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190 results for “knowledge base”
Linear Registration of brain MRI using knowledge-based multiple intermediator libraries
<p>These are the dataset materials, including full data, resampled data, transformation matrices, experimental results and quantitative evaluation for the paper “Linear Registration of brain MRI using knowledge-based multiple intermediator libraries” that is submitted on the Journal of "Frontiers in Neuroscience".</p>
LLM-Based Knowledge Graph Construction from Materials Research Scientific Literature
<p>This dataset was constructed by creating a benchmark of 349 manually annotated triples, which were extracted from four different research articles in the field of materials science.</p>
Official Code and Dataset of Table Tennis Coaching System Based on a Multimodal Large Language Model with Knowledge Base
<p>Official Code and Dataset of Table Tennis Coaching System Based on a Multimodal Large Language Model with Knowledge Base</p>
AsdKB: A Chinese Knowledge Base for the Early Screening and Diagnosis of Autism Spectrum Disorder
<p><strong>Instance</strong></p> <ul> <li>Instance triples</li> </ul> <p><strong>Mapping</strong></p> <ul> <li>mapping.owl: ontology mapping to UMLS and ADAR</li> </ul> <p><strong>Ontology</strong></p> <ul> <li>ontology.owl: AsdKB ontology</li> </ul> <p><strong>For more details, please refer to <a href="https://github.com/SilenceSnake/ASDKB">http://w3id.org/asdkb</a></strong></p>
MMiKG: A Knowledge Graph-based Platform for Path Mining of Microbiota-Mental Diseases Interactions
<p><strong>The original datasets released in MMiKG, containing relevant information like PMID of labels and relations (triples).</strong></p> <p><strong>Background:</strong></p> <p>Gut microbiota has been demonstrated to be crucial in gut-brain axis. In this research, knowledge graphs was leveraged to aggregate and assimilate relevant information on the microbiome-gut-brain axis and its intricate relationships with mental diseases and formed a knowledge graph platform named MMiKG</p> <p> ► <strong>Advantages of MMiKG:</strong></p> <ul> <li> Assist users in semantic search and visualization operations</li> <li> Make the scattered knowledge machine-readable and interpretable</li> <li> Boost users’ confidence in the accuracy of the information</li> <li> Support better decision-making</li> </ul> <p> ► <strong>What users can do with MMiKG:</strong></p> <ul> <li>Integrate diverse resources</li> <li>Infer potential associations between gut microbiota and mental diseases</li> </ul> <p><strong>Tools:</strong></p> <p>MMiKG contains '770' entities and '1,257' triples among them. These items cover '20' common mental illnesses, '270' types of gut microbes, and '480' distinct intermediates.</p> <p> ► <strong>MMiKG's original data includes two folders:</strong></p> <p> ♦ The labels folder:</p> <ul> <li>Present information of each entity’s 'Id', 'Name', 'Degree', 'Type'</li> <li>'Disease.csv', 'Intermediate.csv', 'Microbiota.csv</li> </ul> <p> ♦ The relationships folder: </p> <ul> <li>Present information of 'SourceId', 'TargetId', 'Number', 'Reference PMID'</li> <li>'Disease.csv', 'Intermediate.csv', 'Microbiota.csv' </li> </ul>
An Electronic Health Record-based Approach to Increase PrEP Knowledge and Uptake: the EMC2 PrEP Strategy
ClinicalTrials.gov study NCT05709860. IPD Sharing: NO. Countries: 1. Publications: 1.
Effectiveness of a Knowledge-based Intervention for Patients With Cutaneous Lupus Erythematosus
ClinicalTrials.gov study NCT01629784. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Implementation of Knowledge-Based Palliative Care
ClinicalTrials.gov study NCT02708498. IPD Sharing: NO. Countries: 1. Publications: 12.
Assessing the Impact of a Social Media-Based Educational Intervention Using WhatsApp Video Messages on Dental Caries Prevention Knowledge, Oral Hygiene Practices, and Attitudes Toward Dental Health Am
ClinicalTrials.gov study NCT07363317. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Sámi knowledge and ecosystem-based adaptation strategies for managing pastures under threat from multiple land uses
Open the record for dataset details and reuse information.
Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"</p>
Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)
<p>Dataset accompanying the paper "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)".</p>
Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
<p>This project contains datasets used for Temporal Knowledge Base Completion (TKBC) paper [1].<br> Find more details here: https://github.com/dair-iitd/tkbi</p> <p>[1] "<a href="https://arxiv.org/abs/2005.05035">Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols</a>" Jain, Prachi*, Sushant Rathi*, Mausam and Soumen Chakrabarti. EMNLP 2020.</p>
Artifact for "Diagnosis of Package Installation Incompatibility via Knowledge Base"
<p>This is the artifact for the paper entitled "Diagnosis of Package Installation Incompatibility via Knowledge Base"</p>
Knowledge base of two- and three-dimensional activity cliffs
<p>The result of up-to-date surveys and systematic analyses of 2D-cliffs including clusters, 3D-cliffs, and extensions of 3D-cliffs is made freely available in four separate data files. These files contain the list of 2D-cliffs and cliff clusters, 3D-cliffs, 3D-cliff-MMPs, and superpositions of complex X-ray structures and 3D ligands for selected targets. The data organization and information is detailed in <em>README.doc</em>.</p> <p> </p>
Artifact for "Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base"
<p>This is the artifact for the paper entitled "Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base"</p>
Dataset for "Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision"
<h2>Overall</h2> <p>A strawberry dataset for the paper "Qi Yang, Licheng Liu, Junxiong Zhou, Mary Rogers, Zhenong Jin, 2024. Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision, Computers and Electronics in Agriculture, 220, 108911. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.compag.2024.108911" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compag.2024.108911</a>"</p> <h2>Plant traits measurements</h2> <p>The folder "measurement.zip" includes treatment-level and fruit-level ground truth data. </p> <h3>Treatment-level</h3> <pre><code>data_dryMatter_2022.csv data_dryMatter_2023.csv data_freshMatter_2022.csv data_freshMatter_2023.csv data_fruitNumber_2022.csv data_fruitNumber_2023.csv data_plantBiomass_2022.csv data_plantBiomass_2023.csv</code></pre> <h3>Fruit-level</h3> <p>Fruit conditions with five classes, 1-5 represent Normal, Wizened, Malformed, Wizened & Malformed, and Overripe, respectively.</p> <pre><code>data_size_freshWeight_condition_2022_0N.csv data_size_freshWeight_condition_2022_50N.csv data_size_freshWeight_condition_2022_100N.csv data_size_freshWeight_condition_2022_150N.csv</code></pre> <p>Fruit size for tagged fruits</p> <pre><code>data_taggedFruit_diameter_2022.csv data_taggedFruit_diameter_2023.csv data_taggedFruit_length_2022.csv data_taggedFruit_length_2023.csv</code></pre> <p>Fresh yield and lifespan for tagged fruits (only available in experiment 2023)</p> <pre><code>data_taggedFruit_freshMatter_2023.csv data_taggedFruit_lifespan_2023.csv</code></pre> <h3>Weather data</h3> <pre><code>weather_daily_2022.csv weather_daily_2023.csv</code></pre> <h2>Image data with label</h2> <h3>Object and phenology detection</h3> <p>The folder "strawberry_img_random.zip" contains images and the corresponding JSON labels for object and phenological stages detection.</p> <h3>Fruit size and decimal phenological stage</h3> <p>The folder "strawberry_img_tagged.zip" contains images and the corresponding JSON labels for fruit size and decimal phenological stages detection.</p> <pre><code>For example, "label": "small g, 8.84, 7.62, 0.4", This label means the fruit has an 8.84mm diameter and 7.62mm length, with the main stage being small green and the decimal stage being DS-4 </code></pre> <h3>Merge and split Data</h3> <p>A Python script, "datasetProcessing.py", can be used to merge and split the image data into training and testing set.</p> <h3>Pre-trained models</h3> <p>models.zip</p> <p> </p> <p><em>Data collector: Dr. Qi Yang, University of Minnesota, USA. Email: qiyang577@gmail.com</em></p> <p><em>All the files belong to Prof. Zhenong Jin, University of Minnesota, USA. Email: jinzn@umn.edu</em></p>
FIGURE 11 in Contribution to the knowledge of Chinese Gryllacrididae (Orthoptera: Ensifera: Stenopelmatoidea) XXVI: New additions based on the specimens from Southern China
FIGURE 11. Tenuigryllacris hainanensis sp. nov. Female: A. head in frontal view; B–C. head and pronotum: B. dorsal view, C. lateral view; D. second and third abdominal tergites in lateral view; E. apex of abdomen in lateral view; F. seventh abdominal sternite and subgenital plate in ventral view.
FIGURE 9 in Contribution to the knowledge of Chinese Gryllacrididae (Orthoptera: Ensifera: Stenopelmatoidea) XXVI: New additions based on the specimens from Southern China
FIGURE 9. Phryganogryllacris guangxiensis sp. nov. Male: A. head in frontal view; B–C. head and pronotum: B. dorsal view, C. lateral view; D. second and third abdominal tergites in lateral view; E–I. apex of abdomen: E. dorsal view, F. apical view, G. lateral view, H–I. ventral view.
FIGURE 10 in Contribution to the knowledge of Chinese Gryllacrididae (Orthoptera: Ensifera: Stenopelmatoidea) XXVI: New additions based on the specimens from Southern China
FIGURE 10. Tenuigryllacris hainanensis sp. nov. Male: A. head in frontal view; B–C. head and pronotum: B. dorsal view, C. lateral view; D. second and third abdominal tergites in lateral view; E–I. apex of abdomen: E. lateral view, F. dorsal view, G. apical view, H. apical and ventral view, I. ventral view.
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.