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Dataset results
846 results for “commit”
Microenvironment commits breast tumor ECs to dedifferentiation by micro-RNA-200-b-3p regulation and extracellular matrix remodelling (miRNA-Seq)
GEO Series GSE224147. Homo sapiens. 4 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Single-cell immune profiling reveals thymus-seeding populations, T cell commitment, and multi-lineage development in the human thymus
GEO Series GSE195812. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Plasticity and lineage commitment of individual Th1 cells are determined by stable T-bet expression quantities (ChIP-Seq)
GEO Series GSE266175. Mus musculus. 84 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Esrrb extinction triggers dismantling of naïve pluripotency and marks commitment to differentiation [Microarray]
GEO Series GSE118906. Mus musculus. 12 samples. Type: Expression profiling by array.
TP53 promotes lineage commitment of human embryonic stem cells through ciliogenesis and sonic hedgehog signaling [CRISPR screen]
GEO Series GSE168585. Homo sapiens. 4 samples. Type: Other.
Transcriptional plasticity, priming and commitment in hematopoietic lineages [RNA-seq]
GEO Series GSE92575. Mus musculus. 326 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome Sequencing During Mouse Brain Development Identifies Long Non-Coding RNAs Functionally Involved in Neurogenic Commitment
GEO Series GSE51606. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
PFNF54-Pfs16-GFP-LUC gametocyte time course from commitment to maturity
GEO Series GSE104889. Plasmodium falciparum. 16 samples. Type: Expression profiling by array.
The BCL6 RD2 domain governs commitment of activated B-cells to form germinal centers
GEO Series GSE58365. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Gain of 20q11.21 in human pluripotent stem cells impairs TGFβ-dependent ectodermal commitment
GEO Series GSE116372. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Variation of human neural stem cells generating organizer states in vitro before committing to cortical excitatory or inhibitory neuronal fates [human RNAseq #1]
GEO Series GSE144156. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
Deciphering the conserved dynamic transcriptional signature and regulatory software governing neuronal fate commitment
GEO Series GSE179383. Rattus norvegicus. 60 samples. Type: Expression profiling by array.
Etv2-mediated hemangiogenic fate commitment of mesoderm
GEO Series GSE85641. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing; Other.
Commitment to the phosphate starvation response after replenishment of phosphate
GEO Series GSE50654. Saccharomyces cerevisiae. 10 samples. Type: Expression profiling by array.
Dataset: Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems
<p>This package contains the instance data used for the experiments presented in the paper <em>Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems</em>, by Alinson S. Xavier, Feng Qiu and Shabbir Ahmed.</p>
GAP: Forecasting Commit Activity in git Projects
<p>This entry contains the replication package for our paper <em>GAP: Forecasting Commit Activity in git Projects</em> accepted for publication in Journal of Systems and Software.</p> <p>Abandonment of active developers poses a significant risk for many open source software projects. This risk can be reduced by forecasting the future activity of contributors involved in such projects. Focusing on the commit activity of individuals involved in git repositories, this paper proposes a practicable probabilistic forecasting model based on the statistical technique of survival analysis. The model is empirically validated on a wide variety of projects accounting for 7,528 git repositories and 5,947 active contributors. We found that a model based on the last 20 observed days of commit activity per contributor provides the best concordance. We also found that the predictions provided by the model are generally close to actual observations, with slight underestimations for low probability predictions and slight overestimations for higher probability predictions. This model is implemented as part of an open source tool, called gap, that predicts future commit activity.</p> <p><strong>Replication package</strong></p> <p>The model is explained and defined in <em>"notebooks/Survival analysis.ipynb"</em>. This is a Jupyter notebook created with Jupyter Lab. The dependencies required to run this notebook are listed in <em>requirements.txt</em> and can be automatically installed using <code>pip install -r requirements.txt</code>. Consider making use of a virtual environment to ensure a proper replication of the analyses.</p> <p>The data used to validate the model can be found in <em>data/cargo.csv.gz</em>. They were produced with the script <em>data/convert.py</em> that requires file <em>data-raw/cargo_all_proj_commits_id.csv.gz</em>. This file was created by retrieving all the commits of all projects hosted on github that are related to a project distributed on Cargo. To identify such projects, we relied on libraries.io dataset. GitHub API was then queried to obtain the username of each author (if available) to allow some basic identity merging task. Data about repositories were extracted from libraries.io 1.4.0 dataset and can be found in <em>data/repositories.csv.gz</em>.</p> <p><strong>The GAP tool</strong></p> <p>GAP is made available on <a href="https://github.com/AlexandreDecan/gap">https://github.com/AlexandreDecan/gap</a></p>
A mapping between Bot Commit, Projects, Files, and Blobs
<p>This dataset contains information about 150,633,947 file updates by 12,326,137 bot commits, in terms of blob updates.</p> <p>Each line in the file is of the following form:</p> <p>commit_sha; projects (projectOwner_projectName) associated with the commit separated by comma; filename; new blob associated with the file; old blob associated with the file (before the commit)</p> <p>Example:<br> 00000a0cee231959811e6c5d808730240b325417;guyellis_plant;package.json;fadda016e338f970adc62395e77aab80b09fa11b;2e7b221f2bfdeda4ef78d3a56e039e456b6901bb</p> <p><br> Contents of each blob can be accessed using the GitHub API:</p> <p>https://api.github.com/repos/<projectOwner>/<projectName>/git/blobs/<blob id></p> <p>Example:</p> <p>https://api.github.com/repos/guyellis/plant/git/blobs/fadda016e338f970adc62395e77aab80b09fa11b</p> <p>See <a href="https://developer.github.com/v3/git/blobs/">https://developer.github.com/v3/git/blobs/</a> for more deatils.</p> <p>If you're using our data, please do not forget it cite it !!!</p> <p> </p>
Dataset of Grouped Commit Author IDs after Identity Resolution
<p>This Dataset contains the SHA1 values of IDs for 5,427,024 commit authors who have created commits in git version control system, and have more than 1 ID in git. It is a compressed CSV file (separated by ; ) with 14,861,538 author IDs, where the first column is the group ID, which is same as the first (randomly selected) author ID of the group, and the second column is the author ID that is part of the group. If an author was found to have 2 different IDs: <em>I1, I2</em>, then it is recorded in the file in 2 separate lines, with the lines being <strong>I1;I1</strong> and <strong>I1;I2</strong>, i.e. the first column is the group identifier, which is one of the IDs in a group, and the second column contains the different author IDs in separate lines. Author IDs consist of the Author's name and email address in the format: Name <Email>.</p>
Data from: Altruism can evolve when relatedness is low: evidence from bacteria committing suicide upon phage infection
High relatedness among interacting individuals has generally been considered a precondition for the evolution of altruism. However, kin-selection theory also predicts the evolution of altruism when relatedness is low, as long as the cost of the altruistic act is minor compared to its benefit. Here, we demonstrate evidence for a low-cost altruistic act in bacteria. We investigated Escherichia coli responding to the attack of an obligately lytic phage by committing suicide in order to prevent parasite transmission to nearby relatives. We found that bacterial suicide provides large benefits to survivors at marginal costs to committers. The cost of suicide was low because infected cells are moribund, rapidly dying upon phage infection, such that no more opportunity for reproduction remains. As a consequence of its marginal cost, host suicide was selectively favoured even when relatedness between committers and survivors approached zero. Altogether, our findings demonstrate that low-cost suicide can evolve with ease, represents an effective host-defence strategy, and seems to be widespread among microbes. Moreover, low-cost suicide might also occur in higher organisms as exemplified by infected social insect workers leaving the colony to die in isolation.
Data for 'Estimating the timing of geophysical commitment to 1.5 and 2.0°C of global warming'
<p>This dataset contains all FaIR ensemble parameter prior and posterior distributions; modeled forcing and temperature time series for each Shared Socioeconomic Pathway (SSP); and temperature time series for a cessation of emissions in every year along each SSP.</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.