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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 -
JCO Clinical Cancer Informatics Mar 2021For central cancer registries to become a more significant public health resource, they must evolve to capture more timely, accurate, and extensive data. Key...
For central cancer registries to become a more significant public health resource, they must evolve to capture more timely, accurate, and extensive data. Key stakeholders have called for a faster time to deliver work products, data extensions such as social determinants of health, and more relevant information for cancer control programs at the local level. The proposed model consists of near real-time reporting stages to replace the current time and labor-intensive efforts to populate a complete cancer case abstract on the basis of the 12- and 24-month data submission timelines. The first stage collects a cancer diagnosis minimum data set sufficient to describe population incidence and prevalence, which is then followed by a second stage capturing subsequent case updates and treatment data. A third stage procures targeted information in response to identified research projects' needs. The model also provides for further supplemental reports as may be defined to gather additional data. All stages leverage electronic health records' widespread development and the many emerging standards for data content, including national policies related to healthcare and technical standards for interoperability, such as the Fast Healthcare Interoperability Resources specifications to automate and accelerate reporting to central cancer registries. The emergence of application programming interfaces that allow for more interoperability among systems would be leveraged, leading to more efficient information sharing. Adopting this model will expedite cancer data availability to improve cancer control while supporting data integrity and flexibility in data items. It presents a long-term and feasible solution that addresses the extensive burden and unsustainable manual data collection requirements placed on Certified Tumor Registrars at disease reporting entities nationally.
Topics: Data Collection; Data Management; Electronic Health Records; Humans; Neoplasms; Registries
PubMed: 33760641
DOI: 10.1200/CCI.20.00177 -
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 -
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 -
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 -
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 -
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 -
JAMA Network Open Jul 2023
Topics: Humans; Data Management; International Classification of Diseases
PubMed: 37498604
DOI: 10.1001/jamanetworkopen.2023.27991 -
The Pan African Medical Journal 2021the National Primary Health Care Development Agency, African Field Epidemiology Network, United States Centers for Disease Control and Prevention and the Bill and...
Data management needs assessment for the scale-up of district health information system and introduction of routine (essential) immunization module in Bauchi State, Nigeria, 2015.
INTRODUCTION
the National Primary Health Care Development Agency, African Field Epidemiology Network, United States Centers for Disease Control and Prevention and the Bill and Melinda Gates Foundation are implementing a Routine Immunization (RI) Module as part of their Routine Health Data Management System based on the 2013 - 2015 Accountability Framework for RI in Nigeria. To inform planning and evidence-based decision making, a data management needs assessment was conducted in Bauchi state which was one of the states selected for the deployment of the DHIS2 RI module.
METHODS
desk reviews were conducted, and a semi-structured questionnaire was administered in four Local Government Areas (LGAs) in Bauchi state that were selected based on the initial evaluation of the performance of all 20 Bauchi LGAs. Ganjuwa and Shira were selected as high-performing LGAs and Alkaleri and Bogoro as low-performing LGAs. Four Health Facilities (HF) were selected in each LGA based on rural or urban classification, type of HFs (private or public), security and accessibility.
RESULTS
local Immunization Officers (LIOs) prepare monthly reports in high-performing LGAs, and Community Health Care workers are mostly (69%) responsible for report compilation at the HFs. Shira and Alkaleri met 77% and 44% of training indicator targets, respectively, in the previous 12 months. Data recording and reporting was the type of training received the most by health facility personnel. Functioning refrigerators were in all visited LGAs, working thermometer and updated temperature monitoring charts were available in all the cold chain stores. However, no health facility reported having available computers for data-related activities.
CONCLUSION
this assessment provided an improved understanding of the Bauchi state Routine Health Data Management System and informed the content of the state-wide scale-up.
Topics: Data Management; Health Information Systems; Humans; Immunization; Needs Assessment; Nigeria
PubMed: 36157562
DOI: 10.11604/pamj.supp.2022.40.1.32458 -
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