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688 results for “flat”
Topobathymetric DEMs of Short Prong marsh and tidal flat in Hog Island Bay, VA 2022-2023
Seamless DEMs of the marsh edge and tidal flat at Short Prong in 2022 and 2023 were created using a combination of aerial photogrammetry and boat-mounted single-beam echosounding. Aerial photogrammetry surveys of the subaerial intertidal flat and marsh edge were completed during low tide using a DJI Phantom 4 RTK drone. Positioning was determined using six surveyed ground control points and drone on-board RTK. During high tide, a single beam echosounder was used to survey six cross-shore transects of the subtidal and one alongshore transect near the marsh edge. The same flight plan and echosounding survey path was used both years, with surveys taking place on 7/19/22 and 7/11/23. Photogrammetric data was processed in Agisoft Metashape to produce a DEM and a photomosaic. Echosounding data was processed in Hydromagic and interpolated using the natural neighbor method in GIS. Overlap of survey extent between photogrammetric and echosounding DEMs were stitched together in GIS to create seamless DEMs with a cellsize of 3 cm from the lower elevations of the marsh platform into the subtidal flat for 2022 and 2023.
Moita-Montijo Bay saltmarsh and tidal flat change rates (1958-2018)
<p>Saltmarsh and tidal flat change rates were determined for the period of 1958 to 2018, for the Moita-Montijo Bay (Tagus Estuary, Portugal). Rates were determined only for the exposed saltmarsh. No rates were calculated for sheltered saltmarsh (saltmarsh inside abandoned saltpans and similar structures). Saltmarsh margins were delineated in ArcMap, using, as the carthographic basis, orthorectified aerial photgraphs from 1958 and orthophotomaps from 1995 and 2018 (from Direção Geral do Território (DGT)).</p> <p>Tidal flat change rates were determined for the period of 1977 to 2018, in the north and south banks of the Montijo Channel (Moita-Montijo Bay, Tagus Estuary, Portugal). Rates were determined only for the Montijo Channel margins. The tidal flat margins were delineated in ArcMap, using, as the carthographic basis, orthorectified aerial photgraphs from 1977 and orthophotomaps from 1995 and 2018 (from DGT).</p> <p>The Digital Shoreline Analysis System (DSAS) in ArcMap was used to calculate the End Point Rates (EPR) between the margins (See Himmelstoss et al. (2018) for details on how to the EPR is calculated using DSAS).</p> <p>This dataset was produced in the scope of the Project Planta II*. Further details can be consulted in Martins et al. (2023).</p> <p> </p> <p>Dataset:</p> <ul> <li>Saltmarsh change rates in the Moita-Montijo Bay (1958 to 1995) (Tagus Estuary, Portugal) = rates_smarsh_moitamontijobay_1958_1995.shp</li> <li>Saltmarsh change rates in the Moita-Montijo Bay (1995 to 2018) (Tagus Estuary, Portugal) = rates_smarsh_moitamontijobay_1995_2018.shp</li> <li>Tidal flat change rates in the north bank of the Montijo Channel (1977 to 1995) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_nb_moitamontijobay_1977_1995.shp</li> <li>Tidal flat change rates in the north bank of the Montijo Channel (1995 to 2018) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_nb_moitamontijobay_1995_2018.shp</li> <li>Tidal flat change rates in the south bank of the Montijo Channel (1977 to 1995) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_sb_moitamontijobay_1977_1995.shp</li> <li>Tidal flat change rates in the south bank of the Montijo Channel (1995 to 2018) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_sb_moitamontijobay_1995_2018.shp</li> </ul> <p>Please cite the paper where this dataset is discussed as Martins, D., Alves da Silva, A., Duarte, J., Canário, J., & Vieira, G. (2023). Changes in vessel traffic disrupt tidal flats and saltmarshes in the Tagus Estuary, Portugal. <em>Estuaries and Coasts</em>. DOI: <a href="https://doi.org/10.1007/s12237-023-01198-7">https://doi.org/10.1007/s12237-023-01198-7</a></p> <p>*Project Planta II - PTDC/CTA-GQU/31208/2017, co-funded by the European Regional Development Fund (ERDF), through the financing Regional Operational Program of Lisbon, and by the Portuguese Science and Technology Foundation (FCT), through the national funds (PIDDAC).</p>
Drop count and size of 38 numerical simulation of a flat fan spray
<p>Data refer to the drop size distribution used in the paper "Data-driven modelling for drop size distributions"</p> <p>by T. Traverso, T. Abadie, O. K. Matar, and L. Magri (arXiv link: https://arxiv.org/abs/2305.18049)</p> <p></p> <p></p> <p>Each of the 38 .csv file in this folder is associated with a different working condition of the nozzle.</p> <p>Specifically, the name 'alpha##_Re##_We##.csv' contains the working condition of the nozzle as</p> <p>- alpha## (## is the spray angle)</p> <p>- Re## (## is the Reynolds number)</p> <p>- We## (## is the Weber number)</p> <p>Each file contains as many raws as the number of drops.</p> <p>In the i-th raw,</p> <p>1) the first element is the Volume of the i-th drop;</p> <p>2) the second element is the estimated surface of the i-th drop with the method in equation (18) of [1];</p> <p>3) the third element is the equivalent diameter of the i-th drop (i.e., as if it was spherical - computed from the volume)</p> <p>The value of the Weber number found in the Arxiv paper is half of that reported here. The correct one is the one in this database. The paper will be corrected in due time.</p> <p> </p>
Oxygen Fluxes on Virginia Tidal Flats, 2015-2018
Bare tidal flats along the eastern USA coast are increasingly undergoing state changes to one of two systems: human-restored Crassostrea virginica oyster reefs or invasive Agarophyton (Gracilaria) macroalgal mats, with little known about how these changes impact ecosystem metabolism. This dataset details aquatic eddy covariance (AEC) benthic oxygen fluxes, along with physical drivers such as flow speed and light, of 3 oyster reefs, 2 macroalgal beds, and 1 bare mudflat located at the Virginia Coast Reserve (VCR). The oyster reef data spans 4 years (2015 - 2018) and all seasons, while the mudflat and Agaraphyton data are from summers 2015 and 2017, respectively. These flux data are used to calculate the metabolism (R, GPP, NEM) of these systems, in order to better understand how state changes have impacted carbon cycling on the Virginia coast.
Experimental data for bulk valley transport and Berry curvature spreading at the edge of flat bands
<p>This dataset was used in our study of bulk valley transport and Berry curvature spreading at the edge of flat bands in twisted double bilayer graphene.</p>
Poker Flat Incoherent Scatter Radar (PFISR) Observations of E-region Neutral Winds
<p>Updated: 12-15-2021</p> <p><strong>RULES OF THE ROAD:</strong></p> <p>You are welcome to use the data 'as is', however, please inform me via email if you plan to use the dataset. There are a number of small issues with the dataset that are best discussed. We are interested in publications that use the data and derived values that are presented within the dataset. <strong>If you plan to publish these results, please circulate a draft by me (SRK) and we would appreciate an offer of co-authorship or at minimum an acknowledgement. You should include the NSF funding numbers NSF AGS - 1853408</strong></p> <p> </p> <p>As a general warning, the data from PFISR are quite noisy and you may need to perform significant averaging to produce usable results. Again, please contact me and we can discuss this in more detail.</p> <p>Version v0.6.4.2021.07.12 - This was the final processed version at the time that the final report was submitted to the NSF.</p> <p> </p> <p><strong>--------------- Previous from before ------------------</strong></p> <p>This file contains Poker Flat Incoherent Scatter Radar (PFISR) E-region Neutral Winds Data. These data correspond to monthly data files that include the E-region neutral winds and other parameters for the from March 2013-June 2019.</p> <p><strong>Publications of the Joule Heating Results:</strong></p> <p>https://doi.org/10.1029/2021JA029371</p> <p>https://doi.org/10.1029/2021JA029719</p> <p> </p> <p><strong>Publication of Neutral Wind Results:</strong></p> <p>Hopefully we will have something in 2021. </p> <p> </p> <p><strong>RAW ISR Data:</strong> These data were processed from the following files found in: https://data.amisr.com/database/tmp/Kaeppler/winds/ and https://data.amisr.com/database/tmp/Kaeppler/missing_IPY.tar.gz Please note that the error on the line of sight velocities may have been overestimated in these data and we scaled them by a eVLOS/sqrt(10). Interested persons should contact Ashton Reimer or Roger Varney at SRI International for more information about these data, please see amisr.com</p> <p>Truthfully, the ISR data should eventually be reprocessed and then the winds algorithm run over it again. This is a step for future work.</p> <p> </p> <p><strong>Processing Code is available upon request via email.</strong></p> <p> </p> <p><strong>File Documentation:</strong></p> <p> </p> <p><strong>Please see the change log:</strong></p> <p>Purpose: This is the overarching program and functions which process the<br> E region neutral winds from the fitted AC and LP data from PFISR.<br> This is a conversion fo process_eregwinds_srk.py which was originally written by<br> Nicolls into a more formal python class structure.</p> <p>2017-10-05 - v0.2</p> <p>The ProcessEregionNeutralWinds.py file has been validated against process_eregwinds_srk.py<br> using 20161121.001_ac_3min-fitcal.h5, 20170301.013_ac_3min-fitcal.h5, 20170302.001_ac_3min-fitcal.h5.<br> The program to run these is ComparePrograms.py. At this point these program match.<br> I am going to start diverging the code base, first subtly in the Joule Heating<br> since I found that Mike just looped over Nbeams, which isn't quite right, you need to loop<br> over the beams that were selected.</p> <p>Changes from this point forward will produce different results.</p> <p>2017-10-10 - v0.3.2017.10.10</p> <p>Version v0.3, I made some IO changes but I may start processing some data with this version.</p> <p>Version v0.4 - lots of small edits made to the IO and the plotting software. It all seems to work<br> I have also included the SNR and Ne into the monthly plots and other information.<br> Made processing smoother.</p> <p>03 13 2018 - added solar local time converion</p> <p>v0.4.1 - 09 08 2018 added some ability to extract out the raw electron and SNR densities for each altitude bin<br> v0.4.2 - 10 15 2018 added in obtaining the F-region flows - want to check against the electric field.<br> v0.4.3 - 10 29 2018 added in some more altitude into the Joule Heating so I can make better figures<br> v0.4.4 - 11 20 2018 made some pretty major changes to IO to include consistent calculation of<br> Pedersen conductivity from FastConductivity.py. Made some changes to the Joule heating calculation and checked<br> formulas. It is worth checking again.</p> <p>v0.4.5 - 11 20 2018: added in Hall and Pedersen conductivities from fitted electron density data.<br> v0.4.6 - 12 03 2018: Tried to fix some of the double counting and time problems in testMakeMonthlyh5</p> <p>05 22 2019: added some statements to bypass the geophysical parameters. Also need in config file now.<br> Additionally wrote in IOEregionwinds a try except statement</p> <p>07 29 2019: Running the code for the 06 data reprocessed by Ashton</p> <p>v0.5.0 - 10-15-2019: put in some filtering on the LOS velocity discharging bad Chi square and bad error codes on the fit.</p> <p>v0.5.1 - 10-23-2019: changed chi square to 0.01 for lower boundary</p> <p>v0.5.3 - 12-02-2019: Added in that now passing in the Chi2 and Fitcode filtering by Config file<br> Bigger change that I am scaling the AC dVlos by some sort of factor while Ashton figures this out.<br> We decided that a conversative scaling would be to reduce the dVLOS by 1/sqrt(10).<br> The chi square produced in the data Ashton sent me typically was around 0.01, so the uncertaintiies on the LOS velocities<br> may be over estimated. So we are just changing this as a temporary fix while Ashton fixes the uncertainty estimation.</p> <p>v0.5.5 02 01 2020 - Added in calculation of Coriolis, Centrifugal, and Lorentz forcing<br> v0.5.5 02 10 2020 - Added a correction to qvert so that way I can calculate the lorentz term.<br> Found an error where qvert = 0 in the if statement goes to false.</p> <p>v0.5.5 02 15 2020 - Put in nuInscaler into the main program, scaling ALL kappas by the scaler number</p> <p>v0.5.6 02 28 2020 -- Added some more vlos diagostics and the calculation of the scale height. Added Altitude offset</p> <p>v0.5.6.2020.03.12_nuin_fracoff - testing putting in the Brekke formula for ion neutral collision frequency and took out frac</p> <p>v0.5.7.2020.04.10 - Put in Ashton's revised ion neutral collision frequency formulas into IO.<br> Also wrote a testscript and at least for the file I used was only different by 2.5%.<br> Revised where the mag data is being pulled from since the URL is deprecated<br> Added in Kappa which is now being interpolate - plan to see where kappa =1 is located for the paper.<br> commented out nuin scaler just so I am not chasing my tail</p> <p> test v0.5.7.2020.04.13_org commented back in original ion neutral collision frequency method<br> possible mistake that not summing up properly.</p> <p> test v0.5.7.2020.04.13_newnuin_orgsum_noTr800 - new formula for nuin except took off Tr>800.<br> I expect this should be almost the same as before since the formulas are basically the same.<br> did the original sum using frac[0] and frac[1] want to see if I am underestimating</p> <p> v0.5.7.2020.04.13_newnuin_orgsum_yesTr800 - same as above except now including Tr>800.</p> <p> 'v0.5.7.2020.04.13_newnuin_newsum_noTr800' - using the new sum now and new col freq</p> <p> v0.5.7.2020.04.13_updatedorg - updated original uses original method but including the NO term</p> <p>v0.6.0.2020.04.15 -- Now think I have the new ion neutral collision frequency working and validated.<br> Found a mistake in how I was calculating the ion neutral collision frequency that<br> the fraction weight I was using only included the O+ and O2+ terms and not NO+<br> Turns out I was basically weighting by about 0.5, so I was effectively reducing the<br> ion neutral collision frequency by about a factor of 0.5 or less...<br> From this point forward need to start using any results from > v0.6<br> This revision has changed previous results signi</p> <p>v0.6.0.2020.04.21 -- updated to now include the temperature correction for the O2+</p> <p>v0.6.1.2020.04.23 -- made a number of changes to the geomagnetic files and reprocessed from CDAweb.<br> Wrote new code to be able to process the files from CDAweb in the new format.<br> Also changed the geomagnetic data files</p> <p>v0.6.1.2020.06.07 -- changed the generation of Monthly files to hopefully be in order now<br> Added in missingIPY files given to me by ashton, maybe improve data covarege<br> Some work going to need to be done to make sure that all of the 10, 15, and 20 minute data are there.</p> <p>v0.6.1.2020.06.15_Weijia -- Updated the data for Weijia's study in particular since we are missing a lot of IPY data for 02-04 2013 and 2014.</p> <p>'v0.6.2.2020.07.01' -- Updated the data with new IPY27 mode for 2013 and 2014 Ashton processed. Also now put in mechanical Joule heating term.<br> Put in the conductance and conductivity now too.</p> <p>v0.6.2.2020.07.30 -- Made some changes to IO since Weijia noticed the mechanical heating terms were missing from the monthly files.</p> <p>v0.6.3.2020.10.19 -- Tried to elimated all extra instance of nuinscaler, and also output that variable. Added in variables<br> To get the Ti, Tn, ion neutral collision frequency along the vertical beam for diagnostic purposes<br> included dVest for F-region plasma drifts for Rafael</p> <p>v0.6.4.2020.11.20 -- Extracted some more parameters including F107 and the Hall and Pedersen Drags</p> <p>v0.6.4.2021.07.21 -- Final Run of data for NSF project</p> <p> </p>
Data set for the article "Tides, topography, and seagrass cover controls on the spatial distribution of Pinna nobilis on a coastal lagoon tidal flat"
<p>Data set includes: coordinates of the GNSS points (reference system WGS84 UTM33N); density of P. nobilis and cover of C.nodosa detected in the orthophoto in the 25m<sup>2</sup> cells; tidal levels measured (and, for comparison, simulated with the hydrodynamic model) corrected with respect to the IGM datum; number of emersions and flood duration for different levels of the tidal flat; statistics. The first Excel sheet includes a detailed description of the data.</p>
Full-Body 3D Human Gait Dataset walking on flat ground
<p>This dataset contains full-body 3D gait data collected from 26 healthy participants (10 males, 16 females) with an average age of 28.19 ± 7.77 years. Data was captured using the Xsens Awinda MTw inertial measurement system, comprising 17 wireless sensors operating at a 60Hz sampling frequency.</p> <p>Key Features:</p> <ul> <li>Full-body motion data using MVN Analyze software's full-body model</li> <li>Anthropometric measurements: height (170.5 ± 8.61 cm), foot length (26.47 ± 1.88 cm), shoulder width (39.32 ± 7.79 cm), and wrist span (131.36 ± 8.85 cm)</li> <li>Four distinct walking paths: Mixed (straight and curved), Circle (3m diameter), Turn (180-degree turns), and Zigzag</li> <li>Total of 1,024,295 frames (17,071.58 seconds) of gait recordings</li> <li>Average of 3,568.97 ± 1,204.26 frames per recording (59.48 ± 20.07 seconds)</li> </ul> <p>The dataset includes various walking patterns designed to capture a wide range of gait characteristics, including straight walks, gentle curves, sharp turns, and zigzag movements. Participants were allowed some freedom in executing turns, particularly in the Zigzag and Mixed paths, to introduce natural variations in gait patterns.</p> <p>This comprehensive dataset is suitable for gait analysis, biomechanics research, and the development of motion synthesis algorithms, particularly those focused on normal walking patterns on a fixed surface with various turning scenarios.</p> <p>Dataset Structure:</p> <ol> <li>'<strong>participants.xlsx</strong>': An Excel file containing participant codes and their anthropometric data.</li> <li>'<strong>data</strong>' folder: Contains subdirectories named with participant codes. <ul> <li>Each participant subdirectory contains CSV files of different gait recordings for that participant.</li> </ul> </li> </ol> <p>This dataset was collected as part of the study:</p> <p><strong>Carneros-Prado, D., Dobrescu, C. C., Cabañero, L., Villa, L., Altamirano-Flores, Y. V., Lopez-Nava, I. H., … & Hervás, R. (2024). Synthetic 3D full-body skeletal motion from 2D paths using RNN with LSTM cells and linear networks. </strong><strong><em>Computers in Biology and Medicine, 180,</em></strong><strong> 108943.</strong></p>
Characterizing the gamma-ray variability of the brightest flat spectrum radio quasars observed with the Fermi LAT
<p>The FITS files contain light curves (prefix "lc"), spectral energy distributions (prefix "sed") and best-fit parameters for the whole region of interest (prefix "bestfit_roi") for the gamma-ray analyses of the six brightest flat spectrum radio quasars observed over 9.5 years with the Fermi Large Area Telescope (LAT). The file names also indicate the considered binning (weekly, daily, orbit, sub-orbital) and the considered time range in MJD.<br> The data products have been generated using the fermipy software, please see the documentation for further explanations of the columns provided in these files: <a href="https://fermipy.readthedocs.io/en/latest/">https://fermipy.readthedocs.io/en/latest/</a></p> <p>The analysis catalog are described in detail in the accompanying paper, which is submitted for publication in the Astrophysical Journal. The preprint of the submitted manuscript can be found here: <a href="https://arxiv.org/abs/1902.02291">https://arxiv.org/abs/1902.02291</a></p> <p>Additionally, the code for high level analysis including the light curves and the gamma-ray absorption in the broad line region can be found on github: <a href="https://github.com/me-manu/GaRLiC">https://github.com/me-manu/GaRLiC </a>and <a href="https://github.com/me-manu/blrabsorption">https://github.com/me-manu/blrabsorption</a></p> <p> </p>
Stacks of microCT Scans, Cell size, weight, volume and thallus size data supporting the paper 'Mechanical regulation of tissue flatness in Marchantia'
<div> <div> <div> <p>These data are the supporting elements to the following paper: 'Mechanical regulation of tissue flatness in Marchantia'</p> </div> </div> </div> <p> .tif files contain MicroCT (MCT) scans of 16-day-old <em>Marchantia polymorpha</em> thalli. Three genotypes were analysed here: <strong><em>fer-2</em></strong> mutant (from Mecchia et al., 2022), <strong>FER-OE #9</strong> (proMpEF1::MpFERONIA-mCitrine trangenic line 9)<strong> </strong>from Mecchia et al., 2022), and Tak-1 (WT line). These plants were grown in 3 different media: Gamborgh B5 + vitamins and 0.6, 1.2 and 2.5% agar, and one stress condition consisting of the adjunction of a thin PDMS film at 4, to mimich external mechanical stimulus (only performed on thalli grown on 1.2% agar).</p> <p>MicroCT scans were performed at the faculity of odontology of Université Paris-Cité (Plateform imagerie du vivant) with the technical support of Lotfi Slimani and Baptiste Casel. https://piv.u-paris.fr/micro-ct-haute-resolution/ </p> <p>All files already have embeded scales.</p> <p>Each file name consists of a unique ID number in the following form:</p> <p>P+<LETTER>+<NUMBER>-<CONDITION></p> <p>-LETTER: One letter = one imaging session</p> <p>-NUMBER: Individual and Genotype: 33-40 -> Tak1; 200-207-><em>fer-2</em>; 41-49 -> FER-OE</p> <p>-CONDITION : AGAR0.6/AGAR2.5/PDMS. Absence of condition indicates growth on standard medium (1.2% agar). PDMS indicated growth on standard medium and supplementation of a topping PDMS film at day 4)</p> <p> </p> <p>-Volume data were calculated from MicroCT scans</p> <p>-thallus projected surfaces were calculated from MicroCT scans</p> <p><a href="https://zenodo.org/api/records/13981438/draft/files/Lambda%20curvature%20calculation.ipynb/content" target="_blank" rel="noopener noreferrer">-Lambda curvature calculation.ipynb</a> is suited for MorphographX mesh exported .txt files.</p> <p> </p> <p> </p> <p> </p>
[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria
<p><strong>Highlights:</strong></p> <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul> <p> </p> <p><strong>Collector array description: </strong>The data is from a flat plate collector array with a total gross collector area of 516 m<sup>2</sup> (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.</p> <p> </p> <p><strong>Data files:</strong></p> <ul> <li><strong>FHW_ArcS__main__2017.csv</strong> – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li><strong>FHW_ArcS__main__2017.parquet</strong> – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li><strong>FHW_ArcS__parameters.json</strong> – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li><strong>FHW_ArcS__raw__2017.csv </strong>– Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul> <p> </p> <p><strong>Scripts: </strong></p> <ul> <li><strong>make_data.py</strong> – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li><strong>make_plots.py</strong> – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul> <p> </p> <p><strong>Data collection and preparation</strong>: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria</p> <p> </p> <p><strong>Data owner</strong>: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria</p> <p> </p> <p><strong>Additional information</strong> is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.</p> <p> </p> <p><strong>Note: </strong>A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a></p> <p> </p>
Data for "Detachment of a rigid flat punch from a viscoelastic material"
<p>This dataset contains all the data generated for the publication</p> <p>[1] Papangelo, A., & Ciavarella, M. (2023). Detachment of a rigid flat punch from a viscoelastic material, Tribology Letters, DOI: 10.1007/s11249-023-01720-9<br> </p> <p>The provided data are those which appear in the figures of Ref. [1]. Data are stored using informative named structures in a ".mat" file. The data are easily accessible through the Commercial Software MATLAB (© 1994-2023 The MathWorks, Inc.) or by using the free software GNU Octave. An exemplary code for loading and plotting the data contained in "dataFlatPunch.mat" is the following:</p> <p>%%%%%%%%%%%%%</p> <p>load('dataFlatPunch.mat') % load workspace containing data</p> <p>figure, hold on % open a new figure</p> <p>curve_number = 1; % select the curve to plot</p> <p>plot(Fig1.x{curve_number},Fig1.y{curve_number}) % Plot the curve number "curve_number" of figure "Fig1"</p> <p>%%%%%%%%%%%%%</p> <p>The code can be easily adapted for plotting the curves of all the figures contained in Ref. [1]. </p>
Heat Transfer Physics - Flat Plate Model
<p>The dataset includes unprocessed and processed temperature evolution plots for wide range of experimental conditions corresponding to ice crystal icing performed at the icing wind tunnel of TU Braunschweig within the scope of MUSIC-haic project. In addition to temperature plots the dataset also includes information on the design and components of the test article as well as the respective test matrix. It covers wide range of parametric variation including heat flux, wet bulb temperature, flow velocity and ice water content and provides a sound basis for calibration and validation of numerical tools.</p>
Site environmental, climate, water levels and temperatures, vegetation cover, and GIS change detection for assessing permafrost change in fens on the Tanana Flats, central Alaska
This data package provides data used to assess the roles of climate extremes, ecological succession, and hydrology in repeated permafrost aggradation and degradation in fens on the Tanana Flats, central Alaska. The package provides data on site environmental information, Fairbanks climate, vegetation cover, water levels and temperatures, as well as GIS files for fen change detection. The Site data include information on observers, locations, geomorphology, hydrology, soils, vegetation, and disturbance. The table has numerous fields that uses coding for class characteristics and these codes are described in the metadata as well as compiled in the ELS_Arctic_Boreal_Site_Soil_Veg_Code_Sheet_2020.docx. Alaska Climate records for Fairbanks (UAF Experiment Station) from 1904 to 2019 were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/). Additional data were obtained for the Nenana station (about 70 km southwest of Fairbanks), to fill in small data gaps (particularly precipitation/snow depth ruler measurements) in the Fairbanks record. We attributed the data with fields for summer (May-September) and winter periods (November-March) and hydrologic year (October-September) and calculated mean air temperature, precipitation, and snow depth by seasonal period (average of daily values) and year. The broad summer and winter periods were of interest because warmer and wetter summers increase soil heat input and warmer and snowier winters reduce soil heat loss. Fen hydrology data include information on fen water level/pressure and temperatures collected every two hours at seven sites within fens from 2011 to 2014. Vegetation composition and cover of fens, scrub, and forests was sampled to assess effects of thermokarst on vegetation change. Plant cover was determined by point-sampling at 100 points (including repetitive “hits” for all layers) distributed along 5 equally spaced rows (4-m long, 20 points per row) across the 10-m l
Fig. 8 in Optimal fishing samplers to reveal the morphological structure of a fish assemblage in a subtropical tidal flat
Fig. 8. Redundancy analysis (RDA): individual species-gear relationships explained by morphological shape variability, using incidence data. In blue, fishing gear acronyms: beach seine (BS), cast net (CN), fish trap (FT), hook and line (HL), inner encircling gillnet (EG1), large gillnet (LG), marginal encircling gillnet (EG2), otter trawl (OT), small gillnet (SG).
Fig. 4 in Optimal fishing samplers to reveal the morphological structure of a fish assemblage in a subtropical tidal flat
Fig. 4. Morphospace represented by shape variation of all species in the nine fishing gears grouped, using the relative warps: a. 1 and 2; b. 1 and 3. Acronyms: Anc tri, Anchoa tricolor; Bat sop, Bathygobius soporator; Bot oce, Bothus ocellatus; Cen par, Centropomus parallelus; Chi spi, Chilomycterus spinosus; Dia rho, Diapterus rhombeus; Elo sau, Elops saurus; Epi mar, Epinephelus marginatus; Hip rei, Hippocampus reidi; Lut ana, Lutjanus analis; Lut cya, Lutjanus cyanopterus; Mal del, Malacoctenus delalandii; Myr pun, Myrophis punctatus; Oph gom, Ophichthus gomesii; Pol vir, Polydactylus virginicus; Sca cri, Scartella cristata; Sco bra, Scomberomorus brasiliensis; Sel set, Selene setapinnis; Sel vom, Selene vomer; Sph tes, Sphoeroides testudineus; Str mar, Strongylura marina; Sym tes, Symphurus tesselatus; Tra myo, Trachinocephalus myops. Threatened species (Hip rei, Lut ana, Lut cya, Epi mar) are highlighted in red.
Fig. 1 in The flat bark beetles (Coleoptera, Silvanidae, Cucujidae, Laemophloeidae) of Atlantic Canada
Fig. 1. Distribution of Silvanus bidentatus, Laemophloeus fasciatus, Uleiota debilis, Uleiota d. dubius, Charaphloeus convexulus, and C. sp. nr. adustus in Atlantic Canada.
FIG. 5. — Dissomphalus schubnelli n in First fossil of the extant flat wasp genus Dissomphalus Ashmead, 1893 (Hymenoptera: Bethylidae: Pristocerinae)
FIG. 5. — Dissomphalus schubnelli n. sp., holotype MNHN.F.A71308; arrow, hypopygium. Scale bar: 0.05 mm.
FIG. 3. — Dissomphalus schubnelli n in First fossil of the extant flat wasp genus Dissomphalus Ashmead, 1893 (Hymenoptera: Bethylidae: Pristocerinae)
FIG. 3. — Dissomphalus schubnelli n. sp., holotype MNHN.F.A71308: A, head and anterior part of mesosoma, arrow metanotum; B, head, arrow, clypeus. Scale bars: A, 0.4 mm; B, 0.2 mm.
FIG. 4. — Dissomphalus schubnelli n in First fossil of the extant flat wasp genus Dissomphalus Ashmead, 1893 (Hymenoptera: Bethylidae: Pristocerinae)
FIG. 4. — Dissomphalus schubnelli n. sp., holotype MNHN.F.A71308, mandibles: A, front view; B, side view; ellipses, mandibles. Scale bars: 0.08 mm.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.