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  • 2020-2024
  • Open Source

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    Authors: Noordijk, Ben; Nijland, Reindert; Carrion, Victor J.; Raaijmakers, Jos; +2 Authors

    BaseLess reduces your MinION sequencing device to a simple species detector. As a trade-off, it runs on inexpensive computational hardware like single-board computers. The baseLess code base can be found here. All data and analysis results described in the paper describing baseLess are stored in this file.

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    Authors: van Hees, M.L.M.; Lepelaar, Mark; van Vorsselen, Jeroen; Oskam, I.; +1 Authors

    In order to get an idea of the scope of repurpose, desk research was used to search for as many existing examples as possible both national as international with maximum variety. This has led to a database with more than a hundred repurpose examples that represent the scope of current repurpose practice. These examples are included in the database that is searchable for some characteristics of repurpose. Use the database as inspiration and extra knowledge in the field of repurpose. The practical examples can be filtered with a number of variables in relation to origin, origin of the base material, critical factors, typology, new application, characteristics of new products, business models, impact and sources or references. The database with inspiring repurpose examples is part of the research Repurpose Driven Design & Management co-funded by SiA, the taskforce for Applied Research.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HvA (Hogeschool van ...arrow_drop_down
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    Dataset . 2022
    Data sources: Datacite; NARCIS
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    Dataset . 2022
    Data sources: Datacite; NARCIS
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HvA (Hogeschool van ...arrow_drop_down
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      Dataset . 2022
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      Dataset . 2022
      Data sources: Datacite; NARCIS
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    The dataset contains results of experiments for the study of our research https://arxiv.org/abs/2011.12770. Data is used to generate results with this reproducible notebook: https://github.com/RafalKucharskiPK/ExMAS/blob/master/ExMAS/spinoffs/corona/02_plots.ipynbFiles:- png with maps- .csv with epidemic modelling results (For any given day, the model outputs information about the number of travellers in each state (S-I-Q-R) and newly infected travellers, based on which we can reproduce epidemic spreading profiles.)Abstract of the study:Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.

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    Group template created from the subjects of the AHEAD database, for quantitative R1, quantitative R2* and QSM.

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    Authors: Oortwijn, Wytse Hendrikus Marinus; Huisman, Marieke; Joosten, Sebastiaan Jozef Christiaan; van de Pol, Jan Cornelis;

    This dataset contains the artifact for our TACAS'20 article titled: Automated Verification of Parallel Nested DFS. More specifically, it contains the VerCors implementation of the parallel NDFS algorithm described in Section 3 of the paper, as well as the two optimisations of parallel NDFS described in Section 4. It also contains a release of VerCors for verifying these three implementations.

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    This is a dataset consisting of features for tracks fetched using Spotify's Web API. The tracks are labeled '1' or '0' ('Hit' or 'Flop') depending on some criterias of the author. This dataset can be used to make a classification model that predicts whethere a track would be a 'Hit' or not. (Note: The author does not objectively considers a track inferior, bad or a failure if its labeled 'Flop'. 'Flop' here merely implies that it is a track that probably could not be considered popular in the mainstream.) Here's an implementation of this idea in the form of a website that I made. {http://www.hitpredictor.in/}

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    Authors: Santos Oliván, Daniel; Torres Sánchez, Alejandro; Vilanova Caicoya, Guillermo; Bayat, Mohammad Reza; +3 Authors

    hiperlife is a high performance parallel library for finite elements. The aim of this library is to provide a computational framework to address problems of cell and tissue mechanobiology for a wide range of cases and users, with special focus on curved surfaces (the cell membrane, the cell cortex, epithelial monolayers,etc.). It is designed to handle the multiphysic nature of problems in mechanobiology, to manage unstructured grids that deal with complex geometries and to allow arbitrary higher-order basis functions that describe the curvature of interfaces. hiperlife is written in C++, uses the Message Passage Interface (MPI) paradigm for parallelism, and is built on top of several packages of the Trilinos Project. These particular choices set the basis for the main features of the library, namely, parallelism, flexibility, user-centered design and sustainability.

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    Authors: Vasiliki Panagi;

    hutch-workflow executes rquest in one shot.

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    Authors: Haris Zafeiropoulos; Martin Beracochea;

    # metaGOflow: A workflow for marine Genomic Observatories' data analysis ![logo](https://raw.githubusercontent.com/hariszaf/metaGOflow-use-case/gh-pages/assets/img/metaGOflow_logo_italics.png) ## An EOSC-Life project The workflows developed in the framework of this project are based on `pipeline-v5` of the MGnify resource. > This branch is a child of the [`pipeline_5.1`](https://github.com/hariszaf/pipeline-v5/tree/pipeline_5.1) branch > that contains all CWL descriptions of the MGnify pipeline version 5.1. ## Dependencies To run metaGOflow you need to make sure you have the following set on your computing environmnet first: - python3 [v 3.8+] - [Docker](https://www.docker.com) [v 19.+] or [Singularity](https://apptainer.org) [v 3.7.+]/[Apptainer](https://apptainer.org) [v 1.+] - [cwltool](https://github.com/common-workflow-language/cwltool) [v 3.+] - [rdflib](https://rdflib.readthedocs.io/en/stable/) [v 6.+] - [rdflib-jsonld](https://pypi.org/project/rdflib-jsonld/) [v 0.6.2] - [ro-crate-py](https://github.com/ResearchObject/ro-crate-py) [v 0.7.0] - [pyyaml](https://pypi.org/project/PyYAML/) [v 6.0] - [Node.js](https://nodejs.org/) [v 10.24.0+] - Available storage ~235GB for databases ### Storage while running Depending on the analysis you are about to run, disk requirements vary. Indicatively, you may have a look at the metaGOflow publication for computing resources used in various cases. ## Installation ### Get the EOSC-Life marine GOs workflow ```bash git clone https://github.com/emo-bon/MetaGOflow cd MetaGOflow ``` ### Download necessary databases (~235GB) You can download databases for the EOSC-Life GOs workflow by running the `download_dbs.sh` script under the `Installation` folder. ```bash bash Installation/download_dbs.sh -f [Output Directory e.g. ref-dbs] ``` If you have one or more already in your system, then create a symbolic link pointing at the `ref-dbs` folder or at one of its subfolders/files. The final structure of the DB directory should be like the following: ````bash user@server:~/MetaGOflow: ls ref-dbs/ db_kofam/ diamond/ eggnog/ GO-slim/ interproscan-5.57-90.0/ kegg_pathways/ kofam_ko_desc.tsv Rfam/ silva_lsu/ silva_ssu/ ```` ## How to run ### Ensure that `Node.js` is installed on your system before running metaGOflow If you have root access on your system, you can run the commands below to install it: ##### DEBIAN/UBUNTU ```bash sudo apt-get update -y sudo apt-get install -y nodejs ``` ##### RH/CentOS ```bash sudo yum install rh-nodejs (e.g. rh-nodejs10) ``` ### Set up the environment #### Run once - Setup environment - ```bash conda create -n EOSC-CWL python=3.8 ``` - ```bash conda activate EOSC-CWL ``` - ```bash pip install cwlref-runner cwltool[all] rdflib-jsonld rocrate pyyaml ``` #### Run every time ```bash conda activate EOSC-CWL ``` ### Run the workflow - Edit the `config.yml` file to set the parameter values of your choice. For selecting all the steps, then set to `true` the variables in lines [2-6]. #### Using Singularity ##### Standalone - run: ```bash ./run_wf.sh -s -n osd-short -d short-test-case -f test_input/wgs-paired-SRR1620013_1.fastq.gz -r test_input/wgs-paired-SRR1620013_2.fastq.gz `` ##### Using a cluster with a queueing system (e.g. SLURM) - Create a job file (e.g., SBATCH file) - Enable Singularity, e.g. module load Singularity & all other dependencies - Add the run line to the job file #### Using Docker ##### Standalone - run: ``` bash ./run_wf.sh -n osd-short -d short-test-case -f test_input/wgs-paired-SRR1620013_1.fastq.gz -r test_input/wgs-paired-SRR1620013_2.fastq.gz ``` HINT: If you are using Docker, you may need to run the above command without the `-s' flag. ## Testing samples The samples are available in the `test_input` folder. We provide metaGOflow with partial samples from the Human Metagenome Project ([SRR1620013](https://www.ebi.ac.uk/ena/browser/view/SRR1620013) and [SRR1620014](https://www.ebi.ac.uk/ena/browser/view/SRR1620014)) They are partial as only a small part of their sequences have been kept, in terms for the pipeline to test in a fast way. ## Hints and tips 1. In case you are using Docker, it is strongly recommended to **avoid** installing it through `snap`. 2. `RuntimeError`: slurm currently does not support shared caching, because it does not support cleaning up a worker after the last job finishes. Set the `--disableCaching` flag if you want to use this batch system. 3. In case you are having errors like: ``` cwltool.errors.WorkflowException: Singularity is not available for this tool ``` You may run the following command: ``` singularity pull --force --name debian:stable-slim.sif docker://debian:stable-sli ``` ## Contribution To make contribution to the project a bit easier, all the MGnify `conditionals` and `subworkflows` under the `workflows/` directory that are not used in the metaGOflow framework, have been removed. However, all the MGnify `tools/` and `utils/` are available in this repo, even if they are not invoked in the current version of metaGOflow. This way, we hope we encourage people to implement their own `conditionals` and/or `subworkflows` by exploiting the currently supported `tools` and `utils` as well as by developing new `tools` and/or `utils`.

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    Authors: Romein, John W.; Veenboer, Bram; Vlugt, Steven; Mancini, Mattia; +8 Authors

    C++ Wrappers for the CUDA Driver API.

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546 Research products
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    Authors: Noordijk, Ben; Nijland, Reindert; Carrion, Victor J.; Raaijmakers, Jos; +2 Authors

    BaseLess reduces your MinION sequencing device to a simple species detector. As a trade-off, it runs on inexpensive computational hardware like single-board computers. The baseLess code base can be found here. All data and analysis results described in the paper describing baseLess are stored in this file.

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    Authors: van Hees, M.L.M.; Lepelaar, Mark; van Vorsselen, Jeroen; Oskam, I.; +1 Authors

    In order to get an idea of the scope of repurpose, desk research was used to search for as many existing examples as possible both national as international with maximum variety. This has led to a database with more than a hundred repurpose examples that represent the scope of current repurpose practice. These examples are included in the database that is searchable for some characteristics of repurpose. Use the database as inspiration and extra knowledge in the field of repurpose. The practical examples can be filtered with a number of variables in relation to origin, origin of the base material, critical factors, typology, new application, characteristics of new products, business models, impact and sources or references. The database with inspiring repurpose examples is part of the research Repurpose Driven Design & Management co-funded by SiA, the taskforce for Applied Research.

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    The dataset contains results of experiments for the study of our research https://arxiv.org/abs/2011.12770. Data is used to generate results with this reproducible notebook: https://github.com/RafalKucharskiPK/ExMAS/blob/master/ExMAS/spinoffs/corona/02_plots.ipynbFiles:- png with maps- .csv with epidemic modelling results (For any given day, the model outputs information about the number of travellers in each state (S-I-Q-R) and newly infected travellers, based on which we can reproduce epidemic spreading profiles.)Abstract of the study:Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.

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    Group template created from the subjects of the AHEAD database, for quantitative R1, quantitative R2* and QSM.

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    Authors: Oortwijn, Wytse Hendrikus Marinus; Huisman, Marieke; Joosten, Sebastiaan Jozef Christiaan; van de Pol, Jan Cornelis;

    This dataset contains the artifact for our TACAS'20 article titled: Automated Verification of Parallel Nested DFS. More specifically, it contains the VerCors implementation of the parallel NDFS algorithm described in Section 3 of the paper, as well as the two optimisations of parallel NDFS described in Section 4. It also contains a release of VerCors for verifying these three implementations.

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