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2,015 results for “context”
Data set of "Context-sensitive Requirements Search in Natural Language Specifications"
<p>We use this data set for the evaluation of our Context-sensitive Requirements Search approach. It contains three files and 15 searches.</p> <p> </p> <p><strong>Attribution</strong></p> <p>The foundation of this data set is PURE:<br> A. Ferrari, G. O. Spagnolo, and S. Gnesi. <em>PURE: a Dataset of Public Requirements Documents</em>. Version 1.0. Sept. 2018.<br> Available at: <a href="https://doi.org/10.5281/zenodo.1414117">https://doi.org/10.5281/zenodo.1414117</a></p> <p>The original data is licensed under <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a></p> <p> </p> <p><strong>Annotations</strong></p> <ul> <li>Each of the provided files contains annotations that we created with Label Studio for the corresponding file of the PURE data set.</li> <li>These annotations depict the search results of Plain Keyword Search (PKS) and the corresponding gold standard.</li> <li>The following is an example of an annotation in the .json file:</li> </ul> <pre><code class="language-json">{ "id": "UnaxUjB6oY", "meta": { "text": ["0"] }, "type": "labels", "value": { "end": "/text()[2]", "text": "speed", "start": "/text()[2]", "labels": ["speed: train speed profile"], "endOffset": 364, "startOffset": 359, "globalOffsets": { "end": 1403, "start": 1398 } }, "origin": "manual", "to_name": "text", "from_name": "ner" },</code></pre> <p> </p> <p><strong>Important Json attributes to consider</strong></p> <p><strong>Meta</strong></p> <p>We encode the relevance of each result in its metadata.</p> <p><em>Example:</em></p> <pre><code class="language-json">"meta": { "text": ["0"] },</code></pre> <ul> <li>1 = relevant for this search and found by PKS</li> <li>0 = irrelevant for this search but found by PKS</li> <li>-1 = relevant for this search and not found by PKS</li> </ul> <p> </p> <p><strong>Labels</strong></p> <p>This element is the search identifier we use in Label Studio. It corresponds to the table in the thesis that describes each search in detail.</p> <p><em>Example:</em></p> <pre><code class="language-json">"labels": ["speed: train speed profile"],</code></pre> <p> </p> <p><strong>GlobalOffsets</strong></p> <p>"globalOffsets" describes the position of a label in the text.</p> <p><em>Example:</em></p> <pre><code class="language-json">"globalOffsets": { "end": 1403, "start": 1398 }</code></pre> <p> </p>
The dataset of "GNN-CONTEXT: A GNN-based Code Context Prediction Approach for Software Development Tasks"
<p>This research dataset accompanies our paper "GNN-CONTEXT: A GNN-based Code Context Prediction Approach for Software Development Task".<br>The dataset comprises 3,879 code context models along with their corresponding source code, extracted from three Java open source projects: Mylyn, Platform, and PDE.</p> <p>Each code context model directory contains two essential files:</p> <p>1. code_context_model.xml<br> This XML file encapsulates the code context model's metadata within a <code_context_model> wrapper, including commit ID, start datetime of the first edit event, and end datetime of the last edit event. The file structure is as follows:<br> - <graph> components represent connected components in the code context model.<br> - Each <graph> consists of <vertices> and <edges>.<br> - <vertex> attributes:<br> - id: Unique identifier within the <graph><br> - ref_id: Reference to its source code in my_java_codes<br> - kind: Vertex type (class, function, or variable)<br> - label: Declared name in the source code<br> - stereotype: Vertex stereotype derived from JStereoCode<br> - <edge> attributes:<br> - start: Source vertex ID<br> - end: Destination vertex ID<br> - label: Edge type (declares, calls, inherits, or implements)<br>2. my_java_codes.csv<br> This CSV file contains two columns:<br> - id: Composite of the code context model's directory index and the vertex's ref_id<br> - code: Corresponding source code for the code element</p>
EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
<p>Understanding emotional states is pivotal for the development of next-generation human-machine interfaces. Human behaviors in social interactions have resulted in psycho-physiological processes influenced by perceptual inputs. Therefore, efforts to comprehend brain functions and human behavior could potentially catalyze the development of AI models with human-like attributes. In this study, we introduce a multimodal emotion dataset comprising data from 30-channel electroencephalography (EEG), audio, and video recordings from 42 participants. Each participant engaged in a cue-based conversation scenario, eliciting five distinct emotions: neutral, anger, happiness, sadness, and calmness. Throughout the experiment, each participant contributed 200 interactions, which encompassed both listening and speaking. This resulted in a cumulative total of 8,400 interactions across all participants. We evaluated the baseline performance of emotion recognition for each modality using established deep neural network (DNN) methods. The Emotion in EEG-Audio-Visual (EAV) dataset represents the first public dataset to incorporate three primary modalities for emotion recognition within a conversational context. We anticipate that this dataset will make significant contributions to the modeling of the human emotional process, encompassing both fundamental neuroscience and machine learning viewpoints.</p>
Genetic variation in four highland populations in the context of broader genetic diversity in Daghestan.
GEO Series GSE93106. Homo sapiens. 62 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array.
Common modes of ERK induction resolve into context specific signalling via a combination of signal duration and cell type specific transcriptional repression [RSC_RNA]
GEO Series GSE259233. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [Cut&Tag]
GEO Series GSE209833. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Eosinophil-derived IL-4 promotes nematode growth in an innate context
GEO Series GSE67136. Mus musculus. 24 samples. Type: Expression profiling by array.
Common modes of ERK induction resolve into context specific signalling via a combination of signal duration and cell type specific transcriptional repression [EpiLC_RNA]
GEO Series GSE259234. Mus musculus. 15 samples. Type: Expression profiling by high throughput sequencing.
Adapters drive context-dependent Mycobacterial ClpX activity by altering conformational sampling
GEO Series GSE240561. Mycobacterium tuberculosis H37Rv. 9 samples. Type: Expression profiling by high throughput sequencing.
In-Depth Functional Analysis of BRD9 in Fetal Hematopoiesis Reveals Context-Dependent Roles [RNA-seq]
GEO Series GSE269138. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Functional role and interactions of a recalcitrant to cultivation Sphingomonas degrading the persistent thiabendazole in the context of a microbial enrichment
GEO Series GSE134575. soil metagenome. 18 samples. Type: Expression profiling by high throughput sequencing.
Chromatin accessibility and gene expression during adipocyte differentiation identify context-dependent effects at cardiometabolic GWAS loci
GEO Series GSE178796. Homo sapiens. 62 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Context-dependent actions of Exendin-4 on β-cell function and dynamic changes in islet gene expression over time in vivo
GEO Series GSE37936. Mus musculus. 60 samples. Type: Expression profiling by array.
Robust regulatory interplay of enhancers, facilitators, and promoters in a native chromatin context [PRO-Seq]
GEO Series GSE299354. Homo sapiens. 30 samples. Type: Other.
Transcriptional regulation of amino acid metabolism by KDM2B, in the context of ncPRC1.1 and in concert with MYC and ATF4 [ChIP-seq]
GEO Series GSE245828. Homo sapiens. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
In-Depth Functional Analysis of BRD9 in Fetal Hematopoiesis Reveals Context-Dependent Roles [ATAC-seq_2]
GEO Series GSE278845. Mus musculus. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Comparison of transcriptomic data obtained from stimulated and unstimulated samples for the stratification of patients with a compromised immune system (in the context of sepsis and allogeneic hematop
GEO Series GSE285098. Homo sapiens. 256 samples. Type: Other.
Reinforcement of DNA methylation in CHH context through RNA-directed DNA methylation ensures sexual reproduction in rice
GEO Series GSE152155. Oryza sativa. 24 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
RNA-seq analysis of YAP-overexpressing mouse embryonic fibroblasts in the context of reprogramming
GEO Series GSE131232. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Directed Evolution of an Adenine Base Editor withIncreased Activity and Context Compatibility [sgRNA-target library]
GEO Series GSE242012. Homo sapiens. 14 samples. Type: 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)
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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.