• COVID-19
• Research dataclose
• Other research productsclose
• 2014-2023close
• EU close
• IE close

{"references": ["Greiss, J. et al. (2022). Food aid in Europe in times of the COVID-19 crisis. An international survey project. CSB Working Paper Series, University of Antwerp."]} The dataset is linked to a cross-sectional study on the organisation of food aid in different European countries before and during the COVID-19 crisis. Funding: The Research Foundation – Flanders (FWO) (G056121N)

Additional file 12: Figure 6. Description of data: Use of medication in propensity score-matched groups. Differences of use of (a) corticosteroids, (b) remdesivir, and (c) chloroquine between anakinra group and control group during 10 days before and 10 days after alignment day (day 0). p values were calculated using Fisher’s exact tests.

Supplementary tables for "A comprehensive, flexible collection of SARS-CoV-2 coding regions"

In late 2019, Covid-19 emerged and was soon declared a pandemic causing until now a massive health disruption and a huge impact on the global economy. Several governments around the world are still forced to take containment measures to curb the spread of the virus including partial or full lockdowns. At the same time, they rely heavily on human resources to perform manual Contact Tracing (CT) for alerting known contacts of the confirmed cases and breaking the infection chains early enough. However, CT does not scale well when the cases increase exponentially, due to the limited capacity of national public health authorities, and cannot identify possible hidden infections due to random encounters with strangers in crowded spaces such as restaurants, bars, theaters, public transportation, etc. To this end, Digital Contact Tracing (DCT) is becoming increasingly popular to enhance and empower CT, enabling automatic and faster identification and notification of exposed users. This presentation will first overview the different generations of DCT solutions from the privacy-invasive use of subscriber location data provided by cellular operators, to location monitoring mobile apps on GPS-equipped smartphones, to privacy-preserving mobile apps based on proximity sensing through Bluetooth. It will discuss the findings of recent studies with regards to the effectiveness of DCT and debate whether it has been ��� or has the potential to become ��� a game changer. Finally, it will outline the latest developments and trends in this active research field that are of interest to the IPIN community including presence tracing that aims to notify anonymously those users that have been in the same place (especially indoors) with an infected user, without necessarily satisfying the proximity constraint. Keynote @ 2021 International Conference on Indoor Positioning and Indoor Navigation.

Additional file 3: Table S3. Logistic regression models for each blood and physiological measure tested separately in the KCH training cohort, for 14- and 3-day ICU/death.

Geographic layers Geographic layers on which the different data records are geo-referenced (e.g. mobility, COVID-19 cases). The different layers can be grouped into those that cover the whole territory of pain (e.g. municipalities) and those that are restricted to a specific region (Table1). Among those that cover the full territory of Spain, the record accounts for the first four levels of administrative division, that is, autonomous communities, provinces, municipalities and districts. Visit https://flowmaps.life.bsc.es/flowboard/data for more information about the data. Layers (geo-json format): cnig_ccaa : Comunidades Aut��nomas CNIG cnig_provincias : Provincias CNIG cnig_municipios : Municipios CNIG ine_sec : Secciones censales INE mitma_mov : ��reas de movilidad MITMA zbs_07 : Zonas B��sicas de Salud de Cy abs_09 : ��rees B��siques de Salut GenCat zon_bas_13 : Zonas b��sicas sanitarias de Madrid oe_16 : Osasun Eremuak (Zonas de Salud) Euskadi zbs_15 : Zonas B��sicas de Salud del Servicio Navarro de Salud This work was supported by the Generalitat de Catalunya through the project PDAD14/20/00001, by the H2020 Programme under Grant Agreement 825070 (INFORE) and by the INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF).

This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway, identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph. The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.4723499.

VirAnimalOne is a project focused on large scale mining of publicly deposited genomic and transcriptomic datasets available from ENA/SRA and derived from pets, livestock and wild animal species: to identify unexpected coronavirus sequences; to mine the host animal genomes for potential variants that might confer resistance or susceptibility to SARS-CoV-2 and other coronaviruses known to infect both humans and animals; to phylogenetically and structurally evaluate host receptor conformations and infer potential animal susceptibility to coronavirus infections, with particular attention for SARS-Cov-2. Here, we report the pipeline, preliminary datasets and results produced in this project and related to the VirAnimalOne Technical Report: Technical_Report_VirAnimalOne_Pipeline. It reports and describes the bioinformatic pipeline used for the mining of the omics datasets. Tables S1-S6. Genome datasets used for mining activities. Table S7. List of host genes involved in coronavirus infection and used for mining polymorphisms in the host genome using the retrieved datasets. Table S8-S13. List of annotated variants in the selected host genes involved in coronavirus infection. Tables S14-S10. Viral sequences identified in the next-generation sequencing datasets generated from the selected domestic species. Warning: Please, check for updated versions as data can present some inaccuracies. Fundings: EOSCsecretariat.eu has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.