected and difficult to solve, thus increasing the risk of data exposure and data loss. In mitigating the risk of data exposure and data loss in this article, a framework is proposed to utilize algorithmic classification and workflow capabilities to provide a consistent approach toward data evaluations across the organizations. A rule-based system, implementing the corporate data classification policy, will minimize the risk of exposure by facilitating users to identify the approved guidelines and enforce them quickly. The framework includes an exception handling process with appropriate approval for extenuating circumstances. The system was implemented in a proof of concept working prototype to showcase the capabilities and provide a hands-on experience. The information system was evaluated and accredited by a diverse audience of academics and senior business executives in the fields of security and data management. The audience had an average experience of ∼25 years and amasses a total experience of almost three centuries (294 years). The results confirmed that the 3Vs are of concern and that Variety, with a majority of 90% of the commentators, is the most troubling. In addition to that, with an approximate average of 60%, it was confirmed that appropriate policies, procedure, and prerequisites for classification are in place while implementation tools are lagging.
PubMed: 37906117
DOI: 10.1089/big.2022.0201 -
JCO Clinical Cancer Informatics Sep 2023Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical,...
PURPOSE
Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage.
MATERIALS AND METHODS
Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions.
RESULTS
The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals.
CONCLUSION
The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.
Topics: Child; Humans; Databases, Factual; Data Management; Genomics; Osteosarcoma
PubMed: 37956387
DOI: 10.1200/CCI.23.00104 -
Blockchain in Healthcare Today 2024Properly managing healthcare data is a complex endeavor that must balance the requirements and interests of many stakeholders. In this paper, we present the findings... (Review)
Review
Properly managing healthcare data is a complex endeavor that must balance the requirements and interests of many stakeholders. In this paper, we present the findings from a panel discussion with healthcare professionals and academics, who elaborate on the current situation in healthcare data management as well as the future role that blockchain could play in this sector. Based on the findings of this panel, we structure the research field of healthcare data management and provide numerous avenues for future research. The outcome is a framework that highlights the important role of healthcare data and puts them into context. From a patient's perspective, we specifically elaborate on trust and privacy as well as the expected benefits. Additionally, four important data aspects are identified: integrity, security, interoperability, and, finally, sharing and transfer. We also outline the importance of current problems and derive several relevant and timely research questions that build the foundation of a research agenda for blockchain-driven innovation in healthcare data management. In summary, the framework will inform practitioners of blockchain's potential in healthcare and structure the area for researchers, who are called upon to investigate the respective topics in greater detail.
PubMed: 38715763
DOI: 10.30953/bhty.v7.301 -
Frontiers in Neurology 2023Efficient data sharing is hampered by an array of organizational, ethical, behavioral, and technical challenges, slowing research progress and reducing the utility of... (Review)
Review
Efficient data sharing is hampered by an array of organizational, ethical, behavioral, and technical challenges, slowing research progress and reducing the utility of data generated by clinical research studies on neurodegenerative diseases. There is a particular need to address differences between public and private sector environments for research and data sharing, which have varying standards, expectations, motivations, and interests. The Neuronet data sharing Working Group was set up to understand the existing barriers to data sharing in public-private partnership projects, and to provide guidance to overcome these barriers, by convening data sharing experts from diverse projects in the IMI neurodegeneration portfolio. In this policy and practice review, we outline the challenges and learnings of the WG, providing the neurodegeneration community with examples of good practices and recommendations on how to overcome obstacles to data sharing. These obstacles span organizational issues linked to the unique structure of cross-sectoral, collaborative research initiatives, to technical issues that affect the storage, structure and annotations of individual datasets. We also identify sociotechnical hurdles, such as academic recognition and reward systems that disincentivise data sharing, and legal challenges linked to heightened perceptions of data privacy risk, compounded by a lack of clear guidance on GDPR compliance mechanisms for public-private research. Focusing on real-world, neuroimaging and digital biomarker data, we highlight particular challenges and learnings for data sharing, such as data management planning, development of ethical codes of conduct, and harmonization of protocols and curation processes. Cross-cutting solutions and enablers include the principles of transparency, standardization and co-design - from open, accessible metadata catalogs that enhance findability of data, to measures that increase visibility and trust in data reuse.
PubMed: 37545729
DOI: 10.3389/fneur.2023.1187095 -
Journal of Hospital Librarianship 2024Librarians support researchers by promoting open science and open data practices. This article explores five freely available tools that support and facilitate open...
Librarians support researchers by promoting open science and open data practices. This article explores five freely available tools that support and facilitate open science practices. Open Science Framework provides a platform for project management, data sharing, and data storage. OpenRefine cleans and formats data. DMPTool has templates for data management and sharing plans that comply with funder mandates. The NIH Common Data Elements is a repository for standardized data elements, and finally, the NLM Scrubber is a tool for de-identifying clinical text data. Information professionals can add these tools to their repertoire and share them with researchers at their institution.
PubMed: 38883700
DOI: 10.1080/15323269.2024.2326787 -
Journal of Synchrotron Radiation Nov 2023High-data-throughput and multimodal-acquisition experiments will prevail in next-generation synchrotron beamlines. Orchestrating dataflow pipelines connecting the data...
High-data-throughput and multimodal-acquisition experiments will prevail in next-generation synchrotron beamlines. Orchestrating dataflow pipelines connecting the data acquisition, processing, visualization and storage ends are becoming increasingly complex and essential for enhancing beamline performance. Mamba Data Worker (MDW) has been developed to address the data challenges for the forthcoming High Energy Photon Source (HEPS). It is an important component of the Mamba experimental control and data acquisition software ecosystem, which enables fast data acquisition and transmission, dynamic configuration of data processing pipelines, data multiplex in streaming, and customized data and metadata assembly. This paper presents the architecture and development plan of MDW, outlines the essential technologies involved, and illustrates its current application at the Beijing Synchrotron Radiation Facility (BSRF).
PubMed: 37729071
DOI: 10.1107/S1600577523006951