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Project Source Code at https://github.com/pandem2 Package published at https://pypi.org/project/pandem-source Description Identify, map and integrate multiple pandemic related data into a coherent pandemic-management database. Developed within the H2020 project PANDEM-2, Pandem-Source allows users to systematically capture, standardize and analyze data coming from international and national surveillance databases, participatory surveillance projects, social networks and mass media. This tool is focused on flexibility so adding new sources or variables can be easily done as is required during a pandemic episode. Target users Public health data experts and data managers needing integrate several surveillance data. Approach Data integration follow a semantic approach. Data sources need to be described using a Data Labelling Schema (DLS) file that specifies the acquisition chennel (URL, git, local file or script) the format and how to map the input data to Pandem-Source variables. If further customistions are needed, the user can define them using simple python scripts. The DLS ensures each source and variable is properly documented knwing its origin, meaning and data quality.

Data and code for preprint paper Data-driven recombination detection in viral genomes. Alfonsi T., Bernasconi A., Chiara M., Ceri S.. bioRxiv 2023.06.05.543733; doi: https://doi.org/10.1101/2023.06.05.543733. The repository contains: - A manuscript_supplementary_material, containing also a guide to the files - The RecombinHunt application, with: a README file including system requirements, installation guide (running recombinhunt-3.3.3-py3-none-any.whl), example code snippets; a demo folder with Jupyter notebook (Python code) and example input/output datasets; a src folder containing the source code; a environments folder with preprocessed datasets and lineage/mutation probability tables and a validation_data folder with supporting files.

EBI Tools is a project that aims to provide programmatic access to the various databases and retrieval and analysis services that the European Bioinformatics Institute (EBI) provides through Simple Object Access Protocol (SOAP) and other related web service technologies. Example tools include those to compute sequence similarity searches, pairwise/multiple sequence alignment and protein functional analysis.

Scottish COVID-19 Data Curation Interface This Links prototype provides an interface to curate the weekly data available from National Records of Scotland. It allows for the uploading of individual CSV files, adds new data to the database, and indicates when there are new values for existing data. It uses the temporal features of Links to provide valid-time information about fatality counts. It also displays the data, as well as changes to the data, at three levels. Individual counts (specified by the week and subcategory that the data applies to) Grouped by week Grouped by subcategory It supports the answering of questions such as the following. How do the Male and Female subcategories compare in terms of the change in fatalities? How do the Male and Female subcategories compare in terms of number of updates to existing values? Note that this is a prototype hence some functionality has not been implemented and not all buttons in the interface will respond. The CSV file uploading makes the assumption that uploads will occur in the order the files were originally released, and unexpected behaviour may occur for a different ordering of uploads. Additionally, the CSV files for upload which can be found in the data directory have been modified, and uploading the CSV files from the original site will result in errors.

Added licence and final correction after publishing the corresponding manuscript.

Tinker-HP is a tool for high-performance massively parallel evolution of tinker on CPUs & GPUs. It is used to accelerating molecular dynamics simulations of large complex systems with advanced point dipole polarizable force fields using GPUs and multi-GPU systems.

Code and analysis for the manuscript "Climatic signatures in the different COVID-19 pandemic waves across both hemispheres" published in Nature Computational Science (https://doi.org/10.1038/s43588-021-00136-6).

A platform for segmentation and assessment of COVID19 in CT datasets. The software provides a client application for DICOM metadata anonymization and upload along with a cloud backend for image-analysis processing and job management. The solution is available free of charge at https://smartcad.smart-radiology.com/.

Data and code for paper "Analysis of amino acid change dynamics reveals SARS-CoV-2 variant emergence", now under revision. The repository contains: two files for each of 44 locations (including 19 countries and 25 US states) CoV_<location>_mut5_obs3_absolute.csv (a matrix of amino acid changes [rows], collection weeks [columns], counts of sequences with amino acid change [cells]) CoV_<location>_mut5_obs3_totals.csv (a vector of total collected sequences per each week [columns]) one lineage dictionary (lins_changes75_2_5000_1000.csv) one MATLAB script for reproducing all the results of the paper (variants_clustering.m)

This release provides an open source software and hardware reference implementation for an epidemic dosimeter. Just as a radiation dosimeter measures dose uptake of external ionizing radiation, the epidemic dosimeter tracks potential exposure to viruses or bacteria associated with an epidemic. The dosimeter measures a person's exposure to an epidemic, such as COVID-19, based on exposure to contacts that have been tested positive. The epidemic dosimeter is designed to be widely accessible and to safeguard privacy. Specifically, it is designed to run on the $10 open-hardware Raspberry Pi Zero-W computer, with a minimal user interface, comprising LED indicators regarding operation and exposure risk and a physical interlock switch to allow the release of contact data. The software is based on the DP3T contact tracing "unlinkable" design and corresponding reference implementation code, gratefully acknowledging the team's amazing work. Epidose is partly funded through the EOSC Secretariat. EOSCsecretariat.eu has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.