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5,805 results for “Data model”
Supplemental 3D Model Data - New insights into the evolutionary history of Fungi from a 407 million year old blastocladiomycota-like fossil showing multiple sporangia and an extensive hyphal network (SPIERSView and VAXML format)
<p>Three-dimensional reconstruction models of Fungi from a 407 million year old blastocladiomycota-like fossil showing multiple sporangia and an extensive hyphal network in SPIERSView and VAXML format. 2D and 3D (Red/Cyan) images also provided as a PDF.</p> <p>Notes:</p> <ol> <li>SPIERSView file (.SPV) models can conveniently be viewed using the SPIERSView software, freely available in both Windows and Mac versions from http://www.spiers‐software.org. However, note that low-performance computers may not possess a sufficiently powerful graphics card to render and rotate the model.</li> <li>VAXML file format models are saved as a ZIP-compressed VAXML datasets. VAXML uses one or more .STL files to define the geometry of objects that comprise the dataset, together with one .VAXML file that provides metadata on the dataset as a whole, and specifies how the .STL files should be put together. We recommend using the free SPIERS software to view this model format (http://spiers-software.org/). However, .STL files can be opened independently in several freely available software programs (e.g. MeshLab, Blender). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm.</li> </ol>
Representations of language in a model of visually grounded speech signal: Data
<p>The set of datafiles to reproduce results from:</p> <ul> <li>Chrupała, G., Gelderloos, L., & Alishahi, A. (2017). Representations of language in a model of visually grounded speech signal. ACL. arXiv preprint: https://arxiv.org/abs/1702.01991</li> </ul>
Experimental data for "Spot-On: robust model-based analysis of single-particle tracking experiments"
<p><strong>Overview of experimental spaSPT data</strong></p> <p>To comprehensively test Spot-On over many different conditions, we conducted 1064 spaSPT experiments. The raw data is freely available and the purpose of this ReadMe file is to describe the organization, acquisition parameters and format of the data. The data is for 4 different cell lines imaged over 15 different conditions yielding a total of 60 different conditions. The four cell lines were:</p> <ul> <li> <p>U2OS C32 Halo-CTCF</p> </li> <li> <p>U2OS H2B-Halo-SNAP</p> </li> <li> <p>U2OS Halo-3xNLS</p> </li> <li> <p>mESC (JM8.N4) C3 Halo-Sox2</p> </li> </ul> <p>The cell lines were constructed in different ways. U2OS C32 Halo-CTCF was made by homozygous endogenous N-terminal tagging of CTCF in human osteosarcoma U2OS cells using CRISPR/Cas9-mediated genome-editing as described (C32 refers to clone number 32)<sup>1</sup>. We note the CTCF is an essential gene and that N-terminal tagging did not appear to affect CTCF function or expression level according to a series of control experiments<sup>1</sup>. Moreover, C32 Halo-CTCF has been authenticated using Short Tandem Repeat (STR) profiling (performed by Dr. Alison N. Killilea at the UC Berkeley Cell Culture Facility) against the following loci: THO1, D5S818, D13S317, D7S820, D16S539, CSF1PO, AMEL, vWA and TPOX. The C32 Halo-CTCF cell line showed a 100% match with U2OS.</p> <p>U2OS H2B-Halo-SNAP was made through random integration of a H2B-HaloTag-SNAP-Tag transgene expressed using the EF1a promoter with an IRES-NeoR gene for drug selection. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously<sup>1</sup>. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS.</p> <p>U2OS Halo-3xNLS was made through random integration of a FLAG-Halo-3xNLS (3x SV40 NLS: PKKKRKV) transgene expressed using the EF1a promoter. NeoR for drug selection was separately expressed using an SV40 promoter. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously<sup>1</sup>. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS.</p> <p>mESC C3 Halo-Sox2 was made through homozygous N-terminal tagging of Sox2 in JM8.N4<sup>2</sup> mouse embryonic stem cells using CRISPR/Cas9-mediated genome editing as previously described (C3 refers to clone number 3)<sup>3</sup>. The functionality of the C3 Halo-Sox2 knock-in was validated through control experiments and pluripotency through teratoma assays as described previously<sup>3</sup>.</p> <p>Each file contains single-molecule trajectories from a single cell imaged over 30,000 frames. Localization and tracking was performed using a custom-written Matlab implementation of the MTT-algorithm<sup>4</sup> and the following settings: Localization error: 10<sup>-6.25</sup>; deflation loops: 0; Blinking (frames): 1; max competitors: 3; max <em>D</em> (m<sup>2</sup>/s): 20.</p> <p>The same 15 conditions were used for each of the 4 cell lines.</p> <p><strong>ExpA PA-JF549</strong></p> <p>The purpose of this experiment was to test the effect of “motion-blurring” on the Spot-On estimated <em>D</em><sub>FREE</sub> and <em>F</em><sub>BOUND</sub>. 5 different experimental conditions were considered. Full details are given in the Methods section. Briefly, cells were grown overnight on plasma-cleaned 25 mm circular coverslips either directly (U2OS) and MatriGel coated as described<sup>1</sup>. Cell were labeled with 5-50 nM PA-JF549<sup>5</sup> for around 15-30 min, washed twice and medium exchanged to phenol-red free medium. 30,000 frames were collected at a camera exposure time (Andor iXon Ultra 897; frame-transfer mode; vertical shift speed: 0.9 μs; -70C) of 9.5 ms which together with a ~447 μs camera integration time gave a frame rate of ~100 Hz. PA-JF549 dyes were photo-activated during the ~447 μs camera integration time using 405 nm pulses and the 405 nm pulse intensity optimized to achieve a mean density of 1 molecule per frame per nucleus. The JF549 dye was excited using a 561 nm laser and the total number of excitation photons kept constant but either delivered during a 1 ms pulse, a 2 ms pulse, a 4 ms pulse, a 7 ms pulse or with constant illumination.</p> <p>For each cell line and condition, 4 replicates were performed. We count a replicate as an independent experiment performed on a different day. For each replicate around 5 cells were imaged. Occasionally, fewer than 5 cells are available. To avoid tracking errors, we removed cells with too high a localization density from the analysis. All of this information is available in the file name. For example, “U2OS_C32_Halo-CTCF_PA-JF549_1ms-561nm_100Hz_rep2_cell03” refers to the third cell imaged in the second replicate of U2OS C32 Halo-CTCF using a 1 ms excitation pulse of 561 nm laser at a frame rate of 100 Hz. Similarly, “U2OS_C32_Halo-CTCF_PA-JF549_cont-561nm_100Hz_rep4_cell01” refers to the first cell imaged in the fourth replicate of U2OS C32 Halo-CTCF using constant 561 nm laser at a frame rate of 100 Hz.</p> <p>The five ExpA_PAJF549 conditions are separated by cell line such that each cell line is provided in a separate directory. E.g. the directory “U2OS_H2B_ExpA_PAJF549” contains all data for the U2OS H2B-Halo-SNAP cell line.</p> <p><strong>ExpA PA-JF646</strong></p> <p>This experiment was exactly identical to the “ExpA_PA-JF549” experiment except cell were labeled with PA-JF646<sup>5</sup> and excited using a 633 nm laser. The file names and data organization was otherwise the same and the same five excitation conditions were considered.</p> <p><strong>ExpB PA-JF646</strong></p> <p>The purpose of this experiment was to test if the Spot-On estimated <em>D</em><sub>FREE</sub> and <em>F</em><sub>BOUND</sub> values would depend on the frame rate. In particular, all four proteins exhibit some levels of apparent anomalous diffusion, which could cause a dependence on the frame rate. Cells were labeled with PA-JF646 and grown and imaged as described above. Photo-activation took place during the ~447 μs camera integration time and JF646 dyes were excited using 1 ms stroboscopic 633 nm excitation pulses. To change the frame rate, the camera exposure time was set to 4.5 ms (~201 Hz), 5.5 ms (~167 Hz), 7 ms (~134 Hz), 13 ms (~74 Hz) and 19.5 ms (~50 Hz) when also counting the ~447 μs camera integration time. All of this information is available in the file name. For example, “U2OS_Halo-3xNLS_PA-JF646_1ms-633nm_74Hz_rep2_cell04” refers to the fourth cell imaged in the second replicate of U2OS Halo-3xNLS using a 1 ms excitation pulse of 633 nm laser at a frame rate of 74 Hz. Similarly, “mESC_C3_Halo-Sox2_PA-JF646_1ms-633nm_201Hz_rep1_cell03” refers to the third cell imaged in the first replicate of mESC Halo-Sox2 using a 1 ms excitation pulse of 633 nm laser at a frame rate of 201 Hz.</p> <p><strong>Data format</strong></p> <p>All data is available in two different formats: CSV-files and Matlab MAT-files. Both file formats are readable by the web-version of Spot-On. The Matlab version of Spot-On is only able to read the MAT-files. The CSV format consists of comma-separated values and contains headers. If opened with Microsoft Excel, it should appear as shown:</p> <p>Here the “frame” column contains the frame number in which the molecule was detected. The “t” column contains the timestamp. The “trajectory” column contains the trajectory number. For example, trajectory number 1 was only detected in frame 13 after which it disappeared. In contrast, trajectory number 4 was detected in frames 20, 21 22, 23 and 24. Finally, the “x” and “y” columns contain the x,y coordinates of the localization in units of micrometers (μm).</p> <p>The MAT-files contain a structure array named “trackedPar”. trackedPar contains three variables:</p> <ul> <li> <p>trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of localizations in that trajectory. The first column is the x-coordinate and the second column is the y-coordinate. The units are micrometers (μm).</p> </li> <li> <p>trackedPar.Frame: “Frame” is a column vector where each element is the frame where the particle was localized.</p> </li> <li> <p>trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint where the particle was localized.</p> </li> </ul> <p>Each element in the structure array “trackedPar” correspond to a different trajectory.</p>
Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments"
<p><strong>Generation of simulated data</strong></p> <p>To systematically evaluate the performance of Spot-On as well as other common analysis tools such as MSD<sub>i</sub> and vbSPT, we considered a comprehensive set of 3480 realistic SPT simulations spanning the range of plausible dynamics. The simulations were performed using simSPT, which is freely available at GitLab: https://gitlab.com/tjian-darzacq-lab/simSPT. The simulation methods are described in detail at GitLab. A full description of the parameters which allows exact reproduction of the simulations is available together with the data (see Data Availability section). Briefly, we parameterized simSPT to consider that particles diffuse inside a sphere (the nucleus) of 8 µm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 µm), with an axial detection range of ~700 nm, centered at the middle of the HiLo beam. Molecules are assumed to have a half-life of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. The localization error was set to 25 nm and the simulation was run until 100000 in-focus trajectories were recorded. More specifically, the effect of the exposure time (1 ms, 4 ms, 7 ms, 13 ms, 20 ms), the free diffusion constant (from 0.5 µm²/s to 14.5 µm²/s in 0.5 µm²/s increments) and the fraction bound (from 0 % to 95 % in 5 % increments) were investigated, yielding a dataset consisting of 3480 simulations. The advantage of simulations is that the ground truth is known. This allows a quantitative assessment of which method works the best.</p> <p><strong>Content of the archives:</strong></p> <ol> <li>170718_simSPT_simulations.zip the code and instructions to reproduce the simulations</li> <li>4um.tar.bz2 simulated data inside a 4 µm nucleus</li> <li>20um.tar.bz2 simulated data inside a 20 µm nucleus, in which virtually no confinement occurs.</li> <li>subsampled.tar.bz2 is a set of subsampled datasets, containing either 99999, 30000, 10000, 3000, 1000, 300, 100 or 30 trajectories. Each subsampling was done 50 times, yielding 50 files per subsmpling.</li> </ol> <p><strong>Formats:</strong></p> <p>The data is provided both in CSV and .mat formats. .mat files are provided in the following dataset: 10.5281/zenodo.835541</p>
Experimental Data for Natural Disaster Mobility Model and Typhoon Haiyan Scenario
<p>The experimental data set for running the <em>Typhoon Haiyan</em> scenario with the <em>Natural Disaster Mobility Model</em> presented in the paper:</p> <p>Milan Stute, Max Maass, Tom Schons, and Matthias Hollick, “<strong>Reverse Engineering Human Mobility in Large-scale Natural Disasters</strong>,” to appear in <em>ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)</em>, November 2017, Miami Beach, USA.</p>
Data and scripts for "Joint species-trait distribution modelling: The role of intraspecific trait variation in community assembly"
<p>The README explains how to reproduce the analyses presented in the paper <strong>"Joint species-trait distribution modelling: The role of intraspecific trait variation in community assembly"</strong> by Abrego et al.</p> <p>The input data for the script pipeline is the file “Kilpisjarvi_plant_data.csv”. This file includes the data on the plants and their traits in the long format. Hence, each row of the data matrix corresponds to measurements on one plant species in one study plot. The joint species-trait distribution modelling (JSTDM) pipeline that analyses these data consists of the following R-scripts.</p> <p>· <strong>S1_define_JSTDM_models.R</strong>. This script defines the JSDTM models (null model and environmental model) that include five response types for each species: the presence-absence, abundance conditional on presence, and the plot-level trait values of specific leaf area (SLA), leaf area (LA) and mean height (MH). The model is defined in the Hierarchical Modelling of Species Communities (HMSC) framework utilizing the R-package Hmsc. The models are saved in the file “unfitted_models.RData”.</p> <p>· <strong>S2_fit_models.R. </strong>This script loads the unfitted models and fits them using the posterior sampling methods implemented in the R-package Hmsc. The models are fitted with increasing thinning until thin=100, which value was used to generate the results of the paper. The fitted models are saved in the file "models_thin_100_samples_250_chains_4.Rdata".</p> <p>· <strong>S3_plot_Omega_matrices.R. </strong>This script loads the fitted models and plots the association matrices (Fig. 2 of the paper). The csv file containing the values used to construct Fig. 2 is also given (figure2Cdata.csv and figure2Ddata.csv).</p> <p>· <strong>S4_show_VP_Beta_Gamma.R. </strong>This script loads the fitted models and extracts information on the variance partitionings (VP; Figs. S3 and S4 of the paper), the relationships between response types and environmental predictors (beta; Fig. S2 of the paper), and the relationships between response types and species-level traits (gamma; Fig. S5 of the paper). The csv file containing the values used to construct Fig. S2 (figureS2Adata.csv and figureS2Bdata.csv), Fig. S3 (figureS3data.csv), Fig. S4 (figureS4data.csv) and Fig S5 (figureS5data.csv) are also given.</p> <p>· <strong>S5_conditional_cross_validation.R</strong>. This script performs 10-fold cross validation to the data to test the predictive power related to the modelled plant traits. The script performs both regular (unconditional) cross-validation where all data are masked for the test fold, and conditional cross-validation where only the trait data (but not the abundance data) are masked for the test fold.</p> <p>· <strong>S6_show_conditional_cross_validation_results.R. </strong>This script plots the results of cross-validation (Fig. 3 of the paper). The csv file containing the values used to construct Fig. 3 is also given (figure3Adata.csv and figure3Bdata.csv).</p> <p>· <strong>S7_scenario_predictions.R</strong>. This script performs the scenario simulations described and shown in Fig. 4 of the paper. The csv file containing the values used to construct Fig. 4 is also given (figure4Bdata.csv and figure4Cdata.csv).</p>
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
Data Set "Benchmarking of Vibrational Exciton Models Against Quantum-Chemical Localized-Mode Calculations"
<p>This data set accompanies the publication "Benchmarking of Vibrational Exciton Models Against Quantum-Chemical Localized-Mode Calculations" <br>by Anna M. van Bodegraven, Kevin Focke, Mario Wolter, and Christoph R. Jacob <br>(TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory '01_AIM':</p> <p> - input (structure.pdb, topol.top and *_input.txt) and results (*.log and<br> Hamiltonian/AtomPos/Dipole/Parameters.txt) from frequency calculations<br> with the Amide-I-maps (AIM) program for six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip), <br> each in vacuo or water with three different maps (Jansen, Skinner, <br> Tokmakoff) for 11 snapshots based on a MD run.<br> To rerun the calculations, you will have to change the paths in <br> the *_input.txt files (topfile, trjfile, sourcedir) accordingly<br> <br>Directory '02_SNF':</p> <p> - results (*.dat, *.out, coord and control) from frequency calculations <br> using Turbomole, SNF, and LocVib for six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip) <br> each in vacuo or water for snapshots based on an MD run.<br> <br>Directory '03_NMA':</p> <p> - coordinates and results from pyADF for NMA molecules each alligned <br> with a peptide bond from the six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip) <br> each in water for 11 snapshots based on a MD run and input <br> (*_input.txt) and results (*.log and Hamiltonian/AtomPos/Dipole/Parameters.txt) <br> from calculations with the Amide-I-maps (AIM) program<br> <br>Directory '04_Handling_Data':</p> <p> - contains all used notebooks to extract the data, plot the figures <br> and calculate the errors<br> - RMSD_Error_vacuo/water.ipynb is used to calculate the overall shifts <br> and generates a map-dependent mean value to shift the frequencies of AIM<br> - Frequencies.ipynb and Couplings.ipynb are used to plot the figures <br> - RMSD_values_vacuo/water.ipynb show the calculations for the statistical <br> analysis<br> To use the notebooks, start with the Dictionary_setup_for_data_for_paper.ipynb <br> to set up the main dictionary from the calculated data</p>
Cross-disease integration of single-cell RNA sequencing data from lung myeloid cells reveals TAM signature in in vitro model
<p>Single cells from a 3D human cell-based model comprising tumor cell line-derived spheroids, cancer-associated fibroblasts and primary monocytes were dissociated and analyzed using scRNAseq. 4 monocyte donors were used in the 3D model, and 3 monocyte donors were used for 2D differentiation of macrophages.</p>
Dissection of core promoter syntax through single nucleotide resolution modeling of transcription initiation (CLIPNET data)
<div>This contains data necessary to reproduce the figures in the CLIPNET paper (preprint <a href="https://www.biorxiv.org/content/10.1101/2024.03.13.583868">here</a>) as well as processed data used to train and evaluate CLIPNET. To preserve subdirectory structure, we've packaged the data into tar archives. Please refer to the README documents in our manuscript GitHub repo for more details on file contents: <a href="https://github.com/Danko-Lab/clipnet_paper/">https://github.com/Danko-Lab/clipnet_paper/</a></div> <div> </div> <div>Pretrained CLIPNET models are archived separately at <a href="../doi/10.5281/zenodo.10408622">DOI 10.5281/zenodo.10408622</a></div> <div> </div> <div>V5: Fixed bug in calculation of profile attribution scores causing them to be off by a factor of exactly 500. Genome-wide DeepSHAP tracks & TF-MoDISco tracks have been accordingly updated. I have not updated the individual examples, as these can be quickly fixed by simply multiplying by 500 when plotting. Additionally, I have uploaded profile and quantity motif calls, which contain genome-wide seqlet annotations. The columns in these files are [chrom, start, end, peak_idx, motif_annotation].</div> <div>V4: Uploaded individual bigWigs. These have been lifted over using CrossMap from the original hg19 (GSE110638) to hg38 and RPM normalized.</div> <div>V3: Final version prior to journal submission. Don't recall exact details of what's changed.</div> <div>V2: evaluation_metrics.tar.gz and evaluation_data.tar.gz have been replaced. Previously, we benchmarked the models by treating each peak in each individual as a separate data point. Here, we instead predicted from the reference genome and compared against the averaged bigWigs.</div>
Data for Final Revised Version of "Photoacclimation and Photoadaptation Sensitivity in a Global Ecosystem Model"
<p>This repository contains the data and codes used to create the figures for the REVISED VERSION of the manuscript "Photoacclimation and Photoadaptation Sensitivity in a Global Earth System Model", by Charles A. Stock, John P. Dunne, Jessica Y. Luo, Andrew C. Ross, Nicolas Van Oostende, Niki Zadeh, Theresa J. Cordero, Xiao Liu and Yi-Cheng Teng. It also contains the COBALT Fortran codes used to generate the simulations. </p> <p>This article has been accepted to the Journal of Advances in Modeling the Earth System (JAMES). This update includes minor corrections implemented during proof corrections to ensure that all Figures and Tables were consistent in their treatment of data from the high Arctic (> 85 deg. N Latitude). The updates can be seen in minor changes to the "residual_and_skill" files.</p> <p>The codebase used for this work can be found in the following Github repository:</p> <p>https://github.com/NOAA-CEFI-Regional-Ocean-Modeling/ocean_BGC/releases/tag/COBALTv3_202501</p>
Data for "Early and Widespread Emergence of Regional Warming is Robust to Observational and Model Uncertainty"
<p>These data can be used to reproduce all figures in "Early and Widespread Emergence of Regional Warming is Robust to Observational and Model Uncertainty". Figure code is hosted at https://github.com/jshaw35/RegionalToE_ShawAndLenssen/releases/tag/v1.0</p>
Data Release for Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model
<p>CESM2 and CISM2 data files for figures in "Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model" (Sommers et al., 2021, Paleoceanography and Paleoclimatology)</p>
Matlab codes implementing the XDROM+ data-driven ENSO forecast model and some analysis of it
<p>This is the BEST forecast model of large scale features of ENSO as of today, beating (Zhao et al. Nature 2024).</p> <p>This archive is supplementary to a comment article concerning (Zhao et al. Nature 2024) intended as a "Matters Arising" piece to be submitted to Nature (https://www.researchsquare.com/article/rs-5336072/v1). Given that i criticise also the handling editor and 3 reviewers of (Zhao et al. Nature 2024) calling their incompetence out, do not be surprised if you have to look for the paper in some other journal instead. Oh well, integrity is above all else, no?! On that note, may I interest you in a bit of sci-fi? https://www.linkedin.com/pulse/crime-punishment-bit-differently-tamas-bodai-g4cvf/?trackingId=WdQkSNjgSuyxlyWVLRonrw%3D%3D</p>
Data for "Assessment of using field-aligned currents to drive the Global Ionosphere Thermosphere Model: A case study for the 2013 St Patrick's Day geomagnetic storm"
GITM Simulation results for the paper "Assessment of using field-aligned currents to drive the Global Ionosphere Thermosphere Model: A case study for the 2013 St Patrick's Day geomagnetic storm"
Global snow water equivalent product derived from machine learning model trained with in situ measurement data
<p>This dataset is a global snow water equivalent dataset using machine learning trained with in-situ measurements. The temporal resolution of the SWEML product is daily, and the spatial resolution is 0.25˚ (approximately 25km). It covers latitudes of 90S to 90N and longitudes of 180W to 180E with global scales, excluding Antarctica. The dataset is provided in NetCDF format, organized by year. Each year contains daily SWE data, including leap days in leap years.</p>
Data accompanying the paper "Regime-dependent turbulence length scale formulation for NWP models based on turbulence kinetic energy, shear and stratification", submitted to Monthly Weather Review
<p>This repository contains the outputs of MicroHH LES (van Heerwaarden et al., 2017) and ALADIN-CZ single-column model simulations of four idealized cases:</p> <p>1) The continental cumulus case utilizing measurements from the Atmospheric Radiation Measurement (ARM) program, and Cloud and Radiation Testbed (CART) site in Oklahoma (Brown et al. 2002; Lenderink et al. 2004)</p> <p>2) The trade wind cumulus case from the Barbados Oceanographic and Meteorological Experiment (BOMEX; Siebesma et al. 2003)</p> <p>3) A drizzling stratocumulus case based on the first research flight data of the second period of the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) campaign (Stevens et al., 2005)</p> <p>4) A stable planetary boundary layer case based on the Global Energy and Water Cycle Experiment (GEWEX) Atmospheric Boundary Layer Study (GABLS1) data (Beare et al. 2006; Cuxart et al. 2006; Holtslag 2006)</p> <p>The MicroHH LES outputs are taken from Reilley et al. (2022) study and can be also found at https://doi.org/10.5281/zenodo.6372434. Additionally, we provide case study outputs from the ALADIN-CZ 3D NWP model, wherein the fields roughly match those verified/shown in Fig. 9.</p> <p> </p> <p>The files are organized in the following way:</p> <p>1) MicroHH LES model configuration files (.ini) and output files (NetCDF format) are stored in the file "LES.zip" within "conf" and "data" folders, respectivelly. Additionally, a sample script to plot LES-derived Turbulence Length Scales (TLS) and those based on NWP formulations (using LES data as input) is provided (plot.py).</p> <p>2) The vertical profiles of (i) conserved variables and (ii) turbulent fluxes from the ALADIN-CZ single-column model for four idealized cases are provided in the file "single-column_simulations.zip", consisted of individual ASCII files (per case and TLS formulation). A detailed description of its content can be found in the associated README file.</p> <p>3) Chosen surface and upper-air fields for (i) 23 November 2019 inversion and (ii) 24 June 2022 mesoscale convection system cases are provided in the file "case_studies.zip" and consisted of individual GRIB files per field and prognostic hour. A detailed description of its content can be found in the associated README file.</p> <p> </p> <p> </p>
Training data for the EPInformer model
<p>Training data for the EPInformer framework deep learning model. The data contains enhancer-gene links of protein coding genes nominated by ABC method (https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction/tree/master)</p>
Data from: Temperature alters the toxicological impacts of plant terpenoids on the polyphagous model herbivore Vanessa cardui
<p>Terpenes are a major class of secondary metabolites present in all plants, and long hypothesized to have diversified in response to specific plant-herbivore interactions. Herbivory is a major biotic interaction that plays out across broad temporal and spatial scales that vary dramatically in temperature regimes, both due to climatic variation across geographic locations as well as the effect of seasonality. In addition, there is an emerging understanding that global climate change will continue to alter the temperature regimes of nearly every habitat on Earth over the coming centuries. Regardless of source, variation in temperature may influence herbivory, in particular via changes in the efficacy and impacts of plant defensive chemistry. This study aims to characterize temperature-driven variation in toxicological effects across several structural classes of terpenes in the model herbivore Vanessa cardui, the painted lady butterfly. We observed a general increase in monoterpene toxicity to larvae, pupa, and adults at higher temperatures, as well as an increase in development time as terpene concentration increased. Results obtained from this study yield insights into possible drivers of seasonal variation in plant terpene production as well as inform effects of rising global temperatures on plant-insect interactions. In the context of other known effects of climate change on plant-herbivore interactions like carbon fertilization and compensatory feeding, temperature-driven changes in plant chemical defense efficacy may further complicate the prediction of climate change impacts on the fundamental ecological process of herbivory.</p>
Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Location Data
<p>This dataset is meant to be used with "Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Results and Data". It provides spatial output data for 4 different setups (spheroid, traditional, 3d gravity, and traditional floating) of an agent-based model of <i>in vitro </i>tuberculosis infection models. </p>
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.