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6 results for “Ubuntu”
Ubuntu One multi-cloud object storage trace sublement to "SkyPIE: A Fast & Accurate Oracle for Object Placement"
<p>These are the workload traces of Ubuntu one used in the evaluation of "SkyPIE: A Fast & Accurate Oracle for Object Placement".</p> <p>These traces derive diverse multi-cloud access pattern on object stores from the trace published in "Dissecting UbuntuOne: Autopsy of a Global-Scale Personal Cloud Back-End." The derivation is described in the SkyPIE paper.</p> <p>The traces are stored in Parquet file format, hence can be read with a Parquet reader such as the one included in Pandas. The file names specify the number of regions issuing accesses and the percentage of accesses to object that originate from regions other than the home region, see the publication.</p>
tags-ask-ubuntu
<h3><strong>Overview</strong></h3><p>This is a temporal hypergraph dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. In this dataset, nodes are tags, and hyperedges are the sets of tags applied to questions on askubuntu.com. The timestamps are in ISO8601 format and are normalized to start at 0. This dataset is derived from tags on Ask Ubuntu posts.</p><h4><strong>Statistics</strong></h4><p>Some basic statistics of this dataset are:</p><ul><li>number of nodes: 3,029</li><li>number of timestamped hyperedges: 271,233</li><li>distribution of the connected components:</li></ul><p>Component Size, Number </p><ul><li>3021, 1</li><li>1, 8</li></ul><h4><strong>Source of original data</strong></h4><p>Sources:</p><ul><li><a href="https://www.cs.cornell.edu/~arb/data/tags-ask-ubuntu/">tags-ask-ubuntu dataset</a></li><li><a href="https://archive.org/details/stackexchange">StackExchange</a></li></ul><h4>References</h4><p>If you use this dataset, please cite these references:</p><ul><li><a href="https://doi.org/10.1073/pnas.1800683115">Simplicial closure and higher-order link prediction</a>. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.</li></ul>
threads-ask-ubuntu
<h3>Overview</h3> <p>This is a temporal higher-order network dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. In this dataset, nodes are users on <a href="https://askubuntu.com/">askubuntu.com</a>, and a hyperedge comes from users participating in a thread that lasts for at most 24 hours. The timestamps are the time of the post, but normalized so that the earliest post starts at 0. </p> <h3><strong>Source of original data</strong></h3> <p>Source: <a href="https://www.cs.cornell.edu/~arb/data/threads-ask-ubuntu/">threads-ask-ubuntu dataset</a></p> <h3><strong>References</strong></h3> <p>If you use this data, please cite the following paper:</p> <ul> <li><a href="https://doi.org/10.1073/pnas.1800683115">Simplicial closure and higher-order link prediction</a>. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.</li> </ul>
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>
Relationship Disappointment Stress Syndrome and Ubuntu Centred Therapy: Case report
<p>This is a data set for the manuscript submitted </p>
Ubuntu - I Am Because We Are
ClinicalTrials.gov study NCT05314179. IPD Sharing: NO. Countries: 1. Publications: 0.
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.