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1,961 results for “Sensing”
FIGURES 12–16 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-24. Odontocheila paraexcisipenis sp. nov., a new species of the O. cajennensis species-group (Coleoptera: Cicindelidae)
FIGURES 12–16. Odontocheila paraexcisipenis sp. nov. Venezuela, San Carlos de Rio Negro, male aedeagus or its apex. 12—HT (USNM ex ASUT); 13—PT (CCJM ex ASUT); 14–15—ditto, cleared, showing internal sac in its left and right lateral view; 16— PT, (RLHC). Bars = 1 mm.
FIGURES 7–11 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-24. Odontocheila paraexcisipenis sp. nov., a new species of the O. cajennensis species-group (Coleoptera: Cicindelidae)
FIGURES 7–11. Odontocheila paraexcisipenis sp. nov. Venezuela, San Carlos de Rio Negro. 7—pronotum, ♂, PT (RLHC); 8–11—elytron: 8—♂, HT (USNM ex ASUT); 9—the same elytron upon different illumination angle; 10—♂, PT (CCJM ex ASUT); 11—♂, PT (RLHC). Bars = 1 mm.
FIGURES 1–6 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-24. Odontocheila paraexcisipenis sp. nov., a new species of the O. cajennensis species-group (Coleoptera: Cicindelidae)
FIGURES 1–6. Odontocheila paraexcisipenis sp. nov. Venezuela, San Carlos de Rio Negro. 1—habitus, ♂, 12.8 mm, HT (USNM ex ASUT); 2—head, ♂, HT; 3—antennomeres 1–4, ♂, HT; 4–6—male labrum: 4—HT; 5–PT (CCJM ex ASUT); 6—PT (RLHC). Bars = 1 mm.
Distributed Sensing with Low-cost Mobile Sensors towards a Sustainable IoT
<p>This zip file contains the dataset used to produce Figure 4 of "Distributed Sensing with Low-cost Mobile Sensors towards a Sustainable IoT".</p> <p>It contains two folders, the first for the stationary sensors and the second for the mobile sensors.</p>
COVID-19 anxiety versus well-being - students' sense of support and self-esteem
<p>This study is aimed to evaluate the impact of fear of the COVID-19 pandemic on the quality of life of university students in Poland. An analysis is made of the correlation of various features of the student cohorts with social support and their self-evaluation, in the context of sensing the chances for success in life and satisfaction from studying online.</p> <p>Fighting the pandemic through socioeconomic closure and remote education is imposing a new routine on university students that restricts the activities important in their development and well-being. The research assumption was made that anxiety (N=973) caused by COVID-19 is a personal experience which is socially and culturally regulated. Culturally, the Polish society shows a very high level of distrust towards institutions and people outside private networks (the "social vacuum" syndrome).</p>
Self-Strain-Sensing: Comparison of Contact Materials
<p>These Datasets were used to compare different contacting methods for the development of Self-Strain-Sensing CFRP parts.</p>
Parasitic cnidarians (Myxozoa) do not retain key oxygen-sensing and homeostasis tool-kit genes: Transcriptome assemblies
<p>For aerobic organisms, both the Hypoxia-Inducible Factor (HIF) pathway and the mitochondrial genomes are key players in regulating oxygen homeostasis. However, recent work has suggested that these mechanisms are not as highly conserved as previously thought, prompting more thorough surveys across animal higher taxonomic levels, which would in turn permit testing of hypotheses about the ecological conditions that may have facilitated evolutionary loss of such genes. The phylum Cnidaria is known to harbor wide variation in mitochondrial genome morphology, from typical single circular chromosomes to fragmented linear chromosomes. More recently, members of the cnidarian clade Myxozoa, comprising obligate endoparasites, were shown to have lost their mitochondrial genome, suggesting that variation in environmental oxygen availability may be a key determinant in the evolution of metabolic gene networks. Here, we surveyed genomes and transcriptomes across 42 cnidarian species for the presence of HIF pathway members (HIFa, EGLN, VHL), as well as for an assortment of hypoxia, mitochondrial, and stress-response toolkit genes. We find that presence of the HIF pathway, as well as number of genes associated with mitochondria, hypoxia, and stress response, do not vary based on mitochondrial genome morphology. More interestingly, we uncover evidence that myxozoans have lost the canonical HIF pathway repression machinery, potentially altering HIF pathway functionality to work under the specific conditions of their parasitic lifestyles. In addition, relative to other cnidarians, myxozoans show loss of large proportions of genes associated with the mitochondrion (~39%), and involved in response to hypoxia (~27.5%) and general stress (~32%). Our results provide additional evidence that the HIF regulatory machinery is evolutionarily labile and that variations in the canonical system have evolved in many animal groups.</p>
Adopt a Pixel 3 km: A Multiscale Data Set Linking Remotely Sensed Land Cover Imagery with Field Based Citizen Science Observation
<p>These datasets were used in an article submitted to the journal Frontiers in Climate in 2021: <a href="https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full">https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full</a></p> <p>Further supplemental links (including general information about GLOBE data) can be accessed at <a href="https://observer.globe.gov/get-data/mosquito-habitat-data">https://observer.globe.gov/get-data/mosquito-habitat-data</a>.</p>
Data from: Odor motion sensing enhances navigation of complex plumes
<p>Odor plumes in the wild are spatially complex and rapidly fluctuating structures carried by turbulent airflows. To successfully navigate plumes in search of food and mates, insects must extract and integrate multiple features of the odor signal, including odor identity, intensity, and timing. Effective navigation requires balancing these multiple streams of olfactory information and integrating them with other sensory inputs, including visual and mechanosensory cues. Studies dating back a century have indicated that, of these many sensory inputs, the wind provides the main directional cue in turbulent plumes, leading to the longstanding model of insect odor navigation as odor-elicited upwind motion. Here, we show that <em>Drosophila</em> shape their navigational decisions using an additional directional cue – <em>the direction of motion of odors</em> – which they detect using temporal correlations in the odor signal between their two antennae. Using a high-resolution virtual reality paradigm to deliver spatiotemporally complex fictive odors to freely-walking flies, we demonstrate that such odor direction sensing employs algorithms analogous to those in visual direction sensing. Combining simulations, theory, and experiments, we show that odor motion contains valuable directional information absent from the airflow alone and that both Drosophila and virtual agents are aided by that information in navigating naturalistic plumes. The generality of our findings suggests that odor direction sensing may exist throughout the animal kingdom and could improve olfactory robot navigation in uncertain environments. </p>
DATASET sensing the position of a single scatterer in an opaque medium by mutual scattering
<p>DATASET of the paper "sensing the position of a single scatterer in an opaque medium by mutual scattering"</p>
Graph data for COMSOL simulations of the main properties of the whispering gallery mode resonators for bio-sensing applications
<p>COMSOL simulations data for Whispering gallery mode resonators. Graph data for publication COMSOL simulations of the main properties of the whispering gallery mode resonators for bio-sensing applications. Contain prism coupling with coupling conditions and tapered fiber width (Fig. 2), various "items" attaching to the surface (Fig. 3), and surface roughness with and without coating (Fig. 4).</p>
Data from: T1R2-mediated sweet sensing in a lizard
<p>Sugars are an important class of nutrients found in the flowers and fruits of angiosperms (flowering plants). Although T1R2-T1R3 has been identified as the mammalian sweet receptor, some birds rely on a repurposed T1R1-T1R3 savory receptor to sense sugars. Moreover, as the radiation of flowering plants occurred later than the last common ancestor of amniotes, sugar may not have been an important diet item for amniotes early in evolution, raising the question of whether T1R2-T1R3 is a universal sugar sensor or only a mammalian innovation. Here, using brief-access behavioral tests and functional characterization of taste receptors, we demonstrate that the nectar-taking Madagascar giant day gecko (<em>Phelsuma</em> <em>grandis</em>) can sense sugars through the T1R2-T1R3 receptor. These results reveal the existence of T1R2-based sweet taste in a non-avian reptile, which has important implications for our understanding of the evolutionary history of sugar detection in amniotes.</p>
Experimental Data for: Machine learning enabled image analysis of time-temperature sensing colloidal arrays
<p>This dataset contains images of colloidal arrays functioning as time-temperature integrating, autonomous sensors. Each image shows a sensor consisting of multiple colloidal crystals with varying compositions of particles with different glass transition temperatures. Details on the composition and manufacturing procedures are explained in the corresponding publication. The data is organized into folders with different temperature setpoints. For each temperature, we investigated 10 samples. The name of the samples corresponds to their creation date. The name of the image files corresponds to their acquisition time. The first image in each sample folder was taken at the very start of the heating period. Hence, the heating time can be calculated by subtracting the start time from the acquisition time.</p>
ML-based magnetic sensing scan data
<p>Experimental data sets of NV ODMR scans for training ML models.</p>
Quantifying flow velocities in river deltas via remotely sensed suspended sediment concentration
<p><strong>bathymetry and model velocity field</strong></p>
Room-temperature several-hundred-of-megahertz charge sensing with single-electron resolution using a silicon transistor
<p>Data set for Appl. Phys. Lett. <strong>122</strong>, 043502 (2023); <a href="https://doi.org/10.1063/5.0131808">https://doi.org/10.1063/5.0131808</a>.</p>
NO2 gas sensing properties of hydrothermally synthesized WO3.nH2O nanostructures
<p><span>Nitrogen dioxide (NO<sub>2</sub>) has been identified as a serious air pollutant that threats to our environment, human life and world ecosystems. Therefore, detection of this air pollutant is crucial. Metal oxide semiconductor (MOS) is one of the best approaches frequently used to detect NO2 at relatively low temperatures. Hydrated tungsten trioxide (</span><span>WO<sub>3</sub>•nH<sub>2</sub>O</span><span>), an n-type semiconductor, is regarded to be a promising material for fabricating gas sensors, which are widely utilized in environmental and safety monitoring. In this work, </span><span>WO<sub>3</sub>•nH<sub>2</sub>O</span><span> nanoparticles have been synthesized using a polyfunctional surfactant-mediated hydrothermal approach in the addition of H<sub>2</sub>C<sub>2</sub>O<sub>4</sub> and K<sub>2</sub>SO<sub>4</sub> at a molar ratio of 1:1. This paper has also reported the effect of reaction temperature (120°C to 200</span><span>°</span><span>C) on morphological changes and gas sensing performance. The characterization of these synthesized nanostructures was carried out by UV–Vis absorption spectroscopy (UV-Vis), X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM). The UV absorption peak was obtained around 300 nm. FESEM analysis showed sheet-like structures come together to form flower-type morphology. The synthesized WO<sub>3</sub>•nH<sub>2</sub>O flower-like structures was then used for </span><span>NO<sub>2</sub></span><span> gas sensing application. The prepared sensors showed considerably better sensor response (R<sub>g</sub>/R<sub>a</sub>=17.48) at 185°C for 25 ppm </span><span>NO<sub>2</sub></span><span>. </span></p>
Quantitative Assessment of the Impact of Future Land Use Changes on Flood Risk Using Remote Sensing, Machine Learning, and a Hydraulic Model
<p> </p> <p>The RF Machine learning code </p> <p>Topological, geomorphology, geology, metrological information of the Tajan watershed.</p> <p>Land use land cover images of the Tajan watershed</p> <p>River, transportation roads, villages map </p> <p>Global damage function datasets.</p>
H2020 RESCUER Radar Sensing tool by CONSERT-UNIWA presented on National Greek Television
<p>H2020 RESCUER Radar Sensing tool by CONSERT-UNIWA presented on National Greek Television</p>
Computer vision: algorithms to make sense of the world
<p><strong>The following video describes how computer vision is used by the ROMI platform for object and species detection in both 2D and 3D, and how it is integral to the weeding tool. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(CAMPRODON) What's computer vision? Mmm … computer vision for me is making sense of pixels. I think computer vision has a profound effect in the way that we understand the world because as humans vision is so centric right. If dogs would be making computers, maybe they wouldn't talk that much about vision. But for us it's so centric in the way that we perceive the world and the way we learn about the world, that actually I think it's easier for that of course to program and think useful ways machines could get information you know through vision. And also especially because vision is one of the much more complex senses that we have.<br> <br> (SOLLAZZO) Image and videos that represent nowadays the 80 percent of the data that we produce and the introduction of computer vision and machine learning becomes necessary to start extrapolating information out of this new source of data.<br> <br> (COLLIAUX) So just a point of clarification because we often talk about AI and so just to be a bit more precise about what we do in ROMI. Because AI is quite a vague term, and so what we do mainly is robotics and computer vision.<br> <br> (SOLLAZZO) Computer vision is at the end a limited set of tools and systems that are basically based on mathematical representation and description of the pixel that represent the image, they are part of the image, and machine learning is based on a different approach of interpretation of those pixels.<br> <br> (COLLIAUX) So the rover is for weeding and to remove the weeds you need to detect the weeds first and so we use a computer vision algorithm to detect where are the weeds where are the salads.<br> <br> (SOLLAZZO) So let's see one by one which are the methods that we implemented, in our algorithm, in our system. So we start with the feature extraction in order to do that in fact we go one by one over the images and we understand which are the pixels in common between one and the other. From these method in fact it's possible to recreate an orthomosaic view, an orthomosaic image, but afterwards we need to align it to all the previous images that we've been creating in the previous analysis. So after the generation of the orthomosaic view, what we do is that we start to cut the main image into a portion into a series of smaller portions. This facilitates the execution of the machine learning algorithm and the possibility to recognise the presence or not, of lettuce in the scene. After the recognition has been performed we put together the images once again and we can reconstruct an orthomosaic view with a detected position of the different lettuce. This is necessary to understand not only the position but also the area of growth that these different lettuce are occupying over time. From the geolocation of every single plant we start to analyse the growing curve over time. This is possible thanks to the implementation of ‘Mask RCNN’. So thanks to the generation of all these different areas that during time, will tell us the growing pattern of every single lettuce, and this will be extremely useful to understand when is the moment to harvest the plant when the plant is in fact bolting, more or less this is ok.<br> <br> (COLLIAUX) So we do what I showed was about 2d computer vision, but we do a lot of 3d computer vision also in the project and so let me show you a bit what we do with a plant scanner. So it is uh used by biologists to study the geometry of the plants so they want to reconstruct the pre-architecture of a plant and study that architecture. So for this we take many images of a plant by turning a camera in a circle around the plant, we generate a mask but again a segmentation algorithm to detect where where the plant is and where the background is, and then we can generate a point cloud by an algorithm called ‘space carving’ or ‘shape from silhouette’ which based on the many silhouettes you collected it looks for it it carves the space for the shape which is the most compatible with all the projection of the shape.<br> <br> (CAMPRODON) So what we're doing in ROMI at the end, I would say in a way we hack existing technologies, we take advantage of the low cost cameras that exist in phones right we don't need to rely anymore in high-end industrial cameras, we take advantage of the low-cost computational power, computing cheaper than ever. So these images that we take we can process them with software in ways that was not possible before, we take advantage of software, of especially of open source software and free software and then we build the training models right, so this software is capable to detect on top of that images insights, to go from data to information.</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.