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1,961 results for “Sensing”
Boreal forest tower-based remote sensing data (solar-induced fluorescence and reflectance-based vegetation indices)
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2021 at the Southern Old Black Spruce site in Saskatchewan Canada and the National Ecological Observatory Network (NEON) Delta Junction. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and Solar-Induced Fluorescence (SIF) and for stand-representative targets. Additionally, we provide half-hourly Photosynthetically Active Radiation (PAR), a fraction of direct vs. diffuse radiation (Df), Air Temperature (Tair) and Gross Primary Productivity (GPP). </p>
Dataset: Remotely sensed soil moisture can capture dynamics relevant to plant water uptake
<p><strong>Dataset Description</strong><br> Stable isotope water uptake profiles were consulted across 45 datasets to determine the primary zone of root water uptake ("Uptake Range Top" to "Uptake Range Bottom"), whether the uptake increases in proportion nearer to the surface ("Decay of Water Uptake With Depth"), and whether uptake temporarily switches to shallow soils ("Temporary Uptake of Upper Layers"). More details on the data collection are shared in our Water Resources Research publication (in revision).</p> <p>Correlation length scales, or the effective depth of representation of L-band satellite soil moisture, are estimates in Short Gianotti et al. 2019 using SMAP surface soil moisture and GPM precipitation retrievals.</p> <p><strong>Citations</strong><br> Those that use the stable isotope table are asked to cite our Water Resources Research publication (in revision) as well as the 45 references contributing to the table.<br> Those that use the correlation length scale dataset are asked to cite:<br> Short Gianotti, D.J., Salvucci, G.D., Akbar, R., McColl, K.A., Cuenca, R., Entekhabi, D., 2019. Landscape water storage and subsurface correlation from satellite surface soil moisture and precipitation observations. Water Resour. Res. 9111–9132. https://doi.org/10.1029/2019wr025332</p>
Acoustic sensing of proglacial discharge in Northwest Greenland, Jul-Aug 2022
<p>The sound data were collected near the outlet stream of Qaanaaq Glacier in Northwest Greenland, 2022. </p> <p>Ambient noise was recorded by a Song Meter Micro (Wildlife Acoustics, Maynard, USA) at a sampling rate of 24 kHz with 16-bit resolution.</p> <p>Timestamps correspond to the local time (UTC-2h). File names are as follows:</p> <p>21-24 July 2022 [RUNOFF_....wav]</p> <p>31 July - 8 August 2022 [LITTLEAUK_....wav]</p> <p>For questions, contact: evgeniy.podolskiyatgmail.com</p>
Engineering Fano-resonant hybrid metastructures with ultra-high sensing performances
<p>Here the data related to our recent work that uses metasurfaces designed on metamaterials to excite Fano resonances and Rabi Split analogue modes for sensing.</p> <p> </p> <p>In this repository the following data are available:</p> <ul> <li>The Reflectance, Transmittance and Absorbance (1-T-R) evaluated over all the diffraction angles (from 0° to 89°) for the proposed structure (ring + cross) and the other tested structures, available in the .mat file.</li> <li>The .m file to repeat all the simulations are available in main folder. The available Matlab codes allow reproducing numerical simulations using Reticolo (RCWA) as the main framework to do the computation.</li> <li>Use "getrefractiveindex.m" to interpolate refractive index on finest wavelength range. Please, note that according to the uploaded codes this function have to be in the same folder of the loaded refractive indices. </li> <li>You can use the function "textprogressbar.m" to get a progress bar in real time to know the status of the current simulation. Please, note that this function have to stay in the "/RETICOLO V8/reticolo_allege" folder. </li> <li>One of the available Matlab code allows displaying the electric and magnetic field evaluated in the structure section or on the top view. </li> <li>The sensing test performed for the reported structures using different surrounding medium, for this purpose it is possible to use the codes that allow studying the parameters variation, the incident angle variation or just a single run changing the refractive index of the surrounding medium. </li> </ul> <p>The proposed code works using the Reticolo main code. To download it please visit the following link <a href="https://www.lp2n.institutoptique.fr/light-complex-nanostructures">https://www.lp2n.institutoptique.fr/light-complex-nanostructures</a> or using Zenodo <a href="https://zenodo.org/record/5905381#.Y8rvbC9abpA">https://zenodo.org/record/5905381#.Y8rvbC9abpA</a></p> <p>Once Reticolo has been downloaded copy and paste its path in the proposed Matlab code for Fano resonances on ENZ cavities as reported in the example codes.</p> <p>In this repository you will find the refractive indices that have been used.</p>
Remote sensing data for crop yield in CONUS
<p><strong>I) SUMMARY</strong></p> <p>This database contains harmonized time series for the study of crop yields using remote sensing data and meteorological data. We collected information on soybean, corn, and wheat yields (t/ha) over the CONUS (continuous US) from <a href="http://quickstats.nass.usda.gov/USDA-NASS">USDA-NASS</a> for years 2015–2018 at a county level, and collocated time series for the following variables:</p> <ul> <li>Enhanced Vegetation Index (EVI) from <a href="https://lpdaac.usgs.gov">MODIS</a> satellite (MOD13C1 v6 product)</li> <li>Soil Moisture (SM) from SMAP satellite through <a href="https://zenodo.org/record/5619583#.Y2OkiXbMKUl">MT-DCA algorithm</a></li> <li>Vegetation Optical Depth (VOD) from SMAP satellite through <a href="https://zenodo.org/record/5619583#.Y2OkiXbMKUl">MT-DCA algorithm</a></li> <li>Maximum temperature (TMAX) from <a href="https://daac.ornl.gov">Daymet</a> v3</li> <li>Precipitation (PRCP) from <a href="https://daac.ornl.gov">Daymet</a> v3</li> </ul> <p><strong>II) CONTACT</strong></p> <p>For questions, please email Laura Martínez-Ferrer at <a href="mailto:laura.martinez-ferrer@uv.es">laura.martinez-ferrer@uv.es</a></p> <p><strong>III) DATABASE</strong></p> <p>For each crop type, we provided CSV files containing the time series of the variables and yield described above. Furthermore, additional information for spatial and temporal identification such as a county identifier and a year are included. Lastly, country-shapefiles (.shp) are added for geospatial representation. Further details in readme.txt file.</p> <p><strong>IV) CITE</strong></p> <p>We kindly encourage to cite the following works if this database is used</p> <p>L. Martínez-Ferrer, M. Piles, G. Camps-Valls, Crop Yield Estimation and Interpretability With Gaussian Processes, IEEE Geoscience and Remote Sensing Letters, 2020, vol. 18, no 12, p. 2043-2047, DOI: <a href="https://doi.org/10.1109/LGRS.2020.3016140">10.1109/LGRS.2020.3016140</a> </p> <p>A. Mateo-Sanchis, J. E. Adsuara, M. Piles, J. Muñoz-Marí, A. Pérez-Suay and G. Camps-Valls, "Interpretable Long-Short Term Memory Networks for Crop Yield Estimation," in IEEE Geoscience and Remote Sensing Letters, DOI: <a href="https://ieeexplore.ieee.org/document/10041987">10.1109/LGRS.2023.3244064</a></p>
An Agnostic Benchmark for Optical Remote Sensing Image Super-Resolution
<p>In remote sensing, image super-resolution (ISR) is a technique used to create high-resolution (HR) images from low-resolution (R) satellite images, giving a more detailed view of the Earth’s surface. However, with the constant development and introduction of new ISR algorithms, it can be challenging to stay updated on the latest advancements and evaluate their performance objectively. To address this issue, we introduce SRcheck, a Python package that provides an easy-to-use interface for comparing and benchmarking various ISR methods. SRcheck includes a range of datasets that consist of high-resolution and low-resolution image pairs, as well as a set of quantitative metrics for evaluating the performance of SISR algorithms.</p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- 2021 Data
<p>This is a remote sensing dataset collected in 2021 that contains orthomosaic images, shape files, analysis scripts, and derived numerical data from each plot. Data was collected using the protocol described here:</p> <p><a href="https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1">https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1</a></p> <p>Provided are "field map" files that denote the location and contents of each plot, a folder from each date that contains the 10 band orthomosiac, surface model image, a cropped and rotated image, shape files indicating the location of each plot, and derived data. The analysis can be replicated by following along with workflow listed in file named: rondon_cpb_2021.R. Derived data from this experiment can be found it the file named: "Rondon_CPB_data_2021_UAS_all.csv"<br> <br> If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p> <p> </p>
Quantifying flood exposure for Pakistan's 2022 floods from remotely sensed data
<p>Workflow for a rapid assessment of flood depth from remotely sensed data for Pakistan's 2022 floods. This workflow is designed to inform strategic and trans-sectoral reconstruction and adaptation to flood hazards. </p>
Dataset for the paper High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing
<p>Information regarding the Dataset, corresponding to the paper: “Thakur, M., Cai, N., Zhang, M. et al. High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing. npj 2D Mater Appl 7, 11 (2023). https://doi.org/10.1038/s41699-023-00373-5”</p> <p>This folder contains the raw data and complete package of codes used to analyze, view, save, and plot data for the publication titled "High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing". The code folder, "OpenNanopore-nanopore-tools", can be used to plot raw data which corresponds to the figures in the paper and supplementary information. </p>
Dataset for "Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 40B2-0_187223) in association with the recent publication entitled “Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies”.</p> <p>This work aims to study the humidity response of eco-friendly materials in the S-band (2-4GHz) using a microstrip line resonating at 3.3GHz. Several sensors coated with different biodegradable materials were tested and simulations have been performed. The S12 signal of the resonator was measured when varying the humidity. The data that was collected in the frame of this work is present in this repository.</p> <p>More information about the content of the dataset is present in the included README file.</p>
Remotely Sensed Paddy Rice Map of South Korea (2017-2021)
<p>This dataset includes paddy rice maps in South Korea from 2017 to 2021 with 10 m resolution, which was produced by analyzing time-series Sentinel-1 images with recurrent U-Net deep learning architecture. The paddy rice maps are a product of deep learning model predictions and DO NOT represent ground truth information.</p> <p>The detailed algorithm for producing the dataset can be found in the following paper: <a href="https://doi.org/10.1080/15481603.2023.2206539">https://doi.org/10.1080/15481603.2023.2206539</a></p> <p>The used modeling architecture is indicated by "RU-net 3" in the paper, and the consisting products in the dataset is as follows:</p> <ul> <li>PR_in_[Year] : Probabiltiy of paddy rice cultivation area inside the paddy boundary (levee)</li> <li>PR_bd_[Year] : Probabiltiy of levee</li> <li>PR_in and PR_bd files have a scale factor vaule of 1,000,000.</li> <li>PR_bi_[Year] : Binary map of paddy rice, Due to the pixel size much larger than the levee width, the pixels labeled as levees inevitably include a large portion of the cultivation area. Therefore, binary map represent the sum of PR_in and PR_bd exceeding 0.5 threshold.</li> </ul> <p>Please cite the paper when using this dataset.</p> <p>This work was supported by the International Research and Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT [2021K1A3A1A78097879], and partially supported by the European Commission under contract H2020-CALLISTO [101004152]</p>
Data set for "Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing"
<p>Data set for: Kiritani T, Pala A, Gasselin C, Crochet S, Petersen CCH (2023) Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing. PLOS ONE 18: e0287174. doi: 10.1371/journal.pone.0287174</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2023_Kiritani_PLOSONE.pdf" is the Open Access pdf of the online publication in PLOS ONE.</p> <p>2. The file named "Kiritani_data_code.zip" (~5 GB) is a zipped version of a folder "Kiritani_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder 'Kiritani_data_code' and all subfolders. Directly from this folder, you should first run the codes in the folder 'Data_Analysis_Codes', sequentially executing 'Analysis_1.m' through to 'Analysis_9.m'. Note, execution of 'Analysis_9.m' can take a long time (~1 hour on a good desktop PC). You can then run the codes in the folder 'Figure_Plotting_Codes' to generate the figures published in the journal article. In the folder 'Data', you can also find a DataViewer to visualise the data sets, which you can run by executing 'DataViewer.m' directly from the subfolder ‘Data’.</p> <p> </p>
Dataset for manuscript "Plants as inspiration for material‑based sensing and actuation in soft robots and machines"
<p>The dataset includes data for Figure 2 in the article "Plants as inspiration for material-based sensing and actuation in soft robots and machines<em>" MRS Bulletin</em> (2023). https://doi.org/10.1557/s43577-022-00470-8</p>
Data set - Measured in a context : making sense of open access book data
<p>For more than a decade, open access book platforms have been distributing titles in order to maximise their impact. Each platform offers some form of usage data, showcasing the success of their offering. However, the numbers alone are not sufficient to convey how well a book is actually performing.</p> <p>Our data set is consists of 18,014 books and chapters. The selected titles have been added to the OAPEN Library collection before 1 January 2022, and the usage data of twelve months (January to December 2022) has been captured. During that period, this collection of books and chapters has been downloaded more than 10 million times. Each title has been linked to one broad subject and the title’s language has been coded as either English, German or other languages.</p> <p>The titles are rated using the TOANI score.</p> <p>The acronym stands for Transparent Open Access Normalised Index. The transparency is based on the application of clear regulations, and by making all data used visible. The data is normalised, by using a common scale for the complete collection of an open access book platform. Additionally, there are only three possible values to score the titles: average, less than average and more than average. This index is set up to provide a clear and simple answer to the question whether an open access book has made an impact. It is not meant to give a sense of false accuracy; the complexities surrounding this issue cannot be measured in several decimal places.</p> <p>The TOANI score is based on the following principles:</p> <ul> <li>Select only titles that have been available for at least 12 months;</li> <li>Use the usage data of the same 12 months period for the whole collection;</li> <li>Each title is assigned one – high level – subject;</li> <li>Each title is assigned one language;</li> <li>All titles are grouped based on subject and language;</li> <li>The groups should consists of at least 100 titles;</li> <li>The following data must be made available for each title: <ul> <li>Platform</li> <li>Total number of titles in the group</li> <li>Subject</li> <li>Language</li> <li>Period used for the measurement</li> <li>Minimum value, maximum value, median, first and third quartile of the platform’s usage data</li> </ul> </li> <li>Based on the previous, titles are classified as: <ul> <li>“Less than average” – First quartile; 25 % of the titles</li> <li>“Average” – Second and third quartile; 50% of the titles</li> <li>“More than average” – Fourth quartile; 25 % of the titles</li> </ul> </li> </ul>
Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging - Data
<p>Raw data and simulation code for the paper "Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging"</p>
Primary data for: "Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long-term rodent datasets"
<p>Datasets</p>
Dataset for "Sub-micrometer mechanochromic inclusions enable strain sensing in polymers"
<p>This dataset contains the raw data for the open-access, peer-reviewed article “Sub-micrometer mechanochromic inclusions enable strain sensing in polymers” (https://doi.org/10.1002/adfm.202304938) accepted for publication on July 20, 2023 in Advanced Functional Materials (Wiley).</p>
Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products
<p>These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include:</p> <p>1. Measured data of global fractional vegetation cover from 2013-2021 </p> <p>2. Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).</p>
Immature leaves are the dominant volatile sensing organs of maize
<p>Plants perceive herbivory induced volatiles and respond to them by upregulating their defenses. So far, the organs responsible for volatile perception remain poorly described. Here, we show that responsiveness to the herbivory induced green leaf volatile (Z)-3-hexenyl acetate (HAC) in terms of volatile emission, transcriptional regulation and jasmonate defense hormone activation is largely constrained to younger maize leaves. Older leaves are much less sensitive to HAC. In a given leaf, responsiveness to HAC is high at immature developmental stages and drops off rapidly during maturation. Responsiveness to the non-volatile elicitor ZmPep3 shows an opposite pattern, demonstrating that this form of hyposmia (i.e. decreased sense of smell) is not due to a general defect in jasmonate defense signaling in mature leaves. Neither stomatal conductance nor leaf cuticle composition explain the unresponsiveness of older leaves to HAC, suggesting perception mechanisms upstream of jasmonate signaling as driving factors. Finally, we show that hyposmia in older leaves is not restricted to HAC, and extends to the full blend of herbivory induced volatiles. In conclusion, our work identifies immature maize leaves as dominant stress volatile sensing organs. The tight spatiotemporal control of volatile perception may facilitate within-plant defense signaling to protect young leaves, and may allow plants with complex architectures to explore the dynamic odor landscapes at the outer periphery of their shoots.</p>
A Prosthetic Hand with Integrated Sensing Elements for Selective Detection of Mechanical and Thermal Stimuli
<p>Flexible electronics have gained popularity because of their capability to combine softness and functionality. Soft resistive sensors are susceptible to mechanical stimuli and detecting temperature selectively remains a challenge. In this study, soft flexible thermistors are developed for detecting temperature changes based on the positive temperature coefficient (PTC) effect, selectively. By thermomechanical analysis and differential scanning calorimetry, it is observed that thermoplastic elastomers with higher thermal expansion and semicrystalline morphology result in a sensitive thermistor response. To achieve a high sensitivity in temperature and low sensitivity in the detection of mechanical stimulus, a low-carbon filler content is required. The opposite trend is seen for the piezoresistive sensors for mechanical strain detection. Both sensory material types are compatible with thermoplastic material extrusion-based additive manufacturing. The method is used for the fabrication of the sensing elements and an open-source prosthetic hand. The strain sensor detects the bending of the fingers and the temperature sensor detects the temperature when in contact with a heated surface, successfully. In addition, the temperature sensor is used as a tactile sensor to detect contact with a non-heated surface. Combining selective multisensory capabilities will significantly affect the future development of sensorized prosthetic devices and wearable electronics.</p>
ScienceDex guides
Understand access before you commit
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.