Find research datasets worth reusing

Fig. 1. Morphological terms and measurement characters for wings. a in A review of the genus Enicospilus Stephens (Ichneumonidae: Ophioninae) from Vietnam, with descriptions of ten new species

Fig. 1. Morphological terms and measurement characters for wings. a. Fore wing (BC = basal cell; DS = discosubmarginal cell; FS = first subdiscal cell; MC = marginal cell; SD = second discal cell). b. Hind wing. c. Central part of fore wing (AI = cd / ab; CI = gf / fh; DI = k / fe; ICI = ab / cb; SDI = fe / ig; SI = l / j; SRI = ce / fe).

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the agricultural land at Demmin, Germany

The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Demmin, Germany [53°52'5.80"N,13°16'6.80"E] (DEGE). It is a subset of the complete data record, consisting of the measurements withEthaturements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 degrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-XR sensor was installed on 22 July 2021 at the top of a 10m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 53.868278°N, 13.268556°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angles. The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with an FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with an FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information)  propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full DEGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely.

opencc-by-4.0Jan 2023View details →

Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: ├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage] ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage] ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content] ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content] ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity] ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity] ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content] ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content] ├── depthWT_30arc_us.tif                                                        [depth to water table] ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor] ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor] ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH] ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH] ├── pondingFre_30arc_us.tif                                                     [ponding frequency] ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content] ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content] ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content] ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content] ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content] └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content] SOC TIF files: ├──30cm SOC mean.tif                             [30 cm depth soil SOC] ├──100cm SOC mean.tif                           [100 cm depth soil SOC] ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation] └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection:  <pre><code>GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre>

opencc-by-4.0Mar 2023View details →

Data for publication "Measuring the environment of a Cs qubit with dynamical decoupling sequences"

Data sets as plotted in the preprint "Measuring the environment of a Cs qubit with dynamical decoupling sequences" are uploaded. The zip file "data" contains a folder for each figure in the preprint (named after the figure). Each folder contains the data for all graphs in the respective figure.

High-resolution throughfall measurement design, Hainich, Germany, project AquaDiva

This dataset contains the sampling design for throughfall data used for the analysis published in Metzger et al. (2017) and Fischer et al. (2023). It gives spatially distributed throughfall measurement points and their forest structural properties. The measurement points are grouped into randomly distributed “kernel” points and “transect” points which are not part of the random design. The field site and sampling design are described in Metzger et al. (2017). The throughfall data is given in an associated published dataset (Metzger and Hildebrandt, 2023).

Full dataset of carbonyl sulfide URBAG measurement campaign May and October 2020

The following table shows the concentrations (ppt) of carbonyl sulfide (OCS) that were sampled during the URBAG measurement campaign during the months of May and October 2020 (see urbag.eu for more information). Air samples were taken at the eight sites in the Metroolitan Area of Barcelona, representing the heterogeneity of land use as follows: agricultural area (located in the Gavà and Prat areas), urban forest (Tibidabo and Collserola), urban green (Montjuic and Guinardó) and urban built environment (Sagrada Familia and Poble Nou).  Wind direction is given in degrees; Wind speed m/s; temperature in ºC, and Pressure is QFE in hPa; relative humidity (RH) is in %; PBLH is extracted from WRF model and is in m. This data set is publically available as part of the Open Research section of the manuscript “Exploring the influence of land use on the urban carbonyl sulfide budget: a case study of the Metropolitan Area of Barcelona”, recently submitted to JGR Atmospheres.

Multiparameter Water Quality Monitoring System for Continuous Monitoring of Fresh Waters Calibration and Measurement Data Set

This data set contains calibration data for all sensors incorporated in the sensor node. It provides comparison measurements of TPL fluorescence taken by the node and reference spectrofluorimeter. Initial test measurements, as well as site measurements, are also provided. Finally, data from a heuristic method of TPL detection in the presence of algae and mud are also given.

Soil temperature profiles, measured using a coil-shaped fiber-optic distributed temperature sensor

Measurements of soil temperature temperature profile, by reference sensors and a coil-shaped fiber optic distributed temperature sensor. Retrieved at the Speulderbos measurement site, 52.251048 N, 5.690061 E.   A full description can be found in: Schilperoort, B. (2022). Heat Exchange in a Conifer Canopy: A Deep Look using Fiber Optic Sensors [Delft University of Technology]. https://doi.org/10.4233/uuid:6d18abba-a418-4870-ab19-c195364b654b

opencc-by-4.0Apr 2023View details →

Data set - Measured in a context : making sense of open access book data

For more than a decade, open access book platforms have been distributing titles in order to maximise their impact. Each platform offers some form of usage data, showcasing the success of their offering. However, the numbers alone are not sufficient to convey how well a book is actually performing. Our data set is consists of 18,014 books and chapters. The selected titles have been added to the OAPEN Library collection before 1 January 2022, and the usage data of twelve months (January to December 2022) has been captured. During that period, this collection of books and chapters has been downloaded more than 10 million times. Each title has been linked to one broad subject and the title’s language has been coded as either English, German or other languages. The titles are rated using the TOANI score. The acronym stands for Transparent Open Access Normalised Index. The transparency is based on the application of clear regulations, and by making all data used visible. The data is normalised, by using a common scale for the complete collection of an open access book platform. Additionally, there are only three possible values to score the titles: average, less than average and more than average. This index is set up to provide a clear and simple answer to the question whether an open access book has made an impact. It is not meant to give a sense of false accuracy; the complexities surrounding this issue cannot be measured in several decimal places. The TOANI score is based on the following principles: <ul> <li>Select only titles that have been available for at least 12 months;</li> <li>Use the usage data of the same 12 months period for the whole collection;</li> <li>Each title is assigned one – high level – subject;</li> <li>Each title is assigned one language;</li> <li>All titles are grouped based on subject and language;</li> <li>The groups should consists of at least 100 titles;</li> <li>The following data must be made available for each title: <ul> <li>Platform</li> <li>Total number of titles in the group</li> <li>Subject</li> <li>Language</li> <li>Period used for the measurement</li> <li>Minimum value, maximum value, median, first and third quartile of the platform’s usage data</li> </ul> </li> <li>Based on the previous, titles are classified as: <ul> <li>“Less than average” – First quartile; 25 % of the titles</li> <li>“Average” – Second and third quartile; 50% of the titles</li> <li>“More than average” – Fourth quartile; 25 % of the titles</li> </ul> </li> </ul>

opencc-by-4.0Feb 2023View details →

Replication code and data for "Comparing measured dietary variation within and between tropical hunter-gatherer groups to the Paleo Diet".

Replication code and data for the publication "Comparing measured dietary variation within and between tropical hunter-gatherer groups to the Paleo Diet" appearing in the American Journal of Clinical Nutritian. Files include: 1) R replication code (5 files that should be run sequentially in numerical order). 2) "raw_diet_data.csv": 1 file that is ingested by the R code and used for the main analyses. 3) "lipid_classes.csv" and "nutriants_by_source_raw.csv": 2 files that are ingested by the R code and used for the supplementary analyses. 4) "wc2.1_30s_bio_1.tif": 1 file of GeoTiff data on annual mean temperature from the WorldClim v. 2.1 dataset (http://www.worldclim.com/version2) that is ingested by the R code. 5) "table_1.csv": a cleaned version of the data used in analyses that is output by the R code. 6) "table_hg_diet_data.csv" and "table_seasonal_diet_data.csv": 2 files containing seasonal and HG diet data that were not used in analyses, which are output by the R code.

First Atmospheric Measurements and Emission Estimates of HFO-1336mzz(Z)

Atmospheric measurement data (mole fractions) for HFO-1336mzz(Z) (((Z)-1,1,1,4,4,4-hexafluoro-2-butene, cis-CF3CH=CHCF3). The data are related to article in ES&T (<a href="https://doi.org/10.1021/acs.est.3c01826">https://doi.org/10.1021/acs.est.3c01826</a>). Observations were made at the sites Beromünster (CH), Sottens (CH), Dübendorf (CH), Jungfraujoch (CH), and Cabauw (NL). Measurements were conducted using Medusa pre-concentration units coupled to gas chromatography and mass spectrometry (GC-MS), as is used within the global AGAGE network.

Stereo PIV measurement of open channel flows in RA8 flume at the University of Sheffield

Stereo PIV measurement of open channel flows in RA8 flume at the University of Sheffield Six flow conditions over a rough bed of spheres with 24mm diameter. PIV plane was at the centerline of the flow shining through the bed of spheres. Where the laser PIV plane shone up, the spheres were replaced with translucent hollow spheres to allow the light to go through. Gradient of the flow was 0.001. <table> <tbody> <tr> <td>Water Depth</td> <td>Flow rate</td> <td>Velocity</td> <td>Reynolds’ number</td> <td>Manning’s number</td> <td>Froude number</td> <td>Weber number</td> <td>Relative Submergence</td> </tr> <tr> <td>(mm)</td> <td>(l/s)</td> <td>(m/s)</td> <td>(with depth)</td> </tr> <tr> <td>49</td> <td>1.87</td> <td>0.08</td> <td>3,740</td> <td>0.049</td> <td>0.11</td> <td>3.96</td> <td>2.04</td> </tr> <tr> <td>69</td> <td>5.05</td> <td>0.15</td> <td>10,100</td> <td>0.031</td> <td>0.18</td> <td>20.53</td> <td>2.88</td> </tr> <tr> <td>89</td> <td>7.46</td> <td>0.17</td> <td>14,920</td> <td>0.031</td> <td>0.18</td> <td>34.74</td> <td>3.71</td> </tr> <tr> <td>109</td> <td>11.21</td> <td>0.21</td> <td>22,420</td> <td>0.028</td> <td>0.2</td> <td>64.05</td> <td>4.54</td> </tr> <tr> <td>129</td> <td>15.4</td> <td>0.24</td> <td>30,800</td> <td>0.026</td> <td>0.21</td> <td>102.14</td> <td>5.38</td> </tr> <tr> <td>149</td> <td>20.7</td> <td>0.28</td> <td>41,400</td> <td>0.023</td> <td>0.23</td> <td>159.77</td> <td>6.21</td> </tr> </tbody> </table>

TEAMx-PC22 (TEAMx pre-campaign 2022) – DWD Radiosonde lauches and drone measurements at Brannenburg

This dataset contains data from 9 radiosonde launches and 6 drone ascents of an IOP on 18./19.7.2022 during the TEAMx pre-campaign 2022. More details about TEAMx can be found at <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org</a>. DATA SET DESCRIPTION 1. Measurement location and measured variables Radiosonde launches and drone measurements were conducted at the site of Brannenburg (456m MSL) at the following coordinates: <table> <thead> <tr> <th scope="col"> </th> <th scope="col">Coordinates</th> <th scope="col">Instrument</th> <th scope="col">Measured Variables</th> <th scope="col">Time of sounding [UTC]</th> </tr> </thead> <tbody> <tr> <td>Radiosonde</td> <td> 47.742376 N 12.121785 E</td> <td>Radiosonde Vaisala RS41</td> <td>GPH, WDIR, WSPEED, T, DT, potT, P</td> <td> 18.07. 12:03 18.07. 13:45 18.07. 16:45 18.07. 19:45 18.07. 22:45 19.07. 01:45 19.07. 04:42 19.07. 07:46 19.07. 08:28</td> </tr> <tr> <td>Drone</td> <td> 47.742840 N 12.121808 E </td> <td>Drone DJI Mavic pro, iMETXQ2</td> <td>T, RH, P</td> <td> 18.07. 12:45 18.07. 13:45 18.07. 16:45 18.07. 22:45 19.07. 01:45</td> </tr> </tbody> </table>

assessment of aldehydes to PTR-MS m/z 69 in indoor air measurements - data set

<ul> <li>contact: Lisa Ernle (lisa.ernle@mpic.de), Nijing Wang (nijing.wang@mpic.de), Jonathan Williams (jonathan.williams@mpic.de)</li> <li>instruments: fast GC-MS SOFIA (MPIC), PTR-ToF-MS 8000 (Ionicon)</li> <li>merged dataset</li> <li>calibrated with VOC standard gas mix (Apel-Riemer Environmental Inc., Colorado, USA)</li> <li>units (filename): <ul> <li>normalized counts per second [ncps] (20210426_p_ncps.txt, bar_mean.txt, bar_std.txt)</li> <li>parts per billion [ppb] (all_sub_20210426_ppb.txt)</li> </ul> </li> <li>for information concerning updated versions, please see ReadMe.txt</li> </ul>

opencc-by-4.0May 2023View details →

Acoustic measurements in the ancient open-air theatre of Tyndaris

The data set represents the measured impulse response in the ancient Theatre of Tyndaris performed  in September 2015 by the Applied Acoustics Research Group of the Department of Energy of the Politecnico di Torino. - Measurement set-up has been described in https://www.mdpi.com/2076-3417/10/16/5680 - Receiver and source positions have been shown in Figure 2 in https://www.mdpi.com/2076-3417/10/16/5680

WALL-E polarization lidar measurements from Cyprus campaign 11/2019

WALL-E lidar measurements during the Cyprus observational campaign, on November 2019.The description of the WALL-E lidar system design and calibration procedures can be found herein: Tsekeri, A., Amiridis, V., Louridas, A., Georgoussis, G., Freudenthaler, V., Metallinos, S., Doxastakis, G., Gasteiger, J., Siomos, N., Paschou, P., Georgiou, T., Tsaknakis, G., Evangelatos, C., and Binietoglou, I.: Polarization lidar for detecting dust orientation: system design and calibration, Atmos. Meas. Tech., 14, 7453–7474, https://doi.org/10.5194/amt-14-7453-2021, 2021. <ul> <li>Orientation flag measurements</li> <li>Measurements for the lidar system calibration</li> </ul>

Measurements on reference materials for SThM measurand versus thermal conductivity calibration curve

This sheet presents the results of measurements carried out on calibration samples using the SThM technique as part of the NanoWires project. The thermal conductivity of the samples was previously characterised using a traceable technique. Measurement results are given with associated standard uncertainty. The thermal conductivity range studied was between 0.187 and 117 W.m-1.K-1.  These measurements were used to construct the thermal conductivity calibration curve for a SThM probe. The 19ENG05 NanoWires project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.

HeatResilientCity II - work package 2.3: Interactions between buildings and open space adaptation measures – Meteorological input data for building performance simulation

This repository contains meteorological data from urban climate simulations that were carried out in districts of the cities of Dresden and Erfurt as part of the <a href="http://heatresilientcity.de/">HeatResilientCity II</a> project. The data was extracted at specific points (receptors) of the urban climate model. In addition to the data, a script is attached that can be utilized to generate a time series for IDA ICE building performance simulations using IceWeather.exe. Therefore, a Microsoft Windows operating system is required. To create a time series, simply use the function createIdaIceInput() at the end of the script createTimeSeries.py. Further explanations can be found at the beginning of the script. Information about the ENVI-met data used to create the IDA ICE input can be found in README_RawENVImetOutput_DD.txt and README_RawENVImetOutput_EF.txt. Some input data files have already been generated and can be directly used for thermal building performance simulations with IDA ICE. These files can be found in the folder 0.3_Input_Timeseries (Climate) for IDA ICE. The naming convention of the final input data files for IDA ICE is as follows: <ul> <li>TOWN_SCENARIO_RECEPTOR_AVERAGING_INTERFACE_LATITUDE_LONGITUDE_VERSION</li> <li>TOWN: Choose between 'Erfurt' and 'Dresden'</li> <li>SCENARIO: See further information in README_RawENVImetOutput_DD.txt and README_RawENVImetOutput_EF.txt</li> <li>RECEPTOR: Location in the modelled area (ENVI-met simulation) where data was extracted.</li> <li>AVERAGING: Information about averaging the hourly values of the urban climate simulation (see createTimeSeries.py and READMEs)</li> <li>INTERFACE: Information on how single days were joined together (see createTimeSeries.py).</li> <li>LATITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function setIceWeatherParams() if you are using other cities/custom ENVI-met simulation data.</li> <li>LONGITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function setIceWeatherParams() if you are using other cities/custom ENVI-met simulation data.</li> <li>VERSION: The version number can be set in the script.</li> </ul> Example: Dresden_2y_A1_a_timeSeries_24-24_51.0468_13.6707_v11.prn Folder overview: <ul> <li>The ENVI-met raw data is stored in 0.1_Input_RawENVImetOutput.</li> <li>The script is stored in 0.2_Input_ScriptsToCreateTimeSeries.</li> <li>The final datasets ready for simulation with IDA ICE are stored in 0.3_Input_Timeseries(Climate)ForIDAICE. This folder also contains some weather data time series that have already been created and can be used for IDA ICE (subfolders Erfurt_v11 and Dresden_v11).</li> </ul>

Measurement Data for CT of Battery Pouch Cell with Defects

These are the raw radiography images used to calculate the CT volume of a battery pouch cell with defects.   Measured on a diondo d2 CT (using XWT-190 CT, Varex 4343DX-I) at TU Dresden. Tube: 150 KV, 100 µA Geometry: FOD 160 mm, FDD 1000 mm Detector: 3000 x 3000 px², 3000 Frames, 5x Framebinning, 3000 ms per Frame Data: 16bit unsigned integer, little endian   The sample was prepared by Johannes Münch. For further details see the following paper: <a href="https://doi.org/10.1002/ente.202300323">https://doi.org/10.1002/ente.202300323</a>