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Canopy Phenology, Remote Sensing and Microclimate at Harvard Forest 2006-2011
Our research at the Harvard Forest walk-up tower site examines how seasonality of canopy leaf area, or canopy phenology, influences, and is influenced by, local climate. As part of this activity we are studying methods for (and limits to) remote sensing of canopy phenology. To address this research topic, we have initiated measurements to quantify how radiation fluxes through a deciduous forest canopy are modified by seasonal canopy leaf dynamics. We continuously measure above- and below-canopy radiation fluxes at a variety of spectral bands (shortwave, photosynthetic) and with digital photography. These measurements provide a surrogate measures of canopy leaf area dynamics, and directly represent the radiation component of the surface energy balance. These measurements complement ongoing microclimate and eddy covariance measurements of water and carbon exchange at the EMS flux tower.
Species-level map of Smith Island, VA from remote sensing 2003
Species-level vegetation map for Smith Island off the tip of the Delmarva Peninsula in Virginia. Created by Charles M. Bachmann of the Naval Research Laboratory based classification of hyperspectral imagery using 3-season data (two PROBE2 scenes and a Hymap scene).
Satellite-based remote sensing of water clarity in the shallow coastal lagoons of Virginia 2013-2021
This dataset contains raw data, analysis products and code for a study of satellite-based estimation of water clarity. The files are: Match-up.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2. Satellite overpasses occurred +/- 0-1 days within in situ sampling. Valid remote sensing reflectance values (Rrs) from NASA SeaDAS (not masked by quality flags) were recovered at 12 of 17 in situ sampling sites: 6 ocean inlet sites, 2 lagoon site, and 3 mainland tidal creek sites. Therefore, there are 12 in situ sites available for comparison with satellite estimates. compare_L8S2.csv: Satellite data and water clarity estimates from 150 randomly sampled sites across 5 clear day images in the Virginia Coast Reserve, 2021. Satellite data are from Landsat-8 and Sentinel-2 and processed/atmospherically-corrected using NASA SeaDAS 8.2. The Virginia Coast Reserve is a coastal lagoon system located in Virginia, USA, near the southern tip of the Delmarva Peninsula. Due to low nitrogen inputs and frequent exchange with the Atlantic Ocean via inlets between barrier islands, water quality is high relative to many other coastal bays in the United States and worldwide. Spatial_averaging_analysis.csv: Secchi depths at in situ water quality sites at 10 m resolution (Sentinel-2 only), 30 m resolution (Landsat-8 and Sentinel-2), and 90 m resolution (Landsat-8 and Sentinel-2) where there are in situ match-ups. atmocorrect.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2 and ACOLITE Version 2022022.00. L8_ALL.csv: All Landsat-8 Secchi depth data available between 2013-2021 at in situ water quality sites. S2_ALL.csv: All Sentinel-2 Secchi depth data availab
WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine
<h1><em><strong>1. General description </strong></em></h1> <p>This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea<em> </em>and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine. </p> <h2><em><strong>1.1 Summary of the shared structural information</strong></em></h2> <p>The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a></p> <h2><em><strong>1.2 Summary of the shared geotechnical information</strong></em></h2> <p>Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016).</p> <p>The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., <em>Undrained shear strength</em>). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is:</p> <ul> <li><em>"epsilon50 [-]" </em>represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> <p>It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA). </p> <h2><em><strong>1.3 Summary of the shared measurement data</strong></em></h2> <p>Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks. </p> <p>The sensor network on NRT-WTG is illustrated in in <strong>Fig. 2, </strong>whereas a description of the sensor types is presented in <strong>Tab.1.</strong> The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall.</p> <table> <tbody> <tr> <td><strong>Data type </strong></td> <td><strong>Sensor type</strong></td> <td><strong>Fs (Hz)</strong></td> <td> <p><strong>Level mLAT (m)</strong></p> </td> <td><strong>Description </strong></td> </tr> <tr> <td>Acceleration (g) </td> <td>Piezo-electric acc. sensor (<strong>ACC</strong>)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (<strong>SG</strong>)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (<strong>FBG</strong>)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> <p><strong>Table 1. Description of sensor types.</strong></p> <p>The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data. </p> <p>The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset. </p> <p>In conjunction to the sensor channels presented in <strong>Tab. 1</strong>, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in <strong>Tab 2.</strong></p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> <p><strong>Table 2. </strong>List of provided SCADA parameters</p> <p> </p> <p>A summary of the selected intervals and relevant corresponding scada parameters is given in <strong>Tab 3</strong>.</p> <table> <tbody> <tr> <td><strong>Scenario </strong></td> <td><strong>T1 (UTC)</strong></td> <td><strong>T2 (UTC) </strong></td> <td><strong>Windspeed</strong></td> <td><strong>RPM </strong></td> <td><strong>Pitch </strong></td> </tr> <tr> <td>Parked</td> <td> <p>03/07 01:30</p> </td> <td> <p>03/07 03:30</p> </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> <p>05/07 22:30</p> </td> <td> <p>06/07 00:30 </p> </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> <p><strong>Table 3. </strong>Selected data intervals and relevant scada parameters</p> <p> </p> <h1><em><strong>2. Included in this version </strong></em></h1> <h2><em><strong>2.1 Version - 0.1.0</strong></em></h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul> <p> </p> <h1><em><strong>3. Importing parquet files </strong></em></h1> <p>To import the measurement data into Python it is recommended to use pandas:</p> <pre>import pandas as pd<br># Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) <br><br>Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
RIV06 Remote sensing in and around riparian zones at Konza Prairie
The goal of this project was the measure changes in woody vegetation cover over time, in riparian and non-riparian locations. The study was retrospective, using high resolution aerial imagery to identify areas dominated by grasslands, shrubs, trees, and woody plant that could not be differentiated as shrubs or trees (referred to as “unk” or “unknown”). These data help us understand rates of woody plant cover over time and how these changes might affect other populations (e.g., avifauna) and processes (e.g. hydrology). These data show and increase in woody plant cover across all three watersheds up until 2010, but with less woody plants expansion in the non-riparian zone of watershed and N1B. Through 2020, woody plant expansion continued in watersheds N1B and N4D. In N2B, tree cover decreased sharply in 2011 and remained low through 2020. This was expected due to the tree removal treatment. However, shrub cover increased rapidly over this same time frame, resulting in little net change in total woody cover (tree plus shrub cover). These results suggest that even an extreme intervention of repeated tree removal is not enough to return the riparian zone to a grassland state.
MCR LTER: Coral Reef: Quantifying 2019 coral bleaching; data for Kopecky et al., 2023 Remote Sensing
This data package contains a dataset generated using image AI-assisted image segmentation of live and dead corals within ortho-photomosaics of benthic reef habitat on the North shore fore reef of Moorea, French Polynesia. The orthophotomosaics were produced through a rigorous method of underwater photogrammetry that allowed for spatial and temporal co-registration of ortho-photomosaics of the same location over time (for full photogrammetric methods, see Nocerino et al. 2020: https://doi.org/10.3390/rs12183036). Using the image segmentation software, TagLab (see Pavoni et al. 2021: https://doi.org/10.1002/rob.22049), we quantified live and dead coral before and after a bleaching event to estimate the amount of coral loss as a result of this event. This data package also contains code necessary to conduct the analyses of the dataset described above and create data visualizations used in the manuscript “Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation”, published in the journal Remote Sensing in 2023, and as part of the dissertation of K. Kopecky. Analyses of these data and full methods descriptions can be found at https://doi.org/10.3390/rs15164077. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Zooplankton collected with a 1.4-m2 frame, 500-μm mesh Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009-2017
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. Other zooplankton reside in the mesopelagic zone and feed on detritus or on other animals. Depth-discrete density of zooplankton taxa was determined at process study stations on the annual Palmer LTER cruises along the western Antarctic Peninsula. Samples were collected with a 1.4-m2 frame, 500-μm mesh Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) towed obliquely to the surface from a depth of typically 500 m. MOCNESS tows were conducted in consecutive day-night pairs at each process study station. Zooplankton depth distributions vary between day and night as these animals conduct diel vertical migrations. Depth distributions also vary among zooplankton taxa based on species feeding ecology and life history traits. Zooplankton diel vertical migration contributes to the export of carbon and nutrients from the surface ocean to the mesopelagic zone.
Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction
<p>Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>.</p> <p>Data are stored in a netcdf format and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension.</p> <p>The included ipython notebooks provide examples and explanations for using these laboratory data.</p>
Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data
<p>This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.<br> 1. Initial data used<br> We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).<br> 2. The calculation method<br> The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.<br> 3. Data sets<br> The data set contains two files:<br> - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)<br> - the yearly average of the EI (period 01/2003 - 12/2018)<br> <br> Two images are joined with this data set:<br> - a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)<br> (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).</p> <p> - a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).<br> (file: Figure-enrichment-index.pdf)<br> <br> An improved version of this index will be available in a near future.</p>
Dataset for paper entitled "A Wireless Inductive Sensing Technology for Soft Pneumatic Actuators Using Magnetorheological Elastomers"
<p>This dataset includes all the experimental and FE results presented in the RoboSoft2019 paper "A Wireless Inductive Sensing Technology for Soft Pneumatic Actuators Using Magnetorheological Elastomers" (DOI: <a href="https://doi.org/10.1109/ROBOSOFT.2019.8722800">10.1109/ROBOSOFT.2019.8722800</a>).</p> <p>https://ieeexplore.ieee.org/abstract/document/8722800</p> <p>List of data:</p> <p>Fig.3-EXP_Coil size.xlsx<br> Fig.4-MRE Characterization.xlsx<br> Fig.6-FE modeling results.xlsx<br> Fig.8-Flat SPA Characterization.xlsx<br> Fig.9-EXP-external load.xlsx<br> Fig.10-Exp-Bending SPA.xlsx</p>
Dataset for paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing"
<p>This dataset includes all results presented in the paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing", published in Advanced Materials Technologies, vol.5, 2000659, 2020<br> DOI: 10.1002/admt.202000659.<br> URL:<br> https://onlinelibrary.wiley.com/doi/full/10.1002/admt.202000659</p> <p>List of data in this dataset:<br> Fig.1-Theoretical Analysis and Basic characteristics.xlsx<br> Fig.2-Experimental results-Coil design.xlsx<br> Fig.3-Cyclic Bending and Folding.xlsx<br> Fig.4-Folding Angle Sensing Performance Evaluation.xlsx<br> Fig.5-Case studies.xlsx</p> <p>All the data included in this dataset were collected and processed by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
Dataset for: A systematic literature review on user factors to support the sense of presence
<p>This dataset was created for a publication of Wiepke, Axel and Heinemann, Birte called "A systematic literature review on user factors to support the sense of presence". In this paper we used the PRISMA-method to collect Papers via Google Scholar on the third of April 2023 with the search term:<br>(framework OR model OR frameworks OR models OR processes OR ontologies) AND ((“personality traits” OR “personality variables” OR “personality factors”) AND “spatial presence”) AND (“virtual reality”) AND (learn OR edu\*)</p> <p>The results were pictured in "agreed" findings, where more than 50% of found studies supported a category of results and in "controversial", where there were significant findings, but less than 50% of the studies reported significance.</p> <p>This dataset contains:</p> <ul> <li>raw data for our literature review in .bib</li> <li>our main findings with categories in .csv</li> <li>a short Jupyter notebook script for one graphic in ipynb</li> <li>other graphics as .png</li> </ul>
Satellite remote sensing dataset for urban climate in Bergen and Prague (TURBAN-D09)
<p><span>Shared dataset contains remote sensing data necessary for a land surface temperature (LST) calculation. Layers were processed for two cities; Bergen (Norway) and Prague (Czech Republic). Original data were downloaded from the U.S. Geological Survey (https://doi.org/10.5066/P975CC9B). For a LST calculation, a land surface emissivity (LSE) algorithm was used.</span></p> <h3><span>Processing of LANDSAT-8 and LANDSAT-9 data</span></h3> <ol> <li><span>Reading metadata file for each scene (*MTL.txt)</span></li> <li><span>Reprojection of scene (note: Bergen scenes have two UTM Zones; 31N and 32N)</span></li> <li><span>Cloud cover raster (see folder 01_CloudCover)</span></li> <li><span>Calculation Top-Of-Atmosphere (TOA) reflectance for bands 10 and 11 (TB_10 and TB_11), saving to folder 02_TOA-reflectance</span></li> <li><span>Calculating of NDVI and Fractional Vegetation Cover (FVC), saving to folder 03_FVC-NDVI</span></li> <li><span>Calculating of LSE for both bands, same as different and mean LSE (folder 04_LSE)</span></li> <li><span>Calculating of LST</span></li> <li><span>Saving of metadata file (see *metadata.txt)</span></li> </ol>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Side-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the side-looking orientation, where the installation angle of radar is 90 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Out1_240522_160925.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Corner_160925.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesCornner_160925.mat' contains the timestamped velocity for the frames that are synchronised with the frames of front-looking radar.</p> <p>(The dataset for the front-looking radar is stored in another repository with DOI: 10.5281/zenodo.14215115)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesCornner_160925.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Corner_240522_160925_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Front-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the front-looking orientation, where the installation angle of radar is 0 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Lab_240522_160943.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Front_160943.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesFront_160943.mat' contains the timestamped velocity for the frames that are synchronised with the frames of side-looking radar.</p> <p>(The dataset for the side-looking radar is stored in another repository with DOI: 10.5281/zenodo.14174138)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesFront_160943.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Front_240522_160943_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
Supplementary data for analysing distributed temperature sensing (DTS) measurements from Helsinki, Finland
<p>Supplementary data used in the analysis of distributed temperature sensing (DTS) measurements from Helsinki, Finland, as described in a journal article manuscript "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland".</p> <p>Eddy covariance, radiation and precipitation data is provided from the SMEAR III station by the Institute for Atmospheric and Earth System Research at the University of Helsinki under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). The data can also be accessed programmatically via https://smear.avaa.csc.fi/. All SMEAR III data is time referenced to UTC+2.</p> <p>The 2-metre temperature data is provided by the Finnish Meteorological Institute under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). All Finnish Meteorological Institute data is referenced to UTC.</p>
DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring
<p> </p> <p> </p> <p>This dataset aims to support the work presented in</p> <blockquote> <p>Bouffaut, L., Taweesintananon, K., Kriesell, H. J., Rørstadbotnen, R. A., Potter, J. R., Landrø, M., Johansen, S. E., Brenne, J. K., Haukanes, A., Schjelderup, O., & Storvik, F. (2022). Eavesdropping at the Speed of Light: Distributed Acoustic Sensing of Baleen Whales in the Arctic. Frontiers in Marine Science, 9, 901348. <a href="https://doi.org/10.3389/fmars.2022.901348">https://doi.org/10.3389/fmars.2022.901348</a>.</p> </blockquote> <p>It contains recordings from a dark fiber optic (FO) cable converted into a distributed acoustic sensing (DAS) array of 120km long spreading from Longyearbyen, Svalbard, Norway, out to the open ocean, through Isfjorden. <a href="https://www.frontiersin.org/files/Articles/901348/fmars-09-901348-HTML/image_m/fmars-09-901348-g002.jpg">This DAS array</a>, measuring nano strain, was spatially sampled every ~4m and had a sampling frequency of 645.16 Hz, generating data stored into spatio-temporal matrices. </p> <p>The exact position of the FO cable is proprietary information belonging to Uninett. The space component is therefore given as a vector in “channel number” (sensing node number along the FO cable) and distance from the shore station (m).</p> <p>The data necessary to produce each manuscript example is saved into multiple files corresponding to subsequent groups of channels along the FO cable, to facilitate storage and sharing. The file naming system satisfies the following: Date in the format <em>YYYYMMDD</em>, UTC time at the beginning of the file, channels, whale_raw, duration of the file L<em>xx</em>s, all separated by underscores “_”. Data is shared as *.mat file saved in HDF format and readable in different programming languages. For example </p> <ul> <li>in <a href="https://www.mathworks.com/help/matlab/ref/load.html">Matlab</a> <pre><code>load('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <ul> <li>in <a href="http://https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat">Python</a> <pre><code>scipy.io.loadmat('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <p><strong>Each file contains the following variables</strong></p> <ul> <li><em>data: </em>The DAS-recorded nano strain data</li> <li><em>info_GL_m:</em> Used gauge length (m)</li> <li><em>info_nsamples</em>: Number of temporal samples in the file</li> <li><em>info_ntraces</em>: Number of spatial samples (channels) in the file</li> <li><em>info_sample_interval_s</em>: Sampling period (s)</li> <li><em>info_sampling_frequency_Hz</em>: Sampling frequency (Hz)</li> <li><em>info_SSI_m</em>: Spatial sampling interval (m)</li> <li><em>info_timestamp</em>: Date and time (UTC) of the first sample</li> <li>info_units: Global unit information</li> <li><em>x1_absolute_channel</em>: Vector containing the absolute channel number</li> <li><em>x1_distance_from_shore_m</em>: Vector containing the distance along the FO cable from shore (m)</li> <li><em>x1_position_m</em>: Vector containing the distance along the FO cable from the interrogator (m)</li> <li><em>x1_recwdepthz_m</em>: Vector containing the water column depth used as a proxy for the fiber optic cable depth at each sensing location (m)</li> <li><em>x1_relative_channel</em>: Vector containing the channel number</li> <li><em>x2_time_s</em>: Time vector (s)</li> </ul> <p> </p> <p><strong>List of the files and related manuscript examples</strong></p> <p>Example of at least 3 vocalizing baleen whales recorded simultaneously at three different locations along the Svalbard fiber optic DAS array - Figure 4 in Bouffaut et al. (2022) - between 35-95 km and on 2020-06-26 between 052440-052720 UTC</p> <ul> <li><em>20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch10001_to_ch15000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch15001_to_ch20000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch20001_to_ch25000_whale_raw_L160s.mat</em> </li> </ul> <p>Example of<strong> </strong>series of blue whale calls recorded with a move out on the Svalbard DAS array - Figure 5 & &B in Bouffaut et al. (2022) - between 85-90 km and on 2020-07-16 between 154300-155500 UTC</p> <ul> <li><em>20200716_154302_ch20001_to_ch21000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch21001_to_ch22000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch22001_to_ch23000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch23001_to_ch24000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch24001_to_ch25000_whale_raw_L720s.mat</em></li> </ul> <p>Example of a blue whale non-stereotyped call recorded inside Isfjorden and further used to provide correlated seismic profiles - Figure 6A n Bouffaut et al. (2022) - between 23-28 km on 2020-06-27 between 192255-192805 UTC</p> <ul> <li><em>20200627_192255_ch05001_to_ch07000_whale_raw_L310s.mat </em></li> <li><em>20200627_192255_ch07001_to_ch08500_whale_raw_L310s.mat </em></li> </ul> <p><strong>--------------</strong></p> <p><strong>Analysis tools </strong></p> <p>To reproduce the paper's result, we suggest using the following Python package available on <a href="https://github.com/leabouffaut/DAS4Whales">GitHub</a>:</p> <blockquote> <p>Léa Bouffaut (2023). DAS4Whales: A Python package to analyze Distributed Acoustic Sensing (DAS) data for marine bioacoustics (v0.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/10.5281/zenodo.7760187</a></p> </blockquote> <p>Here is an example of the use of the DAS4Whales package with this dataset's data format: <a href="https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa">https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa</a></p> <p><strong>--------------</strong></p> <p><strong>Please cite as </strong></p> <blockquote> <p>Léa Bouffaut and Kittinat Taweesintananon, “DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring”. Zenodo, Jan. 10, 2022. doi: <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.5823343">10.5281/zenodo.5823343</a>.</p> </blockquote> <p><strong>--------------</strong></p> <p><strong>Contact</strong></p> <p><a href="mailto:lb736@cornell.edu">Contact</a> | <a href="https://www.birds.cornell.edu/ccb/lea-bouffaut/">Webpage</a> | <a href="https://twitter.com/LeaBouffaut">Twitter</a></p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2020 in China
<p>In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278</p>
Terrasar measurement data of "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing"
<p>This data set was used to test of the method described in "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing". It consists of the related Terrasar data and a MATLAB file to import the data into MATLAB.</p>
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)
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DANDI Archive for NWB datasets
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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.