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Philosophical Transactions. Series A,... Oct 2022Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data...
Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of 'following the science' are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Topics: COVID-19; Data Management; Humans; Pandemics; Software; Workflow
PubMed: 35965468
DOI: 10.1098/rsta.2021.0300 -
Data Management Plans in the genomics research revolution of Africa: Challenges and recommendations.Journal of Biomedical Informatics Oct 2021Drafting and writing a data management plan (DMP) is increasingly seen as a key part of the academic research process. A DMP is a document that describes how a... (Review)
Review
Drafting and writing a data management plan (DMP) is increasingly seen as a key part of the academic research process. A DMP is a document that describes how a researcher will collect, document, describe, share, and preserve the data that will be generated as part of a research project. The DMP illustrates the importance of utilizing best practices through all stages of working with data while ensuring accessibility, quality, and longevity of the data. The benefits of writing a DMP include compliance with funder and institutional mandates; making research more transparent (for reproduction and validation purposes); and FAIR (findable, accessible, interoperable, reusable); protecting data subjects and compliance with the General Data Protection Regulation (GDPR) and/or local data protection policies. In this review, we highlight the importance of a DMP in modern biomedical research, explaining both the rationale and current best practices associated with DMPs. In addition, we outline various funders' requirements concerning DMPs and discuss open-source tools that facilitate the development and implementation of a DMP. Finally, we discuss DMPs in the context of African research, and the considerations that need to be made in this regard.
Topics: Africa; Biomedical Research; Data Management; Genomics; Humans; Research Design
PubMed: 34506960
DOI: 10.1016/j.jbi.2021.103900 -
Journal of Biomedical Informatics Apr 2023Data stewardship is a term that is understood in heterogenous ways. In recent organisational developments and efforts to build infrastructures and hire professional...
Data stewardship is a term that is understood in heterogenous ways. In recent organisational developments and efforts to build infrastructures and hire professional staff for research data management in various scientific fields in Europe, data stewardship is understood as mainly aiming at optimising data management in line with the FAIR principles (findability, accessibility, interoperability, reusability) forpurposes of reuse in the interests of the scientific community and the public. In addition, especially in the health and biomedical sciences some understandings of data stewardship mainly focus on the responsibility to respect the informational rights of data subjects. Following on from these different understandings and from recent developments to include ever more stakeholders in data stewardship, we propose a comprehensive understanding of data stewardship. According to this comprehensive understanding, data stewardship includes responsibilities towards all pertinent stakeholders and to equally consider and respect their legitimate rights and interests in order to build and maintain an efficient, trusted and fair data ecosystem. We also point out some of the practical challenges implied in such a comprehensive understanding.
Topics: Humans; Ecosystem; Europe; Data Management
PubMed: 36935012
DOI: 10.1016/j.jbi.2023.104337 -
Journal of the American College of... Apr 2020
Topics: Adhesives; Data Management; Humans; Recurrence
PubMed: 32220444
DOI: 10.1016/j.jamcollsurg.2020.02.001 -
Cytometry. Part a : the Journal of the... Jan 2021Data management is essential in a flow cytometry (FCM) shared resource laboratory (SRL) for the integrity of collected data and its long-term preservation, as described... (Review)
Review
Data management is essential in a flow cytometry (FCM) shared resource laboratory (SRL) for the integrity of collected data and its long-term preservation, as described in the Cytometry publication from 2016, ISAC Flow Cytometry Shared Resource Laboratory (SRL) Best Practices (Barsky et al.: Cytometry Part A 89A(2016): 1017-1030). The SARS-CoV-2 pandemic introduced an array of challenges in the operation of SRLs. The subsequent laboratory shutdowns and access restrictions brought to the forefront well-established practices that withstood the impact of a sudden change in operations and illuminated areas that need improvement. The most significant challenges from a data management perspective were data access for remote analysis and workstation management. Notably, lessons learned from this challenge emphasize the importance of safeguarding collected data from loss in various emergencies such as fire or natural disasters where the physical hardware storing data could be directly affected. Here, we describe two data management systems that have been successful during the current emergency created by the pandemic, specifically remote access and automated data transfer. We will discuss other situations that could arise and lead to data loss or challenges in interpreting data. © 2020 International Society for Advancement of Cytometry.
Topics: COVID-19; Data Management; Flow Cytometry; Humans; Laboratories; Teleworking
PubMed: 33197114
DOI: 10.1002/cyto.a.24265 -
JAMA Network Open Jul 2023
Topics: Humans; Data Management; International Classification of Diseases
PubMed: 37498604
DOI: 10.1001/jamanetworkopen.2023.27991 -
Environment International Jul 2022Management of datasets that include health information and other sensitive personal information of European study participants has to be compliant with the General Data... (Review)
Review
Management of datasets that include health information and other sensitive personal information of European study participants has to be compliant with the General Data Protection Regulation (GDPR, Regulation (EU) 2016/679). Within scientific research, the widely subscribed'FAIR' data principles should apply, meaning that research data should be findable, accessible, interoperable and re-usable. Balancing the aim of open science driven FAIR data management with GDPR compliant personal data protection safeguards is now a common challenge for many research projects dealing with (sensitive) personal data. In December 2020 a workshop was held with representatives of several large EU research consortia and of the European Commission to reflect on how to apply the FAIR data principles for environment and health research (E&H). Several recent data intensive EU funded E&H research projects face this challenge and work intensively towards developing solutions to access, exchange, store, handle, share, process and use such sensitive personal data, with the aim to support European and transnational collaborations. As a result, several recommendations, opportunities and current limitations were formulated. New technical developments such as federated data management and analysis systems, machine learning together with advanced search software, harmonized ontologies and data quality standards should in principle facilitate the FAIRification of data. To address ethical, legal, political and financial obstacles to the wider re-use of data for research purposes, both specific expertise and underpinning infrastructure are needed. There is a need for the E&H research data to find their place in the European Open Science Cloud. Communities using health and population data, environmental data and other publicly available data have to interconnect and synergize. To maximize the use and re-use of environment and health data, a dedicated supporting European infrastructure effort, such as the EIRENE research infrastructure within the ESFRI roadmap 2021, is needed that would interact with existing infrastructures.
Topics: Computer Security; Data Management; Europe; Health Records, Personal; Humans
PubMed: 35696847
DOI: 10.1016/j.envint.2022.107334 -
Methods in Molecular Biology (Clifton,... 2023The bioinformatics analysis of miRNA is a complicated task with multiple operations and steps involved from processing of raw sequence data to finally identifying...
The bioinformatics analysis of miRNA is a complicated task with multiple operations and steps involved from processing of raw sequence data to finally identifying accurate microRNAs associated with the phenotypes of interest. A complete analysis process demands a high level of technical expertise in programming, statistics, and data management. The goal of this chapter is to reduce the burden of technical expertise and provide readers the opportunity to understand crucial steps involved in the analysis of miRNA sequencing data.In this chapter, we describe methods and tools employed in processing of miRNA reads, quality control, alignment, quantification, and differential expression analysis.
Topics: Computational Biology; MicroRNAs; Data Management; Phenotype; Professional Competence
PubMed: 36441466
DOI: 10.1007/978-1-0716-2823-2_16 -
Journal of Medical Internet Research Jul 2020Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is... (Review)
Review
BACKGROUND
Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system.
OBJECTIVE
This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed.
METHODS
To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases.
RESULTS
Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care.
CONCLUSIONS
There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.
Topics: Biomedical Research; Data Management; Delivery of Health Care; Humans
PubMed: 32348265
DOI: 10.2196/17508 -
Methods in Molecular Biology (Clifton,... 2024Flapjack presents a valuable solution for addressing challenges in the Design, Build, Test, Learn (DBTL) cycle of engineering synthetic genetic circuits. This platform... (Review)
Review
Flapjack presents a valuable solution for addressing challenges in the Design, Build, Test, Learn (DBTL) cycle of engineering synthetic genetic circuits. This platform provides a comprehensive suite of features for managing, analyzing, and visualizing kinetic gene expression data and associated metadata. By utilizing the Flapjack platform, researchers can effectively integrate the test phase with the build and learn phases, facilitating the characterization and optimization of genetic circuits. With its user-friendly interface and compatibility with external software, the Flapjack platform offers a practical tool for advancing synthetic biology research.This chapter provides an overview of the data model employed in Flapjack and its hierarchical structure, which aligns with the typical steps involved in conducting experiments and facilitating intuitive data management for users. Additionally, this chapter offers a detailed description of the user interface, guiding readers through accessing Flapjack, navigating its sections, performing essential tasks such as uploading data and creating plots, and accessing the platform through the pyFlapjack Python package.
Topics: Data Management; Gene Regulatory Networks; Software; Synthetic Biology
PubMed: 38468101
DOI: 10.1007/978-1-0716-3658-9_23