Design of a FAIR digital data health infrastructure in Africa for COVID-19 reporting and research.
Ontology highlight
ABSTRACT:
SUBMITTER: van Reisen M
PROVIDER: S-EPMC8420285 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Publications
Design of a FAIR digital data health infrastructure in Africa for COVID-19 reporting and research.
van Reisen Mirjam M Oladipo Francisca F Stokmans Mia M Mpezamihgo Mouhamed M Folorunso Sakinat S Schultes Erik E Basajja Mariam M Aktau Aliya A Amare Samson Yohannes SY Taye Getu Tadele GT Purnama Jati Putu Hadi PH Chindoza Kudakwashe K Wirtz Morgane M Ghardallou Meriem M van Stam Gertjan G Ayele Wondimu W Nalugala Reginald R Abdullahi Ibrahim I Osigwe Obinna O Graybeal John J Medhanyie Araya Abrha AA Kawu Abdullahi Abubakar AA Liu Fenghong F Wolstencroft Katy K Flikkenschild Erik E Lin Yi Y Stocker Joëlle J Musen Mark A MA
Advanced genetics (Hoboken, N.J.) 20210611 2
The limited volume of COVID-19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS-CoV-2 mutations. The Virus Outbreak Data Network (VODAN)-Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an ...[more]
Similar Datasets
Project description:BackgroundAccording to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness).ObjectiveOur objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness.MethodsWe conducted a comprehensive search and retrieved all open-access articles related to COVID-19 from journals indexed in PubMed, available in the Europe PubMed Central database, published from January 2020 through June 2023, using the metareadr package. Using rtransparent, a validated automated tool, we identified articles with links to their raw data hosted in a public repository. We then screened the link and included those repositories that included data specifically for their pertaining paper. Subsequently, we automatically assessed the adherence of the repositories to the FAIR principles using FAIRsFAIR Research Data Object Assessment Service (F-UJI) and rfuji package. The FAIR scores ranged from 1-22 and had four components. We reported descriptive analysis for each article type, journal category, and repository. We used linear regression models to find the most influential factors on the FAIRness of data.Results5,700 URLs were included in the final analysis, sharing their data in a general-purpose repository. The mean (standard deviation, SD) level of compliance with FAIR metrics was 9.4 (4.88). The percentages of moderate or advanced compliance were as follows: Findability: 100.0%, Accessibility: 21.5%, Interoperability: 46.7%, and Reusability: 61.3%. The overall and component-wise monthly trends were consistent over the follow-up. Reviews (9.80, SD = 5.06, n = 160), articles in dental journals (13.67, SD = 3.51, n = 3) and Harvard Dataverse (15.79, SD = 3.65, n = 244) had the highest mean FAIRness scores, whereas letters (7.83, SD = 4.30, n = 55), articles in neuroscience journals (8.16, SD = 3.73, n = 63), and those deposited in GitHub (4.50, SD = 0.13, n = 2,152) showed the lowest scores. Regression models showed that the repository was the most influential factor on FAIRness scores (R2 = 0.809).ConclusionThis paper underscored the potential for improvement across all facets of FAIR principles, specifically emphasizing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic.
| S-EPMC11573139 | biostudies-literature
Project description:Globally, countries have used diverse methods to report data during the COVID-19 pandemic. Using international guidelines and principles of emergency management, we compare national data reporting systems in African countries in order to determine lessons for future pandemics. We analyse COVID-19 reporting practices across 54 African countries through 2020. Reporting systems were diverse and included summaries, press releases, situation reports and online dashboards. These systems were communicated via social media accounts and websites belonging to ministries of health and public health. Data variables from the reports included event detection (cases/deaths/recoveries), risk assessment (demographics/co-morbidities) and response (total tests/hospitalisations). Of countries with reporting systems, 36/53 (67.9%) had recurrent situation reports and/or online dashboards which provided more extensive data. All of these systems reported cases, deaths and recoveries. However, few systems contained risk assessment and response data, with only 5/36 (13.9%) reporting patient co-morbidities and 9/36 (25%) including total hospitalisations. Further evaluation of reporting practices in Cameroon, Egypt, Kenya, Senegal and South Africa as examples from different sub-regions revealed differences in reporting healthcare capacity and preparedness data. Improving the standardisation and accessibility of national data reporting systems could augment research and decision-making, as well as increase public awareness and transparency for national governments.
| S-EPMC9237488 | biostudies-literature
Project description:BackgroundGaps in electronic health record (EHR) data collection and the paucity of standardized clinical data elements (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understanding the epidemiology and quality of care for opioid use disorder (OUD). We identified existing CDEs and evaluated their validity and usability, which is required prior to infrastructure implementation within EHRs.MethodsWe conducted (a) a systematic literature review of publications in Medline, Embase and the Web of Science using a combination of at least one term related to OUD and EHR and (b) an environmental scan of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Opioid-related data elements identified within the environmental scan were compared with related data elements contained within nine common health data code systems and each element was graded for alignment with match results categorized as "exact", "partial", or "none."ResultsThe literature review identified 5186 articles for title search, of which 75 abstracts were included for review and 38 articles were selected for full-text review. Full-text articles yielded 237 CDEs, only 12 (5.06%) of which were opioid-specific. The environmental scan identified 379 potential data elements and value sets across 9 data systems and libraries, among which only 84 (22%) were opioid-specific. We found substantial variability in the types of clinical data elements with limited overlap and no single data system included CDEs across all major data element types such as substance use disorder, OUD, medication and mental health. Relative to common health data code systems, few data elements had an exact match (< 1%), while 61% had a partial match and 38% had no matches.ConclusionsDespite the increasing ubiquity of EHR data standards and national attention placed on the opioid epidemic, we found substantial fragmentation in the design and construction of OUD related CDEs and little OUD specific CDEs in existing data dictionaries, systems and literature. Given the significant gaps in data collection and reporting, future work should leverage existing structured data elements to create standard workflow processes to improve OUD data capture in EHR systems.
| S-EPMC7350566 | biostudies-literature
Project description:Background Sex and gender are believed to influence vaccine response. Yet, the relationship between sex and gender and COVID-19 vaccine efficacy is poorly understood and remains under-investigated. Methods We conducted a systematic review to determine whether and to what extent post-approval COVID-19 vaccine effectiveness (VE) studies report sex-disaggregated VE data. We searched four publication and pre-publication databases and additional grey literature sources for relevant published/preprint studies released between 1 January 2020 and 1 October 2021 (i.e., pre-Omicron era). We included observational studies providing VE estimates for one or more licensed/approved COVID-19 vaccines and including both males and females. Two reviewers independently assessed study eligibility, extracted data, and assessed risk-of-bias through a modified version of Cochrane’s ROBINS-I tool. A qualitative data synthesis was performed. Results Here we show that, among 240 eligible publications, 68 (28.3%) do not report the sex distribution among participants. Only 21/240 (8.8%) studies provide sex-disaggregated VE estimates, and high between-study heterogeneity regarding design, target population, outcomes, and vaccine type/timing prevent the assessment of sex in determining COVID-19 VE across studies. Conclusions Our findings indicate that few COVID-19 vaccine research publications account for sex. Improved adherence to recommended reporting guidelines will ensure that the evidence generated can be used to better understand the relationship between sex and gender and VE. Plain Language Summary The level of protection that vaccines provide against COVID-19 might depend on a person’s sex or gender. However, sex and gender are not always reported in studies on the effectiveness of COVID-19 vaccines. Here, we systematically reviewed the literature on COVID-19 vaccine effectiveness and looked at whether the studies we found separated out their data on vaccine effectiveness by participants’ sex. Out of the 240 publications we identified, 68 (28.3%) did not report the sex of the participants in their study, and only 21 studies (8.8%) reported vaccine effectiveness data separated by sex. These results show that a substantial proportion of COVID-19 vaccine research publications do not account for sex. Efforts should be made by researchers to study and report the relationship between sex and vaccine effectiveness, to help to optimise vaccination strategies so that all people are adequately protected. Sulis et al. perform a systematic review to investigate to what extent post-approval COVID-19 vaccine effectiveness studies report sex-disaggregated data. They identified 240 relevant publications, of which only a small percentage (8.8%) provided sex-disaggregated effectiveness estimates.
| S-EPMC10196298 | biostudies-literature
Project description:Transparent and FAIR disclosure of meta-information about healthcare data and infrastructure is essential but has not been well publicized. In this paper, we provide a transparent disclosure of the process of standardizing a common data model and developing a national data infrastructure using national claims data. We established an Observational Medical Outcome Partnership (OMOP) common data model database for national claims data of the Health Insurance Review and Assessment Service of South Korea. To introduce a data openness policy, we built a distributed data analysis environment and released metadata based on the FAIR principle. A total of 10,098,730,241 claims and 56,579,726 patients' data were converted as OMOP common data model. We also built an analytics environment for distributed research and made the metadata publicly available. Disclosure of this infrastructure to researchers will help to eliminate information inequality and contribute to the generation of high-quality medical evidence.
| S-EPMC10550904 | biostudies-literature
Project description:The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a "bring the infrastructure to the data" approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.
| S-EPMC7580168 | biostudies-literature
Project description:Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.
| S-EPMC9203546 | biostudies-literature
Project description:BackgroundIn clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized "omics" platform for FAIR data analysis.ResultsTo demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow.ConclusionsWe showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy.
| S-EPMC10821763 | biostudies-literature
Project description:Our work illustrates how high-resolution molecular and spatial profiling of COVID-19 patient tissues collected during rapid autopsies can serve as a hypothesisgenerating tool to identify key mediators driving the pathophysiology of COVID-19 for diagnostic and therapeutic target testing. Here we employ bulk RNA sequencing to identify key regulators of COVID-19 and list specific mediators for further study as potential diagnostic and therapeutic targets. We use single-nuclei RNA sequencing to highlight the diversity and heterogeneity of coronavirus receptors within the brain, suggesting that it will be critical to expand the focus from ACE2 to include other receptors, such as BSG and ANPEP, and we perform digital spatial profiling of lung and lymph node tissue to compare two patients with different clinical courses and symptomatology.
2021-09-03 | GSE183356 | GEO
Project description:Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats-instructions, templates, and tools for consistently formatting data within a discipline-can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.
| S-EPMC9663825 | biostudies-literature