The COVID-19 Data Portal: accelerating SARS-CoV-2 and COVID-19 research through rapid open access data sharing.
Ontology highlight
ABSTRACT:
SUBMITTER: Harrison PW
PROVIDER: S-EPMC8218199 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC7158984 | biostudies-literature
Project description:The COVID-19 pandemic has emerged as a global medico-socio-economic disaster. Given the lack of effective therapeutics against SARS-CoV-2, scientists are racing to disseminate suggestions for rapidly deployable therapeutic options, including drug repurposing and repositioning strategies. Molecular dynamics (MD) simulations have provided the opportunity to make rational scientific breakthroughs in a time of crisis. Advancements in these technologies in recent years have become an indispensable tool for scientists studying protein structure, function, dynamics, interactions, and drug discovery. Integrating the structural data obtained from high-resolution methods with MD simulations has helped in comprehending the process of infection and pathogenesis, as well as the SARS-CoV-2 maturation in host cells, in a short duration of time. It has also guided us to identify and prioritize drug targets and new chemical entities, and to repurpose drugs. Here, we discuss how MD simulation has been explored by the scientific community to accelerate and guide translational research on SARS-CoV-2 in the past year. We have also considered future research directions for researchers, where MD simulations can help fill the existing gaps in COVID-19 research.
| S-EPMC8340580 | biostudies-literature
Project description:BackgroundCOVID-19 has threatened the health of tens of millions of people all over the world. Massive research efforts have been made in response to the COVID-19 pandemic. Utilization of clinical data can accelerate these research efforts to combat the pandemic since important characteristics of the patients are often found by examining the clinical data. Publicly accessible clinical data on COVID-19, however, remain limited despite the immediate need.ObjectiveTo provide shareable clinical data to catalyze COVID-19 research, we present Columbia Open Health Data for COVID-19 Research (COHD-COVID), a publicly accessible database providing clinical concept prevalence, clinical concept co-occurrence, and clinical symptom prevalence for hospitalized patients with COVID-19. COHD-COVID also provides data on hospitalized patients with influenza and general hospitalized patients as comparator cohorts.MethodsThe data used in COHD-COVID were obtained from NewYork-Presbyterian/Columbia University Irving Medical Center's electronic health records database. Condition, drug, and procedure concepts were obtained from the visits of identified patients from the cohorts. Rare concepts were excluded, and the true concept counts were perturbed using Poisson randomization to protect patient privacy. Concept prevalence, concept prevalence ratio, concept co-occurrence, and symptom prevalence were calculated using the obtained concepts.ResultsConcept prevalence and concept prevalence ratio analyses showed the clinical characteristics of the COVID-19 cohorts, confirming the well-known characteristics of COVID-19 (eg, acute lower respiratory tract infection and cough). The concepts related to the well-known characteristics of COVID-19 recorded high prevalence and high prevalence ratio in the COVID-19 cohort compared to the hospitalized influenza cohort and general hospitalized cohort. Concept co-occurrence analyses showed potential associations between specific concepts. In case of acute lower respiratory tract infection in the COVID-19 cohort, a high co-occurrence ratio was obtained with COVID-19-related concepts and commonly used drugs (eg, disease due to coronavirus and acetaminophen). Symptom prevalence analysis indicated symptom-level characteristics of the cohorts and confirmed that well-known symptoms of COVID-19 (eg, fever, cough, and dyspnea) showed higher prevalence than the hospitalized influenza cohort and the general hospitalized cohort.ConclusionsWe present COHD-COVID, a publicly accessible database providing useful clinical data for hospitalized patients with COVID-19, hospitalized patients with influenza, and general hospitalized patients. We expect COHD-COVID to provide researchers and clinicians quantitative measures of COVID-19-related clinical features to better understand and combat the pandemic.
| S-EPMC8485985 | biostudies-literature
Project description:BackgroundPatient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons - ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium.Main bodyIn this paper we describe the main features of the EurOPDX Data Portal ( https://dataportal.europdx.eu/ ), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views.(short) conclusionEurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users' interests.
| S-EPMC8862363 | biostudies-literature
Project description:Genomic data sharing in cancer has been restricted to aggregate or controlled-access initiatives to protect the privacy of research participants. By limiting access to these data, it has been argued that the autonomy of individuals who decide to participate in data sharing efforts has been superseded and the utility of the data as research and educational tools reduced. In a pilot Open Access (OA) project from the CPRIT-funded Texas Cancer Research Biobank, many Texas cancer patients were willing to openly share genomic data from tumor and normal matched pair specimens. For the first time, genetic data from 7 human cancer cases with matched normal are freely available without requirement for data use agreements nor any major restriction except that end users cannot attempt to re-identify the participants (http://txcrb.org/open.html).
| S-EPMC4755126 | biostudies-literature
Project description:Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.
| S-EPMC8985540 | biostudies-literature
Project description:In 1982, the Annals of Virology published a paper showing how Liberia has a highly endemic potential of Ebola warning health authorities of the risk for potential outbreaks; this journal is only available by subscription. Limiting the accessibility of such knowledge may have reduced information propagation toward public health actors who were indeed surprised by and unprepared for the 2014 epidemic. Open access (OA) publication can allow for increased access to global health research (GHR). Our study aims to assess the use, cost and impact of OA diffusion in the context of GHR.A total of 3366 research articles indexed under the Medical Heading Subject Heading "Global Health" published between 2010 and 2014 were retrieved using PubMed to (1) quantify the uptake of various types of OA, (2) estimate the article processing charges (APCs) of OA, and (3) analyse the relationship between different types of OA, their scholarly impact and gross national income per capita of citing countries.Most GHR publications are not available directly on the journal's website (69%). Further, 60.8% of researchers do not self-archive their work even when it is free and in keeping with journal policy. The total amount paid for APCs was estimated at US$1.7 million for 627 papers, with authors paying on average US$2732 per publication; 94% of APCs were paid to journals owned by the ten most prominent publication houses from high-income countries. Researchers from low- and middle-income countries are generally citing less expensive types of OA, while researchers in high-income countries are citing the most expensive OA.Although OA may help in building global research capacity in GHR, the majority of publications remain subscription only. It is logical and cost-efficient for institutions and researchers to promote OA by self-archiving publications of restricted access, as it not only allows research to be cited by a broader audience, it also augments citation rates. Although OA does not ensure full knowledge transfer from research to practice, limiting public access can negatively impact implementation and outcomes of health policy and reduce public understanding of health issues.
| S-EPMC5576373 | biostudies-other
Project description:BackgroundTo advance research on malaria, the outputs from existing studies and the data that fed into them need to be made freely available. This will ensure new studies can build on the work that has gone before. These data and results also need to be made available to groups who are developing public health policies based on up-to-date evidence. The Malaria Atlas Project (MAP) has collated and geopositioned over 50,000 parasite prevalence and vector occurrence survey records contributed by over 3,000 sources including research groups, government agencies and non-governmental organizations worldwide. This paper describes the results of a project set up to release data gathered, used and generated by MAP.MethodsRequests for permission to release data online were sent to 236 groups who had contributed unpublished prevalence (parasite rate) surveys. An online explorer tool was developed so that users can visualize the spatial distribution of the vector and parasite survey data before downloading it. In addition, a consultation group was convened to provide advice on the mode and format of release for data generated by MAP's modelling work. New software was developed to produce a suite of publication-quality map images for download from the internet for use in external publications.ConclusionMore than 40,000 survey records can now be visualized on a set of dynamic maps and downloaded from the MAP website on a free and unrestricted basis. As new data are added and new permissions to release existing data come in, the volume of data available for download will increase. The modelled data output from MAP's own analyses are also available online in a range of formats, including image files and GIS surface data, for use in advocacy, education, further research and to help parameterize or validate other mathematical models.
| S-EPMC3662599 | biostudies-literature
Project description:Extensive, multifactorial data sharing is a crucial prerequisite for current and future (radiotherapy) research. However, the cost, time and effort to achieve this are often a roadblock. We present an open-source based data-sharing infrastructure between two radiotherapy departments, allowing seamless exchange of de-identified, automatically translated clinical and biomedical treatment data.
| S-EPMC5228341 | biostudies-literature
Project description:Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice. In the context of COVID-19, there has been a rush to share data marked by an explosion of population-specific and discipline-specific resources for collecting, curating, and disseminating participant-level data. We conducted a scoping review and cross-sectional survey to identify and describe COVID-19-related platforms and registries that harmonise and share participant-level clinical, omics (eg, genomic and metabolomic data), imaging data, and metadata. We assess how these initiatives map to the best practices for the ethical and equitable management of data and the findable, accessible, interoperable, and reusable (FAIR) principles for data resources. We review gaps and redundancies in COVID-19 data-sharing efforts and provide recommendations to build on existing synergies that align with frameworks for effective and equitable data reuse. We identified 44 COVID-19-related registries and 20 platforms from the scoping review. Data-sharing resources were concentrated in high-income countries and siloed by comorbidity, body system, and data type. Resources for harmonising and sharing clinical data were less likely to implement FAIR principles than those sharing omics or imaging data. Our findings are that more data sharing does not equate to better data sharing, and the semantic and technical interoperability of platforms and registries harmonising and sharing COVID-19-related participant-level data needs to improve to facilitate the global collaboration required to address the COVID-19 crisis.
| S-EPMC10552001 | biostudies-literature