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1,961 results for “Sensing”
Data on spatial distribution of tracers for optical sensing of stream surface flow
<p>Here, we present the numerical and field data used in the manuscript entitled <em>Spatial distribution of tracers for optical sensing of stream surface flow</em>. Numerical data were synthetically generated considering different values of seeding density and aggregation levels of tracers for image-velocimetry analyses. In total, 33,600 synthetic images were generated. Field data correspond with the Basento River case study located in southern Italy. The respective footage at 12 fps, pre-processed and stabilised frames, and reference velocity data are provided in this dataset.</p>
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Isoprene in the Southern Ocean & remote sensed variables
<pre>%%%%%% Variables' names contained in "rodriguezrosetal_2020_isorems_data.csv" %%%%%% "id" = source of the data ("peg" = PEGASO cruise, "ace" = ACE Expedition, "pml" = ANDREXII, "ooki" = Ooki et al. 2015, "hack" = Hackemberg et al. 2017) "solar_time" = solar time estimated with solaR package on R. "month" = month of the year. "iso_pm" = Isoprene concentration (pM) "chla_fluo" = Chlorophyll-a (fluorometric) "chla_matchup" = Chlorophyll-a (MODIS Aqua) "sst_matchup" = Sea Surface Temperature (MODIS Aqua) "zeu_matchup" = Depth of the Euphotic Layer (MODIS Aqua) "poc_matchup" = Particulate Organic Carbon (MODIS Aqua) "pic_matchup" = Particulate Inorganic Carbon (MODIS Aqua) "mld_matchup" = Mixing Layer Depth (Holte et al. 2017) "par_matchup" = PAR radiation (MODIS Aqua) "lat" = Latitude (decimal degrees) "lon" = Longitude (decimal degrees)</pre>
Dataset for Supporting Information of the paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing"
<p>This dataset includes all results presented in the "Supporting information" of 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: 10.5281/zenodo.4099806, DOI: <a href="https://doi.org/10.1002/admt.202000659">10.1002/admt.202000659</a><br> URL:<br> https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fadmt.202000659&file=admt202000659-sup-0001-SuppMat.pdf</p> <p>List of data in this dataset:<br> Fig.S1-Theoretical Analysis.xlsx<br> Fig.S5-LM Coils Folding-Exp and NA.xlsx<br> Fig.S6-CoilFoldingDataARC.xlsx<br> Fig.S7-Cyclic Bending-1000 cycles.xlsx<br> Fig.S8-Cyclic Folding of FPC and LM Coils.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>
Supplementary files for ground-based remote sensing of sesame drydown
<p>This is a collection of supplementary materials in support of the article entitled "Using normalized difference vegetation index to estimate sesame drydown and seed yield," which was published on Dec. 10, 2020 in Journal of Crop Improvement, 35:4, 508-521. </p> <p>Supplementary file 1 - list of computer program. This list documents Minitab macro, dryrate.mac, that was written to do regression analysis for sesame drydown data collected at the Texas A&M AgriLife Research Center at Uvalde. The macro automates the process of running m x 9 linear regressions, where m represents the number of sesame plots/genotypes and 9 the number of vegetation indices.</p> <p>Supplementary file 2 - sample input data to macro dryrate.mac. The file contains nine vegetation indices measured five times from six plots during sesame drydown in the 2019 growing season.</p> <p>Supplementary file 3 - sample output data. This file contains the outputs of dryrate, including selected regression coefficients for<br>each of the six sesame plots.</p> <p>Supplementary file 4 - Figure S1. Scatter plots of average values of nine vegetation indices for 60 sesame genotypes measured five<br>times from 6 September 2019 to 6 October 2019.</p> <p>Supplementary file 5 - Table S1. Values of the slopes depicting the regressions between nine vegetation indices and time for 60 sesame genotypes.</p> <p>Supplementary file 6 - Table S2. Average values of different vegetation indices measured on Day 9 during drydown for 60 sesame<br>genotypes.</p> <p>Supplementary file 7 - Figure S2. Relationships between sesame seed yield and the slopes of regressions between nine vegetation indices and time during the drydown period.</p> <p>Supplementary file 8 - Figure S3. Histograms of the coefficients of determinations for the 60 linear regressions relating different<br>vegetation indices with time (days during sesame drydown).</p> <p>Supplementary file 9 - Figure S4. Ranking from the highest to the lowest of the maximum NDVI (normalized difference vegetation index) for the 60 sesame genotypes, along with the measured seed yields for the corresponding genotypes.<br> </p>
LimnoSat-US: A Remote Sensing Dataset for U.S. Lakes from 1984-2020
<p>LimnoSat-US is an analysis-ready remote sensing database that includes reflectance values spanning 36 years for 56,792 lakes across > 328,000 Landsat scenes. The database comes pre-processed with cross-sensor standardization and the effects of clouds, cloud shadows, snow, ice, and macrophytes removed. In total, it contains over 22 million individual lake observations with an average of 393 +/- 233 (mean +/- standard deviation) observations per lake over the 36 year period. The data and code contained within this repository are as follows:</p> <p><em>HydroLakes_DP.shp: </em>A shapefile containing the deepest points for all U.S. lakes within HydroLakes. For more information on the deepest point see https://doi.org/10.5281/zenodo.4136754 and Shen et al (2015).</p> <p><em>LakeExport.py</em>: Python code to extract reflectance values for U.S. lakes from Google Earth Engine.</p> <p><em>GEE_pull_functions.py: </em>Functions called within LakeExport.py</p> <p><em>01_LakeExtractor.Rmd:</em> An R Markdown file that takes the raw data from LakeExport.py and processes it for the final database.</p> <p><em>SceneMetadata.csv:</em> A file containing additional information such as scene cloud cover and sun angle for all Landsat scenes within the database. Can be joined to the final database using LandsatID.</p> <p><em>srCorrected_us_hydrolakes_dp_20200628: </em>The final LimnoSat-US database containing all cloud free observations of U.S. lakes from 1984-2020. Missing values for bands not shared between sensors (Aerosol and TIR2) are denoted by -99. dWL is the dominant wavelength calculated following Wang et al. (2015). pCount_dswe1 represents the number of high confidence water pixels within 120 meters of the deepest point. pCount_dswe3 represents the number of vegetated water pixels within 120 meters and can be used as a flag for potential reflectance noise. All reflectance values represent the median value of high confidence water pixels within 120 meters. The final database is provided in both as a .csv and .feather formats. It can be linked to SceneMetadata.cvs using LandsatID. All reflectance values are derived from USGS T1-SR Landsat scenes.</p>
Reconstructed remote sensing land surface temperature data in North America in 2002-2018
<p>In order to more accurately study the change trend of land surface temperature in North America in recent years, we combined remote sensing and meteorological station data and used various restoration models to generate more accurate and more complete remote sensing land surface temperature data. Our data covered the North American continent from 2002 to 2018, with a spatial resolution of 0.05°×0.05°. In order to facilitate the statistics of the data, we set the projection mode of the data as World_Cylindrical_Equal_Area. We collated the data from different time dimensions, including month, season and year.</p>
Supplementary Video: Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing
<p>Supplementary Video for Adv. Mater. Technol., DOI: 10.1002/admt.202000659<br> Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing<br> H. Wang,* M. Totaro, S. Veerapandian,M. Ilyas, M. Kong, U. Jeong, L. Beccai*</p> <p>This video (.MP4) includes the following supporting movies:</p> <p>Movie S1. FE modeling of planar coil folding and bending<br> Movie S2. Numerical analysis of planar coil folding and bending<br> Movie S3. Dynamic bending test of FPC coil<br> Movie S4. Dynamic folding test of LM coil<br> Movie S5. Vibration detection with a folded FPC coil<br> Movie S6. Self-sensing origami<br> Movie S7. Sensorized soft pneumatic actuator<br> Movie S8. Wearable sensing</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
Dataset for Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation
<p>Dataset of paper "Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation".</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted in an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing. The shared data contains the IQ data of both transmit and receive signals used during the measurement campaign.</p> <p>The file "main.m" shows how to process and plot the shared data.</p>
Survey data for "Remote Sensing & GIS Training in Ecology and Conservation"
<p>This file provides the raw data of an online survey intended at gathering information regarding remote sensing (RS) and Geographical Information Systems (GIS) for conservation in academic education. The aim was to unfold best practices as well as gaps in teaching methods of remote sensing/GIS, and to help inform how these may be adapted and improved. A total of 73 people answered the survey, which was distributed through closed mailing lists of universities and conservation groups.</p>
Word Sense Change Testset
<p><strong>Overview</strong></p> <p>This testset consists of 23 terms which have experienced word sense change during the past centuries. The main changes for each term were found using Wikipedia, dictionary.com and the Oxford English Dictionary. We consider major changes in usage as well as changes to sense. In cases where multiple (fine-grained) senses were available, we opted to accept the widest sense. E.g. for the term <em>rock</em> we consider a music sense without any distinction between different types of <em>rock music</em>, because our dataset is unlikely to have fine-grained sense differentiations. If a clear time point cannot be pinpointed, we choose the earliest possible. For comparison purposes we also chose a set of 11 terms that have experienced minimal change during the investigated period, i.e., stable terms.</p> <p> </p> <p><strong>Supplementary material</strong></p> <p><em>1. testset.txt </em></p> <p>Contains a list of all terms and the different change types for each term with a short description of the sense and change.</p> <p> </p> <p><em>2. Files of the kind "TERM.txt"</em></p> <p>The header tells us the term, which clustering coefficient was used, which similarity threshold and which similarity measure.</p> <p>A path starts with "Path:". </p> <p>A unit starts with "UNIT:"</p> <p>and the numbers following indicate 1. the number of years that the unit spans, and then a list of all years that the internal clusters stem from.</p> <p>E.g., UNIT: 83 1785, 1787, 1790, 1793, 1798, 1801, 1823, 1867, spanns 83 years and consists of clusters from year 1785, 1787, 1790 etc.</p> <p>Indentation shows the tree structure, more indentation means lower level branch in the tree.</p> <p>As an example, in AEROPLANE.txt unit UNIT: 23 1908, 1909, 1910, 1911, 1914, 1918, 1930, 1908 is the root node and the unit is related to UNIT: 27 1916, 1919, 1924, 1932, 1942, 1916.</p> <p> </p> <p><strong>Interesting findings</strong></p> <p>The longest units and paths are found for stable terms, e.g., <em>newspaper</em>. These are statistically significantly longer than the average units and paths for terms that later evolve.</p> <p><em>Newspaper</em> has a unit that spans 145 years and the first path spans from 1852 - 2007.</p> <p> </p> <p><em>FLIGHT.txt</em></p> <p>For the term <em>flight</em> we find that the first unit captures a name, <em>Flight & Robson</em> who were organ builders.</p> <p>The second unit (it its own path) represents the <em>flight</em> over a hurdle: UNIT: 28 1868, 1869, 1870, 1877, 1885, 1889, 1890, 1892, 1893, 1894, 1895</p> <p>There is a unit (it its own path) that represents the <em>flight</em> of a cricket ball: UNIT: 29 1938, 1957, 1966</p> <p>Finally, the last path represents <em>flight</em> as in a means of transportation, in particular for holidays, starting with UNIT: 19 1962, 1970, 1973, 1980</p> <p> </p> <p><em>TAPE.txt</em></p> <p>The first path for tape is a path related to <em>sowing tape</em>.</p> <p>Then there is a second path starting with UNIT: 38 1970, 1974, 2007 that takes up the <em>musical tape</em>.</p> <p>The last path end in the same units that the second path ends in, also related to the <em>musical tape</em>.</p> <p>The <em>music tape</em> and the <em>sowing tape</em> should be related because of their shape, but we cannot find any relation as there are few or no overlapping terms.</p> <p> </p>
Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"
<p>Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"</p> <p>The source files are to be used with the code on https://github.com/MaximeMaW/CompressedSensingMicroscopy3D (also archived in https://zenodo.org/record/439690)</p> <ol> <li>The files prefixed with "VIZ" are high resolution TIF visualizations.</li> <li>The files come from three experiments on two different setups: <ol> <li>A lattice light sheet microscope (LLSM): beads sample (filed termed "<strong>lattice-beads</strong>" and actin-labelled mESCs (files termed "<strong>lattice-phalloidin</strong>")</li> <li>An epifluorescence microscope: beads sample (files termed "<strong>epifluorescence</strong>")</li> </ol> </li> <li>The acquisitions were either performed using an identity measurement matrix (mimicking the plane-by-plane acquisition mode of a traditional z-stack): files termes "<strong>reference</strong>" or with a Fourier measurement matrix (described in the code mentioned above) with a compression ratio of 2 (files termed "<strong>compressed</strong>".</li> <li>The reconstructions were performed as described in the paper with the code mentioned above. Several reconstructions were computed from the same compressed images by simulating increasing compression ratios. To do so, reconstructions were performed by selecting a subset of the acquired planes (number indicated as "<strong>**frames</strong>")</li> <li>Reconstructions were sparsified using a 2D PSF model computed for our epifliuorescence setup and the LLSM (files termed "<strong>PSF_model</strong>"). These are provided as numpy arrays.</li> </ol> <p> </p>
MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters
<p><strong>The dataset</strong></p> <p>MagicBathyNet is a benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations. MagicBathyNet has been designed to be geographically well distributed. It’s coverage includes two very different coastal areas (in terms of water column characteristics and bottom type): i) Agia Napa area in Cyprus, covering a wide range of typical Mediterranean waters and seabed types, and ii) Puck Lagoon area in Poland, representing in a great degree Baltic Sea waters and bottom.</p> <p>MagicBathyNet contains 3355 RGB co-registered triplets of Sentinel-2 (S2), SPOT-6, and aerial image patches, complemented by 1244 RGB co-registered S2 and SPOT-6 doublets, 3354 DSM (Digital Surface Model) raster patches for the aerial patches and 3396 DSM raster patches for S2 and SPOT-6. Additionally, it contains 533 annotated raster patches for seabed habitat and type, facilitating supervised pixel-based classification. Each patch covers 180x180m, represented by 18x18 pixels in S2 imagery, 30x30 pixels in SPOT-6 imagery and 720x720 pixels in airborne imagery. </p> <p>For the implementation code and pre-trained models visit our project page: <a href="https://www.magicbathy.eu/magicbathynet.html">https://www.magicbathy.eu/magicbathynet.html</a> </p> <p><strong>MagicBathyNet.zip </strong>file contains the original dataset presented in the respective paper.</p> <p><strong>MagicBathyNet_extension_for_Swin-BathyUNet.zip</strong> file is added in the new version to support the experiments and the results presented in "Agrafiotis, P., & Demir, B. (2025). Deep learning-based bathymetry retrieval without in-situ depths using remote sensing imagery and SfM-MVS DSMs with data gaps. <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>, <em>225</em>, 341-361. <a href="https://doi.org/10.1016/j.isprsjprs.2025.04.020">https://doi.org/10.1016/j.isprsjprs.2025.04.020</a> "</p> <p> </p> <p> </p> <p><strong>Citation</strong></p> <p>If you use the code in this repository or the dataset please cite our paper:</p> <p>P. Agrafiotis, L. Janowski, D. Skarlatos, and B. Demir, <a href="https://arxiv.org/abs/2405.15477" target="_blank" rel="noopener noreferrer">"MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters"</a>, arXiv:2405.15477, 2024.</p> <p>or </p> <p>P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, "MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters," <em>IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium</em>, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355.</p> <p><strong>Folder structure</strong></p> <p>┗ 📂 magicbathynet/<br> ┣ 📂 agia_napa/<br> ┃ ┣ 📂 img/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 depth/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 gts/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📜 [modality]_split_bathymetry.txt<br> ┃ ┣ 📜 [modality]_split_pixel_class.txt<br> ┃ ┣ 📜 norm_param_[modality]_an.txt<br> ┃<br> ┣ 📂 puck_lagoon/<br> ┃ ┣ 📂 img/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 depth/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 gts/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📜 [modality]_split_bathymetry.txt<br> ┃ ┣ 📜 [modality]_split_pixel_class.txt<br> ┃ ┣ 📜 norm_param_[modality]_pl.txt</p> <p> </p> <p><strong>Package for benchmarking MagicBathyNet dataset</strong></p> <p>Donwload the package for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification here:</p> <p><a href="https://github.com/pagraf/MagicBathyNet">https://github.com/pagraf/MagicBathyNet</a></p> <p> </p> <p><strong>Version history</strong></p> <p>v1.0.0 - First release</p> <p> </p> <p><strong>License</strong></p> <p>Dataset: Creative Commons Attribution Non Commercial 4.0 International</p> <p>Code: Attribution-NonCommercial-ShareAlike 4.0 International License</p> <p>Copyright (c) 2024 The MagicBathyNet Authors</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work was part of the project MagicBathy which is a research project funded by the European Commission for the period 2023-2025. It is funded under the HORIZON Europe MSCA Postdoctoral Fellowships - European Fellowships (GA 101063294).</p> <p>European Space Agency (ESA) is also acknowledged for providing the SPOT-6 images within its TPM programme in the frame of proposal PP0092443 and Airbus for being the provider of the original SPOT-6 images. The Dep. of Land and Surveys of Cyprus is acknowledged for providing the LiDAR reference data for Cyprus.</p>
In-vehicle Sensing Datasets (e.g., GPS, IMU, and OBD data) In Florida
<p>This data collection and distribution is supported by NSF OAC-1948066. These datasets include a total of 497 trajectory datasets over 2404 km. Each dataset includes 6DOF IMU data (e.g., triaxial acceleration and gyroscope data), GPS data (e.g., latitude, longitude, altitude, speed over ground, the number of connected satellites, Course Over Ground), and OBD data (e.g., rpm, throttle positions, accelerator positions, RPM, air temperature, etc.). The data collection mechanism adopts the asynchronous sampling technologies that make capturing sensor data independent of the recorded signal. Therefore, datasets collected from each sensor are logged in separate files (e.g., time_obd.jsonl, time_gps.jsonl, time_obd.jsonl). By matching the time when each sensor module initiated to log data, one can aggregate/fuse multi-type in-vehicle sensing data.</p><p> </p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: RLG Dataset Coniston A
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full descirption of conducted trials and data structure is mentioned in the attached pdf document.</p><p>STREAM trials were conducted by the University of Birmingham (UoB) and the University of St. Andrews from 29/08/2022 - 02/09/2022 at Coniston Lake in the UK. The primary aim was to gather propagation data across lakes and measure the returns from the lake surface. The data will be used to develop algorithms to extract the information needed for pilotage.</p><p>The experiments were performed with radars operating in the 79, 150, and 300 GHz bands to investigate the Doppler and imaging capabilities of these radars.</p><p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for data collection campaign.</p><p>Contact: a.a.a.pirkani@bham.ac.uk or m.s.gashinova@bham.ac.uk</p>
Dataset for: "Additively manufactured degradable piezoelectric microsystems for sensing and actuating"
<h2>Dataset for "Additively manufactured degradable piezoelectric microsystems for sensing and actuating"</h2><p><strong>Morgan Monroe1,2, Nicolas Fumeaux1, L. Guillermo Villanueva2, and Danick Briand1</strong></p><p><strong>1Soft Transducers Laboratory (LMTS), EPFL, Switzerland</strong></p><p><a href="mailto:morgan.monroe@epfl.ch">morgan.monroe@epfl.ch</a>, <a href="mailto:danick.briand@epfl.ch">danick.briand@epfl.ch</a></p><p><strong>2Advanced NEMS Laboratory (A-NEMS), EPFL, Switzerland</strong></p><p> </p><p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled "Additively manufactured degradable piezoelectric microsystems for sensing and actuating" </p><p><strong>DOI: 10.1002/admt.202300745</strong></p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>Manuscript Abstract: </strong></h3><p>The increasing global overabundance of electronic waste and concerns regarding the energy and material-intensive processes associated with traditional electronics manufacturing is driving the development of solution processed, degradable electronics. In particular, solution processed, degradable piezoelectrics have widespread potential in sustainable electronics, due to their diverse use in both sensing and actuating applications and the current industry predominance of lead-based materials. Yet current eco-friendly multi-material printing processes are limited by both the conventional challenges of multilayer process integration as well as the low-temperature thermal constraints of biodegradable materials. In this study, we present a novel approach to the fabrication of additively manufactured and sustainable piezoelectric devices made with degradable electrode materials on paper substrates. The screen-printed, eco-friendly KNbO3 piezoelectric transducers are combined with degradable carbon- or zinc-based conductive inks. We evaluate the physical, dielectric, and piezoelectric properties of the devices, assessing the influence of electrode material on device performance. We report on effective piezoelectric coefficients as high as 4.6 pC N-1 and 5.1 pC N-1 for printed piezoelectric devices on paper substrates with carbon and zinc electrodes respectively. We then demonstrate the applicability of the developed technology in both sensing and actuating applications. Thus, we present the first instance of sustainable fully additively manufactured piezoelectric force sensors and acoustic speakers. By demonstrating entirely printable piezoelectric devices compatible with various green electrode materials, we work to develop more complex sustainable printed piezoelectric technologies in the future.</p><p> </p><h3>The data set consists of the following folders:</h3><ul><li>Device design files</li><li>Physical characterization data</li><li>Dielectric characterization data</li><li>Force Sensor Demonstrator data</li><li>Speaker Demonstrator data</li></ul><p>Below is a detailed description of the data types and contents of each folder. In many files, the naming convention includes one of two key material indicators. In reference to ink properties, the term "Ink Active Material" indicates the primary ingredient in the ink being characterized. This is either KNbO3, Zinc, or Carbon. The specific ink compositions can be found in the Methods portion of the associated manuscript. In reference to devices being characterized, the term "Electrode Material" indicates the primary component of the printed or deposited electrode layers of the devices (as the piezoelectric layer is always the printed KNbO3 film). The electrode materials are either "Gold" (referring to thermally evaporated Gold of approximately 100nm thickness) which was used as a reference electrode material, "Zinc" (referring to screen printed zinc ink as described in the manuscript), or "Carbon" (Referring to screen printed carbon ink as described in the manuscript).</p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>Data types</strong></h3><p>There are 13 file types in this data set: .pdf, .png, .tif, .txt, .dat, .csv, .svg, .stl, .py, .vi, .dwf3work, .aup3, .wav</p><ul><li><strong>.pdf files </strong><ul><li>pdf files in this repository contain summaries of images used in physical characterization, aggregated for ease of visualization. These are exported from Photoshop files and thus include full image information.</li></ul></li><li><strong>.png and .tif files </strong><ul><li>These files contain scanning electron microscopy images of the printed samples on silicon, in cross-section.</li></ul></li><li><strong>.txt, .csv, and .dat files </strong><ul><li>These files contain raw data from device characterization. These file types are comma delimited and the files can be opened with text editors such as Notepad++. Column headers elaborate on the data contained within each file. </li></ul></li><li><strong>.svg files </strong><ul><li>These files contain vector data displaying the full designs of devices fabricated in this study. This data can be opened with a vector graphics editor such as InkScape. The various layers of each file denote a different layer of the device design, and can be treated individually as masks for the relevant layers.</li></ul></li><li><strong>.stl files </strong><ul><li>These files contain design information for 3D components. These files can be opened with any 3D design software such as FreeCAD. All .stl files included in this repository are reproduced from Shannon Ley (<a href="https://pinshape.com/items/36134-3d-printed-3d-printed-headphones">https://pinshape.com/items/36134-3d-printed-3d-printed-headphones</a>).</li></ul></li><li><strong>.py files </strong><ul><li>These files contain python scripts used to process the raw data after collection. These scripts can be opened and edited with standard python scripting interfaces. Each script is commented with descriptions of the overall file as well as in-line comments for user orientation. </li></ul></li><li><strong>.vi files </strong><ul><li>These files contain LabVIEW scripts used to collect raw data during measurements. These scripts can be opened and edited with LabVIEW from version 2020. The script is commented with descriptions of the overall file as well as in-line comments for user orientation. Only one file in this dataset is of this filetype.</li></ul></li><li><strong>.dwf3work files</strong><ul><li>These files contain Digilent Waveforms workspaces used to collect raw data during measurements. These scripts can be opened using the opensource Digilent Waveforms software (<a href="https://digilent.com/shop/software/digilent-waveforms/">https://digilent.com/shop/software/digilent-waveforms/</a>) to view the recorded data and all relevant recording parameters at time of measurement. </li></ul></li><li><strong>.aup3 files </strong><ul><li>These files contain Audacity workspaces used to collect and process audio data during speaker characterization measurements. These files can be opened using the opensource Audacity software (<a href="https://www.audacityteam.org/">https://www.audacityteam.org/</a>).</li></ul></li><li><strong>.wav files </strong><ul><li>These files are audio files of the data exported from Audacity during speaker device characterization and can be opened with any audio processing software.</li></ul></li></ul><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>01 Characterization Device Design</strong></h3><p>This folder contains the design files associated with the fabrication of the basic printed devices used for characterization studies.</p><h4><strong>01 CapacitorsALL.svg</strong></h4><p>A vector file containing the whole-device design of the capacitor style devices used in primary characterization. Each layer of the file is a layer of the device, and was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.</p><h4><strong>02 BottomElectrode.svg</strong></h4><p>A vector file containing the design of solely the bottom electrode layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.</p><h4><strong>03 PiezoelectricLayer.svg</strong></h4><p>A vector file containing the design of solely the piezoelectric layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.</p><h4><strong>04 TopElectrode.svg</strong></h4><p>A vector file containing the design of solely the top electrode layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.</p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>02 Physical Characteristics</strong></h3><p>This folder contains all the data associated with characterizing the physical properties of the piezoelectric devices in this manuscript.</p><h4><strong>01 Particle Size Analysis</strong></h4><p><strong>01 Images</strong>: A folder of SEM images as .png files. These images are cross-sectional SEM images of samples used to evaluate the particle size distribution for the KNbO3, Zinc, and carbon powders using in device fabrication. A second set of images with an appended filename ("traces") in the same folder portrays the sizing lines used to randomly sample particles for sizing.</p><p><strong>02 ParticleSizeDistribution_RAW.csv</strong>: A data file containing the raw measurements collected using the above images in tandem with ImageJ processing software. Data was used to produce histograms of particle size distributions for the KNbO3, Zinc, and Carbon particles.</p><p> </p><h4><strong>02 Profilometry</strong></h4><p><strong>01 Profilometry Data</strong>: A folder of raw data collected as profilometry measurements in the form of .dat files. Data files are labelled using the convention "Profile_[Electrode Material]_DeviceStack_Sample[#].dat" (Ex: "Profile_Carbon_DeviceStack_Sample2.dat"). All measurements begin with a measure of the paper substrate as reference before approaching the sample, where it crosses all 3 layers of the sample before returning to the paper substrate, creating a series of layer-cake-like steps from which layer thicknesses can be determined.</p><p><strong>02 ProfilometeryDataPlotter.py</strong>: a python script to batch import and plot the above collected raw profilometry data. </p><p> </p><h4><strong>03 Cross-section Optical Images</strong></h4><p>A folder containing optical microscopy images of the printed devices in cross-section when printed on paper substrates. The naming convention used is "Optical_[Electrode Material]_[microscope Magnification]_[Image number in that condition].tif" (Ex: "Optical_Carbon_x50_01.tif").</p><p> </p><h4><strong>04 Cross-section SEM Images</strong></h4><p>A folder containing scanning electron microscopy ("SEM") images of the printed devices in cross-section when printed on silicon substrates. The naming convention used is "SEM_[Electrode Material]_[microscope Magnification]_[Image number in that condition].tif" (Ex: "SEM_Carbon_1k_01.tif").</p><p> </p><h4><strong>05 Ink Rheology </strong></h4><p>Summary data collected during rheological measurements of the KNbO3, Zinc, and Carbon inks, as .txt files. The naming convention is "Viscosity_[Ink Primary Component]Ink.txt" (Ex: "Viscosity_ZincInk.txt")</p><p> </p><h4><strong>06 Degradation Study </strong></h4><p><strong>01 DegradationStudy_MassChange.csv</strong>: A data file containing the raw measurements collected during the degradation studies. Data includes the specific samples under test and their measured mass at specific dates, as measured after drying.</p><p><strong>02 Degradation Study Images.pdf</strong>: a .pdf file containing the raw degradation images used in this study, correlated to the dates of imaging and text conditions. </p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>03 Electrical Characteristics</strong></h3><p>This folder contains all the data associated with characterizing the dielectric and piezoelectric properties of the piezoelectric devices in this paper.</p><h4><strong>01 Impedance Data</strong></h4><p><strong>01</strong> <strong>ImpedanceDataAnalysis.py </strong></p><p>The python analysis script used to batch analyze and plot the raw impedance data collected for these samples. Takes the files in the associated folder as input and outputs arrays of measured capacitance and permittivity values for the data analyzed, as well as plots of the processed impedance data as a function of frequency.</p><p><strong>02 Raw Impedance Data</strong></p><p>The raw impedance data used to evaluate the dielectric properties of the devices, including two file types: </p><p><strong>".csv"</strong>: Impedance data collected for capacitor style devices of each electrode type, in the after exporting the relevant impedance, phase, and capacitance data from the raw collection file format. Naming convention is "Impedance_[electrode material]_ALL.csv" (Ex: "Impedance_Carbon_ALL.csv").</p><p><strong> ".dwf3work"</strong>: Raw Impedance data collected for capacitor style devices of each electrode type, in the original files as collected using Digilent Waveforms Software (open source). Data is split into two files based on the size of the capacitors being measured, (either 5 and 10 mm2 devices, or 20 and 30mm2 devices). The naming convention used is "Impedance_[Electrode Material]__[capacitor surface areas tested].dwf3work" (Ex: "Impedance_Zinc_5&10mm2.dwf3work").</p><p> </p><h4><strong>02 Berlincourt Data</strong></h4><p>This folder contains all the data associated with characterizing the piezoelectric properties of the devices in this manuscript. It includes 3 files, sorted by the electrode material of the devices under test, and reports the average d33,eff values (over 3 repetitions) measured for those devices based on the applied poling Voltage and field. The naming convention used is "BerlincourtData_[Electrode Material].csv" (Ex: "BerlincourtData_Carbon.csv").</p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>04 Force Sensor Demonstrator</strong></h3><p>This folder contains all the data associated with force sensor demonstrator reported on in the associated manuscript.</p><h4><strong>01 TouchGrid.svg</strong></h4><p>A vector file containing the whole-device design of the force sensor device used in this sensing demonstration. Each layer of the file is a layer of the device, and was used for the ordering and fabrication of associated screen printing meshes.</p><p> </p><h4><strong>02 VoltageMeasurement.vi</strong></h4><p>A LabView script file used for recording the output voltage of the piezoelectric devices as a function of time. To be used in combination with an Agilent 34410A or 34411A Multimeter. Outputs Voltage response as a function of time from the initiation of the recorded measurement.</p><p> </p><h4><strong>03 Single Force Data</strong></h4><p>This folder contains data associated with characterizing the force sensor demonstrators reported on in this manuscript. It includes 3 sub-folders, sorted by the electrode material of the devices under test. The naming convention used for the sub-folders is "SingleForce_[Electrode Material]" (Ex: "SingleForce_Zinc"). Within each sub-folder is a series of data files (.csv) detailing the test and data conditions. Each sample was compressed with a set force in a series of pulses. The naming convention used for the files in the sub-folders is "[Electrode Material]_[Max Applied Force]_[MeasuredData].csv" (Ex: "Gold_12.5N_MM.csv"), where the measured data is either "_F" or "_MM" for Force data or Multimeter data respectively.</p><p><strong>"_F.csv": </strong>The files include data recorded by the Instron Pull tester of the force applied to the sample as a function of time. This data is associated with the measured voltage in the paired "_MM.csv" file. </p><p><strong>"_MM.csv": </strong>The files include data recorded by a LabVIEW script in tandem with an Agilent multimeter of the voltage produced by the sample associated with the incident force in the paired "_F.csv" file. </p><p> </p><h4><strong>04 Stepped Force Data</strong></h4><p>This folder contains data associated with characterizing the force sensor demonstrators reported on in this manuscript. It includes 3 sub-folders, sorted by the electrode material of the devices under test. The naming convention used for the sub-folders is "SteppedMeasurements_[Electrode Material]" (Ex: "SteppedMeasurements_Gold"). Within each sub-folder is a series of data files (.csv) detailing the test and data conditions. Each sample was compressed with a series of pulses successively increasing in applied force. The naming convention used for the files in the sub-folders is "[Electrode MaterialSteppedSweep[#]_[MeasuredData].csv" (Ex: "Carbon_SteppedSweep1_F.csv"), where the measured data is either "_F" or "_MM" for Force data or Multimeter data respectively, and the # indicates the repetition number of that specific measurement. Other details are the same as those described above for the subfolder data of 03 Single Force Data.</p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>05 Speaker Demonstrator</strong></h3><p>This folder contains all the data associated with speaker demonstrator reported on in the associated manuscript.</p><h4><strong>01 Speaker Design</strong></h4><p><strong>01</strong> <strong>SpeakersBuzzers.svg </strong></p><p>A vector file containing the design of the components used to fabricate the piezoelectric buzzer for the speaker demonstrator. This includes a baseplate onto which the piezoelectric devices were adhered, two rings as standoffs, and two long traces used for the "contact wires". All components were lasercut from cardboard or cardstock components.</p><p><strong>02</strong> <strong>3D Design Files </strong></p><p>A folder containing the design files (.stl) for 3D printing of the headphone chassis, including the headband, ear cans, and baffles. All designs were provided open source by Shannon Ley (https://pinshape.com/items/36134-3d-printed-3d-printed-headphones).</p><p> </p><h4><strong>02 Speaker Data</strong></h4><p><strong>01</strong> <strong>RawAudioRecordings </strong></p><p>A folder containing the raw audio recordings used in speaker characterization, in the form of .aup3 files, directly from the recording software (Audacity). File naming convention is "[Electrode Material]_[Device Size used]_[Recording sampling rate]_RAW.aup3" (Ex: "Zinc_5x30mm2_44.1khz_RAW.aup3").</p><p><strong>02</strong> <strong>Exported Audio </strong></p><p>A folder containing the exported audio recordings used in speaker characterization after trimming to a consistent length of 15s, and exporting from the recording software (Audacity) in the form of .wav files. Each sub-folder has naming convention of "[Electrode Material]_[Device Size used]_[Recording sampling rate]_export" (Ex: "Zinc_5x30mm2_44.1khz_export"), and contains a number of files. Each file within these folders is labelled with the frequency at which the speaker was actuated for that recording data. These files were then imported into Origin, where an FFT was used to extract the amplitude of the recorded data at that specific actuating frequency.</p><p> </p><h4><strong>03 Speaker LDV</strong></h4><p>A folder containing the laser doppler vibrometry data collected for speakers with either Zinc or Carbon-electroded piezoelectric actuators. The data comes in two formats: as-produced from Digilent Waveforms (.dwf3work), or exported (.csv) files. The naming convention for the raw data is "[Electrode Material]_[Speaker active area]_LDV_Raw.dwf3work" (Ex: "Zinc_5x30mm2_LDV_Raw.dwf3work"). The naming convention for the exported data is "[Electrode Material]_[Speaker active area]_LDV_Export.csv" (Ex: "Zinc_5x30mm2_LDV_Export.csv").</p>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN Distributed temperature sensing, fluxes from eddy covariance measurements, and auxiliary measurements from and at the i-Box station (VF-0) Kolsass
<p><strong>Introduction</strong></p> <p>During the TEAMx-precampaign (TEAMx-PC22) in summer 2022, the Innsbruck Box (i-Box) station at the valley floor in Kolsass (CS-VF0) was extended by a vertical array with fiber-optic distributed temperature sensing (DTS). The i-Box is a testbed for studying boundary layer processes in highly complex terrain (<a href="http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00246.1">Rotach et al. (2017)</a> and <a href="https://fileshare.uibk.ac.at/f/9f1101851849439483de/">i-Box WIKI</a> for further information). The mountain boundary layer is investigated using a 17 m high tower with multi-level observations of turbulence, wind speed, and temperature. DTS measurements with a spatio-temporal resolution of 0.127 m and 1 s were added to these profile measurements. The combination of DTS measurements and point observations has the capability of resolving sub-meso scale motions (<a href="https://doi.org/10.1007/s10546-021-00618-0">Pfister et al. 2021</a>) and can reveal processes within the boundary layer during the morning and evening transition (<a href="https://doi.org/10.1029/2020GL092238">Fritz et al. 2021</a>).</p> <p>The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://doi.org/10.15203/99106-003-1">Serafin et al. (2020)</a> and in <a href="https://doi.org/10.1175/bams-d-21-0232.1">Rotach et al. (2022)</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Location</strong></p> <p>The i-Box valley-floor site is located on the almost flat floor near the town of Kolsass within the Inn Valley roughly 20 km east-north-east of Innsbruck. The site is characterized by different types of agricultural land. The 17-m high tower is a full energy-balance station and is instrumented with three vertical levels of turbulence measurements. The exact location is 47.305341°N, 11.62219°E (UTM: 698215.03 E, 5242420.95 N) at 545 m above mean sea level.</p> <p><strong>2. Temporal coverage</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. The i-Box station is running continuously, however, the provided data only covers the period when DTS data is available. The DTS array was running during the following periods:</p> <ul> <li>08.06.-14.06.2022</li> <li>28.06.-18.07.2022</li> </ul> <p><strong>3. Instrument details</strong></p> <p><em><strong>Distributed temperature sensing</strong></em></p> <p>For spatially continuous measurements of vertical temperature profiles at this tower, a DTS array was installed. Temperatures were measured with two channels at 1~Hz with a spatial resolution of 0.127~m. The used DTS instrument was an Ultima-HS (Silixa Ltd., Hertfordshire, UK) which was combined with a fibre-optic cable (900 µm outer diameter; AFL Telecommunications, Spartanburg, SC, USA) consisting of a bend-optimised optical fiber (125 µm with 50 µm core), buffered with Kevlar in a white plastic jacket. The fiber-optic cable was installed vertically towards the west of the tower such that two temperature profiles could be measured simultaneously. For the full array the approximately 450 m long fiber-optic cable was running from one DTS channel through a warm and cold reference bath towards the tower, then up and down the 17-m tower, and back through the baths towards the second channel. Accordingly, the array could be measured in both directions. For mounting at the top and bottom of the tower PVC pipes (diameter 15 cm) were used. The setup with two channels allows for sampling the array in both directions. Before entering the reference baths roughly 200 m were left on the spool slightly affecting signal-to-noise ratio. The vertical array was mapped by cooling packs. Both reference baths observed at the beginning and end of each fiber-optic cable yielded to four reference sections at two temperatures. The array was a double-ended configuration observed as two single-ended configurations which is different from the manufacturer's provided double-ended mode (<a href="https://doi.org/10.3390/s20082235">des Tombe et al. 2020</a>, <a href="https://doi.org/10.5194/essd-14-885-2022">Lapo et al. 2022</a>). Reference temperature probes were PT100 from the Ultmia-HS itself. DTS data was calibrated using the weighted-least squares approach described in <a href="https://doi.org/10.3390/s20082235">des Tombe et al. (2020)</a> and implemented in the <em>dtscalibration</em> software package (<a href="https://doi.org/10.5281/zenodo.7111585">des Tombe et al. 2022</a>) and all processing was completed using the <em>pyfocs</em> software package (<a href="https://doi.org/10.5281/zenodo.7111585">Lapo and Freundorfer 2020</a>) . As the fiber-optic cable runs through both calibration baths before and after the array creating four locations within a temperature controlled environment. Of those locations three are used for calibration (every time step) and the fourth is used for validation. A schematic of the setup is given within the files.</p> <p>After calibration a mean bias of -0.05 K and root mean squared difference of 0.22 K was determined with the validation water bath.</p> <p>Unfortunately the mounting towards the west created an artifact as the tower was partially shading the fiber-optic cable creating unphysical temperature gradients. Accordingly, data from 04.00 - 09.00 UTC should not be used for data analysis. The given data is only a single fiber of the paired vertical sections on the 17-m tower. Artifacts from the plastic ring holders are removed from the fiber.</p> <p>The DTS experiment was named the Innsbruck DTS Experiment (InnDEX22), hence, data names were chosen accordingly. But keep in mind that InnDEX22 was part of TEAMx-PC22.</p> <p><em><strong>i-Box tower</strong></em></p> <p>Full site description and all data exceeding the DTS observations can be found on <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a>. Utilized and uploaded data are mainly within three categories: eddy covariance (EC) fluxes at level 1 (4 m agl) and at level 2 (8.7 m agl) and low-frequency data:</p> <ul> <li>EC flux level 1:<br>Combination of ultrasonic anemometer CSAT3 (orientation from North: 30°) and infrared gas analyzer EC150 from Campbell Scientific</li> <li>EC flux level 2:<br>Ultrasonic anemometer CSAT3 (orientation from North: 30°) from Campbell Scientific</li> <li>low-frequency data: <ul> <li>pressure: Setra 278 (Setra Systems, Inc., Boxborough, Maine, USA) at 1.4 m agl</li> <li>radiation: ventilated CGR4 pyrgeometers and CMP21 pyranometers (Kipp & Zonen, Delft, Netherlands) at 2 m agl</li> <li>temperature profile: Rotronic HC2-S3 actively ventilated at 2, 4, 8.7, and 16.9m</li> <li>wind profile: 2D ultrasonic anemometer Gill Windsonic4 at 2, 4, 6, and 12m</li> </ul> </li> </ul> <p>For the EC processing further quality criteria can be applied to assure good data quality. More information on processing of data and quality criteria is given here:</p> <ul> <li>EC fluxes processing:<br>Averaging interval of 30 min performed by the software <a href="https://www.geos.ed.ac.uk/homes/jbm/micromet/EdiRe/">EdiRe</a><br>Processing includes despiking; double-rotation of the wind components; detrending with a recursive filter and a time constant of 200 s; and applying frequency-response corrections, heat-flux corrections for humidity effects, oxygen corrections for KH20, and WPL corrections. The datafile contains several quality flags and added as description within the netcdf files; zero-plane displacement height of 0 m</li> <li>EC flux Quality Criteria (QC) flags: <ul> <li>-1: all data</li> <li>0: excluding instrument malfunction</li> <li>1: additionally skewness within range (-2 to 2) and kurtosis <8 following Vickers and Mahrt 1997</li> <li>2: additionally exclude non-stationary data</li> </ul> </li> <li>EC flux Flags: <ul> <li>0: data ok</li> <li>1: data not ok (see description of individual flag for further details)</li> </ul> </li> </ul> <p><strong>4. Data file structure</strong></p> <p><em><strong>Zip folders</strong></em></p> <p>Different data sets were generated, as measurements had different temporal resolutions or different sets of parameter. Accordingly the following data is given:</p> <ul> <li>DTS data (1 s): InnDEX22_distributed_temperature_sensing.zip</li> <li>EC flux level 1 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>EC flux level 2 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>Low-frequency data (1 min): InnDEX22_low_frequency_data.zip</li> </ul> <p><em><strong>File format</strong></em></p> <p>Each above mentioned folder is filled with netcdf files. One for each day. Global information contains location, instrument type etc. Parameter description is given as attributes for each parameter.</p> <p><strong>6. Contact</strong></p> <p>Contact lena.pfister(at)uibk.ac.at for any questions regarding the data set.</p> <p><em><strong>Acknowledgements</strong></em></p> <p>Special thanks to the "Institut für Meteorologie und Klimaforschung Atmosphärische Umweltforschung" (IMK-IFU), KIT-Campus Alpin, Garmisch-Partenkirchen, for lending us the DTS measurement device for TEAMx-PC22.</p> <p><strong>7. References</strong></p> <p>Fritz, A. M., Lapo, K., Freundorfer, A., Linhardt, T., & Thomas, C. K. (2021): Revealing the morning transition in the mountain boundary layer using fiber-optic distributed temperature sensing. <em>Geophysical Research Letters</em>, 48, e2020GL092238. <a href="https://doi.org/10.1029/2020GL092238">https://doi.org/10.1029/2020GL092238</a></p> <p>des Tombe, B., Schilperoort, B., Bakker, M. (2020): Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. <em>Sensors</em>, 20, 2235. <a href="https://doi.org/10.3390/s20082235">https://doi.org/10.3390/s20082235</a></p> <p>des Tombe, Bas François, & Schilperoort, Bart. (2022): Dtscalibration Python package for calibrating distributed temperature sensing measurements (v1.1.2). <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a></p> <p>Pfister, L., Lapo, K., Mahrt, L., Thomas, C.K. (2021): Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics. <em>Boundary-Layer Meteorol</em> 180, 187–202. <a href="https://doi.org/10.1007/s10546-021-00618-0">https://doi.org/10.1007/s10546-021-00618-0</a></p> <p>Lapo, K., Freundorfer, A., (2020): klapo/pyfocs v0.5: Fully-functional python package intended for atmospheric deployments of distributed temperature sensing. <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a></p> <p>Lapo, K., Freundorfer, A., Fritz, A., Schneider, J., Olesch, J., Babel, W., and Thomas, C. K. (2022): The Large eddy Observatory, Voitsumra Experiment 2019 (LOVE19) with high-resolution, spatially distributed observations of air temperature, wind speed, and wind direction from fiber-optic distributed sensing, towers, and ground-based remote sensing, <em>Earth Syst. Sci. Data</em>, 14, 885–906 <a href="https://doi.org/10.5194/essd-14-885-2022">https://doi.org/10.5194/essd-14-885-2022</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, (2020): Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. <em>Innsbruck University Press</em>. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, (2022): A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society</em>, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
DailySense: a Daily Self-reports and Physiological Signals Sensing Dataset for Subjective Health Research in the Wild
<p><strong>Description:<br></strong>This is a daily self-reports and physiological signals sensing dataset for subjective health research in the wild (<strong><em>DailySense</em></strong>). This is a dataset from a consecutive 14-day experiment in real-life settings composed of smartphone-based subjective psychological evaluation and physiological sensing. A Total of 36 healthy Japanese adult remote workers (mean±SD, 35.8±7.5; range, 27–58 years; 21 male and 15 female participants) participated in the experiment through two terms (1st term [1], 18 participants from a Japanese company without rewards; 2nd term, 18 participants from a participant's pool with rewards).</p> <p>The study protocol was approved by the internal review board of Research & Development Group, Hitachi, Ltd., and was conducted in accordance with the Declaration of Helsinki. All participants provided informed consent prior to enrollment in this study. The permission to share the raw data with participants' anonymization was included in this approval and explicitly obtained in that informed consent.</p> <p>This dataset contains the following data:</p> <p> - <strong>pre- and post-term data of<br></strong> - responses to self-reporting questionnaires (i.e., the Japanese versions of NEO-FFI, STAI, CES-D, CFS, PSQI, WHO-QOL, and SF-36v2<sup>©</sup>).<br> - demographics<br> - survey regarding this experiment</p> <p>- <strong>mid-term data of<br></strong> - responses to emotional self-reports (i.e., Affective Slider and I-PANAS-SF) and their behavior in ESM 6 times/day at maximum<br> - responses to subjective health (i.e., degree of fatigue, stress, anxiety, depression, and sleeplessness), wake-up/in-bed times, and work style 1time/day<br> - continuously monitored physiological data (i.e., EDA, PPG, Acc) and event tags obtained by a wristband sensor (E4 wristband, Empatica Inc.) during their waking hours<br> - response profile data estimated using the proposed method<br> - log data of an experience sampling support system (exkuma, Japan Experience Sampling Method Association)</p> <p>Details of data are mentioned in an xlsx file of the root directory. Due to the limitation of questionnaires, descriptions of original instructions of items in each questionnaire are omitted.</p> <p>The details of the experiment are described in [1][2]. Note that, in [1], participant #29 was excluded due to insufficient physiological data quality. In addition, in [2], participant #47 was excluded since he did not complete a personality questionnaire, but #29 was included since he completed responses to all the questionnaires.</p> <p> </p> <p><strong>License:<br></strong>This dataset is made available by <strong>Hitachi, Ltd.</strong> under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p> </p> <p><strong>References:<br></strong>If you use this dataset, please cite the following papers:</p> <p>[1] Shunsuke Minusa, Chihiro Yoshimura, and Hiroyuki Mizuno "Emodiversity evaluation of remote workers through health monitoring based on intra-day emotion sampling," Front. Public Heal., vol. 11, no. August, pp. 1–12, 2023, doi: <a href="https://doi.org/10.3389/fpubh.2023.1196539" target="_blank" rel="noopener">10.3389/fpubh.2023.1196539</a></p> <p>[2] Shunsuke Minusa, Tadayuki Matsumura, Kanako Esaki, Yang Shao, Chihiro Yoshimura, and Hiroyuki Mizuno, "Response Style Characterization for Repeated Measures Using the Visual Analogue Scale," arXiv preprint <a href="https://arxiv.org/abs/2403.10136" target="_blank" rel="noopener">arXiv:2403.10136</a>, 2024.</p> <p> </p> <p><strong>Contact:<br></strong>If there is any problem, please contact us:</p> <ul> <li>Shunsuke Minusa, <a href="mailto:shunsuke.minusa.hd@hitachi.com" target="_blank" rel="noopener">shunsuke.minusa.hd@hitachi.com</a></li> </ul>
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.