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13,117 results for “Maps”
Resolving the Interpretation of Magnetic Coercivity Components from Backfield Isothermal Remanence Curves Using Unmixing of Non-linear Preisach Maps: Application to Loess-Paleosol Sequences
<p>The data set includes:</p> <p>1. Non-linear Preisach measurements for Lunca and Costinești loess-paleosol sections</p> <p>2. IRM coercivity distributions from Lunca and Costinesti interpolated on a common sequence of fields</p> <p>3. Lunca granulometry data</p> <p>4. Median Grain size for Costineși section.</p> <p>5. Magnetic susceptibility data measured at Lunca section (Constantin et al., 2015)</p> <p>6. IRM acquisition curves derived from backfield IRM data through rescaling for Costinesti section</p> <p>7. Costinesti rock magnetic data (Necula et al., 2015)</p>
Global map of Local Climate Zones
<p>A global 100 m spatial resolution Local Climate Zone (LCZ) map, derived from multiple earth observation datasets and expert LCZ class labels.</p> <p>The LCZ map is based on the LCZ typology (Stewart and Oke, 2012) that distinguish urban surfaces accounting for their typical combination of micro-scale land-covers and associated physical properties. The LCZ scheme is distinguished from other land use / land cover schemes by its focus on urban and rural landscape types, which can be described by any of the 17 classes in the LCZ scheme.</p> <p>Out of the 17 LCZ classes, 10 reflect the 'built' environment, and each LCZ type is associated with generic numerical descriptions of key urban canopy parameters critical to model atmospheric responses to urbanisation. In addition, since LCZs were originally designed as a new framework for urban heat island studies (Stewart and Oke, 2012), they also contain a limited set (7) of 'natural' land-cover classes that can be used as 'control' or 'natural reference' areas. As these seven natural classes in the LCZ scheme can not capture the heterogeneity of the world’s existing natural ecosystems, we advise users - if required - to combine the built LCZ classes with any other land-cover product that provides a wider range of natural land-cover classes.</p> <p><em>Stewart ID, Oke TR. (2012). Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1</em></p>
High-resolution maps of rubber and rubber-related deforestation for Southeast Asia
<p>This dataset contains maps of rubber plantations in 2021, and maps of rubber-related deforestation between 1993-2016 for Southeast Asia. The rubber maps have a 10 m pixel size, and the deforestation maps have a 30 m pixel size. The dataset and the methods for generating it are described in Wang et al. 2023. High-resolution maps show that rubber causes substantial deforestation. <em>Nature</em>. <strong>Please note that an update of this dataset will follow in September 2025.</strong> </p>
Data for Linkage mapping of root shape traits associated with market class in two biparental carrot populations
<p> </p> <p>This repository contains essential data to support the findings presented in the forthcoming publication titled "Linkage Mapping of Root Shape Traits Associated with Market Class in Two Biparental Carrot Populations." It includes VCF files for two distinct carrot biparental populations, as well as R code for filtering, constructing linkage maps, and conducting QTL analysis. Furthermore, the repository hosts phenotypic data gathered from these two biparental populations during the years 2020 and 2021.</p> <p>Two carrot genetic maps, one for each population, have been made available alongside their respective phenotypic data.</p> <p>The provided R code contains absolute working directory paths that may not function as intended on your system. The primary purpose of sharing this code is to offer readers insight into the techniques employed in this study. You may need to adapt the directory paths to suit your specific setup. </p> <p>To assist readers in understanding the logical sequence of steps involved in our linkage mapping project, the R code scripts have been sequentially numbered from 0 to 10.</p> <p>For more info contact: vegaalfaro@wisc.edu.</p>
Predicted Surficial Blue Carbon Maps for Blackbird Creek and St. Jones River Tidal Salt Marshes using 2014-2023 Landsat-8 OLI records
These data tables reflect the predictions of a gradient boosted trees model for predicting soil organic matter (SOM) in the surficial layer of tidal marsh soils. The model was trained on soil core data related to its corresponding spectral characteristics from decadal Landsat-8 Operational Land Imager data (see associated publication Warner et al. "Leveraging a decade of Landsat-8 spectral records for mapping blue carbon storage in tidal salt marshes"). This is a modeled data product designed to illustrate spatial patterns of organic matter storage as predicted by satellite data.
Mapping EDI, NEON and DataONE units to the QUDT ontology, 2022
In the metadata of digital environmental datasets, automated processing is hindered by the wide variety of representations for unit that may be human-readable, but may not be unambiguous or machine-interpretable, (e.g., grams per square meter, gm/m2, g/m2, gm-2, g/m^2, g.m-2, g m-2 and gramPerMeterSquared). Matching disparate representations of the same unit into a single unit concept from an ontology assists with interpretation and reuse by providing a linkage to a complete unit definitions with label, description, dimensions. Datasets with shared units can be identified during searches, and are more suitable for automating analyses and potential transformation. This dataset contains data and code associated with a project to map units in ecological metadata collected between 2013 and 2022 by DataONE, the Environmental Data Initiative and the U.S. National Ecological Observatory Network to the QUDT ontology using successive string transformations. Data entities include a) raw metadata as received (355,057 unit instances) b) integrated raw data c) substitution tables for string transformations d) resulting lookup table for 896 distinct units matched to QUDT units e) associated R code used for QUDT matching plus a web service and R functions for adding annotation elements to Ecological Metadata Language metadata documents. Using these substitutions and code, 91% of unit instances in the raw metadata could be matched to QUDT. Data and results are discussed in “Porter JH, M O’Brien, M Frants, S Earl, M Martin, C Laney. (in review) Using a Units Ontology to Annotate Pre-Existing Metadata. Submitted to Scientific Data.
A Comparison of Recreational and Survey-Grade Side-Scan Sonar Systems in Mapping Reservoir Fish Habitat in 3 Southwest Ohio Reservoirs
Littoral zone aquatic habitat is thought to play an important driver of aquatic organism population dynamics, but historically has been difficult to obtain at the whole waterbody scale because it is costly and time-consuming to collect with traditional aquatic habitat sampling methods. Here we used side-scan sonar to quantification of habitat features over large areas using two levels of equipment: recreational (consumer-grade) and professional (survey-grade). Our goal was to compare performance of the different side-scan sonars by analyzing their ability to map shoreline habitat features (wood, vegetation, and substrate) in three southwest Ohio reservoirs that contain the range of habitat features of interest to fisheries biologists. We used a low-cost Lowrance Active Imaging 3-in-1 system (≈$2,000 USD) recreational sonar and an EdgeTech 6205 system (≈$150,000 USD) survey-grade sonar to collect imagery along the shoreline of three reservoirs in Ohio. Using imagery from each system, We manually delineated patches of submerged woody debris, standing timber, aquatic vegetation, and benthic substrate in GIS. We also compared the size of uniquely identifiable submerged wood from paired imagery to understand potential biases between the systems.
Stem maps in 4 1-hectare plots in the FASET and DIRT experiments at the University of Michigan Biological Station, Pellston, MI (2006-07)
Our primary objective is to provide an improved understanding of the biological and climatic controls over carbon (C) and energy cycles during and after a successional shift from a mature aspen to a young mixed confer/deciduous forest ecosystem that will be widely distributed across the upper Great Lakes region in coming decades. In Spring 2008, we implemented the Forest Accelerated Succession ExperimenT (FASET) by stem girdling all aspen and birch (gt;6,700 trees, ~35% canopy LAI) within a 39 ha area. A suite of ongoing ecological and meteorological measurements conducted in treatment and control stands before (2007) and after (2008 onwards) the succession treatment are used to quantify effects of climate, species composition, and canopy structure on the forest C cycle. We have established paired treatment and control plots, and surveyed a 25 m grid system within our treatment plots, begun operation of a eddy-covariance tower within the 33 ha treatment plot, conducted intercomparisons of carbon exchange and N allocation between treatment and control plots, remote sensed forest canopy structure, and begun or continued collaborative projects with investigators utilizing this project as a platform for further studies. Our overarching hypothesis is that forest NEP across much of the upper Great Lakes region will increase following transition from aspen dominated ecosystems to those of later-successional species with biologically and structurally more complex canopies. Specific hypotheses: a) Tree mortality will prompt a short-term reduction in NEP. A rapid recovery and stabilization of NEP above that of the control forest will be linked to the magnitude of N leaching losses and the pattern of redistribution of available N. Stands with more species and structurally diverse canopies and greater allocation of N to photosynthetic tissues will have higher NEP. b) Successional change will increase spatial variation in microclimate and nutrient distribution, both of which con
Forest metrics derived from the 2008 Lidar point clouds, includes canopy closure, percentile height, and stem mapping for the Andrews Experimental Forest.
There are three types of forest metrics within this database. They all are derived from the raw Lidar point clouds using the FUSION software. The three types are canopy closure, height metric, and stem mapping. The canopy closure and height metric grids cover a variety of canopy heights and grid cell sizes. 1. Canopy closure: This metric measures the canopy closure of a given horizontal cell above a given vertical threshold (height break). Canopy closure can inform many landscape models and provide insight on how much light will reach the forest floor. 2. Height Metric: This metric measures the height at which a given percent of the first return points are below. This analysis is done in a given grid cell size. Height metrics give various statistics of the elevation above ground for a given set of Lidar points. In forested landscapes, first return height metrics describe the forest canopy. 3.This stem map locates the approximate center of all trees in the HJ Andrews Research Forest greater than 10 meters. In addition to the stem location, a canopy radius is also provided. FUSION and TreeVaWA software programs were used to develop this data. Watershed Sciences, Inc. (WS) collected Light Detection and Ranging (LiDAR) data from HJ Andrews and the Willamette National Forest (NF) on August 10th and 11th 2008. Total area for this AOI is 17,705 acres. The total area of delivered LiDAR including 100 m buffer is 19,493 acres.
Baltimore Ecosystem Study: Stewardship Mapping And Assessment Project (STEW-MAP) survey results 2011 and 2019
Addressing the challenges of sustainable and equitable city management in the 21st century requires innovative solutions and integration from a range of dedicated actors. In order to form and fortify partnerships of multi-sectoral collaboration, expand effective governance, and build collective resiliency it is important to understand the network of existing stewardship organizations. The term ‘stewardship’ encompasses a spectrum of local agents dedicated to the evolving process of community care and restoration. Groups involved in stewardship across Baltimore are catalysts of change through a variety of conservation, management, monitoring, transformation, education, and advocacy activities for the local environment – many with common goals of joint resource management, distributive justice, and community power sharing. The “environment” here is intentionally broadly defined as land, air, water, energy and more. The Stewardship Mapping and Assessment Project (STEW-MAP) is a method of data collection and visualization that tracks the characteristics of organizations and their financial and informational flows across sectors and geographic boundaries. The survey includes questions about three facets of environmental stewardship groups: 1) organizational characteristics, 2) collaboration networks, and 3) stewardship “turfs” where each organization works. The data have been analyzed alongside landcover and demographic data and used in multi-city studies incorporating similar datasets across major urban areas of the U.S. Additional information about the growing network of cities conducting stewmap can be found here: https://www.nrs.fs.usda.gov/STEW-MAP/ Romolini, Michele; Grove, J. Morgan; Locke, Dexter H. 2013. Assessing and comparing relationships between urban environmental stewardship networks and land cover in Baltimore and Seattle. Landscape and Urban Planning. 120: 190-207. https://www.fs.usda.gov/research/treesearch/44985 Johnson, M., D. H. Locke, E. Svendsen, L
Hubbard Brook Experimental Forest: Hyperspectral Foliar N map and associated field data, 2012
A canopy nitrogen map was created for the Hubbard Brook Experimental Forest and watersheds using airborne imaging spectrometer data collected by SpecTIR LLC (Reno, NV) on August 7, 2012, and associated field data. Leaf samples collected in the field were analyzed for nitrogen concentration, scaled to plot (whole canopy) level, and related to airborne imaging spectrometer reflectance data using partial least squares regression modeling to derive spatially explicit estimates of canopy nitrogen concentration (mass-based) for the spatial extent of the airborne imagery.
Map of ecological sites and ecological states for pastures 1, 4, 14, and 15 on the Chihuahuan Desert Rangeland Research Center, New Mexico
This data package includes an ArcMap geodatabase for the Chihuahuan Desert Rangeland Research Center (CDRRC) pastures 1, 4, 14, and 15: one polygon feature class, one point feature class, associated attribute tables and metadata. The spatial data, CDRRC1_4_14_15_StateMap_v1.gdb.zip, represents the ecological sites and states on Pastures 1, 4, 14 and 15 on the Chihuahuan Desert Rangeland Research Center, and includes field traverse data. CDRRC1_4_14_15_StateMapMetadata.pdf and TraversePointsMetadata.pdf contain the geospatial metadata provided by ArcMap. CDRRC1_4_14_15_StateMap_v1.csv is the attribute table associated with the state map’s polygon feature class, and TraversePoints.xlsx is the attribute table associated with the traverse points feature class and includes a sheet containing detailed attribute metadata.
Map. Central Tibet show key locations
<p>Map. Central Tibet show key locations</p>
A method for generating coherent spatially explicit maps of seasonal palaeoclimates from site-based reconstructions
<p>Reconstruction of climate anomalies in southern Europe for the Last Glacial Maximum (LGM, ca 21,000 years ago), made by combining pollen based reconstructions (from Bartlein et al. 2011) and averaged outputs of LGM simulations from the 3rd round of the Palaeoclimate Model Intercomparison Project (PMIP, Braconnot et al. 2011), under a variational data assimilation technique. Reconstructions made using this technique are designed to be used for data-model comparison, specifically against the results of PMIP4. The dataset consists of 6 variables: moisture index (the ratio precipitation and equilibrium evapotranspiration), mean annual precipitation (mm), mean annual temperature (degrees C), mean temperature of the coldest month (degrees C), mean temperature of the warmest month (degrees C), growing degree days above 5 degrees C (day degrees C). The standard deviation of these variables is also given.</p>
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
Theia OSO Land Cover Map 2018
<p>Land cover map of France based on Sentinel-2 satellite images with iota² chain (https://framagit.org/iota2-project/iota2/)</p>
Map of 15 Ecoclimatic Arid and Semi-Arid zones in Africa (6°S-39°N)
<p>Ecoclimatic Arid and Semi-Arid zones in Africa (6°S-39°N) based were derived from a multiway analysis of the WorldClim database (1950-2000). See details in the paper:<em> Leibovici DG,<strong> </strong>Quillevere G and Desconnets J-C (2007) A method to Classify Ecoclimatic Arid and Semi-Arid Zones in Circum-Saharan Africa Using Dynamics of Multiple Indicators. <strong>IEEE Transaction in Geoscience And Remote Sensing, 45(12), 4000-4007. </strong></em>doi:<a href="https://doi.org/10.1109/TGRS.2007.908878">10.1109/TGRS.2007.908878</a></p> <p>A follow up paper is part of the zip archive for multiscale consideration (Leibovici, D. G., & Jackson, M. (2011). Multi-scale integration for spatio-temporal ecoregioning delineation. <strong><em>International Journal of Image and Data Fusion</em>, <em>2</em>(2), 105-119</strong>. doi:<a href="https://doi.org/10.1080/19479832.2010.542893">10.1080/19479832.2010.542893</a>)</p>
VFA T1 mapping | RTHawk (open) vs Siemens (commercial)
<p>The preliminary variable flip angle (VFA) T1 mapping data acquired using:<br> 1. Fully-open (<a href="https://bit.ly/qMRPullseq">https://bit.ly/qMRPullseq</a>) 3D spoiled gradient-echo based RTHawk application<br> 2. Siemens stock sequence 3D FLASH </p> <p>The analysis (fitting and comparison) can be executed online: <a href="http://bit.ly/qmr_vfat1">http://bit.ly/qmr_vfat1</a> </p> <p>GitHub repository for the code: <a href="https://github.com/agahkarakuzu/ismrm20">https://github.com/agahkarakuzu/ismrm20</a></p>
Fig. 14. Distribution map. 1 in New species and new records of terrestrial isopods (Crustacea, Isopoda, Oniscidea) of the families Philosciidae and Scleropactidae from Brazilian caves
Fig. 14. Distribution map. 1. Albosciajotajota Campos-Filho, Bichuette & Taiti sp. nov. 2. Androdeloscia akuanduba Campos-Filho, Cardoso & Taiti sp nov. 3. Atlantoscia inflata Campos-Filho & Araujo, 2015. 4. Benthana iporangensis Lima & Serejo, 1993. 5. B. longicornis Verhoeff, 1941. 6. B. olfersii (Brandt, 1833). 7. B. picta (Brandt, 1833). 8. B. taeniata Araujo & Buckup, 1994. 9. Metaprosekia igatuensis Campos-Filho, Fernandes & Bichuette sp. nov. 10. Paratlantoscia rubromarginata (Araujo & Leistikow, 1999). 11. Amazoniscus spica Campos-Filho, Aguiar & Taiti sp. nov. 12. Circoniscus bezzii Arcangeli, 1931. Light gray areas denote Brazilian conservation units. AL = Alagoas; BA = Bahia; CE = Ceará; DF = Distrito Federal; ES = Espírito Santo; GO = Goiás; MA = Maranhão; MG = Minas Gerais; MT = Mato Grosso; PA = Pará; PB = Paraíba; PE = Pernambuco; PI = Piauí; PR = Paraná; RJ = Rio de Janeiro; RN = Rio Grande do Norte; SE = Sergipe; SP = São Paulo; TO = Tocantins.
Maps 1–12 in New state records for Coniopterygidae (Neuroptera) with an updated list of species in North America north of Mexico
Maps 1–12. State-level distribution of Coniopterygidae spp. 1) Aleuropteryx arceuthobii. 2) Aleuropteryx arizonica. 3) Aleuropteryx cupressi. 4) Aleuropteryx dragoonica. 5) Aleuropteryx juniperi. 6) Aleuropteryx knowltoni. 7) Aleuropteryx longipennis. 8) Aleuropteryx maculipennis. 9) Aleuropteryx megacornis. 10) Aleuropteryx punctata. 11) Aleuropteryx simillima. 12) Aleuropteryx unicolor.
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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)
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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.