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13,064 results for “predictions”
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Weather prediction dataset
<p>Dataset created for machine learning and deep learning training and teaching purposes.<br> It can, for instance, be used for classification, regression, and forecasting tasks.<br> Complex enough to demonstrate realistic issues such as overfitting and unbalanced data, while still remaining intuitively accessible.</p> <p><strong>Description and units of weather features:</strong></p> <p>Data includes the following features/variables for several European cities:</p> <table> <tbody> <tr> <td>Feature (type)</td> <td>Column name</td> <td>Description</td> <td>Physical Unit</td> </tr> <tr> <td>mean temperature</td> <td>_temp_mean</td> <td>mean daily temperature</td> <td>in 1 °C</td> </tr> <tr> <td>max temperature</td> <td>_temp_max</td> <td>max daily temperature</td> <td>in 1 °C</td> </tr> <tr> <td>min temperature</td> <td>_temp_min</td> <td>min daily temperature</td> <td>in 1 °C</td> </tr> <tr> <td>cloud_cover</td> <td>_cloud_cover</td> <td>cloud cover</td> <td>oktas</td> </tr> <tr> <td>global_radiation</td> <td>_global_radiation</td> <td>global radiation</td> <td>in 100 W/m2</td> </tr> <tr> <td>humidity</td> <td>_humidity</td> <td>humidity</td> <td>in 1 %</td> </tr> <tr> <td>pressure</td> <td>_pressure</td> <td>pressure</td> <td>in 1000 hPa</td> </tr> <tr> <td>precipitation</td> <td>_precipitation</td> <td>daily precipitation</td> <td>in 10 mm</td> </tr> <tr> <td>sunshine</td> <td>_sunshine</td> <td>sunshine hours</td> <td>in 0.1 hours</td> </tr> <tr> <td>wind_speed</td> <td>_wind_gust</td> <td>wind gust</td> <td>in 1 m/s</td> </tr> <tr> <td>wind_gust</td> <td>_wind_speed</td> <td>wind speed</td> <td>in 1 m/s</td> </tr> </tbody> </table> <p><strong>File descriptions</strong></p> <ul> <li><code>weather_prediction_dataset.csv</code> - Main data file, tabular data, comma-separated CSV. Contains the data for different weather features (daily observations, see below for more details) for 18 European cities or places through the years 2000 to 2010.</li> <li><code>weather_prediction_picnic_labels.csv</code> - Optional data to be used as potential labels for classification tasks. Contains booleans to characterize the daily weather conditions as suitable for a picnic (True) or not (False) for all 18 locations in the dataset.</li> <li><code>weather_prediction_dataset_map.png</code>- Simple map showing all 18 locations in Europe.</li> <li><code>metadata.txt</code> - Further information on the dataset, the data processing, and conversion, as well as the description and units of all weather features.</li> </ul> <p> </p> <p>ORIGINAL DATA TAKEN FROM:</p> <p>EUROPEAN CLIMATE ASSESSMENT & DATASET (ECA&D), file created on 22-04-2021<br> THESE DATA CAN BE USED FREELY PROVIDED THAT THE FOLLOWING SOURCE IS ACKNOWLEDGED:</p> <p>Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface<br> air temperature and precipitation series for the European Climate Assessment.<br> Int. J. of Climatol., 22, 1441-1453.<br> Data and metadata available at http://www.ecad.eu</p> <p>For more information see metadata.txt file.<br> The dataset has also been presented at the Teaching Machine Learning Workshop at ECML 2022: https://teaching-ml.github.io/2022/.</p> <p>The Python code used to create the weather prediction dataset from the ECA&D data can be found on GitHub: https://github.com/florian-huber/weather_prediction_dataset<br> (this repository also contains Jupyter notebooks with teaching examples)</p> <p>Versions:</p> <ul> <li><strong>v5</strong>: updated metadata.txt file.</li> <li><strong>v4</strong>: to be more future proof in times of climate change/crisis --> "BBQ weather" prediction is now "picnic weather" prediction. Data itself remains unchanged.</li> <li><strong>v3</strong>: added "light" version of the dataset with less features (only 11 locations and fewer variables, reduction from 163 to 89 features) --> This is meant to be used if training times for hands-on session is becoming an issues</li> <li><strong>v2</strong>: now also contains additional `BBQ_weather` labels, the dataset itself has not changed between versions v1 and v2</li> </ul> <p> </p>
Dataset and structure database for an ML model to predict diffusivity in ZIF variants
<p>This dataset accompanies the publication titled "Data Mining for Predicting Gas Diffusivity in Zeolitic-imidazolate Frameworks (ZIFs)" (DOI: <a href="https://doi.org/10.1039/D2TA02624D">https://doi.org/10.1039/D2TA02624D</a>)</p> <p><a href="https://zenodo.org/api/files/b80f6d07-3bf4-484c-97ac-5d579fb0cc27/ESI_2_dataset.xlsx?versionId=dc4525d0-1c5c-478c-9bef-a156587ad69b">ESI_2_dataset.xlsx</a>: Descriptors for all ZIFs of the publication and simulations output, in the form of diffusivities of gas molecules (He up to iso-butane), in all ZIFs.</p> <p>ZIF_database.zip: ZIP file containing all ZIFs prepared by the authors (as discussed in the publication), through various units replacements, in the SOD topology, in .pdb format.</p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
ESM Atlas v0 representative random sample of predicted protein structures
<p>A representative random sample of the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (<a href="https://esmatlas.com/">https://esmatlas.com</a>) open science resource, released on 2022-11-01.<br> Sample size: 997,405.</p>
ESM Atlas v0 random sample of high confidence predicted protein structures
<p>A random sample out of the 225M high confidence predictions in the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (<a href="https://esmatlas.com/">https://esmatlas.com</a>) open science resource, released on 2022-11-01.<br> High confidence is defined as mean pLDDT > 0.7 and pTM > 0.7 and corresponds to ∼36% of the total 617M proteins folded.<br> This is the random sample used for analysis in the paper as well as visualization on the <a href="http://esmatlas.com/">esmatlas.com</a> Explore page.<br> Sample size: 999,520 based on 999,996 unique randomly sampled IDs and 0.05% missing data in the processing pipeline.</p>
Reproducibility package for Using root economics traits to predict biotic plant soil-feedbacks
<p>Using root economics space to predict biotic plant soil-feedbacks presents a novel framework linking below ground ecological theory to plant soil feedback effects. We show how to calculate root functional distance and location of two plant species in root economics space and how these measures can help to predict the strength and direction of the plant soil feedback between them. </p> <p>Contains data and scripts to reproduce analysis and figures for the manuscript (https://github.com/ggpmrutten/linkingRES-PSF)</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Predictive high-resolution mapping of sea floor rock cover for the UK and Ireland
<p>Predicted seabed rock cover using the machine learning algorithm Catboost and marine environmental predictors.</p> <p>Supporting data for T4.1 of Horizon 2020 project FutureMARES.</p>
Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"
<p>This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh.<br> We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor.</p> <p>Each file has its header, describing each column.</p> <p> </p>
Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad
<p>Here we are uploading a data sheet of the<strong> "Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad." </strong></p>
Spatial predictions of suitable environments for palsas and peat plateaus in the Northern Hemisphere for recent and future periods
<p>Here we provide raster files of suitable environments for palsas and peat plateaus in the Northern Hemisphere. These files are results of a scientific study by Könönen et al. (2022, preprint). Files are provided in TIFF-format, and they describe the occurrence probability of the suitable environments for palsas and peat plateaus.</p> <p> </p> <p>Könönen, O. H., Karjalainen, O., Aalto, J., Luoto, M., and Hjort, J.: Environmental spaces for palsas and peat plateaus are disappearing at a circumpolar scale, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-135, in review, 2022.</p>
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset
<p><b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b></p><p>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.</p>
EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction
<p>This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover.</p> <p>This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture.</p> <p>The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data.</p> <p>Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.</p>
Data for Accident Severity Prediction Modelling for Indian Highways Case Study
<p>Accident Data: Road accidents data is of Indian Highways sections Pune-Solapur and Bengal (BAEL) Section. For the Pune-Solapur Section of NH-9, which is located between Km.144/400 and Km. 249/000 in the state of Maharashtra, accident dates from 2013 to 2018. For the Six-Laning of Barwa-Adda-Panagarh Section of NH-2, which includes Panagarh Bypass and is located in the States of Jharkhand and West Bengal Stretch, accident dates from 2015 to 2019 for the stretch between km 398.240 and km 521.120. </p> <p>The data is sorted and analyzed using Random Forest Machine Learning for Accident Severity Prediction Modelling.</p> <p>Acknowledgement: We highly acknowledge the two organizations 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company for making the raw data available.</p> <p>Source: 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company.</p> <p> </p>
Dataset for Bayesian parametric models for survival prediction in medical applications
<p><strong>Data Source</strong></p> <p>The data for these experiments were derived from these sources:</p> <p>* Hosmer Jr DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modeling of Time-to-Event Data. 2nd ed: John Wiley & Sons; 2008.</p> <p>* Jd K, Prentice R. The statistical analysis of failure time data. New York: John Wiley and Sons; 1980.</p> <p>* Fleming T, Harrington D. Counting Processes and Survival Analysis: John Wiley & Sons; 1991.</p> <p> </p> <p>The raw data was downloaded from web archive.</p> <p>https://web.archive.org/web/20170114043458/http://www.umass.edu/statdata/statdata/data/</p> <p><strong>Contents</strong></p> <p>Each folder contains the original data, a textfile with a description of the data, and the pre-processed version with one-hot encoded variables. An additional YAML file is included with the list of included variables, name of the time and censor variable, name of continuous variables and splitting and partitioning information.</p>
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
Time-of-failure prediction of the Achoma landslide, Peru, from high frequency Planetscope satellites
<p><strong>Introduction</strong></p> <p>This repository contains the data used for the study of the slope instability of Achoma, Peru, described in Lacroix et al. (submitted). Specifically, the repository contains a time series of horizontal ground displacements, obtained from high frequency PlanetScope satellite between 2017 and 2020. It also contains two Digital Elevation Models, one from before the Achoma failure obtained with Pléaides stero images, and the other from just after the Achoma failure obtained with drone imagery.</p> <p>The data and methods used for the elaboration of this data repository are described in detail in Lacroix et al. (submitted). In this repository we also provide a short summary and overview of the data and methods used.</p> <p><strong>Data</strong></p> <p>A total of 79 PlanetScope scenes were used to produce the time series of horizontal horizontal ground displacements maps. Table 1 provides an overview of these data.</p> <p>Table1: Data used for the creation of this repository</p> <table> <tbody> <tr> <td> <p>Application</p> </td> <td> <p>Platforms</p> </td> <td> <p>Acquisition dates</p> </td> </tr> <tr> <td> <p>Pre-failure DEM</p> </td> <td> <p>Pléiades</p> <p> </p> </td> <td> <p>2017/05/13</p> </td> </tr> <tr> <td> <p>Post-failure DEM</p> </td> <td> <p>Drone</p> </td> <td> <p>2020/06/19</p> </td> </tr> <tr> <td> <p>Horizontal ground displacement</p> </td> <td> <p>PlanetScope</p> </td> <td> <p>79 scenes from 2017/11/27 to 2020/06/17</p> </td> </tr> </tbody> </table> <p><br> </p> <p><strong>Methods</strong></p> <p>The horizontal ground displacement maps, both along the NS and the EW directions (file names NSxxxxxxxx.tif and Ewxxxxxxxx.tif, where xxxxxxxx is the date in the format yyyymmdd) were created using the offset tracking methodology described in Bontemps et al. (2018), consisting of: (1) correlation of all the pairs of images using Mic-Mac (Rupnik et al., 2017), (2) masking the low correlation coefficient values (CC<0.7), (3) mosaicking correction, similar to stripe corrections (Bontemps et al., 2018), that we obtained by subtracting the median value of the stacked profile in the along-stripe direction, taking into account only stable areas, (4) least square inversion of the redundant system per pixel, weighted by the time separation between pairs (Bontemps et al., 2018), (5) correction of illumination effects (Lacroix et al., 2019), based on the 2 years of data between November 2017 and December 2019.</p> <p>The pre-failure DEM was computed from Ames Stereo Pipeline (Shean et al. 2016) and the methodology developed in (Lacroix, 2016) applied to the Pléiades stereo images (file name DEM_20170513_shifted_vertical2.tif ).</p> <p>The post-failure DEM was processed using the Structure from Motion-Multi View Stereo (SfM-MVS) methodology with the Agisoft Metashape Professional 1.5.5 software applied on 1824 pictures taken from the drone (file name Achoma_DEM_2020.06.20_UTM19S_50cm.tif ).<br> </p> <p><strong>Acknowledgements</strong></p> <p>P.L. acknowledge the support from the French Space Agency (CNES) through the TOSCA, PNTS, and ISIS programs.</p> <p><strong>Dataset attribution</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License.</p> <p><strong>Dataset Citation</strong></p> <p>Lacroix, P., Huanca, J., Angel, L., Taipe, E.: Data Repository: Time-of-failure prediction of the Achoma landslide, Peru, from high frequency Planetscope satellites. Dataset distributed on Zenodo: 10.5281/zenodo.7866962</p>
A Meta-Learner Approach to Multistep-Ahead Time Series Prediction
<p><strong>Abstract</strong></p> <p>The application of machine learning has become commonplace for problems in modern data science. The democratization of the decision process when choosing a machine learning algorithm has also received considerable attention through the use of meta features and automated machine learning for both classification and regression type problems. However, this is not the case for multistep-ahead time series problems. Time series models generally rely upon the series itself to make future predictions, as opposed to independent features used in regression and classification problems. The structure of a time series is generally described by features such as trend, seasonality, cyclicality, and irregularity. In this research, we demonstrate how time series metrics for these features, in conjunction with an ensemble based regression learner, were used to predict the standardized mean square error of candidate time series prediction models. These experiments used datasets that cover a wide feature space and enable researchers to select the single best performing model or the top N performing models. A robust evaluation was carried out to test the learner's performance on both synthetic and real time series. </p> <p><strong>Proposed Dataset</strong></p> <p>The dataset proposed here gives the results for 20 step ahead predictions for eight Machine Learning/Multi-step ahead prediction strategies for 5,842 time series datasets outlined <a href="https://www.sciencedirect.com/science/article/pii/S2215016121002521">here</a>. It was used as the training data for the Meta Learners in this research. The meta features used are columns C to AE. Columns AH outlines the method/strategy used and columns AI to BB (the error) is the outcome variable for each prediction step. The description of the method/strategies is as follows:</p> <p><strong>Machine Learning methods:</strong></p> <ul> <li>NN: Neural Network</li> <li>ARIMA: Autoregressive Integrated Moving Average</li> <li>SVR: Support Vector Regression</li> <li>LSTM: Long Short Term Memory</li> <li>RNN: Recurrent Neural Network</li> </ul> <p><strong>Multistep ahead prediction strategy:</strong></p> <ul> <li>OSAP: One Step ahead strategy</li> <li>MRFA: Multi Resolution Forecast Aggregation</li> </ul>
Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.)
<p>This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data".</p> <p><strong>Directories</strong></p> <p>Please untar the sciCSR-data-files.tar.gz archive.</p> <p><em><strong>Folder "Simulated_data"</strong></em></p> <ul> <li>'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis.</li> <li>'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes.</li> </ul> <p><em><strong>Folder "Seurat_objects"</strong></em></p> <ul> <li>'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below).</li> <li>'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below)</li> <li>'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775))</li> <li>'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6))</li> <li>'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas.</li> <li>* 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas.</li> <li>'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine.</li> <li>'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data.</li> <li>'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data.</li> <li>'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work.</li> </ul>
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.