Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
648
datasets available to search
ShareScore release 0.9.0
Dataset results
648 results for “uncertainties”
Raw Data and Full Ensemble Output (1880-2020) for "A NASA GISTEMPv4 Observational Uncertainty Ensemble"
<p>Contains the Raw and Final data for "A NASA GISTEMPv4 Observational Uncertainty Ensemble" as accepted at JGR:Atmospheres (August 2024). </p> <p><code>FinalEnsembleOutput/</code> Contains the official GISTEMPv4 uncertainty ensemble from 1880-2020 with anomalies relative to a 1951-1980 climatology. The ensemble is organized into three subdirectories</p> <p><code>FinalEnsembleOutput/FullEnsemble</code> Contains a netCDF file [lon,lat,month] for each of the 200 members on a 2x2 grid.</p> <p><code>FinalEnsembleOutput/GriddedSummary</code> Contains netCDF files [lon,lat,month] of statistics summarizing the 200-member ensemble. The statistics contained are the ensemble mean, ensemble sd, quantiles, and sample size (can be less than 200 due to differences in the homogenization in data-sparse regions). The quantiles provided are (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.975).</p> <p><code>FinalEnsembleOutput/KeySeries</code> Contains 200-member ensembles of key, large scale time series at monthly resolution. Global, hemispheric, and zonal mean series are provided each for land, ocean, and combined (land and ocean) mean temeperature. <strong>Use this data when working with these series rather than calculating yourself from the full ensemble as the method for creating these series incoproates unertainty due to regions without coverage.</strong></p> <p><code>Raw/</code> Contains the raw, source data for the GISTEMP ensemble. This directory is not very large, but contains ~50k files as the GHCN product is distributed as an individual text file for every station in the record.</p> <p>Intermediate data products from the analysis can be found here: <a href="https://doi.org/10.5281/zenodo.13344579" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13344579</a></p>
Probabilistic Volcanic Ash Uncertainty
<h1>Probabilistic Volcanic Ash Uncertainty</h1> <p>Incorporating source parameter (MER and plume height) alongside meteorological variability in volcanic ash hazard dispersion forecasting. Contains code and supporting data submitted alongside manuscript "Incorporating Source Parameter and Meteorological Variability in the Generation of Probabilistic Volcanic Ash Hazard Forecasts" to Journal of Geophysical Research: Atmospheres. Requires The Met Office's <a href="https://www.metoffice.gov.uk/research/approach/modelling-systems/dispersion-model">Numerical Atmospheric-dispersion Modelling Environment</a> (NAME), which is available by licence from the UK Met Office.</p> <h2>Introduction</h2> <p>Airborne volcanic ash is hazardous for aircraft. To manage this risk, Volcanic Ash Advisory Centres (VAACs) provide forecasts of ash clouds following a volcanic eruption. These forecasts are created using dispersion models that predict the transport of ash based on eruption details and weather data. These sets of inputs have large uncertainties that can affect the accuracy of the forecasts. The paper this project is associated with presents a method for producing probabilistic forecasts that account for these uncertainties. Typically, weather uncertainties are handled by using multiple weather predictions, referred to as an ensemble. Dispersion outcomes depend on the eruption plume height and mass eruption rate (MER), which are related but have large associated uncertainties. Our method uses a statistical approach to incorporate these uncertainties into forecasts to allow for the calculation of probabilities of different ash concentration levels for aviation. It uses the same number of model runs as there are ensemble members, and does not require eruption details (plume height, MER, and emission profile) to be specified in advance, making it a computationally efficient approach as the bulk of computations can be done after a small number of initial model runs.</p> <h2>Contents</h2> <p>The zip file consists of four folders: analysis, notebooks, pvauncertainty, and scripts.</p> <h3>analysis</h3> <p>Contains two sub-folders:</p> <ul> <li>fig-scripts: scripts for post-processing of data and generation of figures for the submitted manuscript.</li> <li>data: post-processed output data of NAME simulations.</li> </ul> <h3>notebooks</h3> <p>The Jupyter notebooks get-started-pt1 and get-started-pt2 illustrate how the package can be used with NAME. Users must provide their own NAME input files to simulate volcanic ash dispersion; minimal non-working examples of code block segments that must be changed are given in scripts.</p> <h3>pvauncertainty</h3> <p>The Python package contains classes to set up volcanic ash simulations in NAME and evaluate resultant probabilistic quantities:</p> <ul> <li>Set up NAME inputs for a volcanic ash emission given a plume height observation, or range for the height: <ul> <li>Using deterministic or ensemble meteorology</li> <li>Provides a unit MER for later rescaling</li> <li>Given a plume height range and interval step size, initialises NAME with multiple interval emissions to be saved separately</li> <li>Sets NAME running on a SLURM environment</li> </ul> </li> <li>Evaluate probabilistic quantities of volcanic ash concentrations: <ul> <li>Conditional exceedance probabilities given ensemble member</li> <li>Conditional exceedance probabilities given plume height observation</li> <li>Percentiles of ash concentration given plume height observation</li> <li>Overall exceedance probabilities given plume height distribution (Gaussian distribution by default) by numerical integration (quadrature)</li> <li>Plotting of probabilistic quantities of volcanic ash concentrations</li> </ul> </li> </ul> <p>To install the package, navigate to its parent folder and execute "pip install -e .".</p> <h3>scripts</h3> <p>Contains scripts for setting up ensemble or deterministic NAME runs, given a csv file of plume height and MER values, and minimal example NAME input files.</p> <h2>License</h2> <p>This project is licensed under the BSD 3-Clause License.</p>
Experimental dataset: stationary images for digital image correlation uncertainty quantification
<div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>SUMMARY</strong> ---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>Stereo-DIC 5 MPx system was used to capture sets of stationary images for quantification of DIC uncertainties.</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>FOLDERS </strong>---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div><strong>Image sets:</strong> </div> <div> </div> <div><strong>Set 1: </strong>100 stationary images with cross polarisation to reduce effect of specular reflection. Test sample clamped in the clamps of a uniaxial tensile test bench.</div> <div><strong>Set 2: </strong>Same as set 1, but test sample unclamped at the bottom, displaced by 1 mm vertically. Meant to introduce rigid body motion into teh stationary images. </div> <div>For investigation of the impact of cross-polarisation: image gradients made similar as much as possible by adjusting exposure time and apetrture. </div> <div><strong>Set 3:</strong> With cross polarisation - 100 stationary images.</div> <div><strong>Set 4:</strong> Without cross polarisation - 100 stationary images.</div> <div> </div> <div>Images for stereo calibration:</div> <div> </div> <div><strong>Calib_sets_1_2: </strong>Calibration images for sets 1 and 2 mentioned above </div> <div><strong>Calib_sets_3:</strong> Calibration images for set 3 mentioned above </div> <div><strong>Calib_sets_4: </strong>Calibration images for set 4 mentioned above </div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>------------------------------------------------------------------------ <strong>SUPPORTING NINFORMATION </strong>--------------------------------------------------------------------</div> <div>---------------------------------------------------------------------------------------------------------------------------------------------------------------------- </div> <div> </div> <div>Image folder for each set contains an *.xaml file with image capture settings.</div> <div>Each calibration image folder contains a *.caldat file with intrinsic and extrinsic stereo camera parameters identified by MatchID 2024.2 DIC package.</div>
The Data for 'Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation'
<p>Produced synthetic kilonova spectra for 'Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation' using the Sedona radiative transfer code. Each model is an h5 file with the following groups:</p> <ul> <li>Lnu - Array of length # of timesteps by # of frequency bins that contains the spectral sequence of each kilonova model in cgs units (erg/s/Hz)</li> <li>click - Array of length # of timesteps by # of frequency bins for the number of Monte Carlo particles that make up Lnu</li> <li>mu - Center of polar angular bins (not useful since simulations are 1D)</li> <li>mu_edges - Edges of polar angular bins (not useful since simualtions are 1D)</li> <li>nu - Array of length # of frequency bins that are the central frequencies of a bin (Hz)</li> <li>nu_edges - Array of length # of frequency bins +1 that are the edges of each frequency bin (Hz)</li> <li>phi - Center of azimuthal angular bins (not useful since simulations are 1D)</li> <li>phi_edges - Edges of azimuthal angular bins (not useful since simulations are 1D)</li> <li>time - Times at which each spectrum was generated (Days)</li> <li>time_edges - Edges of time bins (Days)</li> </ul> <p>Each kilonova spectra file is named according to <atomic dataset>_<thermalization prescription>_KN_<lanthanide fraction>X_lan_<characteristic velocity>v_<mass>M_1D_spec.h5 where:</p> <ul> <li>atomic dataset is one of: HULLAC, ATOMIC, or Autostructure</li> <li>thermalization prescription is one of: local or global</li> <li>lanthanide fraction is the fraction of lanthanides in the ejecta by mass</li> <li>characteristic velocity is the kinetic velocity of the ejecta in units of the speed of light</li> <li>mass is the mass of the ejecta in units of solar masses</li> </ul>
Trained Surface Layer Models and Metrics for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
Open the record for dataset details and reuse information.
Code and data for Global sensitivity analysis can unveil the hidden universe of uncertainty in multiverse studies
Open the record for dataset details and reuse information.
Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling
<p>Description of Research Data and Code<br>This repository contains the data and code associated with the research paper: Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling, currently under review at Water Resources Research.</p> <p>Overview<br>To investigate the risk interdependencies arising from hydraulic interactions in cascading reservoir systems, we developed a risk propagation model using Bayesian networks (see file: Risk_propagation_model). Building on this model, we employed EMODPS to create a robust operational model for the reservoirs (see file: Robust_operation_model). Our goal was to minimize the joint risks of insufficient hydropower output and ecological water shortages while formulating robust operating policies to mitigate system performance degradation in the face of uncertain future runoffs (generated from our runoff simulations, see file: runoff simulation).</p> <p>Additionally, we analyzed the relationship between overall risk and risk at individual reservoir sites using a scenario discovery algorithm to pinpoint scenarios that reveal vulnerabilities (see file: python_project_scenariodiscovery).</p> <p>Acknowledgments<br>This project builds upon the code developed by Giuliani et al. (2016) M3O-Multi-Objective-Optimal-Operations (https://mxgiuliani00.github.io/M3O-Multi-Objective-Optimal-Operations/), Hadka and Reed (2013) BORG MOEA (http://borgmoea.org/), and Kevin Patrick Murphy et al. (2007) Bayesian Network Toolbox (https://www.ipcc.ch/report/ar6/wg1/#InteractiveAtlas). We are grateful to the original authors for their contributions.</p> <p>While we have made modifications and extensions to the original code, we have not altered its license. Users should refer to the original repositories for more details and ensure compliance with the terms of the original authors' licenses.</p>
Multi-Stage Integrated Transmission and Distribution Expansion Planning Under Uncertainties with Smart Investment Options - Case Study Assumptions
<p>A document containing the input assumptions for the case study published in the paper "Multi-Stage Integrated Transmission and Distribution Expansion Planning Under Uncertainties with Smart Investment Options".</p>
Data and code for "Compulsivity is linked to suboptimal choice variability but unaltered reinforcement learning under uncertainty"
<p>Data and code for "Compulsivity is linked to suboptimal choice variability but unaltered reinforcement learning under uncertainty". See https://github.com/jlexternal/RLVOLUNP_CIT_ana for directory structure. </p>
Arctique - ARtificial Colon Tissue Images for Qualitative Uncertainty Evaluation
<p>This dataset was introduced and published in the NeurIPS 2024 paper, <em>"Arctique: An Artificial Histopathological Dataset Unifying Realism and Controllability for Uncertainty Quantification"</em>. It includes various versions, in particular a version containing 50,000 training images and 1,000 test images, each paired with corresponding instance and semantic masks, as well as 400 additional variations across 50 selected test images to support research on uncertainty quantification.</p> <p><strong>Versions:</strong></p> <ul> <li><strong>Version v3</strong>: The version used for experimental results presented in the associated research paper. This dataset features improved realism and includes the core images and labels as in <strong>v2</strong>, supplemented with noise-augmented variations specifically used to evaluate algorithmic performance under challenging conditions. It comprises 1,500 synthetic images<br>without noise, 1,500 with added noise, and 1,500 depth mask images, along with a range of noisy variations.</li> <li><strong>Version v2</strong>: The full dataset, consisting of 50,000 training images and 1,000 test images, along with their associated instance and semantic masks. Additionally, this version includes 400 augmented variations for 50 selected test images.</li> <li><strong>Version v1</strong>: A small example subset of the dataset provided for review purposes, containing a limited number of images and annotations to allow preliminary exploration.</li> </ul> <p>Each version of the Arctique dataset is split into training and test sets and variations, each containing the following directories:</p> <p>The <strong>images </strong>directory contains all synthetically generated images stored as PNG files. Each image has a resolution of 512x512 pixels with RGB channels and is named "img_<ID>", where <ID> is a unique integer identifier for each image.</p> <p>The <strong>masks </strong>directory includes subdirectories containing various masks related to the images:</p> <ul> <li><em>cytoplasm</em>: Contains 2D semantic masks for the cell cytoplasm. Each mask corresponds to an image named "<ID>.tif", where "<ID>" is the identifier for that image. The mask file is named using the same identifier.</li> <li><em>instance_3d</em>: Contains a directory for each image, named "<ID>. Inside each directory, there is a 3D stack numpy file representing the instance IDs in a 3D volumetric array. Additionally, it includes a sequence of 2D instance segmentation masks, named "slice_<ID>_<slice_count>.png", each representing equidistant slices through the 3D volume along the depth axis.</li> <li><em>instance</em>: Contains 2D instance masks for the cell nuclei. Each mask corresponds to an image named "<ID>.tif", and the mask file is named with the same identifier.</li> <li><em>semantic</em>: Contains 2D semantic masks for the cell nuclei. Similar to the instance masks, each mask corresponds to an image named "<ID>.tif", with the mask file named using the same identifier.</li> </ul> <p>Note that all semantic masks appear as black images when viewed with a standard image viewer. This is because the cell type IDs, ranging from 1 to 5, are used as greyscale values, which appear dark in the images. The modelled cell types are:</p> <p>Cell Types</p> <table> <tbody> <tr> <td>1</td> <td>Epithelial Cells</td> <td>'EPI'</td> </tr> <tr> <td>2</td> <td>Plasma Cells</td> <td>'PLA'</td> </tr> <tr> <td>3</td> <td>Lymphocytes</td> <td>'LYM'</td> </tr> <tr> <td>4</td> <td>Eosinophils</td> <td>'EOS'</td> </tr> <tr> <td>5</td> <td>Fibroblasts</td> <td>'FIB'</td> </tr> </tbody> </table> <p> </p> <p>The <strong>metadata </strong>directory contains JSON metadata files named "metadata_<ID>" for each image. Each JSON file includes a list of Python dictionaries, one for each cell object visible in the image. Consider submission appendix F for a detailed explanation of each dictionary.</p> <p>The <strong>parameters </strong>directory contains JSON files named "parameters_<ID>", which detail the parameters used to generate each image. Each JSON file is a Python dictionary with all the parameter values necessary to reproduce the scene. Consider submission appendix F for a detailed explanation of each parameter.</p>
Oil Price Uncertainty and Bank Diversification: Empirical Evidence from 447 Banks in China
<p>This is the data set corresponding to the research paper "Oil Price Uncertainty and Bank Diversification: Empirical Evidence from 447 Banks in China".</p>
High quality figures of "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the figures for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations" in their original resolution, ensuring clarity the high-quality visual representations for readers.</p>
Data from: Impact of the spatial uncertainty of seed dispersal on tree colonization dynamics in a temperate forest
An aggregated distribution of dispersed seeds may influence the colonization process in tree communities via inflated spatial uncertainty. To evaluate this possibility, we studied 10 tree species in a temperate forest: one primarily barochorous, six anemochorous and two endozoochorous species. A statistical model was developed by combining an empirical seed dispersal kernel with a gamma distribution of seedfall density, with parameters that vary with distance. In the probability density, the fitted models showed that seeds of Fagaceae (primarily barochorous) and Betulaceae (anemochorous) were disseminated locally (i.e., within 60 m of a mother tree), whereas seeds of Acer (anemochorous) and endozoochorous species were transported farther. Greater fecundity compensated for the lower probability of seed dispersal over long distances for some species. Spatial uncertainty in seedfall density was much greater within 60 m of a mother tree than farther away, irrespective of dispersal mode, suggesting that seed dispersal is particularly aggregated in the vicinity of mother trees. Simulation results suggested that such seed dispersal patterns could lead to sites in the vicinity of a tree being occupied by other species that disperse seeds from far away. We speculate that this process could promote coexistence by making the colonization rates of the species more similar on average and equalizing species fitness in this temperate forest community.
Architectural Uncertainty Analysis for Access Control Scenarios in Industry 4.0 - Data Set
<p>This data set contains additional information to the master's thesis of Nicolas Boltz. Included are the implementation, tests, and model instances of sample scenarios.</p>
Data from: Uncertainty in geographic estimates of performance and fitness
1. Thermal performance curves (TPCs) have become key tools for predicting geographic distributions of performance by ectotherms. Such TPC-based predictions, however, may be sensitive to errors arising from diverse sources. 2. We analyzed potential errors that arise from common choices faced by biologists integrating TPCs with climate data by constructing case studies focusing on experimental sets of TPCs and simulating geographic patterns of mean performance. We first analyzed differences in geographic patterns of performance derived from two pairs of commonly used TPCs. Mean performance differed most (up to 30%) in regions with relatively constant mean temperatures similar to those at which the TPCs diverged the most. 3. We also analyzed the effects of thermal history by comparing geographic estimates derived from (1) a broad TPC based on short-term measurements of insect larvae (Manduca sexta) with a history of exposure to thermal variation versus (2) a narrow TPC based on long-term measurements of larvae held at constant temperatures. Estimated mean performance diverged by up to 40%, and differences were magnified in simulated future climates. 4. Finally, to quantify geographic error arising from statistical error in fitted TPCs, we propose and illustrate a bootstrapping technique for establishing 95% prediction intervals on mean performance at each location (pixel). 5. Collectively, our analyses indicate that error arising from several underappreciated sources can significantly affect the mean performance values derived from TPCs, and we suggest that the magnitudes of these errors should be estimated routinely in future studies.
Data for "Learning from mistakes - Assessing the performance and uncertainty in process-based models"
<p><strong>Data for the publication "Learning from mistakes - Assessing the performance and uncertainty in process-based models"</strong></p> <p>The corresponding python code can be found at <a href="http://github.com/MoritzFeigl/Learning-from-mistakes">github.com/MoritzFeigl/Learning-from-mistakes</a>.</p> <p>This dataset contains data of hydrological and meteorological observations of the Fortress Ski Area (Alberta, Canada) for August 8-26, 2019. It consists of input and output files for the HFLUX models calibration period (C) and the validation periods (V, V3). The input files containes additional data used in the learning from mistakes workflow. The meteorological data and part of the hydrological data were provided by John Pomeroy and the University of Saskatchewan’s Cold Water Laboratory.</p>
Data from the thesis diagnosis of waste, application possibilities and future demand for green coconut shells, with evaluation of information uncertainty
<p>Information on the quantities of green coconut shells produced in coastal regions is not well known, which can make it difficult to manage these frequently wasted materials, as well as the understanding that they can be included in a production process as a raw material, with various possibilities of use and income generation. Given this situation, the objective of this research is to develop a diagnosis of the potential use of green coconut shells, after water consumption, in public places in municipalities in the southern and southern coast of Bahia. It is also intended to identify the future demand of the potential possibility for the recovery of the bark, and provide subsidies that can be used to organize the use of this material. In this context, the method applied in this research consists of five steps: (i) literature review; (ii) diagnosis of bark situation in the study region; (iii) evaluation of the uncertainty of the estimated amount of shells; (iv) possibilities for applying green coconut shells and preparing the analysis of strengths, weaknesses, opportunities and threats (SWOT); and (v) obtaining future demand for the potential output for the region through quantitative techniques for predicting time series with uncertainty assessment. The main scientific contributions of this work consist, therefore, (a) in the diagnosis of the waste of green coconut shells in the region; (b) the possibilities of application of coconut shells; (c) in the demand forecasting method elaborated for time series without trends and with high variability; and (d) in assessing the uncertainty of the quantitative information obtained. Thus, the study developed can be used as a subsidy, either for replicating the research process in other types of waste allocated in any region, or for the adoption of public policies to manage these materials, or even for the encouragement and creation of an appropriate productive scenario for the management of green coconut shells in the study region, which contributes to the understanding of the potential for generating environmental, economic and social value from the use and processing of these wasted materials.</p>
Gaia per-transit RV uncertainty
Part of the one-dataum project
Hotspots of gross emissions from the land use sector: patterns, uncertainties, and leading emission sources for the period 2000–2005 in the tropics
<p>According to the latest report of the Intergovernmental Panel on Climate Change (IPCC), emissions must be cut by 41–72 % below 2010 levels by 2050 for a likely chance of containing the global mean temperature increase to 2 °C. The AFOLU sector (Agriculture, Forestry and Other Land Use) contributes roughly a quarter ( ∼  10–12 Pg CO<sub>2</sub>e yr<sup>−1</sup>) of the net anthropogenic GHG emissions mainly from deforestation, fire, wood harvesting, and agricultural emissions including croplands, paddy rice, and livestock. In spite of the importance of this sector, it is unclear where the regions with hotspots of AFOLU emissions are and how uncertain these emissions are. Here we present a novel, spatially comparable dataset containing annual mean estimates of gross AFOLU emissions (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O), associated uncertainties, and leading emission sources, in a spatially disaggregated manner (0.5°) for the tropics for the period 2000–2005. Our data highlight the following: (i) the existence of AFOLU emissions hotspots on all continents, with particular importance of evergreen rainforest deforestation in Central and South America, fire in dry forests in Africa, and both peatland emissions and agriculture in Asia; (ii) a predominant contribution of forests and CO<sub>2</sub> to the total AFOLU emissions (69 %) and to their uncertainties (98 %); (iii) higher gross fluxes from forests, which coincide with higher uncertainties, making agricultural hotspots appealing for effective mitigation action; and (iv) a lower contribution of non-CO<sub>2</sub> agricultural emissions to the total gross emissions (ca. 25 %), with livestock (15.5 %) and rice (7 %) leading the emissions. Gross AFOLU tropical emissions of 8.0 (5.5–12.2) were in the range of other databases (8.4 and 8.0 Pg CO<sub>2</sub>e yr<sup>−1</sup> in FAOSTAT and the Emissions Database for Global Atmospheric Research (EDGAR) respectively), but we offer a spatially detailed benchmark for monitoring progress in reducing emissions from the land sector in the tropics. The location of the AFOLU hotspots of emissions and data on their associated uncertainties will assist national policy makers, investors, and other decision-makers who seek to understand the mitigation potential of the AFOLU sector.</p>
Replication data for: Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty
<p>Simulated host-parasite communities in Llopis‐Belenguer, C., J. A. Balbuena, I. Blasco‐Costa, A. Karvonen, V. Sarabeev, and J. Jokela. 2022. Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty. Ecology</p> <ul> <li>Full communities</li> </ul> <p>01_full_communities.RDS</p> <ul> <li>Resampled communities affected by host sampling completeness. From 90% to 10% of host sampling completeness every 10% steps</li> </ul> <p>02_resampled_sampling_completeness_90.RDS, </p> <p>03_resampled_sampling_completeness_80.RDS, </p> <p>04_resampled_sampling_completeness_70.RDS, </p> <p>05_resampled_sampling_completeness_60.RDS, </p> <p>06_resampled_sampling_completeness_50.RDS, </p> <p>07_resampled_sampling_completeness_40.RDS, </p> <p>08_resampled_sampling_completeness_30.RDS, </p> <p>09_resampled_sampling_completeness_20.RDS, </p> <p>10_resampled_sampling_completeness_10.RDS</p> <ul> <li>Resampled communities affected by parasite taxonomic resolution at all levels of host sampling completeness. From 90% to 10% of parasite taxonomic resolution and from 100% to 10% of host sampling completeness every 10% steps</li> </ul> <p>11_resampled_taxonomic_resolution_90_sampling_completeness_100-10.RDS, </p> <p>12_resampled_taxonomic_resolution_80_sampling_completeness_100-10.RDS, </p> <p>13_resampled_taxonomic_resolution_70_sampling_completeness_100-10.RDS, </p> <p>14_resampled_taxonomic_resolution_60_sampling_completeness_100-10.RDS, </p> <p>15_resampled_taxonomic_resolution_50_sampling_completeness_100-10.RDS, </p> <p>16_resampled_taxonomic_resolution_40_sampling_completeness_100-10.RDS, </p> <p>17_resampled_taxonomic_resolution_30_sampling_completeness_100-10.RDS, </p> <p>18_resampled_taxonomic_resolution_20_sampling_completeness_100-10.RDS, </p> <p>19_resampled_taxonomic_resolution_10_sampling_completeness_100-10.RDS</p> <p> </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.