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159
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ShareScore release 0.9.0
Dataset results
159 results for “code prediction”
Prediction of Non-Coding sRNAs and Screening of Virulence-Related sRNAs of Salmonella Pullorum
GEO Series GSE269156. Salmonella enterica subsp. enterica serovar Pullorum. 3 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Exploration for blood biomarkers of human long non-coding RNAs predicting oxaliplatin-induced chronic neuropathy through iPS cell-derived sensory neuron analysis
GEO Series GSE290071. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Supplementary tables for Predictive role of ferroptosis long non-coding RNAs for immune microenvironment and microsatellite instability in colon cancer
<p>These are supplementary tables.</p>
Data and code used in the article "A deep learning method for predicting soil moisture in unsaturated areas based on physical constraints"
<p>Data and code used in the article "A deep learning method for predicting soil moisture in unsaturated areas based on physical constraints", specifically included are water content data from 55 in situ observations for the years 2018-2020 (observation frequency of 5min or 10min), and example code for implementing LSTM and PIDL using python (mainly the tensorflow library).These data can help the reader to better understand and replicate our research. All the data and code has been uploaded. </p><p>The paper has been published in <i>Water Resources Research</i>, and the citation is: </p><p>Wang, Y., Wang, W., Ma, Z., Zhao, M., Li, W., Hou, X., et al. (2023). A deep learning approach based on physical constraints for predicting soil moisture in unsaturated zones. <i>Water Resources Research</i>,<i>59</i>, e2023WR035194. https://doi.org/10.1029/2023WR035194</p>
Data and code for: Modeling algal defenses under multiple stressors: impacts on explanatory and predictive performance
<p>First release for Zenodo</p>
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
<p>Dataset for experiment 2</p>
Predicting code comprehension: a novel approach to align human gaze with code using deep neural networks
<p>Supplementary data and scripts intended for submission review only. </p>
META-PREDICT: Dynamic responses of the global human skeletal muscle coding and noncoding transcriptome to exercise
GEO Series GSE154846. Homo sapiens. 1531 samples. Type: Expression profiling by array.
Quantitative predictions of protein interactions with long non-coding RNAs
GEO Series GSE87233. Mus musculus. 3 samples. Type: Other.
Predictive analysis of long non-coding RNA expression profiles in tumor and normal tissues from glioblastoma patients
GEO Series GSE104267. Homo sapiens. 24 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Long Non-coding RNA expression predictive of tumour response in patients treated with preoperative chemoradiotherapy for LARC
GEO Series GSE145666. Homo sapiens. 6 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Genome-wide identification and prediction of long non-coding RNAs in the half-smooth tongue sole (Cynoglossus semilaevis)
GEO Series GSE98425. Cynoglossus semilaevis. 1 samples. Type: Expression profiling by high throughput sequencing.
Integrated analysis of dysregulated long non-coding RNA and mRNA expression profiles and function prediction in SH-SY5Y cells exposed to manganese
GEO Series GSE103485. Homo sapiens. 6 samples. Type: Non-coding RNA profiling by array.
Dataset related to article: SENSE OF OWNERSHIP INFLUENCE ON TACTILE PERCEPTION: IS THE PREDICTIVE CODING ACCOUNT VALID FOR THE SOMATIC RUBBER HAND ILLUSION?
<p><strong><span>Table including demographic data (sex, age, and education), Laterality Quotient score, Proprioceptive drift data, Body Ownership Questionnaire data, and Two-Point Discrimination score</span></strong></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>
Data and code used in the article "Integrating multiphysics processes with deep learning for the Prediction of coupled water-vapor-heat water fluxes in unsaturated zone"
<p>《Integrating multiphysics processes with deep learning for the Prediction of coupled water-vapor-heat water fluxes in unsaturated zone》 (the paper has been submitted to <em>Water Resources Research</em>). The data and code used in the paper, specifically, the water content data and temperature data from in-situ observations from July 1, 2020 to August 29, 2020 (with a frequency of observations of 5 min), and the sample code for implementing cpinn using python (mainly the tensorflow library). These data can help readers better understand and replicate our study. All data and code have been uploaded.</p> <p>The paper has been submitted to <em>Water Resources Research</em>, if the paper is accepted for publication, all data will be made freely available.</p> <p>.</p> <p> </p>
Dataset and Code for the Research Paper: "A Smile is All You Need: Predicting Limiting Activity Coefficients from SMILES with Natural Language Processing."
<p><strong>Dataset and Code for the Research Paper: "A Smile is All You Need: Predicting Limiting Activity Coefficients from SMILES with Natural Language Processing."</strong></p> <p>For detailed instructions on how to utilize the resources, please refer to the <code>README.md</code> and see the original publication: https://doi.org/10.1039/D2DD00058J</p>
Matlab code associated with publication "Mathematical model of the multi-amino acid multi-transporter system predicts uptake flux in CHO cells"
<p>Matlab code associated with publication "Mathematical model of the multi-amino acid multi-transporter system predicts uptake flux in CHO cells" </p> <p>Published version : Ashley Sreejan, Mugdha Gadgil, Chetan J. Gadgil, Mathematical model of the multi-amino acid multi-transporter system predicts uptake flux in CHO cells, Journal of Biotechnology, Volume 344, 2022, Pages 40-49, ISSN 0168-1656</p> <p>Published version available at https://doi.org/10.1016/j.jbiotec.2021.12.003</p> <p>One version of the manuscript is available at doi:10.1101/2021.04.26.441392</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>
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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.