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1,961 results for “Sensing”
Data and code for figures: Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article "Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing", Physical Review Applied 20, 024022 (2023).</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)
<p>The Enhanced Vegetation Index (EVI), Land Surface Temperature (LST) and Precipitation (P) were used as new data sources based on the spatial distance model to construct an optimized multi-source remote sensing dryness index named Temperature-Vegetation-Precipitation Dryness Index based on the shortcomings of the TVPDIorigin (i.e., TVPDI<sub>o</sub>) data source. The TVPDI<sub>n</sub> of the long time series was also compared and analyzed with the classical drought index - Standardized Precipitation Evapotranspiration Index (SPEI-3) on a 3-month scale, different drought response level products of Solar-Induced Chlorophyll Fluorescence (SIF), soil moisture (SM) from ESA CCI (European Space Agency's Climate Change Initiative), and total crop yield, then the sensitivity and validity of the TVPDI<sub>n</sub> for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification:</p> <p>(1) Compared with the original data source TVPDI<sub>o</sub> using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDI<sub>n</sub>, the overall correlation between the two and SPEI-3 was good, with a maximum of 0.57 and 0.56, respectively (p< 0.1), but the overall TVPDI<sub>n</sub> constructed in this study had a better fit compared to the original data source TVPDI<sub>o</sub> and was more sensitive to the monitoring of dry and wet conditions.</p> <p>(2) According to the comparison of TVPDI<sub>n</sub> with ESA CCI sm, TVPDI<sub>n</sub> showed a high correlation of more than 0.9 with soil water content, which proved that TVPDI<sub>n</sub> was highly consistent with soil moisture; compared with SIF, 54.5% of the regional correlation coefficients were greater than 0.8 (p< 0.01), and spatially, the correlation results were better in the northwest than in the east, indicating that the response of TVPDI<sub>n</sub> to vegetation productivity is more agile in regions with continental climate such as the northwest. The results of correlation with grain yield comparison showed that good positive correlations were presented with TVPDI<sub>n</sub> in Liaodong Peninsula, northern North China Plain, and most of Qilian Mountains, southern edge of Qinling Mountains, middle and lower reaches of Yangtze River, and South China, indicating that TVPDI<sub>n</sub> has a high consistency in the changes of agricultural grain production in the above mentioned regions, and also proving the index in monitoring agricultural aridity and guiding agricultural production The good performance of the index in monitoring agricultural aridity and guiding agricultural production.</p> <p> This dataset is version 2.0, and covers all of China's territory, but the temperature-vegetation- precipitation dryness index of the open water surface are often set to a null value. Note:The data format is "TIF", the spatial resolution is "1 km", the time resolution is "1 month" and dimensionless. The pixel value is the NTVPDI value, and the closer the pixel value is to 0, the drier it is, and the larger the data, the wetter the land surface. The practical utility of this dataset is to compare the degree of dryness and wetness of China's land, to monitor short-term and medium-term droughts, and to substitute model parameters related to soil moisture. This is of great value to the impartial formulation of China's environmental and economic policies, regular monitoring and evaluation of drought and flood conditions. This product will be freely available to all users worldwide and will be continuously improved to suit new goals and needs.</p>
Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sounds.
<p>Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.</p>
Minimum dataset for "Liquid-activated quantum emission from pristine hexagonal boron nitride for nanofluidic sensing"
<p>Frames and (linked) localization table used to produce Fig. 2 of the manuscript https://www.nature.com/articles/s41563-023-01658-2.</p> <p>Details are given in 'README.txt'.</p> <p>The rest of the data is provided with the paper at the publisher website.</p>
Psychological Sense of Community: Inhaltsvalidität von Testverfahren in Abhängigkeit von Bezugsgemeinschaften
<p>The data set supplements the doctoral dissertation:</p> <p>Krumbholz, F. (2025). <em>Psychological Sense of Community: Inhaltsvalidität von Testverfahren in Abhängigkeit von Bezugsgemeinschaften</em> [Psychological Sense of Community: Content validity of tests in dependence of reference communities]. Wiesbaden: Springer. https://doi.org/10.1007/978-3-658-47919-0</p>
LAGOS-US LANDSAT: Data module of remotely-sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020
This data package, LAGOS-US LANDSAT, is one of the extension data modules of the LAGOS-US platform that provides six water quality estimates (chlorophyll, Secchi depth, dissolved organic carbon, total suspended solids, turbidity, and true water color) from remote sensing for lakes ≥ 4 ha in the conterminous U.S. (48 states plus the District of Columbia) for the years 1984-2020. These estimates are generated through machine learning models on in-lake water quality matchups from LAGOS-US LIMNO with Landsat 5, 7, and 8 whole lake median reflectance values and pixel-wise band ratios that are subsequently used to make predictions across the U.S. The LANDSAT module contains remotely sensed reflectance values for 136,977 of the 137,465 lakes ≥ 4 ha from the LAGOS-US research platform. Within the module are a total of 45,867,023 sets of reflectance values, a matchup dataset with a window of up to 7 calendar days with in situ data, and associated water quality parameter predictions for each reflectance set. Additional quality control flags are provided for predictions indicating whether reflectance extractions included negative values, the percent of the maximum pixels ever retrieved for that lake that the predictions are based on, and whether there are shared calendar day predictions due to scene overlap.
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.
Predicting aboveground and belowground processes in diverse forest ecosystems using remote sensing and in-situ measurements
The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. This data package examines the connections between aboveground and belowground processes in FAB2. This data package includes information on tree diversity and community composition, forest structure, forest understories, soil microbes, net nitrogen mineralization, and canopy nitrogen. A wide variety of data types are included, such as data from hyperspectral and LiDAR remote sensing, percent cover analysis, soil microbial analyses, and soil assays including C:N, pH, and net nitrogen mineralization. This data package is included in the submission of the manuscript entitled “Predicting aboveground and belowground processes in diverse forest ecosystems using remote sensing and in-situ measurements.”
Remote sensing based species distribution modelling based on GLCM and vegetation fractions for the city of Leipzig
<p>Modelling dataset and fractional vegetation cover dataset used in the study "Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting" Wellmann et al. 2020.</p> <p> </p> <p>Reference:</p> <p></p> <p>Wellmann, T., Lausch, A., Scheuer, S., & Haase, D. (2020). Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting. <em>Ecological Indicators</em>, <em>111</em>(April 2020), 106029. https://doi.org/10.1016/j.ecolind.2019.106029</p> <p></p>
Raw data for: Pressure and inertia sensing drifters for glacial hydrology flow path measurements
<p>Raw data for paper</p> <p>Title: Pressure and inertia sensing drifters for glacial hydrology flow path measurements</p> <p>Authors: A.Alexander, M.Kruusmaa, J.A. Tuhtan, A.J. Hodson, T.V. Schuler, A. Kääb</p> <p>Journal: The Cryosphere</p> <p>Year, 2020</p>
Data for resonator behavior in the paper "Self-sensing, tunable monolayer MoS2 nanoelectromechanical resonators"
<p>Data for resonator behavior in the paper "Self-sensing, tunable monolayer MoS2<br> nanoelectromechanical resonators", <em>Nat. Commun.</em> 10, 4831 (2019); DOI:<a href="https://doi.org/10.1038/s41467-019-12795-1">10.1038/s41467-019-12795-1</a>.</p>
Word-in-Context Target Sense Verification
<pre>Formally, WiC is framed as a <strong>binary classification</strong> task. Each instance in WiC-TSV consists of a target word <em>w</em> with a corresponding target sense <em>s</em> represented by either its definition (subtask 1) or its hypernym/s (subtask 2), and a context <em>c</em> containing the target word <em>w</em>. The task aims to determine whether the meaning of the word <em>w</em> used in the context <em>c</em> matches the target sense <em>s</em>. In the following table there are some examples from the dataset. </pre> <p> </p> <p>Subtasks</p> <p> WiC-TSV has <strong>three subtasks</strong> - participants can submit results in any of the subtasks:</p> <p>Subtask 1: Definitions</p> <p>In Subtask 1 systems make use of <strong>definitions</strong> for deciding whether the target word in context corresponds to the given definition or not.</p> <p>Subtask 2: Hypernyms</p> <p>In Subtask 2 systems make use of <strong>hypernymy</strong> information for deciding whether the target word in context is a hyponym of the given hypernym or not.</p> <p>Subtask 3: Definitions + Hypernyms</p> <p>In subtask 3 systems can make use of <strong>both</strong> sources of information, i.e., definitions and hypernyms.</p>
Global vegetation productivity from 1981 to 2018 estimated from remote sensing data
<p>The MUltiscale Satellite remotE Sensing (MUSES) global vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 1981 to 2018. GPP and NPP were estimated with a light use efficiency (LUE) model and MUSES leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products.</p> <p>The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>The detail information of the MUSES 5-km global GPP and NPP products are as below:</p> <p>Name: MUSES 5-km global GPP and NPP products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.05°</p> <p>Temporal resolution: 8 days</p> <p>Projection: geographic latitude/longitude</p> <p>Data format: Tiff</p> <p>Data type: integer (16bit)</p> <p>Upper left coordinates: -180°E, 90°N</p> <p>Scale factor: 100</p> <p>Unit: gCm<sup>-2</sup>d<sup>-1</sup></p> <p> </p> <p><span>Citation (Please cite these papers when these data are used)</span></p> <p><span>1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L.,</span><span> </span><span>New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. </span><span>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612.</span></p> <p><span>2. Wang, M.J.; Sun, R.; </span><span>Zhu, A.R.;</span><span> Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency</span><span> </span><span>and Gross Primary Productivity Using Three</span><span> </span><span>Different Approaches. <span>Remote Sensing</span>. <span>2020</span>, 12, 1003.</span></p> <p><span>3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. <span>Remote sensing. </span><span>2018, </span>10, 327.</span></p>
A global dataset of SST anomaly evolving processes retrieved from remote sensing products (GDSSTAEP V1.0)
<p> The GDSSTAEP includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively. </p> <p> </p>
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"
<p>Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron.</p> <p>2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘Functions’ contains functions called by the main codes.</p> <p>The main folder contains the following codes:</p> <p><em>Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1.</em></p> <p><em>Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2.</em></p> <p><em>Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3.</em></p> <p><em>Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4.</em></p> <p><em>Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1.</em></p> <p><em>Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.</em></p> <p> </p> <p>The subfolder ‘Data’ contains the data structures used for the different figures:</p> <p><em>data_figure1.mat</em></p> <p><em>data_figure2.mat</em></p> <p><em>data_figure3.mat</em></p> <p><em>data_figure4_MECA.mat</em></p> <p><em>data_figure4_Activation.mat</em></p> <p><em>data_figure4_Inactivation.mat</em></p> <p><em>data_figureS2_Activation.mat</em></p> <p><em>data_figureS2_Inactivation.mat</em></p> <p><em>data_Axon.mat</em></p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>Mouse_Name</em> : name of the mouse.</p> <p><em>Mouse_DateOfBirth</em>: date of birth of the mouse (YMD).</p> <p><em>Mouse_Sex</em>: sex of the mouse (F or M).</p> <p><em>Mouse_Genotype</em>: genotype of the mouse.</p> <p><em>Mouse_Drug</em>: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’).</p> <p><em>Mouse_Virus</em>: virus injected if any.</p> <p><em>Cell_Counter</em>; cell recorded in a given mouse.</p> <p><em>Cell_Type</em>: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP).</p> <p><em>Cell_Depth</em>: depth of the recorded cell relative to pia (µm).</p> <p><em>Cell_TargetedBrainArea</em>: cortical area targeted (C2 column of the barrel cortex = C2).</p> <p><em>Cell_Fluorescence</em>: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence</em>=TRUE is considered as a VIP neuron (cf <em>Cell_Type</em>).</p> <p><em>Sweep_Counter</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s).</p> <p><em>Sweep_Type</em>: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’; Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’; ).</p> <p><em>Sweep_Start_Time</em>: time at the beginning of the sweep recording (YMDHms).</p> <p><em>Sweep_WhiskerAngle</em>: C2 whisker angular position extracted from simultaneous high-speed video filming (deg).</p> <p><em>Sweep_WhiskerAngle_SamplingRate</em>: sampling rate of the whisker angle trace.</p> <p><em>Sweep_WhiskingOnset_Time</em>: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s).</p> <p><em>Sweep_MembranePotential</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>Sweep_MembranePotential_SamplingRate</em>: sampling rate of the membrane potential signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_CurrentInjected</em>: current injected into the cell (pA).</p> <p><em>Sweep_CurrentInjected_SamplingRate</em>: sampling rate of current signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_WhiskerStim_Name</em>: whisker to which the magnetic stimulus was applied to (C2 or B2&C2).</p> <p><em>Sweep_WhiskerStim_Time</em>: onset times of the whisker stimulus (s).</p> <p><em>Sweep_OptoStim_Power</em>: light power applied for optogenetic manipulations (% of the max power).</p> <p><em>Sweep_OptoStim_Time</em>: onset times of the light pulses for optogenetic manipulations (s).</p> <p><em>Cell_ID</em>: unique cell identifier (= <em>Mouse_Name</em>+<em>Cell_Counter</em>).</p> <p><em>SpikeThreshold</em>: spike threshold used to detect APs (mV).</p> <p><em>Trial_WhiskingOnset</em>: data structure containing the cut signals used to compute averaged responses around whisking onset times.</p> <p><em>Trial_WhiskerStim</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times.</p> <p><em>Trial_WhiskerStim_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements.</p> <p><em>Trial_WhiskerStim_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements.</p> <p><em>Trial_Opto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times.</p> <p><em>Trial_OptoAndWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements.</p> <p><em>Trial_OptoAndWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements.</p> <p><em>Trial_OnlyWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyOpto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.</p>
Fiber-optic Distributed Temperature Sensing and Wind Profiler Data during the Shallow Cold Pool Experiment
<p>The <a href="https://www.eol.ucar.edu/field_projects/scp">Shallow Cold Pool (SCP) experiment</a> was an <a href="https://www.eol.ucar.edu/observing_facilities/isfs">Integrated Surface Flux System (ISFS)</a> deployment conducted by the <a href="https://ncar.ucar.edu/">National Center for Atmospheric Research (NCAR)</a>, the <a href="https://ceoas.oregonstate.edu/">College of Earth, Ocean and Atmospheres (CEOAS)</a>, the <a href="https://bee.oregonstate.edu/">Department of Biological & Ecological Engineering (BEE)</a>, and the <a href="https://ctemps.org/">Center for Transformative Environmental Monitoring Programs (CTEMPS)</a> of <a href="https://oregonstate.edu/">Oregon State University</a>, in a shallow gully within the Pawnee Grasslands, Coloradp, USA. The primary goal of SCP was to examine the formation and maintenance of common shallow cold pools. These cold pools had not been previously examined with turbulence measurements and very little was known about their dynamics and interaction with gravity waves and other submesoscale motions.</p> <p>SCP consisted of a dense network of ultrasonic anemometers with 19 units being installed at 1m above ground level (agl) and 8 being mounted at different heights on a 20m high tower. In addition, air temperature, humidity, and carbon dioxide concentrations measurements were taken. This data can be found on <a href="https://data.eol.ucar.edu/project/SCP">https://data.eol.ucar.edu/project/SCP</a>.</p> <p>The unique observational technique featured in SCP was a cross-valley transect of the innovative active and passive fiber-optic distributed sensing technique (FODS) using a Distributed Temperature Sensing (DTS) unit (Model Ultima SR, Silixa, London, UK) as well as a ground-based acoustic wind profiler (SODAR, PCS2000-24, Metek GmbH, Elmshorn, Germany) in addition to the classical sonic anemometer network. The data archived in this submission publishes the FODS data and contains data for nine (9) nights between 16th November until 27th November between the hours of 19:00 and 05:00 MST (Local time). Details of the FODS setup are contained in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a> and <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. (2015</a>).<br> The fiber-optic cross-valley transect was 240m long and stretched from the North to the South shoulder of the gully and contained FODS observations at three heights (0.5m, 1m, 2m agl). By combining passive and active FODS, air temperatures and wind speeds were measured spatially continuously with a temporal and spatial resolution of 5s and 25cm, respectively. Air temperatures were measured with an unheated white-PVC jacketed optical glass fiber cable with an outer diameter of 0.9mm, while for the wind speed measurements an additional actively heated stainless-steel uncoated fiber-optic cable (1.3mm outer diameter) was deployed. Wind speeds were derived from the difference between the heated and unheated fiber-optic pair similar to a hotwire anemometer (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. 2015</a>).<br> The acoustic wind profiler (Sound Detection and Ranging, SODAR) was installed at the gully bottom about 200m down the gully from the fiber-optic transect (between station A18 and A19) and measured with a 5-min resolution, a 10-m gate range, and 17000 Hz, see map in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a>. The observational range was between 10m to 320m agl. The data provided is the cluster data output of the wind profiler, which is quality-controlled by the internal data processing software. The published data include horizontal wind speed (speed), wind direction (direction), unrotated along-wind component (u_unrot), unrotated cross-wind component (v_unrot), and unrotated vertical-wind component (w_unrot).</p> <p>By combining the fiber-optic distributed sensing, the sensor network, and the wind profiler, we were able to investigate specific class of submeso-scale motions in detail. The submeso-scale motion occurred frequently during SCP, significantly impacted air temperature, wind speed and direction, as well as the near-surface turbulence within less than a few minutes. These motions are not described or categorized by existing boundary layer regimes or concepts. Consequently, further research on submeso-scale motions using continuous FODS measurements is necessary to better understand the stable boundary layer.</p> <p> </p> <p>Pfister, L., Sayde, C., Selker, J., Mahrt, L., & Thomas, C. K. (2019). Classifying the Nocturnal Atmospheric Boundary Layer into Temperature and Flow Regimes. <em>Quart. J. Roy. Meteorol. Soc.</em>, <em>145</em>(721), 1515–1534. <a href="https://doi.org/10.1002/qj.3508">https://doi.org/10.1002/qj.3508</a></p> <p> </p> <p>Sayde, C., Thomas, C. K., Wagner, J., & Selker, J. S. (2015). High-resolution wind speed measurements using actively heated fiber optics. <em>Geophys. Res. Lett.</em>, <em>42</em>(22), 10,064–10,073. <a href="https://doi.org/10.1002/2015GL066729">https://doi.org/10.1002/2015GL066729</a></p>
Dataset: Six years of surface remote sensing of stratiform warm clouds in marine and continental air over Mace Head, Ireland
<p>A total of 118 stratiform water clouds observed by ground-based remote sensing instruments at the Mace Head Atmospheric Research Station at the West coast of Ireland from 2009 to 2015 were analyzed in terms of microphysical and optical characteristics as well as the impact of aerosols on these properties. The microphysical and optical cloud properties in the files were obtained using the algorithm SYRSOC (SYnergistic Remote Sensing Of Clouds).</p>
Social sensing of urban land use based on analysis of Twitter users' mobility patterns
<p>A companion dataset for the paper "Social sensing of urban land use based on analysis of Twitter users' mobility patterns". This dataset contains five files and one dictionary depicting the preferential return of Twitter users to their key locations and the urban land use types at these locations. More details can be found in the README file. </p>
3D Printed Antennas for mm-Wave Sensing Applicatins: Dataset
<p>This is the dataset related to the paper "3D Printed Antenass for mm-Wave Sensing Applications":</p> <p>This paper presents three low cost 3D printed antenna concepts for integration with a miniature mm-wave platform. The proposed solutions are optimized to operate in mm-wave ISM band (122GHz-123GHz). Different, inexpensive, detachable antennas can be used with the same platform for various RF sensing applications such as food safety, health and industrial.</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.