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31 results for “docker”
MERRIN: MEtabolic Regulation Rule INference from time series data (Docker image and notebooks)
<p>This record contains notebooks and Docker image for reproducing the results of the paper "MERRIN: MEtabolic Regulation Rule INference from time series data" published as part of the ECCB 2022 conference.</p> <p>Notebooks can be executed interactively within the Docker image <code>bioasp/merrin:v1</code> which extends the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> version <code>2021-02-01.</code></p> <p>Also see <a href="https://github.com/bioasp/merrin-covert">https://github.com/bioasp/merrin-covert</a></p> <p>The Docker image can be executed as follows:</p> <pre><code class="language-bash">docker pull bioasp/merrin:v1 docker run -it --rm -p 8888:8888 bioasp/merrin:v1 </code></pre> <p>then point your browser to <a href="http://127.0.0.1:8888">http://127.0.0.1:8888</a>.</p> <p>The image can be imported using the command <code>docker load</code> with the image file provided in this record:</p> <pre><code>docker load -i image.tar.gz</code></pre> <p>or with the <code>donodo</code> command available at <a href="https://github.com/pauleve/donodo">https://github.com/pauleve/donodo</a>:</p> <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.6670165</code></pre>
Replication data for: "How does Docker affect energy consumption? Evaluating workloads in and out of Docker containers"
<p>Database of raw power measurements and energy summaries for our Docker energy tests.</p> <p>Please cite us if you use this dataset.</p> <p>Schema</p> <pre><code>CREATE TABLE configuration( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE experiment( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE run( id PRIMARY KEY, configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE ); CREATE TABLE measurement( run REFERENCES run(id) ON DELETE CASCADE ON UPDATE CASCADE, timestamp REAL NOT NULL, -- Unix timestamp in milliseoncds power REAL NOT NULL ); CREATE TABLE energy( id PRIMARY KEY REFERENCES run(id), configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE, energy REAL NOT NULL, started REAL NOT NULL, ended REAL NOT NULL, elapsed_time REAL NOT NULL -- in milliseconds );</code></pre>
Studying the Practices of Deploying Machine Learning Projects on Docker
<p>This repository contains the dataset for our study titled above:</p> <p>Below is the abstract:</p> <p>Docker is a containerization service that allows for convenient deployment of websites, databases, applications' APIs, and machine learning (ML) models with a few lines of code. Studies have recently explored the use of Docker for deploying general software projects with no specific focus on how Docker is being used to deploy ML-based projects. In this study, we conducted an exploratory study to understand how Docker is being used to deploy ML-based projects. As the initial step, we examined the categories of ML-based project that use Docker. We then examined why and how these projects use Docker, and the characteristics of the resulting Docker images. Our results indicate that six categories of ML-based projects use Docker for deployment, including ML Applications, MLOps/ AIOps, Tookits, DL Frameworks, Models, and Documentation. We derived the taxonomy of 21 major categories representing the purposes of using Docker, including those specific to models such as model management tasks (e.g., testing, training), data management (e.g., migration, persistent storage, sharing), software testing, interactive development. We then showed that ML engineers use Docker images mostly to help with the platform portability, such as transferring the software across the operating systems, runtimes such as GPU-accelerated, and language constraints. However, we also found that more resources may be required to run the Docker images for building ML-based software projects due to the large number of files contained in the image layers with deeply nested directories. Through this study we hope to shed light on the emerging practices of deploying ML software projects using containers and highlight aspects that should be improved.</p>
Implementation of Genomic Variant Calling Using GATK4, SPARK, WDL, CROMWELL and DOCKER Over Simulated Ebola NGS Dataset.
<p>Ebola genome is manually mutated to contain non-structural as well as structural variants. One ebola genome contains non-structural variants - 10 SNPs, 10 INDELs, 05 TRANSLOCATIONs, 05 INSERSIONs and their reverse complements. Similarly, other two set of mutated genomes contain structural variants. Each set contains seven mutated ebola genome each one for large deletion, insertion, duplication, translocation, inversion, complex variant1 (consecutive three mutations - insertion, duplication and deletion) and complex variants2 (consecutive three mutations - deletion, duplication and deletion). All insertions are novel sequence insertion.</p> <p> </p>
CESM FKESSLER input data for running CESM in docker container
This dataset contains input data for running CESM 2.1.1 with FKESSLER compset and resolution T31_g37. <pre>create_newcase --case /home/cesm/cases/fkessler --compset FKESSLER \ --res T31_g37 --compset FKESSLER --machine espresso --run-unsupported </pre>
CESM F1850 input data for running CESM in docker container
<p>This dataset contains input data for running CESM 2.1.1 with F1850 compset and resolution f09_g17.</p> <p> </p> <pre>create_newcase --case /home/cesm/cases/B1850 --compset B1850 \ --res f09_g17 --machine espresso --run-unsupported && \ cd /home/cesm/cases/B1850 </pre> <p>The case uses <a href="https://bioconda.github.io/recipes/cesm/README.html">cesm from bioconda</a>.</p>
CESM input data for running CESM historic with CAM6 and CLM5 (no ocean) in docker container
<p>CESM docker container for HIST_CAM60_CLM50%BGC_CICE%PRES_DOCN%DOM_MOSART_CISM2%NOEVOLVE_SWAV compset and resolution f19_g17 using <a href="https://bioconda.github.io/recipes/cesm/README.html">bioconda cesm docker</a> as a base image.</p>
Seahorse Dockers School
<p>The documentary video on grassroot primary school - Seahorse Dockers School in Seahorse Village, Kilifi County, founded by Mramba Mweni and Saidi Chengo - where TPAAE researchers are conducting workshops in visual education, supporting the development of children's creativity in intercultural dialogue.</p>
Docker Images Analysis
<p>This replication package is for the Docker container analysis submitted and accepted in SANER2019.</p> <p>Paper title: On The Relation Between Outdated Docker Containers, Severity Vulnerabilities and Bugs</p> <p>This replication package requires Python 3.5+ to be installed, and all the dependencies listed in ``requirements.txt``.<br> They can be automatically installed using ``pip install -r requirements.txt``. <br> The replication package also needs "docker" and "skopeo" (https://github.com/containers/skopeo) tools be installed. However, one can directly use the prepared data and analyze it, without the need to docker or skopeo.<br> These experiments were executed on a Linux Ubuntu OS.</p> <p>Content:<br> + notebooks: contains the notebook with the results and analysis.<br> + data: contains prepared data for the analysis<br> + scripts: contains other scripts needed for inspecting the images before analysis and extracting Debian packages from the Debian snapshot.</p> <p>To obtain the analysis used in the paper, one should execute ``jupyter notebook`` at the root of this replication package, and open the notebook contained in ``notebooks``.</p> <p>For the Data Extraction part, we relied on the tool of ConPan: https://github.com/neglectos/ConPan</p> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license.<br> The source code is under the GNU General Public License.</p>
Implementation of Genomic Variant Calling Using GATK4, SPARK, WDL, CROMWELL and DOCKER Over Simulated Ebola NGS Dataset.
<p>Ebola genome is manually mutated to contain non-structural variants - 10 SNPs, 10 INDELs, 05 TRANSLOCATIONs, 05 INSERSIONs and their reverse complements. Paired-end illumina RNASEQ reads are simulated in fastq format. </p>
Implementation of Genomic Variant Calling Using GATK4, SPARK, WDL, CROMWELL and DOCKER Over Simulated Ebola NGS Dataset.
<p>Ebola genome is manually mutated to contain structural variants. There are seven mutated ebola genomes each one for two large deletions, insertions, duplications, translocations, inversions, one complex variant1 (consecutive three mutations - insertion, duplication and deletion) and one complex variants2 (consecutive three mutations - deletion, duplication and deletion). All insertions are novel sequence insertion. Paired-end illumine RNASEQ reads are simulated in fastq format.</p>
Implementation of Genomic Variant Calling Using GATK4, SPARK, WDL, CROMWELL and DOCKER Over Simulated Ebola NGS Dataset.
<p>Ebola genome is manually mutated to contain structural variants. There are seven mutated ebola genomes each one for large deletion, insertion, duplication, translocation, inversion, complex variant1 (consecutive three mutations - insertion, duplication and deletion) and complex variants2 (consecutive three mutations - deletion, duplication and deletion). All insertions are novel sequence insertion. Paired-end illumine RNASEQ reads are simulated in fastq format.</p>
Community Docker Hub images and the third-party (JavaScript, Python and Ruby) packages installed in them
<p>This dataset comes with the replication package provided for a study that we carried out on third-party JavaScript, Python and Ruby packages installed in DockerHub images.</p> <p>The replication package can be found in: <a href="https://github.com/neglectos/3dPartyPackages_Docker">https://github.com/neglectos/3dPartyPackages_Docker</a>/</p>
Test dataset and docker volume for omero-quay
<p>This is a minimal dataset to test [omero-quay](https://gitlab.in2p3.fr/fbi-data/omero-quay)</p> <p>See omero-quay documentation and tests for usage</p> <p> </p> <p> </p>
Taxonomy database for DeGeCI - without docker
<p>Janusgraph database for taxonomy graph used for taxonomic filtering in DeGeCI (requires no use of docker)</p>
docker-compose for neo4j with paradise papers data loaded: Release v0.1-43
<p><code>docker-compose</code> for neo4j with paradise papers data loaded</p>
Automated Data Quality Assurance with GitLab pipelines and Docker
<ul> <li>The Data Validation and Quality Assurance demonstrator is a tool designed to assist with collaborative research projects, ensuring that collected data complies with established schemas and monitoring these files for unusual or unexpected values. This demonstrator attempts to show how a task of this nature can be attempted in a scalable, cloud based infrastructure, while maintaining compatibility with existing frameworks. In this presentation we will cover a brief use case, showing the results of the demonstrator on an example dataset that contains some intentionally added data quality items</li> </ul>
Docker compose files to analyze structural patterns of containerized microservice applications
<p>The dataset contains 137 docker-compose files found in publicly available repositories on Github. This dataset was used to analyze the structural patterns of containerized microservice applications to figure out how the structure of microservice applications in benchmarks could be improved to resemble more real world structures.</p> <p>For more information and citation please refer to our paper:</p> <p>Vladimir Podolskiy, Maria Patrou, Panos Patros, Michael Gerndt, and Kenneth B. Kent. 2020. The weakest link: revealing and modeling the architectural patterns of microservice applications. In <em>Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering</em> (<em>CASCON '20</em>). IBM Corp., USA, 113–122. https://dl.acm.org/doi/10.1145/3132747.3132772 </p>
A Large-scale Data Set and an Empirical Study of Docker Images Hosted on Docker Hub
<p>The data set schema, fields description, and analysis scripts are in the GitHub artifact repository.</p> <p>https://github.com/linncy/icsme2020-docker-study</p> <p> </p> <p>@inproceedings{LinICSME20,<br>author={Changyuan Lin and Sarah Nadi and Hamzeh Khazaei},<br>title={A Large-scale Data Set and an Empirical Study of Docker Images Hosted on Docker Hub},<br>booktitle={Proceedings of the 36th IEEE International Conference on Software Maintenance and Evolution (ICSME)},<br>year={2020},<br>url_Paper={https://www.dropbox.com/s/3bktcmdr7rlw1ic/LinICSME20.pdf}<br>}</p>
Docker taxonomy database for DeGeCI
<p>Janusgraph database for taxonomy graph used for taxonomic filtering in DeGeCI</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.