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1,572 results for “GRanD”
GRIME AI Water Segmentation Model for the USGS Platte River near Grand Island, NE, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the Platte River, near Grand Island, NE, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NE_Platte_River_near_Grand_Island for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process. This project w
GRIME AI Water Segmentation Model for the USGS Monitoring Site Chippewa River at Grand Ave at Eau Claire, WI, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site Chippewa River at Grand Ave at Eau Claire, WI, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_Chippewa_River_at_Grand_Ave_at_Eau_Claire for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically gen
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NM_Rio_Grande_below_Elephant_Butte_Dam for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated du
An fMRI dataset in response to "The Grand Budapest Hotel", a socially-rich, naturalistic movie
Open the record for dataset details and reuse information.
Middle Rio Grande New Mexico, Bernalillo 550 Bridge, Water Quality Daily Means 2010-2018
This data package includes daily mean data for streamflow, turbidity, light, and gross primary production for a site on the Middle Rio Grande located at the Bernalillo 550 Bridge in Bernalillo New Mexico, USA, from 2010-01-01 to 2018-12-31.
Leaf area index and above ground biomass for Juncus roemerianus in the Grand Bay National Estuarine Research Reserve from 2015 to 2019
Leaf area index (LAI) and above ground biomass were measured in 16 permanent Juncus roemerianus vegetation plots located in the Grand Bay National Estuarine Research Reserve in Mississippi. Data were collected from 2015 to 2019. LAI were measured using a handheld ceptometer. Above ground biomass for each plot were collected from core samples.
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the Grand Bay, Mississippi flux tower site from March 2018 to January 2019
Eddy covariance (EC) CO2 fluxes from March 2018 to January 2019 collected over a Juncus roemerianus marsh located in the Grand Bay National Estuarine Research Reserve (NERR) in Mississippi. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height within the marsh.
Extracted patterns about transport from the French Great National Debate (Grand Débat National)
<p>This data set is composed by 5 geojson files, that can be used to generate maps of mainland France :</p> <ul> <li>motifs_all.geojson : pattern about transport extracted from contributions of the French Great National Debate (Grand Débat National). Original dataset : https://granddebat.fr/pages/donnees-ouvertes</li> <li>bikeway_fr.geojson and railroad_fr.geojson : cycleways and railways of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</li> <li>trainstations.geojson : train stations and halts of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</li> <li>au2010_carto.geojson : categorized urban areas of mainland France. Original dataset : https://www.insee.fr/fr/information/2115011</li> <li>communesimportantes.geojson : the main cities of mainland France</li> </ul> <p>The data set is in French.</p> <p><em>Ce jeu de données est composé de 5 fichiers geojson qui peuvent être utilisés pour générer des cartes en France métropolitaine :</em></p> <ul> <li><em>motifs_all.geojson : motifs à propos du transport extraient des contributions en ligne au Grand Débat National. Jeu de données d'origine : https://granddebat.fr/pages/donnees-ouvertes</em></li> <li><em>bikeway_fr.geojson and railroad_fr.geojson : pistes cyclables et voies ferrées en France métropolitaine, venant d'Open Street Map. Jeu de données d'origine : https://download.geofabrik.de/europe/france.html</em></li> <li><em>trainstations.geojson : gares et petites gares en France métropolitaine, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</em></li> <li><em>au2010_carto.geojson : aires urbaines catégorisées en France métropolitaine, définies par l'INSEE. Jeu de données d'origine : https://www.insee.fr/fr/information/2115011</em></li> <li><em>communesimportantes.geojson : principales villes de France métropolitaine</em></li> </ul>
Bridge2AI Grand Challenge AI-Readiness Evaluation Data Year 2 of 4
<p>This excel workbook and set of radar plots contains current and projected AI-readiness evaluation datasets of four NIH Bridge2AI Program Grand Challenges in Functional Genomics, Clinical Care Informatics, Precision Public Health, and Return to Health (Salutogenesis). These evaluations were collected in late 2024, at the conclusion of Year 2 of the 4-year Bridge2AI program, by Grand Challenge (GC) representatives on the Bridge2AI Standards Working Group, in consultation with their GC leadership team, and will be updated in subsequent years and the program progresses. They assess biomedical AI readiness of existing collected data only. </p>
Exemple of a valorization network based on a 10% collection rate from potential biowaste in the Grand Lyon territory (France).
<p>This study, conducted in the frame of the H2020 DECISIVE project, aims at developing a method to design a decentralized and small-scale AD (mAD) network in urban and peri-urban areas. A mixed integer linear program (MILP) was set up based on the proposed system. It aims at minimizing the impacts of the biowaste and the digestate transportation by minimizing their payload-distances while taking into account notably the technical constraints of the newly developed micro-AD. A Geographic Information System (GIS) based methodology was developed to feed the MILP model with very fine-scale data required to optimized a proximity treatment system. The method allows to locate and estimate the biowaste generation and to locate the digestate outlets, the agricultural areas, and to estimate the maximal amount of digestate usable. The candidate sites for mAD are identified with a GIS multi-criteria analysis that includes the environmental regulations, some urban planning rules and the site accessibility and heat outlet valorization. The method developed is successfully applied in the territory of The Grand Lyon Metropole (534 km²), located in France. The MILP model succeeds at providing a solution even with the very large problem studied (≥10<sup>8</sup> possible combination). Different scenarios can be easily tested to meet the potential needs of the stakeholder: the quantity of biowaste to treat, the type of sources to target, the synergy with the current treatment solution, etc.</p> <p>The data are provided through the open, non-proprietary GeoPackage files (GPKG) commonly recognized in GIS tools. A complementary xml file describe the metadata in compliance with the Inspire directive. A complementary pdf file describe the fields of the datasets.</p> <p> </p>
Grand Mesa 2017-02-01 snow depth estimate
<p>Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series of very-high-resolution commercial stereo imagery. More formal releases of data products will be available in the coming years.<br> <br> Analysis of this dataset is presented in the following publication:</p> <ul> <li>McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H-P., Painter, T., Molotch, N., Elder, K., Hiemstra, C., Brucker, L., (2019), Spatially Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's 2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite Observations, Water Resources Research, 55 (19), 10026-10036, doi:<a href="https://doi.org/10.1029/2019WR024907">10.1029/2019WR024907</a>.</li> </ul> <p>If you use these data, please cite the above publication.</p>
Rainfall at El Verde Field Station, Rio Grande, Puerto Rico since 1975
Rainfall at El Verde Field Station is measured manually using a rain collector placed on the station roof. The collector is mostly open, but some degree of interference from tall trees around the station is possible. A technician measures rainfall daily during workdays. For weekends and holidays, the amount of precipitation is measured on the next working day and divided equally among the days since the last reading. As of July 1st, 2022 we stopped the practice of redistributing rainfall totals after gaps in rainfall collection. Previously for example, If on a Monday rainfall of 15 millimeters was collected and rainfall wasn't collected on Saturday or Sunday but had been on Friday then Monday, Saturday, and Sunday would each have 5 millimeters. Since July 1st, 2022 rainfall is reported as the totals actually collected on the dates they are collected. Rainfall has been measured at the El Verde Field Station since 1964. McDowell and Estrada-Pinto, 1988 presents a description of the collection procedures, raw data from 1964 to 1986, and some summary statistics for this period of record. Precipitation for this period showed some seasonality in monthly means, with a peak in May. Monthly averages for the period of 1975 to current chart can be found at this site. The highest values for the monthly averages for the period of 1975 to 2000 are from August to December with a low in October and November the highest. In this period the highest amount of total annual rainfall was in 1998 with 5293.61 mm and the minimum in 1994 with 1402.87 mm. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Maximum temperature at El Verde Field Station, Rio Grande, Puerto Rico since October 1992
Daily maximum air temperature at the El Verde Field Station since October 1992. Temperature is measured daily using a manual Min-Max thermometer placed in a wooded box in the understory. The box is mostly shaded by surrounding trees, but it is more exposed to sunlight after hurricanes defoliate the forest canopy (e.g., hurricane Maria in 2017). Measurements are available for workdays only, as a technician at the station collects measurements. The thermometer is reset after each reading. Notes: • An automatic Hobo pendant sensor complements reading (see El Verde Field Station Air temperature from automatic sensor). • Daily minimum air temperature available in a separated data set. • Data from before 1992 is available in dataset #181 (1975 -August 1992). Data-missing gaps were filled in by extrapolated data from other sources, making the subsequent manipulations less valuable for interpreting long term trends. Daily emperature has been measured at the El Verde Field Station since 1975 (see methods). These data was divided into two data set: #16, having data from 1992 to current and #181 which has data from1975 till August 1992. Data-missing gaps where filled in by extrapolated data from other sources, making the subsequent manipulations less valuable for interpreting long term trends. Monthly averages have been calculated. Maximum values for maximum temperature were recorded from May to October with a range from 29 to 30 and peaks of 29.7 Centigrade in October. The months of October through December show the most dramatic increase, specially December (see chart). Highest average maximum temperatures during these years were recorded in 1998 and 1999 (See chart). Max monthly temperatures appear to be increasing from this years on. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as pa
Minimum temperature at El Verde Field Station, Rio Grande, Puerto Rico since 1975
Daily minimum air temperature at the El Verde Field Station since October 1992. Temperature is measured daily using a manual Min-Max thermometer placed in a wooded box in the understory. The box is mostly shaded by surrounding trees, but it is more exposed to sunlight after hurricanes defoliate the forest canopy (e.g., hurricane Maria in 2017). Measurements are available for workdays only, as a technician at the station collects measurements. The thermometer is reset after each reading. Notes: • An automatic Hobo pendant sensor complements reading (see El Verde Field Station Air temperature from automatic sensor). • Daily maximum air temperature available in a separated data set. • Data from before 1992 is available in dataset #181 (1975 -August 1992). Data-missing gaps were filled in by extrapolated data from other sources, making the subsequent manipulations less valuable for interpreting long term trends. Daily emperature has been measured at the El Verde Field Station since 1975 (see methods). Monthly averages have been calculated. Lowest average values for minimum temperature were recorded from January to April with nearly 18 Centigrades during this period of time (see chart). During these months, the lowest average minimum temperature was recorded in 1976 ranging from 20 to 22 Centigrades (see chart). Highest minimum montly average records are shown from June to October with peak in the vecinity of 20 Centigrades. The year with lowest minimum tempearture was 1995 and 2000 with the low values near 14 and 15 Centigrades, respectively. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service
Maximum temperature at El Verde Field Station, Rio Grande, Puerto Rico from January 1975 to August 1992
Daily emperature has been measured at the El Verde Field Station since 1975 (see methods). Average record show that maximum values for maximum temperature recorded from May to October with a range from 29 to 30 and peaks of 29.7 Centigrade in October. The months of October through December show the most dramatic increase, specially December. Highest average maximum temperatures during these years were recorded in 1998 and 1999. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Vegetation surveys in the riparian (bosque) corridor of the Middle Rio Grande valley, NM
This dataset contains vegetation cover information from 34 long-term Bosque Ecosystem Monitoring Program (BEMP) sites from 2000 – 2021. Data were collected along ten 30-m transects at each site at the centimeter scale each year in August-early October as funding and site access allowed. At the fullest extent, sites spanned 520 km of the riparian forest along the Rio Grande. The purpose of this dataset is to track plant species at sites along the Rio Grande in New Mexico. From this dataset, changes in plant species abundance, richness, and species diversity can be tracked and analyzed with ecosystem drivers such as flooding, fire, species removal/fuel reduction projects, and climate change. Species are coded using USDA Plant Database codes, allowing species information to be added to each species, including origin (native or nonnative), duration (e.g., annual, biennial, perennial), and plant type (e.g., grass, forb, vine, shrub, tree). This dataset has allowed the tracking of the ascendance of nonnatives in some sites, the recovery of natives in other sites, success or lack of success following restoration projects, and records of new species occurring in various counties and the state of New Mexico.
NRM2018 PET Grand Challenge Dataset
Open the record for dataset details and reuse information.
Grandes patrimonios en la provincia de A Coruña, 1820-1886
<p>Great heritages in the province of A Coruña, 1820-1886.</p> <p>The data set contains the names of the persons and their assets in pesetas corrientes of the year 19013.<br> The origin of the data comes from Archivo Histórico de Protocolos de A Coruña and Archivo Histórico Universitario de Santiago de Compostela. </p> <p>The data is published in:</p> <p>Lindoso Tato, E. (2005). Cuadro 1.17 Grandes patrimonios en la provincia de A Coruña, 1820-1886. En <em>El factor empresarial en la Galicia atlántica, 1820-1913 </em>(p. 98). A Coruña: Universidade.<br> They were first published in the doctoral thesis:<br> Lindoso Tato, Elvira (2005). El factor empresarial en la Galicia atlántica, 1820-1913. </p> <p> </p> <p> </p>
Data from: Grand Theft Empathy? Evidence for the absence of effects of violent video games on empathy for pain and emotional reactivity to violence
<p><strong>Abstract:</strong></p><p>Influential accounts claim that violent video games (VVG) decrease players' emotional empathy by desensitizing them to both virtual and real-life violence. However, scientific evidence for this claim is inconclusive and controversially debated. To assess the causal effect of VVGs on the behavioral and neural correlates of empathy and emotional reactivity to violence, we conducted a prospective experimental study using functional magnetic resonance imaging (fMRI). We recruited eighty-nine male participants without prior VVG experience. Over the course of two weeks, participants played either a highly violent video game, or a non-violent version of the same game. Before and after this period, participants completed an fMRI experiment with paradigms measuring their empathy for pain and emotional reactivity to violent images. Applying a Bayesian analysis approach throughout enabled us to find substantial evidence for the absence of an effect of VVGs on the behavioral and neural correlates of empathy. Moreover, participants in the VVG group were not desensitized to images of real-world violence. These results imply that short and controlled exposure to VVGs does not numb empathy nor the responses to real-world violence. We discuss the implications of our findings regarding the potential and limitations of experimental research on the causal effects of VVGs. While VVGs might not have a discernible effect on the investigated subpopulation within our carefully controlled experimental setting, our results cannot preclude that effects could be found in special vulnerable subpopulations, or in settings with higher ecological validity.<br> </p><p><strong>Dataset:</strong><br>This dataset contains the fMRI data collected for the study in the BIDS-format (https://bids.neuroimaging.io/)</p><ul><li>functional neuroimaging (*_bold.nii.gz) data of 89 human participants, collected during two tasks:<ul><li>Empathy-for-Pain paradigm (Session 1 & 2)</li><li>Emotional Reactivity paradigm (Session 2)</li></ul></li><li>associated event files (*_events.tsv) containing event onsets, durations, and behavioral covariates</li><li>metadata</li></ul><p>FMRI bold timeseries are fully preprocessed, as described in the manuscript.</p><p>Additional data, such as behavioral data in a simpler format, can be accessed on https://osf.io/yx423/</p><p> </p>
Mapa de Localização da Escola Municipal de Ensino Fundamental Santo Antônio, Linha dos Pomeranos, Agudo, Rio Grande do Sul
<p>Mapa de Localização da Escola Municipal de Ensino Fundamental Santo Antônio, Linha dos Pomeranos, Agudo, Rio Grande do Sul, realizado com o software QGIS, versão 3.12.3, com técnica de sombreamento utilizando o relevo sombreado do TOPODATA do Instituto Nacional de Pesquisas Espaciais (INPE).</p>
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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.