Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

High-resolution Canopy Height Model of Hawaii Island 2018-2020

Forest canopy height model for Hawaii Island using lairborne lidar data collected by NOAA in 2018, 2019 and 2020. The maps are produced by year at the resolution of 1 m. The raw point cloud data had am average point cloud density of 8 pulses per squre m. https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/9635/index.html ALS 2018 data was reprocessed using Lastools software to reclassify ground class (2) ALS 2019_20 was also reprocessed using Lastools software to reclassify unclassified (1) points to vegetation (5) The CHM’s generation procedure is composed by four steps. It starts by the creation of 500m x 500m tiles using a 50m buffer, resorting to the lastile function, followed by the lasheight function that is used to compute the elevation of each point above the ground. Then, the lastile function is used again to remove the buffer from the normalised point clouds. These first three steps resort to the LASTools software. The fourth, and final step, consists in the generation of the CHM with a 1 m resolution resorting to the pit-free algorithm implemented in the rasterize_canopy function from the lidR package. The file is a GeoTIFF with LZW compression in ArcGIS pro 3.3  EPSG:6635 Use of these data requires citation of this dataset

opencc-by-4.0Jul 2024View details →

Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features

This dataset supports the study titled "Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features", published in Urban Climate (<a href="https://doi.org/10.1016/j.uclim.2024.102102" target="_new" rel="noopener">DOI: 10.1016/j.uclim.2024.102102</a>).   Content Overview: <ul> <li> Building Label Data for Footprint Detection: <ul> <li>Amsterdam_BDG_Label.rar</li> <li>MiamiDade_BDG_Label.rar</li> </ul> These are the label datasets used for training the building detection segmentation models. They have been instrumental in accurately detecting building footprints in Amsterdam. </li> <li> Amsterdam_3D_Buildings.rar:  CityGML file of 3D building models for Amsterdam, derived from LiDAR data and U-Net3+ model. </li> </ul> <ul> <li> Morphological Features.rar: Contains urban morphological features (in raster format) extracted from LiDAR data used in the study. </li> <li> Training and Test Data for Air Temperature Estimation: <ul> <li>Train_Test_AvgTemp_Amsterdam.rar</li> <li>Train_Test_MaxTemp_Amsterdam.rar</li> <li>Train_Test_MinTemp_Amsterdam.rar</li> </ul> This dataset includes training and testing data for estimating air temperatures in three scenarios: average daily temperature, minimum daily temperature, and maximum daily temperature for the city of Amsterdam. </li> </ul>

opencc-by-4.0Aug 2024View details →

Datasets supporting the paper 'City-scale high-resolution flood models and the role of topographic data: a case study of Kathmandu, Nepal.'

########################################################### Datasets supporting the publication: Watson, C.S., Gyawali, J., Creed, M., and Elliott, J.R. City-scale high-resolution flood models and the role of topographic data: a case study of Kathmandu, Nepal. Geocarto International. DOI: <a href="https://doi.org/10.1080/10106049.2024.2387073">https://doi.org/10.1080/10106049.2024.2387073</a> -Please refer to the publication for details on the production of each dataset. -Please cite the publication and this dataset repository when using the data. ########################################################### Contents: Stream centrelines: streams_fabdem.gpkg streams_GLO30.gpkg streams_kh9_1974.gpkg streams_merit.gpkg streams_merit_hydro.gpkg streams_pleiades.gpkg streams_reference.gpkg Flood maps: fastflood_1in100year_flood_depth_metres.tif flood_depth_difference__fastflood_Shrestha_et_al_2023_metres.gpkg Height_above_channel_metres.tif 1974 Orthoimage: KH9_1974_orthoimage_DZB1209_500101L007001_DZB1209_500101L008001.tif

Aladdin: High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D Cine MRI

The uploaded files include high-resolution 3D images of the left atrium from 18 individuals—10 healthy volunteers and 8 patients with various cardiovascular diseases—along with their corresponding left atrium segmentation maps. Additionally, a deformation atlas based on the 10 healthy cases is also provided. For more information, visit: <a href="https://github.com/cgalaz01/aladdin_cmr_la" target="_new" rel="noopener">https://github.com/cgalaz01/aladdin_cmr_la</a>

opencc-zeroJul 2024View details →

Transwell-Based Microfluidic Platform for High-Resolution Imaging of Airway Tissues

<div> <div> <div> <div> This dataset contains original data collected during our study on the development and characterization of a transwell-based microfluidic platform designed for high-resolution imaging of airway tissues. It includes quantitative measurements of various features of live airway epithelium tissues, such as Trans-Epithelial Electrical Resistance (TEER), Cilia Beating Frequency (CBF), LDH Release (a cytotoxicity assay), tissue thickness, and cell number. Additionally, the dataset provides results from computational simulations modeling the shear stress in the microchannel generated by perfusion. </div> </div> </div> </div>

Collapse of a freestanding rock pillar at Matterhorn Hörnligrat: daily high-resolution images show kinematic precursor

Animated time series of manually selected pictures highlights the visible displacement of a freestanding rock pillar at Matterhorn Hörnligrat two weeks prior the collapse. Data used are available under https://doi.pangaea.de/10.1594/PANGAEA.967586 (Weber et al., 2024).

opencc-by-4.0Aug 2024View details →

High-resolution chain transform fault bathymetry from the PI-LAB experiment

Bathymetry data from PI-LAB, MGL1602 (<a href="https://doi.org/10.1029/2018JB015982">https://doi.org/10.1029/2018JB015982</a>). Format: Lat/Lon/Depth [m] For reference, please cite: Harmon, N., Rychert, C., Agius, M., Tharimena, S., Le Bas, T., Kendall, J. M., & Constable, S. (2018). Marine geophysical investigation of the Chain Fracture Zone in the equatorial Atlantic from the PI‐LAB experiment. Journal of Geophysical Research: Solid Earth, 123(12), 11-016

High-resolution figures of Buchner et al. 2024

High-resolution figures of Buchner et al. 2024 "New data indicate larger decline of morphological diversity in split-footed lacewing larvae than previously estimated" in Insects (MDPI).

Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS

InSAR Line-of-Sight (LOS) velocities and their associated uncertainties in the southeastern Tibetan Plateau, along with the strain rate fields. Citations: Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS. Geophysical Research Letters. Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS [Data set]. Zenodo.  https://doi.org/10.5281/zenodo.13731812

High-resolution spectral characterization of YSES 1 system with VLT/CRIRES+

This repository contains the extracted spectra of YSES 1 and its super-Jovian companions b and c observed with the high-resolution spectrograph VLT/CRIRES+ (R~100,000). The paper from <a href="https://doi.org/10.3847/1538-3881/ad7ea9">Zhang et al. (2024)</a> provides more details of the data analyses.

Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"

This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically: <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 & Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>

High-resolution (250x250m) gridded daily mean wind speed dataset for Austria spanning from 1961 to 2023

Overview: Resolution: 250x250 m Projection: EPSG 31287, Austria Lambert Extent: Austria Period : 1961-2023 Format: NetCDF Application: High-resolution gridded climate data derived from in-situ observations play a crucial role in global and regional climatology. The data are a valuable input for further climate impact studies, particularly in ecological and energy modelling, and can be subsequently used for wind power potential analysis. Moreover, policymakers can make informed decisions based on accurate climate information derived from this dataset, enhancing the effectiveness of climate-related policies and interventions. Additionally, the data can be used for model evaluation and bias adjustment. Methods: 1. Data homogenization Breaks in the station data time series were detected and corrected using the Standard Normal Homogeneity Test (SNHT), which identifies where the mean changes the most. If this change exceeds a certain threshold, the time series is adjusted by aligning the statistical distribution of values before the change to those after the change, assuming the most recent time series is correct. This adjustment is achieved using the Quantile Mapping (QM) approach. 2. Spatial interpolation All methods were tested in a nested 10-fold cross-validation (CV) scheme. This means that 10 % of the stations are left out (outer loop), and the other 90 % are used for training the model (inner loop). The outer loop is solely used for validating the model, whereas the inner loop serves for model optimization. A two-stage approach was applied. Initially, a background field - the climatology for each month - was calculated using Random Forest Regression (RFR) with a defined set of predictors. Subsequently, model residuals were spatially interpolated using the 3D Inverse Distance Weighting (3D IDW) method. Differences between daily values and corresponding monthly climatologies were also interpolated using 3D IDW. The final daily mean wind speed field is calculated by adding the monthly climatology fields to the interpolated daily residuals.

Dataset of High-Resolution Micro-CT Imaging of Tumor Invasion and Metastasis in a Murine Esophageal Cancer PDX Model

This dataset features high-resolution micro-CT imaging data capturing the progression of tumor invasion and metastasis in an orthotopic patient-derived xenograft (PDX) model of esophageal cancer. Using contrast-enhanced micro-CT, we visualized detailed patterns of tumor invasion, including budding, multicellular streaming, and expansive growth, across multiple abdominal organs such as the stomach, pancreas, liver, and spleen. The dataset includes two specimens, highlighting both the primary tumor site and extensive metastases throughout the abdominal cavity. Our imaging preserved the native tissue architecture, providing a unique three-dimensional view of tumor-host interactions. This collection offers valuable insights for researchers studying the dynamics of esophageal cancer invasion and metastasis. Detailed descriptions of the micro-CT scanning parameters, image analysis, and sample preparation are provided within the dataset archive.

The High-resolution 3D QP Model of the China Seismic Experiment Site

The CSES-Q1.0 is the highest resolution 3D QP model in the CSES to date.The first column of the file represents longitude, the second column represents latitude, the third column represents depth, and the fourth column represents QP values.

ENTROPY DR1. High-resolution near-UV/optical spectra of 2MASS J11151597+1937266

<h1>ENTROPY Data Release 1 (v2.0)</h1> <h2>Summary</h2> The files in this data release consist of the flux-calibrated, stacked 1D spectum of the isolated, free-floating accreting planetary-mass object 2MASS J11151597+1937266 (2M1115). The data results from the high-resolution (R~50,000) observations by the Ultraviolet and Visual Echelle Spectrograph (UVES) at ESO's Very Large Telescope (VLT) in Chile, between 10-11 June 2023, taken as part of the ESO programme 0111.C-0166(A). This is the underlying data used for the analysis in the publication <a href="https://doi.org/10.1051/0004-6361/202450881" target="_blank" rel="noopener">Viswanath et al. (2024)</a>  <h2>Details of Observations</h2> The observations of 2M1115 with UVES were carried out between 10-11 June 2023 (MJD 60105, 60106) as part of the ExoplaNeT accretion mOnitoring sPectroscopic surveY (ENTROPY) in #Dichroic 1 mode with both the blue (390 nm) and red (580 nm) arms using a 0.8 arcsec wide slit without AO or chopping. A total of four frames at 740 s exposure (NDIT=1) each were obtained across the two nights, giving a total integration time of 0.82 hr at an average seeing of 1.43 arcsec and an average airmass 1.416. The total wavelength range covered by the spectrum is 320–680 nm. <h2>Target</h2> Identifier: 2MASS J11151597+1937266 ICRS RA (ep=2016.0): 168.816234 deg ICRA DEC (ep=2016.0): +19.623939 deg Distance: 45.21 +- 2.20 pc Age: 5-45 Myr Mass: 6(+8, -4) Mjup Radius: 1.5 +- 0.1 Rjup Effective Temperature: 1816 +- 63 K <h2>Data reduction</h2> Each of the 4 raw data frames from the original set of exposures from the 2 nights were bias subtracted and flat-field corrected using the calibration files from ESO. Inter-order background was also subtracted. Cosmic rays were accounted for by using a horizontal median filtering and masking out pixels higher than 10 times the local variance. The flux was extracted order by order using standard aperture photometry, which included the subtraction of sky background and telluric lines. The flux was calibrated based on the observations of a standard star taken contemporaneously with the same observing setup as the target, and was corrected for the relative slit loss (~4%) from seeing. Wavelength calibration was performed using the arc lamp spectrum and a Th–Ar line list. The wavelengths listed in the spectrum are in air. A barycentric velocity correction was applied to the wavelength calibration to transform the spectral reference system to that of the source. The stacked 1D spectrum available in this data release is obtained by taking the weighted average of the 4 individual spectra, with the weights determined from the respective photon noise. <h2>Description of files</h2> The data release in this second version consists of: a) 1 merged spectrum UVES-2023.06.10-11.2M1115.merged-spectra.txt covering the entire UVES range of these observations (320–680 nm). The flux for the overlapping wavelengths in adjacent orders of each arm were averaged to get a single flux value per wavelength. This allows for easy access of the entire spectral range of all arms together. The ASCII file has 4 columns: Column1 : Wavelength, in ångstrøm Column2 : Flux, in units ergs/s/cm2/ångstrøm Column3 : Flux_stddev, the weighted standard deviation of the flux across the 4 individual spectra, in units of ergs/s/cm2/ångstrøm (This is the recommended error bar) Column4 : Flux_phot_err, Average of the photometric uncertainties in flux across the 4 spectra, in units of ergs/s/cm2/ångstrøm b) The compressed file UVES-2023.06.10-11.2M1115.non-merged-spectra.zip contains one folder per each of the three arms (Blue, RedL, RedU). The folders include ASCII files containing the spectrum for individual orders of each arm, provided for the purpose of preserving the signal-to-noise ratio (S/N) of all orders. Some important emission lines like H𝛽 occur in both adjacent orders but at different signal strengths. Merging the orders by averaging the flux as in file (a) will suppress the S/N of such emission lines. The order numbers of the respective arms containing detected emission lines from 2M1115 can be found in Tables 2 and G.1 in <a href="https://doi.org/10.1051/0004-6361/202450881" target="_blank" rel="noopener">Viswanath et al. (2024)</a>. For the following Balmer lines detected in <a href="https://doi.org/10.1051/0004-6361/202450881">Viswanath et al. (2024)</a>, the corresponding rest wavelengths appear in two consecutive orders as indicated below: <table> <tbody> <tr> <td>Line</td> <td>Arm</td> <td>Orders</td> <td>Detection in Viswanath et al. (2024)</td> </tr> <tr> <td>H𝛽</td> <td>RedL</td> <td>2, 3</td> <td>Both at > 3σ S/N </td> </tr> <tr> <td>Hγ</td> <td>Blue</td> <td>34, 35</td> <td>Both at > 3σ S/N </td> </tr> <tr> <td>Hδ</td> <td>Blue</td> <td>28, 29</td> <td>Only in 28</td> </tr> <tr> <td>H7</td> <td>Blue</td> <td>24, 25 </td> <td>Both, but only ~2σ S/N in 24</td> </tr> <tr> <td>H9</td> <td>Blue</td> <td>20, 21</td> <td>At only ~2σ S/N in both</td> </tr> </tbody> </table>   Each ASCII file has 4 columns with same description as in file (a). To plot the spectrum of all the orders of all the arms, the following python code can be used: <code>import numpy as np</code> <code>from glob import glob</code> <code>from natsort import natsorted</code> <code>arms = ['BLUE', 'REDL', 'REDU']</code> <code>plt.figure()</code> <code>for arm in arms:</code> <code>    files = natsorted(glob(f'UVES-2023.06.10-11.2M1115.non-merged-spectra/{arm}/*.txt'))</code> <code>    print(files)</code> <code>    for i in range(len(files)):</code> <code>        data = np.loadtxt(files[i])</code> <code>        plt.plot(data[:, 0], data[:, 1])</code> <code>plt.show()</code> <pre> </pre> c) 2M1115-Photometry.txt, an ASCII file containing the existing photometry for the target <h2> </h2> <h2>Changes from previous version V1.0</h2> The merged spectrum UVES-2023.06.10-11.2M1115.merged-spectra.txt in this version has been modified to preserve the original spectral resolution in the overlapping wavelegth regions. In V1.0, the corresponding regions had double the resolution due to an error in the interpolation method used. The non-merged spectra of the individual orders have been replaced in this version as a compressed zip folder (instead of .npz files) containing ASCII files for spectra of each individual order to facilitate easier data handling. The photometry file has been updated from that of last version with more information, including two additional filter bands (Gaia Gbp and Grp) as well as effective filter widths of all provided filter bands.

opencc-by-4.0Jun 2018View details →

High-resolution Industrial Production Energy (HIPE)

The High-resolution Industrial Production Energy (HIPE) data set contains smart meter readings of ten machines and the main terminal of a power-electronics production plant over three months. <a href="https://doi.org/10.1145/3208903.3210278" target="_blank" rel="noopener">The accompanying publication (published by ACM)</a> describes the data set and outlines open challenges and use cases with industrial energy data. This includes a discussion of characteristics of industrial machines and of differences to residential appliances. Links: <ul> <li>Publication: <a href="https://doi.org/10.1145/3208903.3210278" target="_blank" rel="noopener">https://doi.org/10.1145/3208903.3210278</a></li> <li>Description: <a href="https://www.energystatusdata.kit.edu/hipe.php">https://www.energystatusdata.kit.edu/hipe.php</a></li> </ul>

opencc-by-4.0Nov 2024View details →

Six years of high-resolution monitoring data of 40 borehole heat exchangers

This dataset provides six years of monitoring data of the ground heat exchanger that supplies heat and cold to the E.ON Energy Research Center located in Aachen, Germany. The data belong to the following publication: <blockquote> Heim, E., Stoffel, P., Müller, D., & Klitzsch, N. (2024). Six years of high-resolution monitoring data of 40 borehole heat exchangers. Scientific Data, 11(1), 1334. <a href="https://doi.org/10.1038/s41597-024-04241-9" rel="nofollow">https://doi.org/10.1038/s41597-024-04241-9</a> </blockquote> The ground heat exchanger consists of 40 double-U-loop borehole heat exchangers (BHE), that are arranged in three subfields. Each subfield is connected to an underground vault, in which sensors for the inlet fluid temperature, the outlet fluid temperature and the volume flow of each BHE are placed. The sensors record data in 30-second intervals. Coherent data is provided from July 1, 2018 to June 30, 2024 in two time resolutions and processing levels: <ul> <li>Raw data: The raw data in its initial resolution, aligned to coherent 30-second timestamps.</li> <li>Prepared data: The raw data was resampled to 5 minute intervals using the weighted mean, with the volume flow as weight. Moreover, data periods that are not representative of the thermal exchange process in the BHE were masked (e.g., no-flow periods).</li> </ul> Both raw and prepared data are provided as .csv files for each month individually. Temperature measurements are given in °C, the volume flow rate in l/min.  In addition, a supporting file indicating the BHE locations is provided. It also contains the length of the horizontal connecting pipes going from the underground vaults (where the sensors are placed) to the BHE heads.  Code explaining how to open and work with the data is provided in a separate github repository: <a href="https://github.com/elimh/ERC_BHEfield_Data_Code">https://github.com/elimh/ERC_BHEfield_Data_Code</a>

dryad36/100

High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning

Biological oscillators adapt to environmental changes with widely tunable frequencies, a property theoretical studies attributed to positive feedbacks. However, no experiments have tested this theory. Here, we created synthetic cells to independently tune the frequency and feedback strength of a cell-cycle oscillator, enabling continuous mapping of period landscape in response to network perturbations. We found that although inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the tunability, the reduction is not as significant as theoretically predicted, and the Cdk1-counteracting phosphatase, PP2A, provides additional machinery to ensure frequency regulation. Additionally, cells exhibit polymorphic responses to PP2A inhibition, showing a monomodal distribution of oscillatory cells at low or high PP2A inhibition or a bimodal distribution at both low and high inhibitions. We explained the polymorphism by a model of two interlinked bistable switches of Cdk1 and PP2A where cell-cycle oscillations exhibit two modes in the presence or absence of PP2A bistability.

opencc-zeroAug 2021View details →