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1,961 results for “Sensing”
Monitoring long-term vegetation dynamics over the Yangtze River Basin, China, using multi-temporal remote sensing data
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Data from: Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations
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Data from: Polyelectrolyte-based wireless and drift-free iontronic sensors for orthodontic sensing
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Climactic data derived from remotely sensed, daily weather parameters (NASA DAYMET): Maricopa County, AZ (2000-2016)
overview There is considerable interest in using climatic variables and bioclimatic predictors not only in ecological species distribution models but in interdisciplinary studies of urban environments. We compiled a downloadable geodatabase of monthly environmental variables on a 1km x 1km spatial resolution including raw climate variables such as precipitation, minimum and maximum air temperature, and water vapor pressure obtained from NASA Earth Science Data and Information System Daily Surface Weather and Climatological Summaries (DAYMET) for Maricopa County, Arizona. This geodatabase of environmental variables provides accessible vital data for the entire Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) study area that can be used in an array of interdisciplinary studies. related data set Annual bioclimatic predictors for Maricopa County (as defined by Nix, 1986 and Hijmans, 2004) generated from data in this data set are accessible from: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=661 literature cited Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A., 2004. The WorldClim interpolated global terrestrial climate surfaces. Version 1.3. Nix, Henry A., 1986, A biogeographic analysis of Australian elapid snakes, in Longmore, Richard, ed., Atlas of elapid snakes of Australia: Canberra, Australian Flora and Fauna Series 7, Australian Government Publishing Service, p. 4‒15.
Separating Functions of the Phage-Encoded Quorum-Sensing-Activated Antirepressor Qtip
<p>Raw gels and microscopy images used in a study on the interaction between Qtip and its partner phage repressor protein.</p> <p> </p> <p> </p>
Modelling Avian Habitat Suitability in Boreal Forest using Structural and Spectral Remote Sensing Data
<p>Data used in research regarding avian habitat suitability models in Harry's River Watershed in Newfoundland, Canada</p>
TJNU-Ground-based-Remote-Sensing-Cloud-Database
<p>The ground-based remote-sensing cloud database (GRSCD) is created to improve the study of reginoal sky conditions.</p>
Data for "Disdrometer measurements under Sense-City rainfall simulator"
<p>The data set corresponds the data presented in the data paper : “Disdrometer measurements under Sense-City rainfall simulator“ which is published in Earth System Science Data” (https://www.earth-system-science-data.net/).</p> <p>More details can be found in the Read_me.txt file and in the paper.</p>
PyonAir: An open design, open source, air quality monitor for community driven particulate matter sensing.
<p>This dataset present some of the data recorded by two low-cost PM sensors, a Plantower PMS5003 and a Sensirion SPS030 located at Southampton AURN reference station on the 2nd December 2019 between 17:00 and 22:00 during a fire that occurred in the city. It also present the data from the Fidas 200, averaged every 15min also located at the AURN station.</p> <p>The file SPS_PMS_fire.csv contain the following rows:</p> <ul> <li>date</li> <li>pm25 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> <li>sensor - Serial number of the sensor</li> <li>site - name of the location of the sensor</li> </ul> <p>The file fidas_15min.csv contains the following rows:</p> <ul> <li>date</li> <li>PM2.5 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> </ul> <p> </p>
Dataset of Synchrotron Low Energy XRF and STXM files used in a manuscript on "Compressive Sensing for Dynamic XRF Scanning"
<p>Synchrotron Low Energy XRF and STXM Dataset used in a research manuscript on "Compressive Sensing for Dynamic XRF Scanning". This dataset includes HDF5 files with XRF (/dante) and STXM (/andor) maps and metadata such as XRF lifetime and sample stage positions (/sample_motors). The dataset also includes as TIFF images various outputs such as the sparse maps, the masked areas and the results of in-painting methods. In the DAT file, there is the output of the fitted XRF data as ASCII from PyMCA. In HTML there is included the relevant part of the electronic logbook (DonkiLOG). These data were acquired during the beamtime experiments 20180178 and 20192072 in the <a href="http://www.elettra.eu/elettra-beamlines/twinmic.html">TwinMic</a> soft X-ray microscopy beamline of Elettra Sincrotrone Trieste.</p> <p> </p> <p> </p>
dataset: Remote sensing retrieval of isoprene concentrations in the Southern Ocean
<p>This is the dataset used in the manuscript entitled "Remote sensing retrieval of isoprene concentrations in the Southern Ocean". Variables' names can be found at "readme.txt"</p>
DATA for "Towards ultrasound enhanced mid-IR spectroscopy for sensing bacteria in aqueous solutions"
<p>Dataset for DOI: 10.1117/12.2290390.</p>
The Supplementary Materials of article "remotesensing-801936" of Journal "Remote sensing"
<p>Figures S1-S3 of article remotesensing-801936 that the integrated maps of the entire (i.e., nine) stations from 30 days before and 15 days after the Jiuzhaigou earthquake at 0.005-0.01 Hz, 0.001-0.005 Hz and 0.01-0.05 Hz, respectively.</p>
ISMRM Reproducible Research Study Group: Data for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge"
<p>Challange data (brain/heart) and supplementary data (cardiac/rawdata_sprial) for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge".</p>
Data from: Quorum-sensing signaling by chironomid egg masses' microbiota affects haemagglutinin/protease (HAP) production by Vibrio cholerae
<p><i>Vibrio cholerae</i>, the causative agent of cholera, is commonly isolated, along with other bacterial species, from chironomid insects (<i>Diptera: Chironomide</i>). Nevertheless, its prevalence in the chironomid egg masses' microbiota is less than 0.5%. <i>V. cholerae</i> secretes haemagglutinin/protease (HAP) that degrades the gelatinous matrix of chironomid egg masses and prevents hatching. Quorum sensing (QS) activates HAP production in response to accumulation of bacterial autoinducers (AIs). Our aim was to define the impact of chironomid microbiota on HAP production by <i>V. cholerae</i>. To study QS signaling, we used<i> V. cholerae</i> bioluminescence reporter strains (QS-proficient O1 El-Tor wild type and QS-deficient mutants) and different bacterial species that we isolated from chironomid egg masses. These egg mass isolates, as well as a synthetic AI-2, caused an enhancement in <i>lux</i> expression by a <i>V. cholerae</i> QS-deficient mutant. The addition of the egg mass bacterial isolate supernatant to the QS-deficient mutant also enhanced HAP production and egg mass degradation activities. Moreover, the <i>V. cholerae</i> wild type strain was able to proliferate using egg masses as their sole carbon source while the QS-deficient was not. The results demonstrate that members of the chironomid bacterial consortium produce external chemical cues that, like AI-2, induce expression of the<i> hapA </i>gene in <i>V. cholerae</i>. Understanding the interactions between <i>V. cholerae</i> and the insects' microbiota may help uncover the interactions between this pathogen and the human gut microbiota.</p>
Data from: Sensing the structural characteristics of surfaces: Texture encoding by a bottom-dwelling fish
<p>The texture of contacted surfaces influences our perception of the physical environment and modulates behavior. Texture perception and its neural encoding mechanisms have traditionally been studied in the primate hand, yet animals of all types live in richly textured environments and regularly interact with textured surfaces. Here we explore texture sensation in a different type of vertebrate limb by investigating touch and potential texture encoding mechanisms in the pectoral fins of fishes, the forelimb homologs. We investigated the pectoral fins of the round goby (<em>Neogobius melanostomus</em>), a bottom-dwelling species that lives on substrate types of varying roughness and whose fins frequently contact the bottom. Analysis shows that the receptive field sizes of fin ray afferents are small and afferents exhibit response properties to tactile motion that are consistent with those of primates and other animals studied previously. In response to a periodic stimulus (coarse gratings), afferents phase lock to the stimulus temporal frequency and thus can provide information about surface texture. These data demonstrate that fish can have the capability to sense the tactile features of their near range physical environment with fins.</p>
Business Intelligence com Qlik Sense aplicado ao Radar Saúde
<p>Este artigo relata a adoção do Qlik Sense no processo de construção<br> do Radar Saúde, software pelo qual o TCE-MT divulga gráficos e indicadores<br> sobre a saúde no estado do Mato-Grosso de modo a garantir a transparência<br> e a prestação de contas ao cidadão. Os resultados mostraram que o Qlik<br> Sense é de fácil utilização e apresenta vantagens em termos de simplicidade,<br> qualidade, diminuição de tempo e aumento de agilidade na implementação da<br> aplicação e gerenciamento de dados. O artigo auxilia no aumento do corpo de<br> conhecimento relacionado a Qlik Sense. Em termos práticos, o artigo é útil<br> para desenvolvedores que buscam formas mais eficientes para gerenciar<br> dados.</p>
Characterization of Industrial Smoke Plumes from Remote Sensing Data
<p><strong>Characterization of Industrial Smoke Plumes from Remote Sensing Data</strong><br> </p> <p>This data set contains imaging data acquired by ESA's <a href="https://earth.esa.int/web/sentinel/missions/sentinel-2">Sentinel-2 Earth-observing satellite constellation</a> for a sample of industrial sites that were picked based on emission information provided by the <a href="https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial">European Pollutant Release and Transfer Register</a>. The images contain scenes of mainly industrial sites, some of which are actively emitting smoke plumes.</p> <p>This data set was created to investigate whether it would be possible to train a deep learning model to automatically identify and segment smoke plumes from remote sensing image data. Please refer to the acknowledgements section for more on information on this project.</p> <p><br> <strong>Description</strong></p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands and georeferencing information, and has a shape of 120 x 120 pixels (corresponding to a square area with an edge length of 1.2 km on the ground). The bands are extracted from Sentinel-2 Level-2A products, except for band 10, which has been extracted from the<br> corresponding Level-1C product (this band has not been utilized in the underlying work).</p> <p>This repository contains a total of 21,350 images. Based on manual annotation, the image sample was split into a sample of 3,750 <em>positive</em> images that contain industrial smoke plumes, and 17,600 <em>negative</em> images that do not contain smoke plumes. Furthermore, this repository contains a collection of JSON files that hold manual segmentation labels for smoke plumes present in 1,437 images. Segmentation labels were generated using <a href="http://https://labelstud.io/">label-studio</a>. Please note that polygon edge coordinates have to be scaled by a factor of 1.2 to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following tarballs are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.tar.gz [6.0GB] - contains 21,350 GeoTIFF images</li> <li>segmentation_labels.tar.gz [350KB] - contains 1,437 JSON files</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Mommert, M., Sigel, M., Neuhausler, M., Scheibenreif, L., Borth, D., "Characterization of Industrial Smoke Plumes from Remote Sensing Data", Tackling Climate Change with Machine Learning workshop at NeurIPS 2020.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at <a href="https://github.com/HSG-AIML/IndustrialSmokePlumeDetection">github</a>.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2019, processed by ESA.</p> <p> </p> <p><strong>Responsible Author</strong></p> <p>Michael Mommert<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> michael.mommert ( at ) unisg.ch</p>
Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)
<p>The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variables operating at distinct scales are considered. These include climate, but also ecosystem functioning attributes (EFAs) related to energy–matter exchanges that characterize land cover types under various environmental settings, at finer scales. Here, we used satellite-based EFAs and climate variables in species distribution models (SDMs) to assess how variables related to ecosystem functioning improve our understanding of current distributions and the identification of suitable areas for White Oak species in Iberia. We developed consensus ensemble SDMs targeting a set of thirteen oaks, including both narrow endemic and widespread taxa. Models combining EFAs and climate variables obtained a higher performance and predictive ability (true-skill statistic (TSS): 0.88, sensitivity: 99.6, specificity: 96.3), in comparison to the climate-only models (TSS: 0.86, sens.: 96.1, spec.: 90.3) and EFA-only models (TSS: 0.73, sens.: 91.2, spec.: 82.1). Overall, narrow endemic species obtained higher predictive performance using combined models (TSS: 0.96, sens.: 99.6, spec.: 96.3) in comparison to widespread oaks (TSS: 0.80, sens.: 92.6, spec.: 87.7). The Iberian White Oaks show a high dependence on precipitation and the inter-quartile range of Normalized Difference Water Index (NDWI) (i.e., seasonal water availability) which appears to be the most important EFA variable. Spatial projections of climate–EFA combined models contribute to identify the major diversity hotspots for White Oaks in Iberia, holding higher values of cumulative habitat suitability and species richness. We discuss the implications of these findings for guiding the long-term conservation of IberianWhite Oaks and provide spatially explicit geospatial information about each oak species (or set of species) relevant for developing biogeographic conservation frameworks.</p>
Remotely sensed forest understory density and nest predator occurrence interact to predict suitable breeding habitat and the occurrence of a resident boreal bird species
<p>Habitat suitability models (HSM) based on remotely sensed data are useful tools in conservation work. However, they typically use species occurrence data rather than robust demographic variables, and their predictive power is rarely evaluated. These shortcomings can result in misleading guidance for conservation. Here, we develop and evaluate a HSM based on correlates of long term breeding success of an open nest building boreal forest bird, the Siberian jay. In our study site in northern Sweden, nest failure of this permanent resident species is driven mainly by visually hunting corvids that are associated with human settlements. Parents rely on understory nesting cover as protection against these predators. Accordingly, our HSM includes a light detection and ranging (LiDAR) based metric of understory density around the nest and the distance of the nest to the closest settlement to predict breeding success. It reveals that a high understory density 15-80 m around nests is associated with increased breeding success in territories close to settlements (<1.5 km). Farther away from human settlements breeding success is highest at nest sites with a more open understory providing a favourable warmer microclimate. We validated this HSM by comparing the predicted breeding success with landscape-wide census data on Siberian jay occurrence. The correlation between breeding success and occurrence was strong up to 40 km around the study site. However, the HSM appears to overestimate breeding success in regions with a milder climate, and therefore higher corvid numbers. Our findings suggest that maintaining patches of small diameter trees may provide a cost-effective way to restore the breeding habitat for Siberian jays up to 1.5 km from human settlements. This distance is expected to increase in the warmer, southern, and coastal range of the Siberian jay where the presence of other corvids is to a lesser extent restricted to settlements.</p>
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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)
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.