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Dataset results
51 results for “learning community”
Data for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the data and external data used by teams in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body" and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directories contain:</p> <p><strong>data.zip:</strong> The training and test data, including metadata, used in the Kaggle competition "HuBMAP + HPA - Hacking the Human Body".</p> <p><strong>Team_1.zip: </strong>External data used by the first place winning solution.</p> <p><strong>Team_2.zip: </strong>External data used by the second place winning solution.</p>
Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directory contains:</p> <p><strong>trained_model_1_weights.zip: </strong>Trained model weights for first place solution (Team 1).</p> <p><strong>trained_model_2_weights.zip:</strong> Trained model weights for second place solution (Team 2).</p> <p><strong>trained_model_3_weights.zip: </strong>Trained model weights for third place solution (Team 3).</p> <p><strong>trained_model_weights_baseline.zip:</strong> Trained model weights for the baseline model.</p>
Data and analytical codes for: Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure
<p>Data and analytical codes for: Ishizawa et al. (2023) Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure, bioRxiv, 2023.07.04.546222</p> <p> https://www.biorxiv.org/content/10.1101/2023.07.04.546222v1</p> <p> </p>
Simulation data from: Dynamics of immune memory and learning in bacterial communities
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ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities
<p>The dataset of paper---ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities.</p> <p>It includes 14,053 and 21,765 contributors, as well as 23,739 and 33,454 issues from the PyTorch and TensorFlow communities. </p>
ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities
<p>The dataset of paper---ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities.</p> <p>It includes 14,053 and 21,765 contributors, as well as 23,739 and 33,454 issues from the PyTorch and TensorFlow communities. </p>
ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities
<p>The dataset of paper---ASE_2022_A Tale of Two Cities: an Empirical Study on Deep Learning OSS Communities.</p> <p>It includes 23,739 and 33,454 issues from the PyTorch and TensorFlow communities. </p>
Leveraging Diversity in Software Engineering Education through Community Engaged Learning and a Supportive Network
<p>This replication package contains the schedule for the first cohort of our program described in the paper titled "Leveraging Diversity in Software Engineering Education through Community Engaged Learning and a Supportive Network".</p>
Dataset: Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering
<p>This dataset contains all relevant data for the manuscript (in submission) "<em>Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering</em>".</p> <p>Code written to analyse this dataset (which may be adapted for other flow cytometry datasets) is found at https://zenodo.org/record/999747</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Naming convention for raw flow cytometry data files (located in /Script 3. Generating raw data subset/input/):</p> <p>[Allparameters] _ [Year] - [Month] - [Date] [Hour] [u] [Minute] _ [Depth]</p> <p>e.g: Allparameters_2014-07-31 08u08_1.0m</p> <p>The date, time and depth indicate the location and time at which the measurement was taken.</p>
KuafuPrimer: Machine learning facilitates the design of 16S rRNA gene primers with minimal bias in bacterial communities
<p>KuafuPrimer is a machine learning-aided method that learns community characteristics from several samples to design 16S rRNA gene primers with minimal bias for microbial communities. It is built on <strong>Python 3.9.0</strong>, <strong>Pytorch 1.12.0</strong>. Here are some large size files required to run KuafuPrimer, and users need to download and put them in correct directories before running the program.</p> <ol> <li>Silva_ref_data.zip: processed files of silva dataset that should be put in <code>Model_data/Silva_ref_data/</code>.</li> <li>DeepAnno16_publicated_model.zip: parameters of the trained DeepAnno16 model that should be put in <code>Model_data/DeepAnno16_publicated_model/</code> .</li> </ol> <p>For more information, please refer to https://github.com/zhanghaoyu9931/KuafuPrimer.</p>
Ab initio design of microbial communities from large-scale seed pools using deep learning and rapid ptimization
<h4>This repository contains the full results of our paper: <strong><em>Ab initio</em> design of microbial communities from large-scale seed pools using deep learning and rapid ptimization.</strong></h4> <p>Authors: Xiaoqing Jiang#, Jiaheng Hou#, Haoyu Zhang#, Jinyuan Guo, Shaohua Gu, Yulin Liao, Xinrun Yang, Peter X. Geng, Yiyan Zhou, Qian Guo, Chunhui Wang, Mo Li, Alexandre Jousset, Zhong Wei*, and Huaiqiu Zhu*</p> <p>The results including:</p> <p><strong>(1)</strong> <strong>GEM.tar.gz</strong>: The eBiota-GEM dataset, containing 21,514 Genome-Scale Metabolic Models (GEMs) constructed using CarveMe based on RefSeq complete genomes.</p> <p><strong>(2) Baterial_evaluation.tar.gz</strong>: The evaluation of the ability to uptake substrates and secret productions for all 21,514 GEMs.</p> <p><strong>(3) Community_results.tar.gz</strong>: The results calculated from eBiota-GEM includes various combinations for two-bacterial consortia, covering strain IDs, substrates, products, yields, dual-bacterial growth, single-bacterial growth, co-occurrence predictions, interactions and total production.</p> <p><strong>(4) DeepCooc_files.tar.gz</strong>: The parameter files of DeepCooc, required by eBiota platform.</p>
Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
ClinicalTrials.gov study NCT05838456. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Graduate student reflections on learning and implementing community science methods to tackle compounding climate extremes
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Whole-child development losses and racial inequalities during the pandemic: Fallouts of school closure with remote learning and unprotective community
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Inclusive STEM teaching learning community facilitator survey data
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Code and Data Repository for "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments"
<p>The repository includes data and R code necessary to reproduce all figures and analyses presented in the study titled "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments".</p>
Supplementary material 3 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Common OTU lists and Statistical analysis : Explanation note: This file contains detected OTUs in both methods and biodiversity analysis (GLM and t-test)
Supplementary material 2 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Biodiversity workflow in R : Explanation note: Bundle of files for biodiversity analysis in R. All necessary input files and a commented script of R-commands are provided.
Supplementary material 4 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Master data sheet : Explanation note: Spreadsheet file containing information about read abundances of operational taxonomic units (OTUs) and sample metadata. Here, data were prepared for subsequent biodiversity analysis in R.
Supplementary material 1 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Bioinformatics pipeline : Explanation note: This file provides all steps and commands necessary for quality filtering and demultiplexing of raw paired fastq sequences.
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.