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1,552 results for “patch”
The nucleosome acidic patch directly interacts with subunits of the Paf1 and FACT complexes and controls chromatin architecture in vivo
GEO Series GSE121543. Saccharomyces cerevisiae. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Genes regulated by RANK in the epithelium of Peyer's patches
GEO Series GSE93319. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Comprehensive Gene ExpressionProfiling of Peyer’s Patch M Cells, Villous M-like Cells, and Intestinal Epithelial Cells
GEO Series GSE7838. Mus musculus. 9 samples. Type: Expression profiling by array.
3D Bioprinted Endometrial Patch Enhances Regeneration and Fertility through Organotypic Endometrial Architecture
GEO Series GSE308303. Rattus norvegicus. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic and metabolic responses of mycorrhizal roots to nitrogen patches under field conditions
GEO Series GSE30270. Solanum lycopersicum. 30 samples. Type: Expression profiling by array.
The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"
<p>This is the online repository of the paper "Context-Aware Code Change Embedding for Better Patch Correctness Assessment" under review by SANER2021. We release the source code of Cache, the patches used in our evaluation, as well as the experiment results.</p> <ul> <li> <p>Patches: Three patch benchmarks included in our study. </p> <ul> <li> <p>Tian: The patches from Tian's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">ASE20</a> paper.</p> </li> <li> <p>Wang: The patches from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</p> </li> <li> <p>Cache: The patches collected by ourselves, which is consist of 17,377 deduplicated overfitting patches from <a href="https://dl.acm.org/doi/10.1145/3338906.3338911">RepairThemAll</a> and 17,377 instances from <a href="https://dl.acm.org/doi/10.1145/3379597.3387491">ManySStuBs</a>(used as correct patches).</p> </li> </ul> </li> <li> <p>Results:</p> <ul> <li> <p>RQ1: The detailed result files in RQ1, which are named by the format of <em>[model]_[classifier]</em>.csv.</p> <p>For example, the file named <strong>BERT_DT.csv</strong> in the folder <strong>Tian's_dataset</strong> means that this file is the result of patches from <strong>Tian's</strong> study embedded by <strong>BERT</strong> and classified by <strong>Decision Tree</strong>.</p> <ul> <li> <p>Tian's_dataset : The detailed result files on Tian's dataset. </p> </li> <li> <p>Cache_dataset : The detailed result files on our own dataset. </p> </li> <li> <p>Cross_dataset : The detailed result files of representation learning techniques when training on our own dataset and testing on Tian's dataset.</p> </li> </ul> </li> <li>RQ2: The detailed result files in RQ2. <ul> <li> <p>Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/">ASE20</a>.</p> </li> <li> <p>ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's <a href="https://dl.acm.org/doi/10.1145/3180155.3180182">ICSE18</a> paper. We directly compare against the results reported by the authors of ODS on 139 patches from Xiong's paper since the data and source code of ODS is unavailable.</p> </li> </ul> </li> </ul> </li> </ul> <p>Source: The source code and lib for running Cache is available at <a href="https://github.com/APR-Study/Cache">https://github.com/APR-Study/Cache</a>.</p>
Gene expression profiles of distinct mesenchymal cell populations in Peyer's patches
GEO Series GSE93320. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Expression data of ileal Peyer's patches from Salmonella Typhimurium infected pigs
GEO Series GSE101442. Sus scrofa. 8 samples. Type: Expression profiling by array.
NWRA AIA Active Region Patches (AARP) Database
The "AIA Active Region Patches" comprise an unbiased multi-wavelength set of FITS files of data extracted from SDO/AIA full-disk data. The spatial extractions are based on SDO/HMI "Active Region Patch" (HARP) definitions, and match the HARP numbering. However, the AARPs have extended bounding-boxes to cover the larger visible extent of coronal loops. There is no spatial binning and no additional geometric or regridding performed. The spectral down-sampling comprises the omission of 1700 images; all other filters are included (94, 131, 171, 193, 211, 304, 335, 1600). The temporal sampling consists of 11 images at 72s cadence (13 min) for each wavelength, centered at 15:48, 16:48, 17:48, 18:48, 19:48, 20:48 and 21:48, daily. No AEC-invoked (shorter exposure) data were included; exposure times were consistent but not multiplied through (data are presented in DN). The SSW aia_prep and corrections for time-dependent sensitivity degradation were applied.
The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"
<p>The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment".</p> <p>This is the online repository of the paper "Context-Aware Code Change Embedding for Better Patch Correctness Assessment" under review by ISSTA2021. We release the source code of Cache, the patches used in our evaluation, as well as the experiment results.</p> <ul> <li> <p>Patches: Two patch benchmarks included in our study.</p> <ul> <li>Small: The 1,183 deduplicated patches from Tian's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">ASE20</a> paper and Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li>Large: The patches collected by ourselves, which is consist of totally 49,694 patches from <a href="https://dl.acm.org/doi/10.1145/3338906.3338911">RepairThemAll</a> and <a href="https://dl.acm.org/doi/10.1145/3379597.3387491">ManySStuBs</a>.</li> </ul> </li> <li> <p>Results</p> <ul> <li> <p>RQ1: The detailed result files in RQ1, which are named by the format of <em><code>[model]_[classifier].csv</code></em>. For example, the file named <em><code>BERT_DT.csv</code></em> in the folder <em><code>Small</code></em> means that this file is the result of patches from <strong>Small</strong> dataset embedded by <strong>BERT</strong> and classified by <strong>Decision Tree</strong>.</p> <ul> <li>Small: The detailed result files on Small dataset.</li> <li>Large: The detailed result files on Large dataset.</li> <li> <p>Cross: The detailed result files of representation learning techniques when training on Large dataset and testing on Small dataset.</p> </li> </ul> </li> <li> <p>RQ2: The detailed result files in RQ2.</p> <ul> <li>Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li> <p>ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's <a href="https://dl.acm.org/doi/10.1145/3180155.3180182">ICSE18</a> paper. We directly compare against the results reported by the authors of ODS on 139 patches from Xiong's paper since the data and source code of ODS is unavailable.</p> </li> <li> <p>Table_5_Effectiveness_APCA.xlsx: The detailed version of <strong>Table 5</strong> in the paper.</p> </li> <li> <p>Table_6_Effectiveness_ODS.xlsx: The detailed version of <strong>Table 6</strong> in the paper.</p> </li> </ul> </li> </ul> </li> <li>Source: The source code and lib for running Cache. Guidance for replicating our study is available at <em><code>source/Readme.md</code></em>. We will build a homepage for Cache on GitHub upon acceptance.</li> </ul>
A note on the small mammals of small, isolated forest patches in the Eastern Cape, South Africa
<p>Analyses for the above publication in African Journal of Ecology</p>
Comparison Between the Patch-Type Mobile 12-Lead ECG System and the Standard 12-Lead ECG System: Timely Acquirement of 12-Lead ECG
<p>Video S1: Video clip of performing a 12 lead ECG using the patch-type mobile 12-lead ECG system.</p> <p>Video S2: Video clip of performing a 12 lead ECG using the standard 12-lead ECG system.</p>
ScienceDex guides
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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.