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1,961 results for “Sensing”
Dataset for the paper "A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors"
<p>The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medical institutions from the US and India, and an initial unsupervised machine learning based solution as a baseline for the community to accelerate research in this area.</p> <p>Additional data for more days (from January-August 2022) for the same locations presented in our paper can be requested for research purposes by contacting the authors.</p> <p>Our GitHub repository - https://github.com/ai4society/PowerIoT-State-Identification</p> <p>If you are using this data, please cite,</p> <blockquote> <pre>@inproceedings{midas-state-id, author = {Bharath C Muppasani and C J Anand and Chinmayi Appajigowda and Biplav Srivastava and Lokesh Johri}, title = {A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors}, booktitle = {Proc. Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-23)}, year = {2023}, keywords = {Signal Processing (eess.SP), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences}, copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International} }</pre> </blockquote>
Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.
<p>This dataset corresponds to the article: <strong>"Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, <em>Remote Sensing in Ecology and Conservation</em>"</strong></p> <p>DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a></p> <p>1 Department of Ecology and Environmental Science, Umeå University, Sweden<br>2 School of Biosciences, University of Nottingham, Loughborough, UK<br>3 Department of Ecology and Evolutionary Biology, University of Michigan, US</p> <p>The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a></p> <p>In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information).</p> <p>Paper abstract:</p> <p>High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the<em> Betula nana</em> vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.</p>
Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States
<p>Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with crown nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) at the continental scale. With-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first study on hyperspectral imagery indicates promise for future monitoring of reproductive potential. The fact that both ISP and CSP decline at high foliar P levels has immediate applications in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.</p>
Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery
<blockquote> <p>Version 2: Added missing files to <code>sniffing_data.zip</code></p> </blockquote> <p>See GitHub repository for data processing functions and examples: <a href="https://github.com/soerenbrandt/sniffing-sensor">https://github.com/soerenbrandt/sniffing-sensor</a></p> <p><strong>Abstract</strong>:<br>Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.</p>
Radar-based sensing of wind turbines blades based on 35 GHz FMCW sensors installed at operational wind turbine towers
<p>The dataset contains radar-based measurements of rotor blades from three operational wind turbines as part of a structural health monitoring system. For this purpose, a sensor box with a 35 GHz radar sensor (about 1 000 measurements per second) and a camera system (about 100 images per second), is mounted on each wind turbine tower at approximately 100 m height. In order to distinguish individual rotor blades, a machine-readable marker printed on a self-adhesive film was applied on the blade’s surface. When a rotor blade passes the sensor, the camera captures an image of the marker while the radar records a measurement. The marker is then identified and the recorded data is assigned to a particular rotor blade. The measurements demonstrate that the damage detection methodology can be transferred to an image processing problem. The challenge is to manage the strong influence from variable environmental and operational conditions, e.g. wind speed, azimuth orientation, that modify the rotor blade appearance in the radargram significantly. The dataset contains measurements from the intact turbine blade conditions, because it was not possible to introduce structural damage.</p>
FCH and FS Datasets for the paper "Integrating Multi-Source Remote Sensing Data for Mapping Boreal Forest Canopy Height and Species in interior Alaska in Support of Radar Modeling"
<p>This dataset provides forest canopy height and forest species in Delta Junction, interior Alaska in 2017. This dataset was produced based on the multi-source remote sensing datasets (AirMOSS, UAVSAR, Sentinel-1, Sentinel-2, topography), using a XGBoost approach.</p>
Dataset: Vision Sensing Acquisition Corp. (VSACW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vision Sensing Acquisition Corp. (VSACU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vision Sensing Acquisition Corp. (VSAC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Functionalization of PEDOT:PSS for aptamer-based sensing of IL6 using organic electrochemical transistors
<div>Explanation of included data for following publication:</div> <div>-------------------------------------------------------------------------------------</div> <div> </div> <div>Functionalization of PEDOT:PSS for aptamer-based sensing of IL6 using organic electrochemical transistors</div> <div> <div>DOI: 10.1038/s44328-024-00007-w</div> </div> <div> </div> <div>Bernhard Burtscher (1), Chiara Diacci (1), Anatolii Makhinia (1,2), Marios Savvakis (1), Erik O Gabrielsson (1), Lothar Veith (3), Xianjie Liu (1), Xenofon Strakosas (1), Daniel T Simon (1)</div> <div> </div> <div>1. Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, 60174 Norrköping, Sweden</div> <div>2. RISE Research Institutes of Sweden, Digital Systems, Smart, Hardware, Printed, Bio- and Organic Electronics, 60221 Norrköping, Sweden</div> <div>3. Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany</div> <div> </div> <div>-------------------------------------------------------------------------------------</div> <div>The data is structured according to the first figure they appear in the manuscript.</div> <div> </div> <div>Figure 2:</div> <div>a) Transfer curves data as .txt at various stages of the functionalization process with information of</div> <div>(Time / s; Gate Voltage / V; Drain Voltage / V; Drain Current / A; Gate Current / A; VR (measured voltage at the reference electrode) / V)</div> <div>1) PEDOT:PSS</div> <div>2) 4-ABA + PEDOT:PSS</div> <div>3) Aptamer + 4-ABA + PEDOT:PSS</div> <div> </div> <div>b) Measurement data of constant sensing experiments of either IL6 (6 meas.), BSA (3 meas.) or a control without aptamers (4ABA-ETA_Control_IL6-measurement) with explanation of when analyte was changed to which concentration.</div> <div> </div> <div>c) Data of constant measurement of fully functionalized OECT in bovine plasma after incubation over night in bovine plasma and information regarding time and concentration of changes</div> <div> </div> <div> </div> <div>Figure 3: Raw image files of fluorescent test of AL4083 PEDOT:PSS formulation with PLL-FITC as .nd2 files:</div> <div>- 0.25% Gops with PLL-FITC (name: AL4083-Gops025_sample5_PLLFITC_FITC)</div> <div>- 0.25% Gops without PLL-FITC (name: AL4083-Gops025_sample3_4ABA_noPLLFITC_FITC_5s)</div> <div>- 1% Gops and 1% PSS with PLL-FITC (name: AL4083-Gops1_PSS1_sample4_PLLFITC_FITC)</div> <div>- 1% Gops and 1% PSS without PLL-FITC (name: AL4083-Gops1_PSS1_sample3_4ABA_noPLLFITC_FITC_5s)</div> <div> </div> <div> </div> <div>Figure 4: Frequency dependent OECT measurements of IL6 with PBS and different concentration of IL6. Each folder contains the nominal applied voltage (as .csv file) and the measurement voltage (in V) from the DAQ (as .tdms file in the ao1 column for each frequency). Data can then be processed with Python to fit the sine wave for each frequency to observe changes in the amplitude and phase.</div> <div> </div> <div>Figure 5: Data taken from Figure 2b and Figure 4</div>
Figure 4 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 4. Spatial variability of the climatic rate of the Black Sea level change (cm/yr) for period from 1993 to 2015.
Toward liquid cell quantum sensing: Ytterbium complexes with ultra-narrow absorption
<p>The energetic disorder induced by fluctuating liquid environments acts in opposition to the precise control required for coherence-based sensing. Overcoming fluctuations requires a protected quantum subspace that only weakly interacts with the local environment. We reported a ytterbium complex that exhibited an ultra-narrow absorption linewidth in solution at room temperature with a full-width at half-maximum of 0.625 meV. Using spectral hole-burning, we measured an even narrower linewidth of 410 peV at 77 K. Narrow linewidths allowed low-field magnetic circular dichroism at room temperature, used to sense Earth-scale magnetic fields. These results demonstrated that ligand protection in lanthanide complexes could significantly diminish electronic state fluctuations. We termed this system an 'atom-like molecular sensor' (ALMS) and proposed approaches to improve its performance.</p>
Plate XXV, Fig. M.1 – River Cold Sense, near Zollhaus (FR), Switzerland, late March. Rare example of type A and type B in near syntopy. Black arrow: nymphal biotope of type A; white arrow: of type B. in Steps towards a revision of the Perla bipunctata Pictet, 1833 species complex (Plecoptera: Perlidae)
Plate XXV, Fig. M.1 – River Cold Sense, near Zollhaus (FR), Switzerland, late March. Rare example of type A and type B in near syntopy. Black arrow: nymphal biotope of type A; white arrow: of type B.
Plate III, Figs A.16–A.21 – Type A (French and Swiss Alps). A.16, head and pronotum of nymph (Swiss Alps); A.17, markings on tergites of nymph (Gryonne valley); A.18, markings on tergites of nymph (Sense); A.19, markings on the femora of legs, dorsal view (Gryonne Valley); A.20, sclerotized apex of extracted aedeagus of an adult ♂ (Vercors); A.21, sclerotized apex of aedeagus of Perla grandis, from Aubert 1949: 225, his Fig. 4. in Steps towards a revision of the Perla bipunctata Pictet, 1833 species complex (Plecoptera: Perlidae)
Plate III, Figs A.16–A.21 – Type A (French and Swiss Alps). A.16, head and pronotum of nymph (Swiss Alps); A.17, markings on tergites of nymph (Gryonne valley); A.18, markings on tergites of nymph (Sense); A.19, markings on the femora of legs, dorsal view (Gryonne Valley); A.20, sclerotized apex of extracted aedeagus of an adult ♂ (Vercors); A.21, sclerotized apex of aedeagus of Perla grandis, from Aubert 1949: 225, his Fig. 4.
Fig. 8 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 8. Phenotypic characters mapped against broad molecular phylogenies of haemosporidian parasites. Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text.
Fig. 7 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 7. Phenotypic characters mapped against broad molecular phylogenies of haemogregarine parasites. Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text.
Fig. 6 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 6. Phenotypic characters mapped against broad molecular phylogenies of haemococcidian parasites (blood-borne genera shown in red). Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 5 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 5. Developmental cycles and hosts for apicomplexan blood parasites (DH = definitive host; IH = intermediate host; PH = paratenic host; bm = blood meal; bmi = injected during blood meal; ve = vector eaten).
Fig. 3 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 3. Developmental stages formed by kinetoplastid flagellates (blood-borne genera shown in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 2. Geological time periods with milestones in the development of life on Earth, together with historical extent of fossil records for particular assemblages.
ScienceDex guides
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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.