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31 results for “docker”
DELiVR supporting files (training weights, additional components for inference docker container)
<p>Initial Release of the supporting data for the DELiVR cleared-brain analysis pipeline, as described in our 2024 publication: </p> <p>Virtual reality-empowered deep-learning analysis of brain cells</p> <p>Kaltenecker, Al-Maskari, Negwer, et al., Nature Methods 2024</p> <p>https://doi.org/10.1038/s41592-024-02245-2</p> <p>This repository contains the following datasets:</p> <ul> <li>Training weights for microglia inference</li> <li>Training weights for c-fos inference (as used in the paper)</li> <li>The copy of Terastitcher that we used to build the inference container </li> <li>The copy of mBrainAligner that we used to build the inference container</li> <li>The Dockerfile that we used to build the inference container </li> <li>The Ilastik version that we used to build the inference container </li> </ul> <p>Source code for TeraStitcher: https://github.com/abria/TeraStitcher</p> <p>Source code for mBrainaligner: https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/mBrainAligner </p> <p>Download for this specific version of the Ilastik binaries: https://files.ilastik.org/ilastik-1.4.0b8-Linux.tar.bz2</p>
Improvement of the Power Consumption of Docker Container Environments by evaluation and adaptation of logging techniques
<pre>Container technologies are becoming increasingly important in cloud computing, data centers, and software development. As a result, the number of computing units increases significantly, leading to an increase in power consumption. If the energy consumption of a computing instance is changed minimally by an adapted configuration, this can have a large impact on entire container environments. This paper uses the example of docker logging drivers to show what these effects could be in terms of CPU consumption, reachability, power consumption and power costs.</pre>
OSGeo tools in Docker Hub
<p>This repository shows the results of 60 queries using the Docker Hub API to measure the presence of OSGeo tools in Docker images. The results have been obtained using <a href="https://github.com/beatcracker/PSDockerHub">PSDockerHub</a>. It is a PowerShell module written to access the official <a href="https://hub.docker.com/">Docker Hub/Registry</a>. Its main goal is to make sure that you have never had to use the public part of Docker Hub site in the browser. </p> <p>These files are obtained using this script <a href="https://github.com/sergitrilles/OSGEO_docker">https://github.com/sergitrilles/OSGEO_docker</a>. All the files have been manually modified to choose the correct search results.</p>
abc-finder docker tar file
<p>abc-finder docker tar file</p>
Packages installed in Debian-based Docker images
<p>This dataset comes with the replication package provided for a study that we carried out on packages installed in Docker images.<br> <br> <strong>Title of the study:</strong> ``A multi-dimensional analysis of technical lag in Debian-based Docker images"<br> <strong>Authors :</strong> Ahmed Zerouali (VUB, UMONS), Tom Mens (UMONS), Alexandre Decan (UMONS), Jesus Gonzalez-Barahona (URJC) and Gregorio Robles (URJC).<br> <strong>Journal</strong>: Empirical Software Engineering </p> <p>The replication package can be found in: <a href="https://github.com/neglectos/dockerhub_analysis/">https://github.com/neglectos/dockerhub_analysis/</a></p>
Data from: The impact of base image selection on the energy efficiency of containerized applications in Docker
<p>Data collected from the workloads in <a href="https://github.com/btjiong/docker-energy">https://github.com/btjiong/docker-energy</a></p><p> </p>
Reproduction Package (Docker container) for the ESEC/FSE 2022 Article `A Retrospective Study of one Decade of Artifact Evaluations`
<p>This is the artifact accompanying our study of artifact evaluations at SE/PL conferences and their effects, accepted for presentation at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2022.<br> For ease of artifact evaluation and usage, we ship our artifact as a Docker container, which comprises our datasets, the tools we built to collect those datasets, and the scripts used to obtain the results presented in the paper.<br> It also contains the Dockerfile to create the submitted image in order to make the software dependencies for our artifact explicit.</p>
Replication package for the Docker Inheritance network analysis
<p><strong>Container Image Inheritance on DockerHub: Empirical Analysis and Insights</strong></p> <p>This repository represents a replication package for our SCAM paper on DockerHub inheritance network.</p> <p>This replication package requires Python 3.5+ to be installed.</p> <p>These experiments were executed on a Linux Ubuntu OS.</p> <p>This replication package contains four folders:<br> - data: contains all datasets required.<br> - scripts: contains all scripts needed to collect the data.<br> - notebooks: contains notebooks where we analyze data. <br> - figures: contains figures saved from the notebooks.</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>The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.</p>
Docker Image for Reproducibility Study
<p>This is the archive of the full Docker image used for the artifact of the ICSE 2023 paper "On the Reproducibility of Software Defect Datasets".</p> <p>Link to the artifact and paper: https://github.com/ucd-plse/On-the-Reproducibility</p> <p>Link to the Docker image on Docker Hub: https://hub.docker.com/r/ucdavisplse/reproducibility</p> <p> </p> <p> </p>
RiboDoc: a Docker-based package for ribosome profiling analysis
GEO Series GSE173856. Saccharomyces cerevisiae. 4 samples. Type: Expression profiling by high throughput sequencing.
Energiemessungen der Docker-Komponenten
<p>Hier werden die Ergebnisse der Energiemessungen der Docker-Komponenten dargestellt. Zu finden sind alle Messungen der letzten Messreihe.</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.