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ShareScore release 0.9.0
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32 results for “linux”
Linux Kernel CTF trace
Trace obtained using lttng
Figure 1a from: Ioanas H, Saab B, Rudin M (2017) Gentoo Linux for Neuroscience - a replicable, flexible, scalable, rolling-release environment that provides direct access to development software. Research Ideas and Outcomes 3: e12095. https://doi.org/10.3897/rio.3.e12095
Figure 1a - Minimal (excluding all optional features) dependency graph of the contributed neuroscience package set.
Figure 1b from: Ioanas H, Saab B, Rudin M (2017) Gentoo Linux for Neuroscience - a replicable, flexible, scalable, rolling-release environment that provides direct access to development software. Research Ideas and Outcomes 3: e12095. https://doi.org/10.3897/rio.3.e12095
Figure 1b - Maximal (including all optional features) dependency graph of the contributed neuroscience package set.
binary_c V2.2.4 with binary_c-python V1.0.0 on Ubuntu 22.04 (Linux Gnome) desktop
<p>The <strong><em>binary_c</em></strong> software framework models the evolution of single, binary and multiple stars, including stellar evolution and nucleosynthesis. It is desgined for stellar population calculations, so is lightweight and versatile, and its support software contains tools for development and data analysis. <strong><em>binary_c</em></strong> is happy in standalone, virtual and HPC environments. Builds on <a href="https://en.wikipedia.org/wiki/Linux">Linux</a>, <a href="https://en.wikipedia.org/wiki/MacOS">OSX</a> and <a href="https://en.wikipedia.org/wiki/Windows_Subsystem_for_Linux">WSL</a>.</p> <p>This is version 2.2.4 of binary_c, with binary_c-python 1.0.0, installed on Kubuntu 24.04 desktop running a Linux kernel. Many useful tools are pre-installed, such as GCC, Clang, meson, ninja, valgrind, emacs, GSL, kcachegrind, gdb, gnuplot, and Jupyter so you can run binary_c-python's notebooks. Also included are commands to run either the latest stable versions or master versions straight from the gitlab repository.</p> <p>Homepage: <a href="https://binary_c.gitlab.io/">https://binary_c.gitlab.io/</a></p> <p>Login details:</p> <ul> <li>User: binary_c</li> <li>Password: binary_c</li> </ul> <p>The virtual machine is compressed with xzip, so you will need to decompress it before use.</p>
binary_c V2.2.4 with binary_c-python V1.0.0 on Kubuntu 24.04 (Linux) desktop
<p>The <strong><em>binary_c</em></strong> software framework models the evolution of single, binary and multiple stars, including stellar evolution and nucleosynthesis. It is desgined for stellar population calculations, so is lightweight and versatile, and its support software contains tools for development and data analysis. <strong><em>binary_c</em></strong> is happy in standalone, virtual and HPC environments. Builds on <a href="https://en.wikipedia.org/wiki/Linux">Linux</a>, <a href="https://en.wikipedia.org/wiki/MacOS">OSX</a> and <a href="https://en.wikipedia.org/wiki/Windows_Subsystem_for_Linux">WSL</a>.</p> <p>This is version 2.2.4 of binary_c, with binary_c-python 1.0.0, installed on Kubuntu 24.04 desktop running a Linux kernel. Many useful tools are pre-installed, such as GCC, Clang, meson, ninja, valgrind, emacs, GSL, kcachegrind, gdb, gnuplot, and Jupyter so you can run binary_c-python's notebooks. Also included are commands to run either the latest stable versions or master versions straight from the gitlab repository.</p> <p>Homepage: <a href="https://binary_c.gitlab.io/">https://binary_c.gitlab.io/</a></p> <p>Login details:</p> <ul> <li>User: binary_c</li> <li>Password: binary_c</li> </ul> <p>The virtual machine is compressed with xzip, so you will need to decompress it before use.</p>
Build and measurements of Linux kernel configurations across different versions
<p>With large scale and complex configurable systems, it is hard for users to choose the right combination of options (i.e., configurations) in order to obtain the wanted trade-off between functionality and performance goals such as speed or size. Machine learning can help in relating these goals to the configurable system options, and thus, predict the effect of options on the outcome, typically after a costly training step. However, many configurable systems evolve at such a rapid pace that it is impractical to retrain a new model from scratch for each new version. Taking the extreme case of the Linux kernel with its ≈ 14, 500 configuration options, we investigate how binary size predictions of kernel size degrade over successive versions (and how transfer learning can be adapted and applied to mitigate this degradation). </p> <p>We used and are sharing a unique and large dataset constituted of the binary sizes (compressed and non-compressed) of thousands of configurations for different versions of the kernel, spanning three years (4.13, 4.15, 4.20, 5.0, 5.4, 5.7, and 5.8). Overall, around 200K configurations over 10K+ options/features and 6 versions. </p> <p>This dataset has been used in the Transactions of Software Engineering (TSE) article "Transfer Learning Across Variants and Versions: The Case of Linux Kernel Size" (preprint: https://hal.inria.fr/hal-03358817) </p>
Package Freshness in Linux Distributions
<p>Edit: this version is obsolete, a new version with a larger dataset has been made available.</p> <p> </p> <p>This is a survey conducted on the package freshness in Linux distributions.</p> <p>The survey can be found <a href="https://docs.google.com/forms/d/1xd5mYIcBH4i6EWfRhrCKQeh2NokG1DE02xBUTi33WcM">here</a>.</p> <p>An anonymised version of the responses is included, in csv format:</p> <p>- The first 3 columns correspond to the answers to the first question, that is to say the ranking of the distributions used by respondents.</p> <p>- The "perception" columns correspond to the answers to the second question: "according to you, how long does it take for the latest upstream version to be made available in the official repositories of that distribution?"</p> <p>Packages were divided into 6 categories:</p> <p>OSS: open-source end-user software, such as Firefox, LibreOffice or GIMP;<br> PS: proprietary end-user software, such as Adobe Reader, Spotify or Skype;<br> DT: development tools, such as git, Emacs or Eclipse;<br> STL: system tools and libraries, such as openSSL;<br> PLL: programming language libraries, such as Numpy, Lodash or Boost;<br> PLR: programming language runtimes such as Python, node.js or Java.</p> <p>- The "importance" columns correspond to the answers to the third question: "how important is it for you to stay up to date with upstream packages of the following types?"</p> <p>- The "reasons" columns correspond to the answers to the fourth question: "what are your main reasons for updating packages?"</p> <p>- The "mechanisms" columns correspond to the answers to the fifth question: "which mechanism do you use to install specific versions of packages?"</p> <p>The possible mechanisms are:</p> <p>Official package manager the distribution and its official repository (off),</p> <p>Official package manager of the distribution with community repositories (com),</p> <p>Third-party package managers (3rd)</p> <p>Installing manually from binaries (bin)</p> <p>Installing manually from source files (sources)</p>
Package Freshness in Linux Distributions.
<p>Survey conducted on the package freshness in Linux distributions. A preprint of the paper can be found <a href="https://arxiv.org/pdf/2007.16123.pdf">here</a>.</p> <p>The survey form can be found <a href="https://docs.google.com/forms/d/1xd5mYIcBH4i6EWfRhrCKQeh2NokG1DE02xBUTi33WcM">here</a>.</p> <p>An anonymised version of the responses is included, in csv format:</p> <p>- The first 3 columns correspond to the answers to the first question, that is to say the ranking of the distributions used by respondents.</p> <p>- The "perception" columns correspond to the answers to the second question: "according to you, how long does it take for the latest upstream version to be made available in the official repositories of that distribution?"</p> <p>Packages were divided into 6 categories:</p> <p>OSS: open-source end-user software, such as Firefox, LibreOffice or GIMP;<br> PS: proprietary end-user software, such as Adobe Reader, Spotify or Skype;<br> DT: development tools, such as git, Emacs or Eclipse;<br> STL: system tools and libraries, such as openSSL;<br> PLL: programming language libraries, such as Numpy, Lodash or Boost;<br> PLR: programming language runtimes such as Python, node.js or Java.</p> <p>- The "importance" columns correspond to the answers to the third question: "how important is it for you to stay up to date with upstream packages of the following types?"</p> <p>- The "reasons" columns correspond to the answers to the fourth question: "what are your main reasons for updating packages?"</p> <p>- The "mechanisms" columns correspond to the answers to the fifth question: "which mechanism do you use to install specific versions of packages?"</p> <p>The possible mechanisms are:</p> <p>Official package manager the distribution and its official repository (off),</p> <p>Official package manager of the distribution with community repositories (com),</p> <p>Third-party package managers (3rd)</p> <p>Installing manually from binaries (bin)</p> <p>Installing manually from source files (sources)</p>
DXC Framework Linux
DXC Framework for Linux and Unix type operating systems
Linux Firmware Corpus - Full LFwC Meta Data for Replication
Open the record for dataset details and reuse information.
Dataset of An Empirical Study of Concurrency Vulnerabilities in Linux
<p>Dataset of An Empirical Study of Concurrency Vulnerabilities in Linux</p>
Supplemental linux commands [Augustus commands and also TransDecoder commands]
<p>Supplemental linux commands [Augustus commands and also TransDecoder commands]</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)
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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.