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datasets available to search
ShareScore release 0.9.0
Dataset results
2,015 results for “context”
Reinforcement of DNA methylation in CHH context through RNA-directed DNA methylation ensures sexual reproduction in rice [sRNA-seq]
GEO Series GSE152154. Oryza sativa. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Cav3.2 T-type calcium channel-dependent gene expression after retrieval of context-associated memory
GEO Series GSE30846. Mus musculus. 16 samples. Type: Expression profiling by array.
Generic and context-dependent gene modulations during Hydra whole body regeneration
GEO Series GSE111534. Hydra vulgaris. 153 samples. Type: Expression profiling by high throughput sequencing.
ASCL1 Drives Tolerance to Osimertinib in EGFR Mutant Lung Cancer in Permissive Cellular Contexts [Cell Line RNA-seq]
GEO Series GSE243565. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
Chromatin accessibility and gene expression during adipocyte differentiation identify context-dependent effects at cardiometabolic GWAS loci [RNA-seq]
GEO Series GSE178795. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Cav3.2 T-type calcium channel-dependent gene expression after retrieval of context-associated memory (right hippocampus)
GEO Series GSE30845. Mus musculus. 8 samples. Type: Expression profiling by array.
Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [APL PBMCs ATAC-seq]
GEO Series GSE215101. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Histone chaperone CAF-1 is a synthetic lethal vulnerability in the context of ARID1A deficiencies [ATAC-Seq]
GEO Series GSE125220. Homo sapiens. 80 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Reinforcement of DNA methylation in CHH context through RNA-directed DNA methylation ensures sexual reproduction in rice [Bisulfite-seq]
GEO Series GSE152152. Oryza sativa. 6 samples. Type: Methylation profiling by high throughput sequencing.
MRO CONTEXT CAMERA EXPERIMENT DATA RECORD LEVEL 0 V1.0
The CTX Experiment Data Record (EDR) Archive, a collection of image data from the Context Camera on the Mars Reconnaissance Orbiter spacecraft.
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>
Seascape context matters more than habitat condition for fish assemblages in coastal ecosystems
<p>Data collect for the a project between University of the Sunshine Coast and Healthy Land and Water for manuscript <em>Seascape context matters more than habitat condition for fish assemblages in coastal ecosystems</em></p>
Cross-Project-Code-Context-Prediction_Dataset
<p>The dataset of 3,469 code context models (Code Context Models.zip) and 14,510 code context patterns (Topological Patterns.zip) from Platform, PDE, and ECF.</p> <p><strong>Code Context Models</strong></p> <p>There are 10 files in each directory (i.e., working period), including:</p> <p>1. interaction events.txt: the interaction events during this working period</p> <p>2. event timestamps.txt: the timestamps of this working period, including:</p> <ul> <li>start datetime of the first event</li> <li>start datetime of the last event</li> <li>end datetime of the last event</li> <li>duration of interaction events (in seconds)</li> </ul> <p>3. code elements_extracted.txt: the code elements the developer accesses during this working period, extracted from interaction events (e.g., org.eclipse.ecf.provider.irc.ui/src&lt;org.eclipse.ecf.internal.irc.ui.wizards{IRCConnectWizardPage.java[IRCConnectWizardPage~getConnectID)</p> <p>4. code elements_resolved.txt: the resolved code elements (e.g., 'org.eclipse.ecf.internal.irc.ui.wizards.IRCConnectWizardPage[getConnectID')</p> <p>5. repository urls.txt: the urls of the git repositories identified from the code elements</p> <p>6. code elements_total.txt: all the code elements extracted from the code snapshots</p> <p>7. code context model.txt: the code context model with the code elements in [4]. This file is like:</p> <p>vertices:</p> <p>A@#c0</p> <p>B@#f0</p> <p>edges:</p> <p>c0@#f0@#declare</p> <p>A and B are the code elements, while c0, f0 are their ids (c: class, f: function). The relation between them is 'declare'. </p> <p>8. code context model_total.txt: the code context model with the code elements in [6]</p> <p>9. stereotype roles_total.txt: the stereotype roles of all the code elements in [6]</p> <p>10. code context model_abstract.txt: the abstract code context model of which the code elements are abstracted as stereotype roles</p> <p><strong>Topological Patterns</strong></p> <p>There are 9, 8, and 7 pattern groups mined from Platform, PDE, and ECF respectively. Each pattern group is named 'project_X_MinSupp', where 'X' denotes the X-th cluster of code context models and 'MinSupp' = 0.02. The information about the X-th cluster can be found in the paper's appendix. There are various patterns in a pattern group. For example:</p> <p>t # 25<br> v 0 DATA_PROVIDER <br> v 1 COLLABORATOR-CONSTRUCTOR <br> e 0 1 declare<br> 12</p> <p>The id of this pattern is 25, and there are two vertices 0 and 1, corresponding to DATA_PROVIDER and COLLABORATOR-CONSTRUCTOR respectively. There is a 'declare' edge from DATA_PROVIDER to COLLABORATOR-CONSTRUCTOR. This pattern occurs 12 times in all the abstract code context models</p>
Minimal Archive of Annotation Scripts, In Context
<p>This is a minimal archive of annotation scripts. Scripts should be run from their respective local directories.</p>
Archive of annotation scripts, in context
<p>%% Supplemental scripts<br> %% For use with Matlab 2022a, linux version<br> %% These scripts should not be moved from their respective location(s) within zenodo_archive_scripts_in_context<br> %% Run the scripts from their respective location(s)<br> %% These scripts are provided "as is" and may not be useful for any application other than as supporting material.</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.