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5,506 results for “variability”
Certain rigid maximally mutable Laurent polynomials in three variables
<p>This dataset contains certain rigid maximally mutable Laurent polynomials (rigid MMLPs) in three variables. Rigid MMLPs are defined in reference [1].</p> <p>The Newton polytopes of these Laurent polynomials are three-dimensional canonical Fano polytopes. That is, they are three-dimensional convex polytopes with vertices that are primitive integer vectors and that contain exactly one lattice point, the origin, in their strict interior. See references [2] and [3].</p> <p>Although the rigid MMLPs specified in this dataset have 3-dimensional canonical Fano Newton polytope, this is by no means an exhaustive list of such Laurent polynomials. The dataset contains examples of rigid MMLPs that correspond under mirror symmetry to three-dimensional Q-Fano varieties of particulaly high estimated codimension: see reference [4].</p> <p>The file "rigid_MMLPs.txt" contains key:value records with keys and values as described below, separated by blank lines. Each key:value record determines a rigid MMLP, and there are 130 records in the file. An example record is:</p> <p>canonical3_id: 231730<br> coefficients: [1,1,1,1,1,1,1,1,1,1]<br> exponents: [[-1,-1,-1],[0,1,0],[0,1,1],[1,0,0],[1,0,1],[1,2,2],[2,1,2],[2,1,3],[3,3,5],[4,2,5]]<br> period: [1,0,0,12,24,0,540,2940,2520,33600,327600,693000,2795100,35315280,129909780,354666312,3816572760,20559258720,59957561664,435508321248,2969362219824]<br> ulid: 01G5CBH3F86NRYF0TJ8MYWM41H</p> <p>The keys and values are as follows, where f denotes the Laurent polynomial defined by the key:value record.</p> <p>canonical3_id: an integer, the ID of the Newton polytope of f in reference [3]<br> coefficients: a string of the form "[c1,c2,...,cN]" where c1, c2, ... are integers. These are the coefficients of f.<br> exponents: a string of the form "[[x1,y1,z1],[x2,y2,z2],...,[xN,yN,zN]]" where x1, y1, z1, ..., xN, yN, zN are integers. These are the exponents of f.<br> period: a string of the form "[d0,d1,...,d20]" where d0, d1, ..., d20 are non-negative integers that give the first 21 terms of the period sequence for f.<br> ulid: a string that uniquely identified this entry in the dataset</p> <p>The sequences defined by the keys "coefficients" and "exponents" are parallel to each other. The period sequence for f is defined, for example, in equations 1.2 and 1.3 of reference [1].</p> <p>References</p> <p>[1] Tom Coates, Alexander M. Kasprzyk, Giuseppe Pitton, and Ketil Tveiten. Maximally mutable Laurent polynomials. Proceedings of the Royal Society A 477, no. 2254:20210584, 2021.</p> <p>[2] Alexander M. Kasprzyk. Canonical toric Fano threefolds. Canadian Journal of Mathematics, 62(6):1293–1309, 2010.</p> <p>[3] Alexander M. Kasprzyk. The classification of toric canonical Fano 3-folds. Zenodo, https://doi.org/10.5281/zenodo.5866330, 2010.</p> <p>[4] Liana Heuberger. Q-Fano threefolds and Laurent inversion. Preprint, arXiv:2202.04184, 2022.</p>
Variability in the global ocean carbon sink from 1959-2020 by correcting models with observations (LDEO-HPD)
<p><strong>* The latest versions of this dataset are maintained and available here: <a href="https://zenodo.org/record/7901433">https://zenodo.org/record/7901433</a> *</strong></p> <p>The ocean reduces human impact on the climate by absorbing and sequestering CO2. From 1950s to the 1980s, observations of pCO2 and related ocean carbon variables were sparse and uncertain. Thus, global ocean biogeochemical models (GOBMs) have been the basis for quantifying the ocean carbon sink. The LDEO-Hybrid Physics Data product (LDEO-HPD) interpolates sparse surface ocean pCO2 data to global coverage by using GOBMs as priors, applying machine learning to estimate full-coverage corrections. The largest component of the GOBM corrections are climatological. This is consistent with recent findings of large seasonal discrepancies in GOBMs, but contrasts the long-held view that interannual variability is a major source of GOBM error. This supports extension of the LDEO-HPD pCO2 product back to 1959, using a climatology of model-observation misfits prior to 1982. Consistent with previous studies for 1980 onward, air-sea CO2 fluxes for 1959-2020 demonstrate response to atmospheric pCO2 growth and volcanic eruptions.</p> <p>This data is the final reconstruction of air-sea CO2 fluxes for 1959-2020 using the mean pCO2 from the corrected models. Both annual flux time series and spatially explicit fluxes are included. RIVERINE CARBON EFFLUX ADJUSTMENTS ARE NOT INCLUDED WITHIN THESE FILES. File metadata provides units.</p>
Data associated with the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data".
<p>This dataset refers to the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data". https://doi.org/10.5194/acp-2022-15.</p> <p> </p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Data for the Manuscripts of "Variability of Jakarta Rain-Rate Characteristics Associated with the Madden-Julian Oscillation and Topography" and "Subdaily Rain-Rate Properties in Western Java Analyzed Using C-Band Doppler Radar"
<p>This archive consists of the post-processed data of C-Band Doppler Radar (CDR) over Jakarta and surrounding regions for the studies of "Variability of Jakarta Rain-Rate Characteristics Associated with the Madden-Julian Oscillation and Topography" and "Subdaily Rain-Rate Properties in Western Java Analyzed Using C-Band Doppler Radar".</p> <p>The dataset is a gridded rainfall data derived from the local relationship of Z (reflectivity) from the CDR and rainfall (R) from stations. The derived rainfall data are in daily estimates from 2009 to 2012 with the format in NetCDF files.</p> <p>The CDR data were obtained from the projects “Hydrometeorological Array for Intraseasonal Variation-Monsoon Automonitoring (HARIMAU)” (JFY 2005-2009), and the Science Technology Research Partnership for Sustainable Development (SATREPS) “Maritime Continent Center of Excellence (MCCOE) (JFY 2009-2013) of the Japan Science and Technology Agency (JST)/Japan International Cooperation Agency(JICA) under a collaboration of the Agency for the Assessment and Application of Technology (BPPT)-Indonesia and Japan Agency for Marine-earth Science and Technology (JAMSTEC)-Japan.</p>
Variability and drivers of the Oxygen Minimum Zone in the Tropical Pacific during 1981-2020
<p>This dataset contains model outputs simulated by a basin-scale model (OGCM-DEEC V1.4). It is supplementary of the paper "Variability and drivers of the Oxygen Minimum Zone in the Tropical Pacific during 1981-2020". Please let me know if you need any more information.</p>
CCZ-Inequivalent quadratic vectorial Boolean bent functions in 8 variables
<p>This dataset contains representatives of CCZ-equivalence classes of quadratic vectorial Boolean bent functions in 8 variables.</p>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
Data from: Accounting for predator species identity reveals variable relationships between nest predation rate and habitat in a temperate forest songbird
<p><strong>Abstract</strong></p> <p>Nest predation is the primary cause of nest failure in most ground-nesting bird species. Investigations of relationships between nest predation rate and habitat usually pool different predator species. However, such relationships likely depend on the specific predator involved, partly because habitat requirements vary among predator species. Pooling may therefore impair our ability to identify conservation-relevant relationships between nest predation rate and habitat. We investigated predator-specific nest predation rates in the forest-dependent, ground-nesting wood warbler <em>Phylloscopus sibilatrix </em>in relation to forest area and forest edge complexity at two spatial scales, and to the composition of the adjacent habitat matrix. We used camera traps at 559 nests to identify nest predators in five study regions across Europe. When analysing predation data pooled across predator species, nest predation rate was positively related to forest area at the local scale (1,000 m around nest), and higher where proportion of grassland in the adjacent habitat matrix was high but arable land low. Analyses by each predator species revealed variable relationships between nest predation rates and habitat. At the local scale, nest predation by most predators was higher where forest area was large. At the landscape scale (10,000 m around nest), nest predation by buzzards <em>Buteo buteo</em> was high where forest area was small. Predation by pine martens Martes martes was high where edge complexity at the landscape scale was high. Predation by badgers <em>Meles meles </em>was high where the matrix had much grassland but little arable land. Our results suggest that relationships between nest predation rates and habitat can depend on the predator species involved and may differ from analyses disregarding predator identity. Predator-specific nest predation rates, and their relationships to habitat at different spatial scales, should be considered when assessing the impact of habitat change on avian nesting success.</p>
Global Soil Bioclimatic variables at 30 arc second resolution
<p>Soil temperature layers were calculated by adding monthly soil temperature offsets to monthly air-temperature maps from CHELSA (date range 1979-2013) (Karger et al. 2017, Sci Data). These soil temperature layers were then used to calculate annual means, temperature ranges, standard deviation, warmest and coldest months and quarters. Wettest and driest quarters were identified for each pixel based on CHELSA monthly values. A quarter is a period of three months (1/4 of the year).</p> <p>When using any of these layers, please cite: Lembrechts et al., Global maps of soil temperature (2021). Global Change Biology. DOI: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16060">10.1111/gcb.16060</a></p> <p>For each Soil Bioclim layer, two depth intervals are available: 0 - 5 cm and 5 - 15 cm.</p> <p>We have followed the generally accepted definitions of BIO 1 - BIO 11: </p> <ul> <li>SBIO1 = Annual Mean Temperature</li> <li>SBIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))</li> <li>SBIO3 = Isothermality (BIO2/BIO7) (×100)</li> <li>SBIO4 = Temperature Seasonality (standard deviation ×100)</li> <li>SBIO5 = Max Temperature of Warmest Month</li> <li>SBIO6 = Min Temperature of Coldest Month</li> <li>SBIO7 = Temperature Annual Range (BIO5-BIO6)</li> <li>SBIO8 = Mean Temperature of Wettest Quarter</li> <li>SBIO9 = Mean Temperature of Driest Quarter</li> <li>SBIO10 = Mean Temperature of Warmest Quarter</li> <li>SBIO11 = Mean Temperature of Coldest Quarter</li> </ul> <p>These layers are also publicly available as Google Earth Engine assets. These are acessible via:</p> <ul> <li><a href="https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v1_0_5cm">https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm</a></li> <li><a href="https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v1_5_15cm">https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v2_5_15cm</a></li> </ul> <p>Also available are monthly maps of soil temperature, for two depth intervals (0-5 cm and 5-15 cm). For example, the soil temperature map for month 1 (January) at 0-5 cm is named soilT_1_0_5cm.tif'. </p> <p>To mask pixels by the proportion of extrapolation, the files 'PCA_int_ext_0_5cm.tif' and 'PCA_int_ext_5_15cm.tif' can be used. </p>
Variable Optical True Time Delay Line Breaking Bandwidth-Delay Constraints - Dataset
<p>Dataset for the Letter "Variable Optical True Time Delay Line Breaking Bandwidth-Delay Constraints", in Optics Letters</p>
Synthetic business population containing simulated business variables
<p>This dataset contain simulated data for a fully synthetic business population. The dataset contains 900,000 records, each of which represents a simulated business. It resembles the real-world population of employing businesses in Australia in terms of the distribution of businesses across size categories, industry classes and geographic regions (state). The data for the population has been generated using a combination of published survey outputs available from the Australian Bureau of Statistics (ABS) website, and employee tax data and survey data sourced from the Business Longitudinal Analysis Data Environment (BLADE) in the ABS DataLab.</p>
Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models (Ocean and Climate variables from Simple Climate Models)
<p>This dataset provide global ocean and climate variables from 8 Simple Climate Models used in the study "Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models"</p>
Reproduction package for the paper "Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stae1315">"Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring" by Sutlieff et al. (2024)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
Mean NDVI Values (1982-2018) and Future Predictions Using CHELSA Bioclim Variables for Türkiye
<p>This dataset contains mean Normalized Difference Vegetation Index (NDVI) values from 1982 to 2018 and their future predictions based on CHELSA bioclimatic variables, specifically for the region of Türkiye. The data is provided in .asc format and includes both historical and projected NDVI values under different climate scenarios.</p> <h4>Contents:</h4> <ul> <li><strong>Historical NDVI Data (1982-2018)</strong>: Mean NDVI values derived from remote sensing data.</li> <li><strong>Future NDVI Predictions</strong>: NDVI projections for the periods 2011-2040, 2041-2070, and 2071-2100 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5.</li> </ul> <h4>Methodology:</h4> <ol> <li><strong>Model Training</strong>: <ul> <li>A Random Forest Regressor was used to model the relationship between NDVI and the selected bioclim variables.</li> <li>The model achieved an R² of 0.9341, Mean Absolute Error of 0.0275, and Root Mean Squared Error of 0.0499.</li> </ul> </li> <li><strong>Future Predictions</strong>: <ul> <li>Future NDVI values were predicted using the trained model and future CHELSA bioclim projections.</li> <li>Predictions were made for three future periods (2011-2040, 2041-2070, 2071-2100) under three SSPs (SSP1-2.6, SSP3-7.0, SSP5-8.5).</li> </ul> </li> </ol> <h4>Data Specifications:</h4> <ul> <li><strong>Extent</strong>: Covers the geographical area of Türkiye and adjacents.</li> </ul> <h4>Sources:</h4> <ul> <li><strong>NDVI Data</strong>: <ul> <li>Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., & Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sensing, 14(15), 3639.</li> </ul> </li> <li><strong>CHELSA Bioclim Data</strong>: <ul> <li>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017). Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122" target="_new" rel="noreferrer">https://doi.org/10.1038/sdata.2017.122</a></li> <li>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4" target="_new" rel="noreferrer">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a></li> </ul> </li> </ul>
Seasonal Carbonate Chemistry Variability in Marine Surface Waters of the Pacific Northwest. Data Archive.
<p>This archive includes two .nc files (NetCDF format) containing observational data (discrete and mooring) from marine surface waters of the Pacific Northwest that have not yet been submitted to a long-term data repository. These data contributed to the development of seasonal cycle data products described in the manuscript by Fassbender et al. A metadata file is provided for the discrete data subset (upper 10 m of discrete observational data); however, the complete cruise datasets and metadata will be submitted for archival in the National Centers for Environmental Information’s (NCEI) Ocean Carbon and Acidification Data repository (<a href="https://www.nodc.noaa.gov/oceanacidification/">https://www.nodc.noaa.gov/oceanacidification/</a>). Data subsets are provided here for accelerated public access. Data users are encouraged to download the complete datasets from NCEI once they are available (<a href="https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html">https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html</a>). Metadata for the University of Washington Oceanic Remote Chemical/Optical Analyzer (ORCA) mooring observations used by Fassbender et al., including the temperature and salinity data from the Dabob Bay and Twanoh moorings, are not provided here. Quality control protocols applied to the ORCA mooring data are outlined in the Quality Assurance Project Plan (<a href="http://nwem.ocean.washington.edu/ORCA_QAPP.pdf">http://nwem.ocean.washington.edu/ORCA_QAPP.pdf</a>; Newton and Devol, 2012).</p>
Characterizing the gamma-ray variability of the brightest flat spectrum radio quasars observed with the Fermi LAT
<p>The FITS files contain light curves (prefix "lc"), spectral energy distributions (prefix "sed") and best-fit parameters for the whole region of interest (prefix "bestfit_roi") for the gamma-ray analyses of the six brightest flat spectrum radio quasars observed over 9.5 years with the Fermi Large Area Telescope (LAT). The file names also indicate the considered binning (weekly, daily, orbit, sub-orbital) and the considered time range in MJD.<br> The data products have been generated using the fermipy software, please see the documentation for further explanations of the columns provided in these files: <a href="https://fermipy.readthedocs.io/en/latest/">https://fermipy.readthedocs.io/en/latest/</a></p> <p>The analysis catalog are described in detail in the accompanying paper, which is submitted for publication in the Astrophysical Journal. The preprint of the submitted manuscript can be found here: <a href="https://arxiv.org/abs/1902.02291">https://arxiv.org/abs/1902.02291</a></p> <p>Additionally, the code for high level analysis including the light curves and the gamma-ray absorption in the broad line region can be found on github: <a href="https://github.com/me-manu/GaRLiC">https://github.com/me-manu/GaRLiC </a>and <a href="https://github.com/me-manu/blrabsorption">https://github.com/me-manu/blrabsorption</a></p> <p> </p>
Corresponding spreadsheet to the Paper 'Variability in the assessment of childcare in 30 European countries'
<p>The spreadsheet provides the list of indicators reported by the national experts to assess the quality of child care in the relevant countries along with those gathered from official documents provided by the experts. It has been adopted to the Paper 'Variability in the assessment of childcare in 30 European countries'. </p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</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.