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Fig. 3 in Predicting Wing Trait Gaps: The Case of Andean Rove Beetles (Coleoptera: Staphylinidae)
Fig. 3. Difference (δ = delta) between the estimated and observed values when pronotum width is used as a predictor variable of wing width (A), wing length (B) in all rove beetle species, and wing area for other subfamilies (C) and for Staphylininae species (D).
Fig. 1 in Predicting Wing Trait Gaps: The Case of Andean Rove Beetles (Coleoptera: Staphylinidae)
Fig. 1. Morphological traits measured in rove beetles. a) Dorsal view of Belonuchus sp., b) Hind wing. Body length (BL), pronotum width (PW), pronotum length (PL), elytra length (EL), elytra width (EW), clypeal apical margin to apex of elytra length (CEL), wing length (WL), wing width (WW), wing area (WA).
Fig. 2 in Predicting Wing Trait Gaps: The Case of Andean Rove Beetles (Coleoptera: Staphylinidae)
Fig. 2. Linear regressions using pronotum width as a predictor variable. A) Wing width, B) Wing length, C, D) Wing area. The gray band indicates the prediction intervals with 95% confidence.
Gene Enhancer Predictions in C2C12 (Mouse Myoblast)
<p>This file contains the complete enhancer prediction output of the analysis done in this study published on Nature Communications:</p> <p><a href="https://www.nature.com/articles/s41467-025-57758-x" target="_blank" rel="noopener">https://www.nature.com/articles/s41467-025-57758-x</a></p> <p>using the Activity by Contact Model following the pipeline described here: <a href="https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction">https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction</a></p> <p>To be used in this analysis, a genomewide Hi-C interaction matrix was generated with <a>Juicer </a> v1.6, expression counts for each gene were generated with STAR v2.7.10b and <a>Rsubread</a> v2.8.2, and the sequence alignment maps of ATAC-seq and H3K27ac ChIP-seq were generated with NextGenMap v0.5.5, all using raw sequencing reads downloaded from <a>SRA</a>: Hi-C (SRR16220088), RNA-seq (SRR074113 and SRR074114), <a>ATAC-seq (SRR2999996) and H3K27ac ChIP-seq (SRR358589, SRR358590, and SRR358591).</a></p> <p><a>The chromosome coordinates are of the GRCm38 - mm10 assembly, and the gene IDs are from ENSEMBL annotation.</a></p> <p> </p> <p>This research was funded in whole or in part by the Austrian Science Fund (FWF) [P29713-B28, P32512-B and P36503-B] to Roland Foisner and a doctorate program funded by the Austrian Science Fund (FWF) [W1261-B28].</p> <p> </p> <p>For more information please refer to our publication that used these enhancer predictions titled:</p> <p>MyoD1 localization at the nuclear periphery is mediated by association of WFS1 with active enhancers</p>
MvGraphDTA: Multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs
<h1>MvGraphDTA</h1> <p>MvGraphDTA:通过引入图形和折线图,基于多视图的图形深度模型用于药物-靶点亲和力预测</p> <h2>要求</h2> <p><span><span>numpy</span></span><span>==1.23.5</span></p> <p><span><span>pandas</span></span><span>==1.5.2</span></p> <p><span><span>biopython</span></span><span>==1.79</span></p> <p><span><span>scipy</span></span><span>==1.9.3</span></p> <p><span><span>torch</span></span><span>==2.0.1</span></p> <p><span><span>torch_geometric</span></span><span>==2.3.1</span></p> <h2>示例用法</h2> <h3>1. 使用我们的预训练模型</h3> <p>在本节中,我们提供了 pdbbindv2016 的核心集数据和 Li 的数据(过滤后的 casf2013 和 casf2016),您可以直接执行以下命令来运行我们的预训练模型并在核心集上获取结果。</p> <pre># Run the following command.<br>python test_pretrain.py</pre> <h3>2. 在数据集上运行</h3> <p>在本节中,您必须提供药物的 .sdf 文件以及靶标的 .pdb 文件。</p> <div> <p># 您可以通过运行以下命令获取药物和靶点的图形和折线图。<br>Python data_process.py</p> <p># 当所有数据都准备好后,您可以通过运行以下命令来训练自己的模型。<br>Python training.py</p> </div>
The source code for a new capillary and adsorption‒force model predicting hydraulic conductivity of soil during freeze‒thaw processes
<p>The source code is related to "A New Capillary and Adsorption‒Force Model Predicting Hydraulic Conductivity of Soil during Freeze‒thaw Processes" (Shufeng Qiao, Rui Ma, Yunquan Wang, Ziyong Sun, Helen Kristine French, Yanxin Wang)</p>
Protein structure data for "AI-predicted protein deformation encodes energy landscape perturbation"
<p>AF2-predicted protein structures of WT and mutant proteins that have corresponding ddG measurements in the ThermoMutDB database of protein mutant stability measurements. PDB structures are compressed using <a href="https://github.com/steineggerlab/foldcomp/">FoldComp</a>, and saved in "structures.zip".</p> <p>Summary of the final dataset and results can be found in "results_summary.pkl".</p> <p>Code used to plot figures can be found in "code4figs.zip".</p>
Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework
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Visualisation Scripts: Predictability of the 2020 strong vortex in the Antarctic stratosphere and the role of ozone
<p>NCL (https://www.ncl.ucar.edu/) scripts to generate Figures 1-15 and Supplementary Figures S1-S7. </p>
Pretrained Model of Channel Mixer Layer for Spatiotemporal Predictive Learning
<p>Model supplementary material for <em><strong>Space Weather</strong></em> journal paper with the title: Channel Mixer Layer: Multimodal Fusion Towards Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content</p>
Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks
<p>The datasets used to evaluate Enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) against predicting the pro-longevity or anti-longevity effect of model organism gene. This repo also includes the pretrained encoders that obtained the best predictive performance for each organism (see Table 2).</p>
Generating aerial flood prediction imagery
<h1>Flood Generation Dataset</h1> <h2>Dataset Overview</h2> <p>The dataset contains paired sets of pre- and post-flooding satellite images, which can be used for training models on flood prediction tasks, particularly on generating photorealistic visualisations of floods. Each image is also associated with topographical factors, which can be used to assist the model in making predictions. These factors are a digital elevation model (DEM), flow accumulation, distance to rivers, and cartographical map. Every image set was manually verified and adjusted, in order to ensure perfect pixel alignment, where each pixel represents the same 0.5m x 0.5m geographical area in all of the factors.</p> <p>The dataset contains 1229 unique image sets, 235 of Hurricane Florence, 335 of Hurricane Harvey, 147 of the Midwest floods, and 512 of the India monsoon. 408 of the image sets (from Hurricane Harvey and some of the Midwest floods) have an additional alternative version with a 1m/pixel resolution DEM.</p> <h2>Dataset Structure</h2> <p>The <strong>dataset input </strong>folder contains images with the pre-flooding satellite image and topographical factors represented in 9 stacked channels (see the table below) and stored as a .tif file. The <strong>dataset output </strong>folder contains the post-flooding satellite image, stored as a .tif file. The name of the file contains which disaster it represents.</p> <table> <tbody> <tr> <td><strong>Channel</strong></td> <td><strong>Contents</strong></td> </tr> <tr> <td>0, 1, 2</td> <td>Pre-flooding satellite image</td> </tr> <tr> <td>3</td> <td>DEM</td> </tr> <tr> <td>4</td> <td>Flow accumulation</td> </tr> <tr> <td>5</td> <td>Distance to rivers</td> </tr> <tr> <td>6, 7, 8</td> <td>Cartographical map</td> </tr> </tbody> </table> <h2>Dataset Contents</h2> <h3>Pre- and post satellite images.</h3> <p>Each image has 1024x1024 pixels in a three-band RGB format, with a resolution of approximately 0.5 metres/pixel. The satellite images were extracted from the xBD dataset, captured by the Maxar Open Data Program. It was released under the Creative Commons Attribution-Noncommercial-Sharealike 4.0 International licence (CC BY-NC-SA 4.0). </p> <h3>Digital elevation model and flow accumulation</h3> <p>The USGS 3D Elevation Program DEM, which has a 1/3 arc-second (10 metre) resolution, describes the topography of the images within the USA. The elevations in this DEM represent the topographic bare-earth surface. The Copernicus GLO-30 DEM, which has a resolution of only 30 metres, was used for the flood areas in India. The flow accumulation was calculated from the DEMs.</p> <p>The USGS 3D Elevation Program DEM is released by the U.S. Geological Survey, 2023, 1/3rd arc-second Digital Elevation Models (DEMs) - USGS National Map 3DEP Downloadable Data Collection. All 3DEP products are public domain. The Copernicus Global Digital Elevation model was produced using Copernicus WorldDEM-30 DLR e.V. 2010-2014 and Airbus Defence and Space GmbH 2014-2018, provided under COPERNICUS by the European Union and ESA.</p> <h3>Distance to rivers and cartographical map</h3> <p>OpenStreetMap data was downloaded from Planet OSM and processed using the Osmium tool. The Maperitive software was then used to apply a custom ruleset to the maps' appearance, removing all of the text, and enhancing the clarity of the land use types. The distance to rivers representation was producing by creating buffer distances (at 0.5km intervals) to all major rivers and waterways, as classified by the Open Street Map. The OpenStreetMap (OSM) data is distributed under the Open Database License (ODbL). https://www.openstreetmap.org/copyright</p> <h1>Flood Segmentation Dataset</h1> <h2>Dataset Overview</h2> <p>In order to evalate the flood predictive accuracy of post-flooding images generated by a model that has been trained on the flood generation dataset, the synthetic and ground truth post-flooding images can be compared using a flood segmentation model trained on this dataset. A post-flooding satellite image is input into the the segmentation model, and it then classifies whether each pixel is flooded or not, and outputs a 1-channel binary flood mask, where white (1) corresponds to a predicted flooded pixel, and black (0) corresponds to a predicted non-flooded pixel. The flood masks corresponding to both real and synthetic images can then be compared using metrics such as MSE, precision, recall, etc, thus determining whether the generative model correctly flooded the same areas that are actually flooded in the real ground truth image.</p> <h2>Dataset Structure</h2> <p>The flood segmentation dataset contains 1028 images, depicting the same disasters as in the flood generation dataset. The <strong>masks input</strong> folder contains 256x256 pixel 3-channel RGB post-flooding .tif images, which are comprised of a mix of ground truth and synthetic images generated by different GAN architectures. The <strong>masks output </strong>folder contains the corresponding 1 channel binary flood masks.</p> <p>332 of the flood masks were published openly by: https://huggingface.co/datasets/blutjens/eie-earth-intelligence-engine. The rest of the masks were manually labelled by myself.</p>
Data for 'Cooperative thalamocortical circuit mechanism for sensory prediction errors'
<p>Data and code for 'Cooperative thalamocortical circuit mechanism for sensory prediction errors'</p> <p>Nature (2024)</p> <p>DOI: 10.1038/s41586-024-07851-w</p>
A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer
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Predicting Weather Disruptions for the ICC Champions Trophy 2025 in Pakistan Using Machine Learning and Data Analytics
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First global machine learning model to predict solar flare impact on Earth's ionosphere
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Structure Databases: Predicted Influence of Organic Structure Directing Agents on Al Distributions in CHA Zeolites
<p>Structures and energies associated with the paper: Predicted Influence of Organic Structure Directing Agents on Al Distributions in CHA Zeolites</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF) and Convolutional Neural Network (CNN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <div>[1] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</div> <div>[2] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Figure data for "Machine Learning For Early Dynamic Prediction Of Functional Outcome After Stroke"
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FIGURE 5. Predicted niche for Serjania perulacea and the new varieties. A. S in Geographic variation of Serjania perulacea (Paullinieae, Sapindaceae) and description of two new varieties
FIGURE 5. Predicted niche for Serjania perulacea and the new varieties. A. S. perulacea var. perulacea (morphotype A) with a niche restricted to Cerrado and Semideciduous Chiquitano Forest. B. S. perulacea var. krapovickasii (morphotype B) wide niche, from Brazil to Bolivia, with a gap in Paraguay. C. S. perulacea var. chacoensis (morphotype C) central-east of Paraguay.
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.