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5,805 results for “Data model”
Data for "Martian Infrasound: Numerical Modeling and Analysis of InSight's Data"
<p>This dataset complements the paper "Martian Infrasound: Numerical Modeling and Analysis of InSight's Data", submitted to the Journal of Geophysical Research - Planets.</p>
TTnet - Model validation data
<p>This repository contains the raw data collected to validate the <em>TTnet</em> time and energy model, which is described in</p> <ul> <li><strong>Time-Triggered Wireless Architecture</strong><br> Romain Jacob, Licong Zhang, Marco Zimmerling, Samarjit Chakraborty, Lothar Thiele <br> Accepted to ECRTS 2020 <br> <a href="https://arxiv.org/abs/2002.07491">arxiv.org/abs/2002.07491</a></li> <li><strong>Leveraging Synchronous Transmissions for the Design of Real-time Wireless Cyber-Physical Systems</strong><br> Romain Jacob<br> Doctoral dissertation , 2020<br> <a href="https://doi.org/10.5281/zenodo.3510184">10.5281/zenodo.3510184 </a>(Chapter 5)</li> </ul> <p>The processing of these data is described in detailed in the following GitHub repository:<br> <a href="https://github.com/romain-jacob/TTW-Artifacts">github.com/romain-jacob/TTW-Artifacts</a></p> <p><strong>Files description</strong></p> <ul> <li><strong>data_raw.zip</strong><br> Contains the serial logs and test configuration files, for all three test series</li> <li><strong>serieX_all_data.zip</strong><br> Contains all the test results (serial logs + GPIO traces + power traces) for the test of series X.<br> Missing for series 3 (we forgot to save it before it got deleted from the server... sorry about that)</li> </ul>
Supplemental data for "Initial land use/cover distribution substantially affects global carbon and local temperature projections in the integrated Earth System Model." Article published as Global Biogeochemical Cycles publication 2019B006383
<p>These are supporting data for Global Biogeochemical Cycles publication 2019B006383: "Initial land use/cover distribution substantially affects global carbon and local temperature projections in the integrated Earth System Model." They include data for all of the regular and supplemental figures.</p>
uEMEP startup configuration and data file for GMD uEMEP model description publication
<p>These zip files contain the configuration and example data files for running uEMEP to reproduce the results in the uEMEP model description GMD publication (DOI: ?). Code and make files are accessible through github or through the associated repository here (DOI: 10.5281/zenodo.3756008).</p> <p>The zip files, once installed, allow the simulation of one day 01.01.2017. The example simulation is limitted to one day due to the size of the daily EMEP files used and the NORTRIP road dust emissions files. EMEP data for the rest of the year is accessible though '<a href="https://thredds.met.no/thredds/catalog/data/fou-kl/uEMEP/EMEP4NO/EMEP4NO_rerun_2017_v2/catalog.html">https://thredds.met.no/thredds/catalog/data/fou-kl/uEMEP/EMEP4NO/EMEP4NO_rerun_2017_v2/catalog.html</a>' if a complete calculation for a year, as appears in the publication, is to be made (size 5.8 GB per day). The remaining NORTRIP files for 2017 are available on request (70GB zipped file).</p> <p>The '<a href="https://zenodo.org/api/files/9b9eda53-6e17-4c8f-b24a-866fc7e3b121/uEMEP_demo_startup_files.zip?versionId=9aa47918-2bd8-418c-85bc-fe6c1e7f1cf5">uEMEP_demo_startup_files.zip</a>' zip file contains a bash script 'uEMEP_script_demo_startup.sh' and configuration files for calculating this day. The config file 'uEMEP_EMEP_config_hourly_default_startup_v1.txt' is the main default configuration file for uEMEP which contains explanations of the variables. Two simulations are possible with additional configuration files. One that reproduces a mapping calculation for a small region of Oslo 'Oslo_100m_5km.txt' and one that will calculate concentrations only at positions of specified measurement site receptors in norway 'uEMEP_EMEP_extra_config_Norway_station_startup_v1.txt'.</p> <p>It is necessary to change the route directories in the config files and the script file after installation and to tailor the script file to own machines and que systems.</p> <p>Example log files from the two runs are included along with the resulting uEMEP output for the simulations to check reproducability.</p> <p>Also included is 'matlab_plotting_scripts.zip', which contains the matlab scripts used to plot the validation results shown in the GMD article.</p>
Data for "An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore"
<p>Data for the article “An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore”</p> <p>By LT Beumer, J Pohle, NMS Schmidt, M Chimienti, JP Desforges, LH Hansen, R Langrock, SH Pedersen, M Stelvig, FM van Beest</p> <p> </p> <p>The data set includes three files: A readme file describing the data files and two data files accompanying the above publication.</p> <p>Combined, the two data files represent the dataset collected by GPS collars fitted on 19 female muskoxen in northeast Greenland (28 muskox-years with 153-1062 observation days/animal) and associated extracted covariates, divided into a summer and winter season dataset as modelled in the article. Data here are given as included in the models (for a description of cleaning procedures, see article). All continuous, non-cyclical covariates were standardised to have zero mean and unit standard deviation to improve numerical stability of parameter estimation. This is indicated by “_scaled” in the column name.</p> <p>For further queries please contact nms@bios.au.dk</p>
Data from: Applied use of alternate stable state modeling in restoration ecology
<p>The concept of alternate stable states is important in ecological theory and models, but the application and implementation of these models have the potential to make significant future advances in the field of patterned landscapes. The bi-stable, ridge and slough landscape is a central feature of Everglades restoration and provides an important opportunity to test stable state theory with multistate transition models. We used these models to estimate environmental parameters associated with state changes (water depths, edaphic factors, etc.) to develop a quantitative method to measure resilience and stability. The multistate model indicates that long-term, local hydrology (15-year mean maximums and 15-year mean amplitude) and edaphic factors control the local scale shifts between ridge and slough states. We show that multistate models can provide hydrologic envelopes for managers, produce a tool to help assess future water management scenarios, and address issues of sustainability, resilience, and restoration for any bi-stable system.</p>
Restore Centre of Excellence: High-resolution mapping of Louisina vegetation remote sensing data for surge modelling
<p>Satellite derived Leaf Area Index map of the Louisiana coast, translated into plant dimensions using field data from CMRS stations and dedicated project sampling. Plant dimensions have been used to prescribe hydraulic roughness fields for a hydrodynamic model (Delft3D) used to asses the effect of wetlands on storm surge levels.</p>
data from "The chloroplast land plant phylogeny: analyses employing better-fitting tree- and site-heterogeneous composition models"
<p>The nucleotide, codon-degenerate and protein concatenated alignments of 83 chloroplast genes, with the respective character sets, in nexus format.</p>
Experimental data and linear model for "Asymmetric internal tide generation in the presence of a steady flow"
<p>This dataset contain the experimental data, analyses, and linear model that are described in the manuscript</p> <p>"Asymmetric internal tide generation in the presence of a steady flow". submitted to Journal of Geophysical Research - Oceans.</p> <p>The folder data_density_fields_fluxes contains the experimental density fields (itXX/results/densityfields) and energy fluxes (itXX/results/fluxes) for the five experiments described in the manuscript: exp I (it17), exp II (it16), exp III (it15), exp IV (it13) and exp (V) (it 18).</p> <p>The Dossmannetal_wavesolution.m file is the linear model described in the manuscript for internal wave generation over a ridge.</p>
Datasets: New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys
<p>These data set contains data from 11 surveys of radio-collared using remotely piloted aircraft systems (RPAS), thermal <br> imaging and automated detection.The MODELDEV .csv contains data points used to develop the modified Horvitz Thompson estimator models in the paper "New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys" which is accepted into publication in the journal Ecology and Evolution as of 11/06/2020. The TESTING .csv contains data points used for model testing in in the same publication. </p>
MMS SITL Ground Loop: Data for the GLS-MP Magnetopause Model
<p>Data required to run the mp-dl-unh model used for magnetopause classification by NASA's Magnetospheric Multiscale (MMS) mission. Included are the model weights, the scikit-learn scaling parameters, and the training and validation dataset used when creating the model. The model itself is available through GitHub and best run on Google Colabs.</p> <p><strong>Releases associated with the data:</strong></p> <p><a href="https://doi.org/10.5281/zenodo.3891992">GLS-MP</a>: Notebooks that used the data in this deposit to train, validate, and run the unh-mp-dl model</p> <p><a href="http://github.com/colinrsmall/mp-dl-unh">mp-dl-unh</a>: Software used to run the model at the MMS SDC</p> <p><a href="https://doi.org/10.5281/zenodo.3891944">MMS SITL Ground Loop</a>: Notebooks used to create tables and figures in the published paper</p> <p><a href="https://doi.org/10.5281/zenodo.3894873">PyMMS</a>: Softwares used to download MMS data and burst selections</p>
Data archive for paper "WRF‐TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model"
<p><strong>WRF-TEB data archive</strong></p> <p>This archive contains data and tools to reproduce results as included in <a href="https://doi.org/10.1029/2019ms001961">Meyer et al. (2020)</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li><a href="https://sylabs.io/">Singularity</a> version >= 3.</li> </ul> <p><strong>Usage</strong></p> <p>To run all models and plotting scripts included in integration test and meteorological evaluation, run the following command from your command-line interface.</p> <pre><code>NPROC=8 TYPE=evaluate tools/singularity/run.sh</code></pre> <p>where <code>NPROC=8</code> is the maximum number of processes to use. The output can be found in the <code>work/</code> folder.</p> <p><strong>HPC</strong></p> <p>If you want to use this in an HPC environment, use <code>tools/hpc</code> as a template. As an example, to run the evaluation on Imperial HPC using PBS (Portable Batch System), use:</p> <pre><code>qsub -v REPO_ROOT=$(pwd),TYPE=evaluate tools/hpc/job_imperial.sh</code></pre> <p><strong>Copyright and License</strong></p> <p>Copyright and licensing information are included at the top of source files or as separate files in folders.</p> <p><strong>References</strong></p> <p>Meyer, D., Schoetter, R., Riechert, M., Verrelle, A., Tewari, M., Dudhia, J., Masson, V., Reeuwijk, M., & Grimmond, S. (2020). WRF‐TEB: implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model. Journal of Advances in Modeling Earth Systems. <a href="https://doi.org/10.1029/2019ms001961">https://doi.org/10.1029/2019ms001961</a></p>
Model simulation data used in "Attributing ozone and its precursors to land transport emissions in Europe and Germany" (Mertens et al., ACP, 2020)
<p>This dataset contains the output of the MECO(n) regional model simulations analysed and discussed in Mertens et al. (ACP., 2020). An overview of the numerical experiments is given in the article. The README files in the dataset give additional information about the metadata.</p>
Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions"
<p>Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions" submitted to Water Resources Research.</p> <p>Contains:</p> <ul> <li>SWMM template files.</li> <li>Objective function values for all model runs.</li> <li>Jupyter Notebooks used to create figures and tables for the article.</li> <li>Copy of rainfall runoff data available from <a href="https://doi.org/10.5281/zenodo.3931582">https://doi.org/10.5281/zenodo.3931582</a></li> </ul> <p>For python implementations of the Hydrograph Matching Algorithm (Ewen 2011) see <a href="https://doi.org/10.5281/zenodo.3923792">https://doi.org/10.5281/zenodo.3923792</a></p> <p>Ewen, John. “Hydrograph Matching Method for Measuring Model Performance.” <em>Journal of Hydrology</em> 408, no. 1–2 (September 2011): 178–87. <a href="https://doi.org/10.1016/j.jhydrol.2011.07.038">https://doi.org/10.1016/j.jhydrol.2011.07.038</a>.</p>
Data for the parameterization of radiative transfer processes in urban climate models
<p><em>Radiative Transfer</em> <em>Model</em> (RTM) is a key component in microscale building resolving urban climate models (<em>UCM</em>), which are used to simulate the flow within urban area. We use different parameterizations of RTMs in the model system <a href="https://gmd.copernicus.org/articles/13/1335/2020/gmd-13-1335-2020.html">PALM</a> version 6.0 to show how much detail modellers should include in their simulation.</p> <p>We introduce the output PALM model results for two examples: (1) A simplified urban geometry consisting of an urban crossing (UC) and (2) a realistic urban domain located at the town square Ernst-Reuter-Platz in Charlottenburg in Berlin (ER). The netCDF files contain the radiative flux received by each surface in the domains, including the shortwave (direct and diffuse) radiation as well as the longwave radiation. Also, the data set includes the 3D flow variables (<em>u</em>, <em>v</em>, <em>w</em>) and the potential temperature. The model drivers (input data) for both examples are included as well.</p> <p>The data set consists of the following model input/output data:</p> <p>1) Simplified urban domain (UC):</p> <ul> <li>Input driver for the model PALM for UC (UC_model_driver.tar.gz)</li> <li>Radiation fluxes for UC when using RTM_01: radiation for horizontal surfaces (UC_RTM_01.nc)</li> <li>Radiation fluxes for UC when using RTM_02: sky view effect (building shadows) (UC_RTM_02.nc)</li> <li>Radiation fluxes for UC when using RTM_03: vegetation interaction with SW radiation (UC_RTM_03.nc)</li> <li>Radiation fluxes for UC when using RTM_04: receiving radiation from surface emission (UC_RTM_04.nc)</li> <li>Radiation fluxes for UC when using RTM_05: vegetation interaction with LW radiation (UC_RTM_05.nc)</li> <li>Radiation fluxes for UC when using RTM_06: single reflection (UC_RTM_06.nc)</li> <li>Radiation fluxes for UC when using RTM_07: vegetation interaction with reflected radiation (UC_RTM_07.nc)</li> <li>Radiation fluxes for UC when using RTM_08: multiple reflections (UC_RTM_08.nc)</li> <li>3D data for the UC reference case which includes u,v,w,theta</li> </ul> <p>2) Realistic urban domain (ER):</p> <ul> <li>Input driver for the model PALM for ER (ER_model_driver)</li> <li>Radiation fluxes for ER when using RTM_01: radiation for horizontal surfaces (ER_RTM_01.nc)</li> <li>Radiation fluxes for ER when using RTM_02: sky view effect (building shadows) (ER_RTM_02.nc)</li> <li>Radiation fluxes for ER when using RTM_03: vegetation interaction with SW radiation (ER_RTM_03.nc)</li> <li>Radiation fluxes for ER when using RTM_04: receiving radiation from surface emission (ER_RTM_04.nc)</li> <li>Radiation fluxes for ER when using RTM_05: vegetation interaction with LW radiation (ER_RTM_05.nc)</li> <li>Radiation fluxes for ER when using RTM_06: single reflection (ER_RTM_06.nc)</li> <li>Radiation fluxes for ER when using RTM_07: vegetation interaction with reflected radiation (ER_RTM_07.nc)</li> <li>Radiation fluxes for ER when using RTM_08: multiple reflections (ER_RTM_08.nc)</li> <li>3D data for the ER reference case which includes u,v,w,theta</li> </ul> <p>For more information and analysis, please check out the relevant publication in the international journal Geoscientific Model Development: Salim et. al, Importance of radiative transfer processes in urban climate models:A study based on the PALM model system 6.0, submitted to GMD.</p>
Numerical model output data for publication "Control of the oceanic heat content of the Getz-Dotson Trough, Antarctica, by the Amundsen Sea Low"
<p>Contains MITgcm model output data to reproduce the analyses of the paper "Control of the oceanic heat content of the Getz-Dotson Trough, Antarctica, by the Amundsen Sea Low", by Dotto and co-authors, published in Journal of Geophysical Research-Oceans (doi: 10.1029/2020JC016113). The model simulation is presented and described in Kimura et al. (2017; doi: 10.1002/2017JC012926) and in Dotto et al. (2019; doi: 10.1175/JPO-D-19-0064.s1). See the 'ReadMe.txt' file for a description of the data. </p> <p> </p>
"Shifts in Phytoplankton Composition and Stepwise Climate Change during the Middle Miocene" - Age-depth models and calcareous nannofossil census data
<p>This is a data supplement to the paper "Shifts in Phytoplankton Composition and Stepwise Climate Change during the Middle Miocene" (Paleoceanography and Paleoclimatology).</p> <p><strong>Data Set S1</strong>. Age-depth models.</p> <p>This data set includes the file SI_Tables S2-S5_Henderiks_etal.xlsx containing raw age-depth tie point compilations for each site and sample age estimates, as well as the final, site-specific input files and output (age assignments) from the <em>Undatable </em>Matlab software Version 1.1 (Lougheed and Obrochta, 2019; https://doi.org/10.1029/2018PA003457). The age-depth models presented in this study can be reproduced by running the age-depth model input files in the <em>Undatable</em> graphical user interface (GUI), whereby the necessary settings for the specific number of Monte Carlo iterations, xfactor and bootstrapping are contained in the header of the input files. Note that input and output files are grouped in two zipped folders: a cm- and meter-depth scale version (the latter decreases computing time and produced the age-depth plots shown in Figures S1 and S2 of the paper).</p> <p><strong>Data Set S2</strong>. Calcareous nannofossil census data.</p> <p>The file SI_ds02_Henderiks_etal.xlsx consists of two separate data sheets:<br> 1. Middle Miocene nannofossil abundance estimates (N/g) and genus-level census counts (%, ±95% CI) at 5 different Atlantic deep-sea sites (Sites 982, 608, 925, 926 and 1264).<br> 2. Middle Miocene census counts (%, ±95% CI) of <em>Coccolithus</em> and <em>Reticulofenestra</em> morphospecies and size categories for Sites 982, 608, 925 and 926.</p>
WASHTREET - Hydraulic, wash-off and sediment transport experimental data obtained in an urban drainage physical model
<p><strong>WASHTREET</strong><strong> -</strong> <strong>Hydraulic, wash-off and sediment transport experimental data obtained in an urban drainage physical model.</strong></p> <p>This dataset contains the results from the tests carried out at a laboratory physical model in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain) as part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>. The objective of the project is to perform a series of high-resolution experiments where urban surface wash-off and sediment transport through gully pots and pipes were accurately measured in laboratory-controlled conditions in a separate drainage system.</p> <p>The experimental facility is a 36 m2 full-scale street section and consists of a rainfall simulator placed over a concrete street surface with two gully pots that drain runoff into an underground pipe system. Further details of the physical model are provided in ‘1_Physical_model_description.pdf’. Two zip files with rain intensity distributions (‘2_Rain_intensity_maps.zip’) and model topographies (‘3_Elevation_data.zip’) complete physical model information as accurately measured inputs for hydraulic, wash-off and sediment transport experiments. ‘4_Hydraulic_tests_description.pdf’ describes experimental procedure, equipment, measuring points, results and data set files of the hydraulic characterization of the experiments. Data regarding these hydraulic tests is included in ‘5_Hydraulic_tests.zip’. In these tests, flow in both gully pots and in the pipe system outlet, and a total of 6 surface and 6 pipe depths were measured by ultrasound distance sensors for the different simulated rains.</p> <p>‘6_Washoff_tests_description.pdf’ includes information of the experimental initial conditions, the different sediment granulometries used, measuring points, experimental procedure and result files regarding wash-off and sediment transport experiments. Data files of a total of 23 tests are included in ‘7_Wash-off_tests.zip’. In these experiments, an initial mass of sediment is distributed over the model surface, and the wash-off and sediment transport processes are measured during a steady and uniform rainfall by total suspended solids (TSS) and particle size distribution (PSD) samples at the entrance of gully pots and at the pipe system outlet. Online turbidity measurements at pipe system outlet, pipe depths and flow at pipe system outlet are also measured during the experiments. Results regarding mass balances, which are performed at the end of the experiment to assess the final distribution of sediments, are also included. At last, some relevant photos and videos taken during the experiments are provided in ‘8_Multimedia.zip’. </p> <p>Flow measurements have been used in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">10.1016/j.jhydrol.2019.05.003</a>), together with the related datasets <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a> and <a href="http://www.doi.org/10.5281/zenodo.3241337">WASHTREET - Structure from Motion data</a>, to calibrate a 2D shallow water model.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p> </p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13. <a href="https://doi.org/10.1038/s41597-020-0384-z">https://doi.org/10.1038/s41597-020-0384-z</a></li> <li>Naves, J., Rieckermann, J., Cea, L., Puertas, J., & Anta, J. (2020). Global and local sensitivity analysis to improve the understanding of physically-based urban wash-off models from high-resolution laboratory experiments. <em>Science of The Total Environment</em>, <em>709</em>, 136152. <a href="https://doi.org/10.1016/j.scitotenv.2019.136152">https://doi.org/10.1016/j.scitotenv.2019.136152</a></li> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Development and Calibration of a New Dripper-Based Rainfall Simulator for Large-Scale Sediment Wash-Off Studies. <em>Water</em>, <em>12</em>(1), 152. <a href="https://doi.org/10.3390/w12010152">https://doi.org/10.3390/w12010152</a></li> </ul>
Data from: Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling
<p>As stated in the Read Me file:</p> <p>These data and resources are associated with the manuscript:</p> <p><em>Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling</em>, Mersmann et. al., as submitted to biorXiv in July 2020.</p> <p>The raw imaging data relates to Figure 5, S1, S2 and Table S1. The images are fluorescent micrographs displaying immobilised adenovirus particles bound to a monoclonal antibody 9C12.</p> <p>Each experiment folder is numbered, as in Table S1, and appended with the mixing proportion (Fl), as defined in the manuscript. Within each folder there are 6 subfolders, representing samples incubated with different concentrations of 9C12 antibody.</p> <p>Each image is a 3 channel 1024x1024 tif. Channel 1 = 9C12 Alexa Fluor 647. Channel 2 = 9C12 Biotin + QDot655. Channel 3 = Adenovirus Alexa Fluor 488. Samples were illuminated in TIRF mode using a 100X objective, images were captured on a Hamamatsu OCRA Flash 4 sCMOS camera. Further details are available in the header of each file.</p> <p>The control samples are labelled with 100% 9C12 Alexa Fluor 647 or 100% 9C12 Biotin, as described in the manuscript.</p> <p>The data analysis script is an imageJ macro. It runs on the FIJI version of ImageJ with the NanoJ package installed (https://github.com/HenriquesLab). It outputs fluorescent measurements for each identified AdV particle. Note that the script rearranges the channel order such that Channel 1 = Adenovirus Alexa Fluor 488, Channel 2 = 9C12 Alexa Fluor 647, Channel 3 = 9C12 Biotin + QDot655. </p> <p>The channels require registration due to chromatic aberration, this is achieved using the Realign Channels function in NanoJ, appropriate translation masks are provided along with the script.</p> <p>Any question about the data or script should be addressed in Joe Grove (j.grove@ucl.ac.uk)</p>
Training data for "Apply modeling on population or community data to see effect of year, habitat or site on species abundance"
<p>Datasets for the "Apply modeling on population or community data to see effect of year, habitat or site on species abundance" Galaxy for ecology tutorial</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.