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Dataset results
846 results for “commit”
RNA methylation maintains hematopoietic stem cell identity and symmetric commitment
GEO Series GSE132357. Mus musculus. 30 samples. Type: Expression profiling by high throughput sequencing.
JNK2 inhibits mammary luminal cell commitment
GEO Series GSE40226. Mus musculus. 19 samples. Type: Expression profiling by array.
Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells [ATAC-seq]
GEO Series GSE156733. Homo sapiens. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Tumor-induced double positive T cells display distinct lineage commitment mechanisms and functions (4)
GEO Series GSE203186. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
ATAC-seq profiling of ER-Hoxa9-Lys-GFP cells before and after inducing irreversible commitment to myeloid differentiation
GEO Series GSE178392. Mus musculus. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Myelo-Erythroid Lineage Commitment Regulation [HPC7_ChIP]
GEO Series GSE307957. Mus musculus. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
The ACTonHEART Study: a Randomized Controlled Clinical Trial of a short intervention based on Acceptance and Commitment Therapy vs. usual care for cardiac rehabilitation patients with coronary heart disease.
<p>The ACTonHEART study evaluates the effectiveness of adding an ACT-based intervention to usual secondary prevention care of coronary heart disease, in order to promote healthy lifestyle changes and improve psychological wellbeing and quality of life among CR patients; psychological flexibility was a hypothesized mechanism of change.</p> <p>Ninety-two patients were enrolled and randomized, following an unbalanced randomization ratio of 2:1, to the ACT group (N= 59) and the control group (N= 33). The control group completed standard CR (Usual Care; UC) and the experimental subjects participated in the ACTonHEART group intervention in addition to standard CR. The ACTonHEART intervention consisted of three, two-hour, group sessions, focused on integrating acceptance and mindfulness skills into educational topics on heart-healthy behaviors. The primary study hypothesis is that the ACTonHEART group is superior to the UC group in the following primary outcome measures: LDL-cholesterol, resting systolic blood pressure, body mass index, and psychological wellbeing.</p> <p>Participants were assessed at baseline and at the end of the rehabilitation period. Linear mixed models analyses were used to detect the group x time interaction. Across all outcome variables, no time x treatment effect was found. The results of this study may inform the future implementations of ACT in the cardiac rehabilitation context.</p> <p>Corresponding author: Chiara Spatola (chiara.spatola@unime.it)</p>
Data of corporate social responsibility, employee green behavior, green organizational identity, and organizational commitment
<p>The dataset presented originates from a study conducted by a team at the business laboratory of Isfahan University of Technology. It encompasses information related to corporate social responsibility, employee green behavior, green organizational identity, and organizational commitment. This information was collected to support the team's research objectives and ultimately contribute to a greener future for the planet.</p>
Self-Admitted Technical Debt in Commit Messages: Comparing Java, Python, and R
<p><strong><span>The folder organization and datasets within each are as follows:</span></strong></p> <p><strong><span>Collection Folder:</span></strong><span> the original dataset that we scraped is placed. We have removed the user names and email addresses to keep the users’ privacy. </span><strong><span>RQ1 Folder</span></strong><span> has three subfolders: </span></p> <p><span><span>❖<span> </span></span></span><strong><span>Manual Training:</span></strong><span> The initial manually labeled data we used to initially train the classifiers is included. Note that columns A-O in this dataset are all extracted from GitHub’s API. Column O (heading “message”) is the commit message itself. The following columns P and Q (heading “author_a” and “author_b”) are the final classification (upon which the Cohen Kappa was calculated). Column R (heading “notes”) contains some commentaries on specific cases that may be meaningful.</span></p> <p><span><span>❖<span> </span></span></span><strong><span>Predicted:</span></strong><span> The results of the automatic classifiers (both 1st and 2nd round) are included. The additional columns are generated by the classifiers.</span></p> <p><span><span>❖<span> </span></span></span><strong><span>Verifications</span></strong><span> contain the manually labeled data that we used as 1st and 2nd verification rounds. This is a simplified dataset with the commit’s sha and the parsed message. The authors classified columns E and F independently and individually. The labels stated here are those that the authors agreed to (without having access to column D). Note that column D was added afterward by sha-matching by another author to calculate the Cohen Kappa. The yellow rows are those with disagreements.</span></p> <p><span> </span><strong><span>RQ2_RQ3 Folder</span></strong><span> contains the manually labeled dataset for RQ2 and RQ3 (SATD Types and Activities). </span></p> <p><span>NOTE: Kindly note that many messages or classifications are <em>multiline</em>. This means that the cells have to be expanded to be capable of reading all text included in a cell.</span></p>
Kevin: Knowledge-Enhanced Conventional Commit Message Generation
<div> <h1>Introduction</h1> <br> <div>We purpose a novel conventional commit message generation framework Kevin. In this directory, we give all code, dataset, and experiment results.</div> <br> <div>We use the dataset MCMD to evaluate our model, and filter some meaningless data on [`it`](dataset/MCMD_filter.zip).</div> <br> <div>Now we introduce the purpose of each folder:</div> <br> <div>1. `code`: this is the implement of our approach.</div> <div> 1. `commit_type_classification`: this is implement of our approach in commit type classification, and there are detailed documents in this folder.</div> <div> 2. `commit_description_classification`: this is implement of our approach in commit description generation, and there are detailed documents in this folder.</div> <div>2. `dataset`: this is the dataset we used to evaluate our approach, `MCMD_filter.zip` contains the filtered version of the dataset MCDM, `metrics.zip` includes 10 code change metrics used in commit type classification.</div> <div>3. `empirical_study`: this is the result of empirical study abour the correlation between code change situations and 8 commit types.</div> <div>4. `experiment_results`: this is all original experiment results of Kevin and six state-of-the-art approaches.</div> <div> 1. `RQ1 Overall Effectiveness`: this is for experiment RQ1.</div> <div> 2. `RQ2 Ablation Study`: this is for experiment RQ2.</div> <div> 3. `RQ3 Human Evaluation`: this is for experiment RQ3.</div> <div> 4. `metrics`: this is the implement of automated evaluation metrics BLEU, ROUGE-L, and METEOR.</div> <div>5. `checkpoints`: this directory includes five checkpoints of commit description generation model across five languages.</div> <br> <div># Usage</div> <br> <div>Before run the model, unzip all dataset in directory `dataset/`.</div> <br> <div>``` shell</div> <div>unzip MCMD_filter.zip</div> <div>unzip metrics.zip</div> <div>```</div> <br> <div>In the directory `code/commit_type_classification` and `code/commit_description_generation`, there are detailed document for install dependencies and run the model.</div> <br> <div>To run commit type classification model,</div> <br> <div>``` shell</div> <div>python classification.py</div> <div>```</div> <br> <div>If extract features from original dataset, configure parameters in function `main()` of `code/commit_type_classification/get_feature.py`, and</div> <div>``` shell</div> <div>python get_feature.py</div> <div>python classification.py</div> <div>```</div> <br> <div>To run commit description generation model, prepare the data modify the parameters in function `main()`.</div> <div>``` shell</div> <div>python prepare.py</div> <div>```</div> <br> <div>configure parmeters in directory `code/commit_description_generation/conf/*.py`, includes the path of dataset, model, and hyperparameters.</div> <div>``` shell</div> <div>CUDA_VISIBLE_DEVICES=0 python eval.py +model=codet5</div> <div>```</div> <br> <div>If train the model,</div> <div>``` shell</div> <div>CUDA_VISIBLE_DEVICES=0 python train.py +model=codet5</div> <div>```</div> </div>
Conventional commit message of dataset MCMD
<div> <h1>Introduction</h1> <br> <div>This directory includes the dataset MCMD and its conventional subset.</div> <br> <div><code>MCMD.zip</code> is the filtered dataset MCMD.</div> <div><code>MCMD_conventional.zip</code> is the conventional subset of the dataset MCMD.</div> </div>
dataset related to article "ACCEPTANCE AND COMMITMENT THERAPY FOR HIGH FREQUENCY EPISODIC MIGRAINE WITHOUT AURA FINDINGS FROM A RANDOMIZED PILOT INVESTIGATION"
<p>Dataset including data (SSPS files .sav) referred to the Italian subsample of the “ACT for Migraine” multicenter study</p>
Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells.
GEO Series GSE156735. Homo sapiens. 33 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Reprogramming lineage committed cells into iMSC-LC
GEO Series GSE49798. Mus musculus. 8 samples. Type: Expression profiling by array.
Genome-wide maps of chromatin state in pluripotent and lineage-committed cells.
GEO Series GSE158378. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
RNA-seq profiling of ER-Hoxa9-Lys-GFP cells before and after inducing irreversible commitment to myeloid differentiation
GEO Series GSE178866. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
The transcription factor KLF4, together with PAX6, is essential for the commitment of corneal epithelial cell fate
GEO Series GSE198977. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Mutant GFAP alters lineage commitment of neural organoids [AxDorganoids_bulk_experiment1]
GEO Series GSE267510. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
Zfp335 establishes neonatal immune tolerance through safeguarding effector Treg lineage commitment and maintenance [RNA-seq]
GEO Series GSE189075. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Tumor-induced double positive T cells display distinct lineage commitment mechanisms and functions (3)
GEO Series GSE203184. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing; Other.
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.