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24 results for “environmental maps”
Environmental Materiality Map:MSCI
<p>The dataset was collected from <a href="https://www.msci.com/our-solutions/esg-investing/esg-industry-materiality-map">MSCI Industry Materiality Map: </a> "MSCI ESG Ratings assess the resilience of companies to long-term, financially relevant environmental, social, and governance (ESG) risks. Our ESG Industry Materiality Map is a representation of the current ESG Key Issues and their contribution to companies' ESG Ratings. This map is part of our ESG Ratings transparency initiatives, through which we have made ESG Ratings of <a href="https://www.msci.com/esg-ratings">companies</a> and <a href="https://www.msci.com/esg-fund-ratings">funds</a> accessible to the public." - MSCI (2023)</p>
Screened literature for mapping review of environmental problem shifting (EPS)
<p>Final literature sample used in manuscript. (506 publications). </p><p>Version 2 after revision. </p><p>Columns from the Scopus API:</p><ul><li>DOI, ISSN, ISBN</li><li>Subjabbr: Abbreviation of Scopus subject area</li><li>Publication year</li><li>Pubname: The name of the journal</li><li>Authkeywords: The article keywords</li><li>Title: The document title</li></ul><p> </p><p>Columns from our classification:</p><ul><li>Articletype: Classification into empirical, review and conceptual-theoretical.</li><li>Documenttype: The type of publication: journal article, book chapter, reports.</li><li>Counter: Marks the 13 studies that find no shifting or positive environmental impacts only.</li><li>Miti measures: The type of mitigation option.</li><li>Split measures: The subtype of mitigation. Only coded for <i>CDR</i>; <i>Solar, hydro and wind power</i> and <i>Other</i>.</li><li>Methods: The method(s) used in empirical papers</li><li>Impacts: The environmental impact.</li><li>Split impacts: The environmental impact specified for subtypes of mitigation.</li></ul>
Hyperspectral environmental illumination maps for outdoor and indoor scenes
<p>This repository contains a dataset of hyperspectral illumination maps collected from 6 outdoor and 4 indoor scenes.</p> <p> </p> <p>If you use this dataset in your research, please cite:</p> <p> </p> <p>Takuma Morimoto, João M. M. Linhares, Sérgio M. C. Nascimento, and Hannah E. Smithson, “How many surfaces can you distinguish by color? Real environmental lighting increases discriminability of surface colors,” Optics Express (in press).</p> <p> </p> <p>Technical details about data acquisition are described in:</p> <p> </p> <p>Takuma Morimoto, Sho Kishigami, João M.M. Linhares, Sérgio M.C. Nascimento, and Hannah E. Smithson, “Hyperspectral environmental illumination maps: characterizing directional spectral variation in natural environments,” Optics Express, 27, 22, 32277 - 32293. (2019). <a href="https://doi.org/10.1364/OE.27.032277">https://doi.org/10.1364/OE.27.032277</a></p> <p> </p> <p>Each file includes the following formats:</p> <p> </p> <ol> <li><strong>png</strong>: RGB image for visualization.<br><br></li> <li><strong>mat</strong>: Hyperspectral image with wavelengths from 400 nm to 700 nm in 10 nm steps. Each pixel value represents spectral radiance in W m−2 sr−1 nm−1. The image and wavelength range are stored in the variables ‘radiance’ and ‘wls’, respectively.</li> </ol> <p>The images have an average spatial resolution of 1019 (height) × 2035 (width) across 10 scenes.</p> <p>File names follow the format X_sceneY, where X is the scene type (outdoor or indoor) and Y is the scene number.</p> <p> </p> <p>For Python users, the mat file can be loaded using e.g. scipy.io.loadmat. More information: <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html</a></p> <p> </p> <p>Note: To use for hyperspectral renderings (e.g., Mitsuba), convert the hyperspectral image to the OpenEXR format.</p>
Data for "Microstructural mapping of A. islandica shells reveals environmental and physiological controls on biomineral size"
<p>This data contains the image segmentation workflow, data processing procedure and all data generated for the publication "Microstructural mapping of A. islandica shells reveals environmental and physiological controls on biomineral size" currently under review.</p>
Results of the expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps
<p>This is the repository for the results of the 'expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps'.</p> <p>Note: check the most recent version in the sidebar</p> <table> <tbody> <tr> <td>Current version</td> <td>v.0.2</td> </tr> <tr> <td>Date</td> <td>2024/01/10</td> </tr> <tr> <td>Respondants</td> <td>30</td> </tr> </tbody> </table> <p><strong>Available files:</strong></p> <table> <tbody> <tr> <td>File</td> <td>Type</td> <td>Description</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_public.csv">responses_v01_public.csv</a></td> <td>CSV table</td> <td>Survey raw results (anonymous)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_stats.csv">responses_v01_stats.csv</a></td> <td>CSV table</td> <td>Questions statistics</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_mean_sd.jpg">responses_v01_mean_sd.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (mean and standard deviation)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_bands.jpg">responses_v01_bands.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (uncertainty bands)</td> </tr> </tbody> </table> <p>The column descriptions in the statistical table are as follows:</p> <p>Prefixes:</p> <ul> <li>HABITAT: habitat suitability score</li> <li>WEIGHT: Threat weight</li> <li>MAX_DIST: Maximum distance of negative influence (impact)</li> </ul> <p>Suffixes:</p> <ul> <li>mean: Average</li> <li>std: Standard deviation</li> <li>min: Minimum value</li> <li>p05: 5th percentile</li> <li>p25: 25th percentile</li> <li>p50: 50th percentile (median)</li> <li>p75: 75th percentile</li> <li>p95: 95th percentile</li> <li>max: Maximum value</li> </ul> <p>These prefixes and suffixes describe various statistical measures used to analyze the environmental modeling data.</p>
Fig. 1. Map showing sites where P in Wing shape is influenced by environmental variability in Polietina orbitalis (Stein) (Diptera: Muscidae)
Fig. 1. Map showing sites where P. orbitalis populations were collected: 1. Mbaracayú (25◦17Ɩ S, 54◦49Ɩ W) in Paraguay and 2. Fênix (23◦54Ɩ S, 51◦58Ɩ W), 3. Jundiaí do Sul (23◦26Ɩ S, 50◦14Ɩ W), 4. Guarapuava (25◦23Ɩ S, 51◦ 27Ɩ W), 5. Ponta Grossa (25◦ 05Ɩ S, 50◦ 09Ɩ W) and 6. Colombo (25◦17Ɩ S, 49◦13Ɩ W) in Brazil.
Environmental map data (OS1-128)
<p>Environmental maps associated with https://zenodo.org/deposit/7233945 .</p> <p>Maps are provide in two formats (PLY and PCD).</p>
Environmental Localization Mapping and Guidance for Visual Prosthesis Users
ClinicalTrials.gov study NCT04359108. IPD Sharing: NO. Countries: 1. Publications: 1.
Indigenous Peoples and environmental research and monitoring within the Laurentian Great Lakes Basin: A systematic map
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Map of the proportion of threatened endemic species per country in relation with environmental and socioeconomic drivers
<p>This dataset is a shapefile representing the proportion of threatened endemic species (both plants and animals) in 247 countries along with associated environmental and socioeconomic drivers. The geographic coordinate system is World Geodetic System 1984 (EPSG: 4326).</p>
Mapping differences in mammalian distributions and diversity using environmental DNA from rivers
<p>Finding more efficient ways to monitor, and estimate the diversity of, mammalian communities is a major step towards their management and conservation. Environmental DNA (eDNA) from river water has recently been shown to be a viable method for biomonitoring mammalian communities. Yet, most of the studies to date have focused on the potential for eDNA to detect individual species, with little focus on describing patterns of community diversity and structure. In this study, we focus on the sampling effort required to reliably map the diversity and distribution of semi-aquatic and terrestrial mammals and allow inferences of community structure surrounding rivers.<br> <br> Location: Southeastern England<br> <br> Methods: We used eDNA metabarcoding on water samples collected along two rivers and a beaver enclosure over two days, targeting terrestrial and semi-aquatic mammals. Mammalian community diversity and composition was assessed based on species richness and β-diversity. Differences between river communities were calculated and partitioned into nestedness and turnover, and the sampling effort required to rapidly detect semi-aquatic and terrestrial species was evaluated based on species accumulation curves and occupancy modelling.<br> <br> Results: eDNA metabarcoding efficiently detected 25 wild mammal species from five orders in two days of sampling, representing the vast majority (82%) of the species expected in the area. The required sampling effort varied between orders, with common species (generally rodents, deer and lagomorph species) more readily detected, with carnivores detected less frequently. Measures of species richness differed between rivers (both overall and within each mammalian order) and patterns of β-diversity revealed the importance of species replacement in sites within each river, against a pattern of species loss between the two rivers.<br> <br> Main conclusions: eDNA metabarcoding demonstrated its capability to rapidly detect mammal species, allowing inferences of community composition that will better inform future sampling strategies for this Class. Importantly, this study highlights the potential use of eDNA data for investigating mammalian community dynamics over different spatial scales.</p>
Mapping environmental injustices within the U.S. prison system: a nationwide dataset
<p>This open-access geospatial dataset (downloadable in csv or shapefile format) contains a total of 11 environmental indicators calculated for 1865 U.S. prisons. This consists of all active state- and federally-operated prisons according to the Homeland Infrastructure Foundation-Level Data (HIFLD), last updated June 2022. This dataset includes both raw values and percentiles for each indicator. Percentiles denote a way to rank prisons among each other, where the number represents the percentage of prisons that are equal to or have a lower ranking than that prison. Higher percentile values indicate higher vulnerability to that specific environmental burden compared to all the other prisons. Full descriptions of how each indicator was calculated and the datasets used can be found here: <a href="http://https://github.com/GeospatialCentroid/NASA-prison-EJ/blob/main/doc/indicator_metadata.md.">https://github.com/GeospatialCentroid/NASA-prison-EJ/blob/main/doc/indicator_metadata.md.</a></p> <p>From these raw indicator values and percentiles, we also developed three individual component scores to summarize similar indicators, and to then create a single vulnerability index (methods based on other EJ screening tools such as Colorado Enviroscreen, CalEnviroScreen and EPA’s EJ Screen). The three component scores include climate vulnerability, environmental exposures and environmental effects. Climate vulnerability factors reflect climate change risks that have been associated with health impacts and includes flood risk, wildfire risk, heat exposure and canopy cover indicators. Environmental exposures reflect variables of different types of pollution people may come into contact with (but not a real-time exposure to pollution) and includes ozone, particulate matter (PM 2.5), traffic proximity and pesticide use. Environmental effects indicators are based on the proximity of toxic chemical facilities and includes proximity to risk management plan (RMP) facilities, National Priority List (NPL)/Superfund facilities, and hazardous waste facilities. Component scores were calculated by taking the geometric mean of the indicator percentiles. Using the geometric mean was most appropriate for our dataset since many values may be related (e.g., canopy cover and temperature are known to be correlated).</p> <p>To calculate a final, standardized vulnerability score to compare overall environmental burdens at prisons across the U.S., we took the average of each component score and then converted those values to a percentile rank. While this index only compares environmental burdens among prisons and is not comparable to non-prison sites/communities, it will be able to heighten awareness of prisons most vulnerable to negative environmental impacts at county, state and national scales. As an open-access dataset it also provides new opportunities for other researchers, journalists, activists, government officials and others to further analyze the data for their needs and make comparisons between prisons and other communities. This is made even easier as we produced the methodology for this project as an open-source code base so that others can apply the code to calculate individual indicators for any spatial boundaries of interest. The codebase can be found on GitHub (<a href="https://github.com/GeospatialCentroid/NASA-prison-EJ">https://github.com/GeospatialCentroid/NASA-prison-EJ</a>) and is also published via Zenodo (<a href="https://zenodo.org/record/8306856">https://zenodo.org/record/8306856</a>).</p> <p> </p>
Data from: Ecological genomics meets community-level modeling of biodiversity: mapping the genomic landscape of current and future environmental adaptation
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Data from: Vulnerability mapping as a tool to manage the environmental impacts of oil and gas extraction
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Mapping differences in mammalian distributions and diversity using environmental DNA from rivers
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Map of the proportion of threatened endemic species per country in relation with environmental and socioeconomic drivers
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Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence
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Facilitating circularity of end-of-life photovoltaic in China by mid-century with environmental benefits and costs informed by a high-resolution waste map
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Environmental map layers for Los Angeles environmental assessment project
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Mapping multi-layered regulation in response to environmental stress
GEO Series GSE113171. Homo sapiens. 72 samples. Type: Expression profiling by high throughput sequencing; Other.
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.