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Journal of Pharmaceutical Sciences Apr 2020The process of assembling regulatory documents for submission to multiple global health agencies can present a repetitive cycle of authoring, editing, and data... (Review)
Review
The process of assembling regulatory documents for submission to multiple global health agencies can present a repetitive cycle of authoring, editing, and data verification, which increases in complexity as changes are made for approved products, particularly from a chemistry, manufacturing, and controls (CMC) perspective. Currently, pharmaceutical companies rely on a workflow that involves manual CMC change management across documents. Similarly, when regulators review submissions, they provide feedback and insight into regulatory decision making in a narrative format. As accelerated review pathways are increasingly used and pressure mounts to bring products to market quickly, innovative solutions for assembling, distributing, and reviewing regulatory information are being considered. Structured content management (SCM) solutions, in which data are collated into centrally organized content blocks for use across different documents, may aid in the efficient processing of data and create opportunities for automation and machine learning in its interpretation. The US Food and Drug Administration (FDA) has recently created initiatives that encourage application of SCM for CMC data, though many challenges could impede their success and efficiency. The goal is for industry and health authorities to collaborate in the development of SCM for CMC applications, to potentially streamline compilation of quality data in regulatory submissions.
Topics: Commerce; Data Management; United States; United States Food and Drug Administration; Workflow
PubMed: 32004537
DOI: 10.1016/j.xphs.2020.01.020 -
Current Opinion in Biotechnology Oct 2020Biological samples such as tissues, blood and other body fluids, plants or seeds, prokaryotic and eukaryotic cells or isolated biomolecules as well as associated data... (Review)
Review
Biological samples such as tissues, blood and other body fluids, plants or seeds, prokaryotic and eukaryotic cells or isolated biomolecules as well as associated data are the essential raw material for research and development in medicine, biotechnology and agriculture. The collection, processing, preservation, and storage of these resources, in addition to provision of access, are key activities of biobanks or biological resource centres. Biobanks have to ensure proper quality of samples and data, ethical and legal compliance as well as transparent and efficient access procedures. In this context the review places special emphasis on pre-analytical procedures and international standards, which are essential to improving analytical data reliability and reproducibility, as well as on the increasing importance of data management. These requirements of biobanks are demonstrated using the example of pathogen-containing and microbiome biobanks, and refer to needs in cancer research and development.
Topics: Biological Science Disciplines; Biological Specimen Banks; Biomedical Research; Containment of Biohazards; Data Management; Precision Medicine; Reference Standards; Reproducibility of Results
PubMed: 31896493
DOI: 10.1016/j.copbio.2019.12.004 -
Trials Mar 2022Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in...
BACKGROUND
Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran.
METHOD
This was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used.
RESULTS
The five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes.
CONCLUSION
According to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.
Topics: Data Management; Diabetes Mellitus; Humans; Iran; Qualitative Research; Research Personnel
PubMed: 35241149
DOI: 10.1186/s13063-022-06110-5 -
Bioinformatics (Oxford, England) Sep 2022Environmental DNA (eDNA), as a rapidly expanding research field, stands to benefit from shared resources including sampling protocols, study designs, discovered...
MOTIVATION
Environmental DNA (eDNA), as a rapidly expanding research field, stands to benefit from shared resources including sampling protocols, study designs, discovered sequences, and taxonomic assignments to sequences. High-quality community shareable eDNA resources rely heavily on comprehensive metadata documentation that captures the complex workflows covering field sampling, molecular biology lab work, and bioinformatic analyses. There are limited sources that provide documentation of database development on comprehensive metadata for eDNA and these workflows and no open-source software.
RESULTS
We present medna-metadata, an open-source, modular system that aligns with Findable, Accessible, Interoperable, and Reusable guiding principles that support scholarly data reuse and the database and application development of a standardized metadata collection structure that encapsulates critical aspects of field data collection, wet lab processing, and bioinformatic analysis. Medna-metadata is showcased with metabarcoding data from the Gulf of Maine (Polinski et al., 2019).
AVAILABILITY AND IMPLEMENTATION
The source code of the medna-metadata web application is hosted on GitHub (https://github.com/Maine-eDNA/medna-metadata). Medna-metadata is a docker-compose installable package. Documentation can be found at https://medna-metadata.readthedocs.io/en/latest/?badge=latest. The application is implemented in Python, PostgreSQL and PostGIS, RabbitMQ, and NGINX, with all major browsers supported. A demo can be found at https://demo.metadata.maine-edna.org/.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Metadata; DNA, Environmental; Data Management; Software; Databases, Factual
PubMed: 35960154
DOI: 10.1093/bioinformatics/btac556 -
F1000Research 2022: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR...
: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. : An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. : The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. : The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.
Topics: Humans; Data Management; Metadata; Information Dissemination; Surveys and Questionnaires; Workflow
PubMed: 36405555
DOI: 10.12688/f1000research.121714.2 -
American Journal of Biological... Nov 2022Previous research has shown that while missing data are common in bioarchaeological studies, they are seldom handled using statistically rigorous methods. The primary...
OBJECTIVES
Previous research has shown that while missing data are common in bioarchaeological studies, they are seldom handled using statistically rigorous methods. The primary objective of this article is to evaluate the ability of imputation to manage missing data and encourage the use of advanced statistical methods in bioarchaeology and paleopathology. An overview of missing data management in biological anthropology is provided, followed by a test of imputation and deletion methods for handling missing data.
MATERIALS AND METHODS
Missing data were simulated on complete datasets of ordinal (n = 287) and continuous (n = 369) bioarchaeological data. Missing values were imputed using five imputation methods (mean, predictive mean matching, random forest, expectation maximization, and stochastic regression) and the success of each at obtaining the parameters of the original dataset compared with pairwise and listwise deletion.
RESULTS
In all instances, listwise deletion was least successful at approximating the original parameters. Imputation of continuous data was more effective than ordinal data. Overall, no one method performed best and the amount of missing data proved a stronger predictor of imputation success.
DISCUSSION
These findings support the use of imputation methods over deletion for handling missing bioarchaeological and paleopathology data, especially when the data are continuous. Whereas deletion methods reduce sample size, imputation maintains sample size, improving statistical power and preventing bias from being introduced into the dataset.
Topics: Archaeology; Sample Size; Research Design; Data Management; Bias
PubMed: 36790608
DOI: 10.1002/ajpa.24614 -
ENeuro Feb 2023Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new...
Research Data Management and Data Sharing for Reproducible Research-Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience.
Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.
Topics: Data Management; Biomedical Research; Surveys and Questionnaires; Information Dissemination; Neurosciences
PubMed: 36750361
DOI: 10.1523/ENEURO.0215-22.2023 -
Computer Methods and Programs in... Nov 2021In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these...
BACKGROUND AND OBJECTIVES
In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these clinical tools have design limitations, since they are currently not able to cover the needs of adaptation to the continuous changes in the practice of the trials due to the heterogeneous and dynamic nature of the clinical research data. These systems are usually proprietary solutions provided by vendors for specific tasks. In this work, we propose FIMED, a software solution for the flexible management of clinical data from multiple trials, moving towards personalized medicine, which can contribute positively by improving clinical researchers quality and ease in clinical trials.
METHODS
This tool allows a dynamic and incremental design of patients' profiles in the context of clinical trials, providing a flexible user interface that hides the complexity of using databases. Clinical researchers will be able to define personalized data schemas according to their needs and clinical study specifications. Thus, FIMED allows the incorporation of separate clinical data analysis from multiple trials.
RESULTS
The efficiency of the software has been demonstrated by a real-world use case for a clinical assay in Melanoma disease, which has been indeed anonymized to provide a user demonstration. FIMED currently provides three data analysis and visualization components, guaranteeing a clinical exploration for gene expression data: heatmap visualization, clusterheatmap visualization, as well as gene regulatory network inference and visualization. An instance of this tool is freely available on the web at https://khaos.uma.es/fimed. It can be accessed with a demo user account, "researcher", using the password "demo".
CONCLUSION
This paper shows FIMED as a flexible and user-friendly way of managing multidimensional clinical research data. Hence, without loss of generality, FIMED is flexible enough to be used in the context of any other disease where clinical data and assays are involved.
Topics: Data Management; Databases, Factual; Gene Regulatory Networks; Humans; Internet; Software; User-Computer Interface
PubMed: 34740063
DOI: 10.1016/j.cmpb.2021.106496 -
Histochemistry and Cell Biology Sep 2023
Topics: Data Management; Microscopy; Data Analysis
PubMed: 37646975
DOI: 10.1007/s00418-023-02226-0 -
Human Resources For Health Dec 2019Healthcare providers (HCPs) are recognized as one of the cornerstones and drivers of health interventions. Roles such as documentation of patient care, data management,... (Review)
Review
BACKGROUND
Healthcare providers (HCPs) are recognized as one of the cornerstones and drivers of health interventions. Roles such as documentation of patient care, data management, analysing, interpreting and appropriate use of data are key to ending vaccine-preventable diseases (VPDs). However, there is a great deal of uncertainty and concerns about HCPs' skills and competencies regarding immunization data handling and the importance of data use for improving service delivery in low- and middle-income countries (LMICs). Questions about the suitability and relevance of the contents of training curriculum, appropriateness of platforms through which training is delivered and the impact of such training on immunization data handling competencies and service delivery remain a source of concern. This review identified and assessed published studies that report on pre- and in-service training with a focus on HCPs' competencies and skills to manage immunization data in LMICs.
METHODS
An electronic search of six online databases was performed, in addition to websites of the WHO, Global Alliance for Vaccines and Immunization (GAVI), Oxfam International, Save the Children, Community Health Workers Central (CHW Central), UNAIDS and UNICEF. Using appropriate keywords, MeSH terms and selection procedure, 12 articles published between January 1980 and May 2019 on pre- and in-service training of HCPs, interventions geared towards standardized data collection procedures, data documentation and management of immunization data in LMICs, including curriculum reviews, were considered for analysis.
RESULTS
Of the 2705 identified references, only 12 studies met the inclusion criteria. The review provides evidence that shows that combined and multifaceted training interventions could help improve HCPs' knowledge, skills and competency on immunization data management. It further suggests that offering the right training to HCPs and sustaining standard immunization data management is hampered in LMICs by limited or/lack of training resources.
CONCLUSION
Pre-service training is fundamental in the skills' acquisition of HCPs; however, they require additional in-service training and supportive supervision to function effectively in managing immunization data tasks. Continuous capacity development in immunization data-management competencies such as data collection, analysis, interpretation, synthesis and data use should be strengthened at all levels of the health system. Furthermore, there is a need for periodic review of the immunization-training curriculum in health training institutions, capacity development and retraining tutors on the current trends in immunization data management.
Topics: Community Health Workers; Curriculum; Data Management; Developing Countries; Humans; Immunization; Inservice Training; Poverty
PubMed: 31791352
DOI: 10.1186/s12960-019-0437-6