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992 results for “imagery”
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
Ground-truthing of satellite imagery to track harmful algal blooms in Pigeon Lake, Alberta, Canada 2017-2022
This data was collected to create a calibrated model that would enable the use of satellite imagery to track harmful algal blooms by using chlorophyll a estimates as a proxy for cyanobacteria in the lake. Samples from Pigeon Lake were collected on the same day that the Sentinel-2 satellite would pass over the lake. These samples were analyzed for different algal pigments and enumerated to genus level to ensure that the satellite imagery was of cyanobacteria rather than different algal groups. An algorithm was developed which we termed the three band index (TBI) that best matched with the cholorophyll a from the in situ samples. This model was used on satellite imagery from 2017-2022 of Pigeon Lake to get chlorophyll a estimates for every 20 x 20 pixel of each image of the lake. This pixel data was used to determine different bloom metrics like the intensity, the area (extent) and severity.
Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona
This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset. Locations and areas of PASS study neighborhood boundaries and source imagery used to calculate these summaries are available through the Environmental Data Initiative - see resouce listing in the methods of this data set.
Normalized Difference Vegetation Index (NDVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Vegetation and invertebrate communities in 500 plots in the Duplin and Dean Creek watersheds: ground truth data for matching hyperspectral imagery
We measured characteristics of vegetation (Aster tenuifolius, Batis maritima, Borrichia frutescens, Distichlis spicata, Iva frutescens, Juncus roemerianus, Limonium carolinianum, Salicornia biglovii, Salicornia virginica, Spartina alterniflora, Spartina patens, Sporobolus virginicus), soil (salinity, proportion organic and proportion water) and densities of common gastropods and bivalves in 500 plots in the Duplin and Dean Creek watersheds on Sapelo Island on June 20-26, 2006. Plot locations were determined using a high precision hand-held GPS. These data were used to help ground-truth hyperspectral aerial images collected at the same time by Dr. John Schalles.
2006 AISA hyperspectral imagery of the GCE domain for water
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included six flight lines flown for the examination of water spectral properties for the Satilla River, Altamaha River, and Sapelo Sound. Imagery was acquired for 97 bands from 435-950 nm at a 1 m spatial resolution. The bandwidths were preselected by investigators with CALMIT to to capture the photoplankton red reflectance feature and carotenoid and chlorophyll driven absorption behaviors. These data were acquired for the following purposes: 1) calculate suites of remote sensing phytoplankton indices, (2) produce algorithms for predicting plant and phytoplankton chlorophyll and accessory pigments and productivity, 3) assess water quality, and (4) perform atmospheric corrections.
2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included ten flight lines flown for the examination of salt marsh and upland vegetation spectral properties near Blackbeard Creek, the Duplin River, Dean Creek, and the Altamaha River. Imagery was acquired for 63 bands from 400-980 nm at a 1 m spatial resolution. The bandwidths were preselected by investigators with CALMIT to to capture the vegetative red reflectance feature, leaf water content related NIR reflectance, and carotenoid and chlorophyll driven absorption behaviors. These data were acquired for the following purposes: 1) calculate suites of remote sensing vegetation indices, (2) produce algorithms for predicting plant and phytoplankton chlorophyll and accessory pigments, vegetation biomass, 3) assess vegetative health, and (4) perform atmospheric corrections.
Maximum likelihood classification of 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included four flight lines flown for the examination of vegetation for the Duplin River salt marshes. Imagery was acquired for 63 bands from 400-980 nm at a 1 m spatial resolution. Imagery were classified using the maximum likelihood classifier (MLC) and a post-classification decision tree to achieve an overall classification accuracy of 90%. Classification training and validation data were obtained from the 2006 Hyperspectral ground survey. See Hladik (2012) and Hladik, Alber, and Schalles (2013) and Schalles, et. al. (2013) for additional details.
NDVI images derived from the 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included ten flight lines flown for the examination of salt marsh and upland vegetation and water for spectral properties at 1 m spatial resolution. For all vegetation images, the Normalized difference vegetation index (NDVI) was calculated. NDVI uses the ratio of reflectance in the red and NIR wavelengths (NDVI = (NIR799 - RED675)/ (NIR799 + RED675)) to derive an index of plant vigor (Rouse et al., 1974). The subscript values are the wavelength band centers used to calculate NDVI. Values indicate the amount of green vegetation present in the pixel—higher NDVI values indicate more green vegetation. Vallid results fall between -1 and +1.
Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis.
We used Landsat 5 TM satellite imagery to derive aboveground biomass estimates for the three height classes (tall, medium, short) of Spartina alterniflora on the Centeral Georgia Coast. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora aboveground biomass to landscape level estimates using 294 Landsat images acquired between 1984 to 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1,222 pixels covering 1.1 million m^2. Using univariate and linear multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. Drivers included monthly mean values for Altamaha River Discharge, Palmer Drought Severity Index, Standardized Precipitation Index, Mean Sea Level, Precipitation, and Temperature.
Uncalibrated RGB orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.
Uncalibrated RGB data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Calibrated Red/Near Infrared orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.
Red/Near Infrared data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
SEV LTER: Tracking Vegetation Phenology Using PhenoCam Imagery at the Sevilleta National Wildlife Refuge, New Mexico, 2014-2024
As of 03/03/2024, the Sevilleta Long-Term Ecological Research Program is equipped with a total of 65 digital RGB cameras, or PhenoCams, across the Sevilleta National Wildlife Refuge. These cameras are installed on eddy covariance flux towers and at a number of precipitation manipulation experiments to track vegetation phenology and productivity across dryland ecotones. PhenoCams have been paired with eddy covariance flux tower data at the site since 2014, while some Mean-Variance Experiment PhenoCams were installed as recently as June 2023. For information on PhenoCam data processing and formatting, see Richardson et al., 2018, Scientific Data (https://doi.org/10.1038/sdata.2018.28), Seyednasrollah et al., 2019, Scientific Data (https://doi.org/10.1038/s41597-019-0229-9), and the PhenoCam Network web page (https://phenocam.nau.edu/webcam/). The PhenoCam Network uses imagery from digital cameras to track vegetation phenology and seasonal changes in vegetation activity in diverse ecosystems across North America and around the world. Imagery is uploaded to the PhenoCam server hosted at Northern Arizona University, where it is made publicly available in near-real time, every 30 minutes from sunrise to sunset, 365 days a year. The data are processed using simple image analysis tools to yield a measure of canopy greenness, from which phenological metrics are extracted, characterizing the start and end of the growing season. These transition dates have been shown to align well with on-the-ground observations at various research sites. Long-term PhenoCam data can be used to track the impact of climate variability and change on the rhythm of the seasons.
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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.