Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,509
datasets available to search
ShareScore release 0.9.0
Dataset results
1,509 results for “CALIBRATION”
Simulated calibration dataset for 4D scanning transmission electron microscopy
<p>4D-STEM data frequently requires a number of calibrations in order to make accurate measurement: for instance, in various cases, it can be essential to measure and correct for diffraction shifts, account for ellipticity in the diffraction patterns, or determine the rotational offset between the real and diffraction planes.</p> <p>We've prepared a simulated 4D-STEM dataset which includes diffraction shifting, elliptical distortion, and an r-space/k-space rotational offset. Two HDF5 files each include the simulated data for two different electron probes: a standard probe, using a circular probe-forming aperture, and a 'bullseye' probe, using a patterned aperture. Each HDF5 file contains the following data objects:</p> <p>(a) the 'experimental' 4D-STEM scan of a strained single-crystal gold nanoparticle (size: (100,84,250,250) )</p> <p>(b) a 4D-STEM scan of a calibration sample of polycrystalline gold (size: (100,84,250,250) )</p> <p>(c) a stack of diffraction images of the electron probe over vacuum (size: (250,250,20) )</p> <p>(d) a single image of the electron probe over the sample and far from focus, such that the CBED forms a shadow image (size: (512,512) )</p>
Dataset of publication "Investigation of the discharge coefficient in the laminar boundary layer regime of critical flow Venturi nozzles calibrated with different gases including hydrogen"
<p>The attached files contain experimental raw data and fluid properties for the nozzles 1 and 2 mentioned in the paper. The data in the attached files can be used to calculate the Cd values published in the paper.</p>
Data and results of the example used in the SI-Hg D1 protocol for the SI-traceable calibration of elemental mercury (Hg0) gas generators used in the field
<p>During the SI-Hg project a metrological traceable protocol for the calibration of mercury gas generators used in the field was developed and validated. The SI-Hg calibration protocol specifies the procedures for establishing traceability to the SI units for the quantitative output of elemental mercury generators that are employed in regulatory applications for emission monitoring or testing. This protocol provides methods for</p><ul><li>the experimental procedures to compare the output of elemental mercury gas generators</li><li>the data processing for determination of mercury concentration and the expanded uncertainty of the mercury concentration obtained from the elemental mercury gas generator.</li></ul><p>In the protocol examples are given to explain the data processing, determining the mercury concentration and corresponding uncertainty. In this repository the raw data and results used for the example calculated with the data processing script can be found.</p>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, underlying data
<p>These data files include 2 years (2018-2020) of high-frequency in-situ chlorophyll-a and phycocyanin fluorescence sensor (Turner Designs, Cyclops 7F) data, together with data from various tests conducted with these sensors. Morever, in-vitro chlorophyll-a data for 2018-2020 period is also included.</p> <p>These data are collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). There is data from 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors.</p> <p>In this Dataset folder, there are:</p> <ul> <li>one <strong>Metadata </strong>(*.xlsx)<strong> </strong>file with 3 sheets; <ul> <li>"<em>Descriptive</em>": comprises information on location, authors, study period and the main instrument used, </li> <li>"<em>Structural</em>": includes detailed information on each dataset.</li> <li>"<em>Relational</em>": includes a figure showing the relations between the datasets</li> </ul> </li> <li>thirteen files (*.csv) in LemCP_DataORE that were used to; <ul> <li>conduct in-situ fluorescence sensor tests (i.e. blank variation, linearity check, DOC effect check), </li> <li>calibrate 24 in-situ fluorescence chlorophyll-a sensors</li> <li>plot various figures</li> </ul> </li> </ul>
iRON_Soil Moisture_Calibrated
<p>**<strong>Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa for data sets uploaded prior to February 2024. Datasets uploaded after Feb 22, 2024 have the sensor labels corrected <em>for all dates going back to installation.</em>**</strong><br><br>***<strong>An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets</strong>. <strong>Files with FullRecord in the name are in correct, UTC time codes</strong>***</p> <p>These data are the complete data set from installation through March 15, 2018 for AGCI's interactive Roaring Fork Observation Network (iRON). The network consists of 9 stations in the Southern Rockies (Colorado, USA). Descriptions of soil type and site locations can be found at: www.agci.org/iron/stations</p> <p><strong>Please note:</strong> Measurements taken for rain are recorded via tipping bucket rain gauge and may not be accurate for winter precipitation events (e.g. snow). Error readings are represented by -888.88, -138.25, or -162.469 represents a sensor error. Absent data is represented by N/A.</p> <p>Sensors used for soil moisture readings are Decagon EC-5 Dielectric probe (5cm/2in depth); Decagon 10-HS (20cm/8in; 50cm/20in depths). All other equipment is Onset, and the logger device is the RX3000 model. Data collection is generally set to every 20 minutes (most stations) or every hour (Independence Pass station), but maintenance resets, etc. may occasionally have contributed to other collection intervals, e.g. every 5 minutes. Conversely, on occasions of logger failure, there may be full days of data absent.</p> <p>Snow data are available for Northstar Transition Zone and Independence Pass by request.</p> <p> </p> <p><strong>Updated calibrations (V2) are anticipated in the summer of 2018 for the following stations: Glassier Ranch (3), Glenwood Springs (5), Northstar Transition Zone (7), </strong><strong>Spring Valley (8)</strong></p> <p>Some instruments have been replaced over the life of a station. In 2015, all original U30 logger boxes were replaced with the RX3000 model. For complete metadata or records of instrumentation, please contact project advisor E. Osenga at eliseo at agci.org.</p> <p><strong>**Glassier Ranch site is a wetland and the soils are often completely saturated (leading to sensor difficulties in taking accurate readings) much of the year.</strong></p>
Example data from a virtual mass comparison using the Digital Calibration Certificate (DCC) scheme
<p><strong>The example data is an outcome of a project on a fully automated evaluation of a virtual mass comparison using the Digital Calibration </strong><strong>Certificate (DCC) schema.</strong></p> <p>We present the proof of concept for the first fully automated evaluation of a virtual mass comparison. The focus of this comparison is to demonstrate a possibility of digital transformation in metrology using an automated evaluation chain of different tools as well as machine-interpretable files for data transfer and final report. This publication provides the example data being used as input and output of the automated comparison evaluation tool.</p>
1.3A (Ge337) calibration data for new Ge115 monochromator installed on Echidna Neutron Powder Diffraction Instrument
<p>In early October 2024 the Echidna neutron powder instrument located at the OPAL reactor, ANSTO, installed a new monochromator with Ge115 cut. The present calibration data were collected shortly afterwards from a standard LaB6 sample in a 6mm diameter Vanadium can. The instrument was set to 140 degrees takeoff angle and monochromator angle 85.08 degrees, corresponding to the Ge337 reflection. Raw data in NeXus format are contained in <strong>ECH0034261.nx.hdf</strong>. These data were corrected for variable detector response using the information in <strong>eff_2024-10-06.cif</strong> and pixel vertical positions adjusted according to the table in <strong>vertical_offsets_2024-10-06.txt. </strong>Deviations from the ideal detector 1.25 degree angular spacing were applied using <strong>echidna-Apr2018.ang</strong>. The detector response was then recorrected based on overlapping measurements using the algorithm described in <a href="https://doi.org/10.1107/S1600576718014048">Avdeev and Hester (2018)</a> resulting in a 1D pattern suitable for fitting wavelength and peak shapes. This 1D pattern is provided here as a plain table (<strong>ECH0034261_LaB6.xyd</strong>) and as a pdCIF file (<strong>ECH0034261_LaB6.cif</strong>) including metadata on data collection and reduction. Details of data reduction are described in the above paper, and the data reduction routines used are included in the <a href="https://github.com/Gumtree/Echidna_scripts">Gumtree package as python code</a>.</p>
Calibration of non-local damage models from full-field measurements: application to discrete element fields.
<p>The codes, datasets, and results from the manuscript 'Calibration of non-local damage models from full-field measurements: application to discrete element fields' are available here.</p> <p> </p> <p>This repository is organized into four folders:</p> <ol> <li><strong>'Ideal Case' folder</strong>: This corresponds to Section ‘3. Application of the calibration method on a 1D ideal case' of the manuscript. In this folder, you will find the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM+</a> and <a href="https://www.python.org/" target="_blank" rel="noopener">Python</a> scripts and a makefile to run them. These codes reproduce all the figures, as well as the complete dataset associated with Figures 1-7.</li> <li><strong>'Virtual Testing Machine' folder</strong>: This corresponds to Section 4, 'Introduction of a Virtual Testing Machine' of the manuscript. Here, you will find: <ul> <li>The data for Figure 9, contains the force vs. CMOD response for all sizes and geometries.</li> <li>The data for Figure 10, contains the scores associated with this parametric identification.</li> </ul> </li> <li><strong>'Green Functions' folder</strong>: This includes the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM++</a> code to solve the boundary value problem for obtaining Green's function of the Eikonal equation. The code is demonstrated with both a non-damaged case and a polynomial damage case. In the first case, Green’s function corresponds to the weighting functions of the Implicit Gradient method (<a href="https://doi.org/10.1002/(SICI)1097-0207(19961015)39:19<3391::AID-NME7>3.0.CO;2-D" target="_blank" rel="noopener">Peerlings et al., 1996</a>).</li> <li><strong>'Real Case' folder</strong>: This corresponds to Section 5, 'Application of the Proposed Calibration Procedure to Virtual Tests.' Here, you will find: <ul> <li>The complete 'Damage vs. Strain' Dataset was generated with the Virtual Testing Machine. Figures 12-15 correspond to this dataset, processed as explained in the manuscript.</li> <li>The 'damage vs. damage driving variable' dataset for each characteristic length. Figure 16 in the manuscript corresponds to this dataset.</li> <li>The data for Figures 17 and 18, contain the evolution of the error with the characteristic length for different evolution laws.</li> </ul> </li> </ol>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, extended data
<p>These files include a scheme showing the sensor and tank (mesocosm) system, plots from high frequency in-situ chlorophyll-a and phycocyanin fluorescence data and in-vitro chlorophyll-a data collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). These plots are based on 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors from 24 tanks/mesocosms. There is also a table showing the steps for cleaning the high-frequency data.</p>
Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments"
<p>Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments."</p> <p>Data includes representative real and simulated bead trajectories used in the manuscript.</p> <p>Code includes all simulations, analysis, and plot details for the Figures in the manuscript. </p> <p>See included README.txt for more details.</p>
NASA's Airborne Topographic Mapper (ATM) ground calibration data for the Arctic Spring campaign 2019
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>This particular data set was used in a publication by Studinger <em>et al.</em>, 2022 (<a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>) that developed methods for estimating water depths of supraglacial lakes on the Greenland Ice Sheet.</p> <p>In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM waveform data from the T6 and T7 lidars, as well as the true ranges and a MATLAB® function to read ground test waveform data.</p> <p><strong><strong>See also:</strong> </strong></p> <p>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></p> <p>User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></p> <p>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></p> <p>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p>
Gaia Data Release 3: Basis function configuration for internally calibrated BP/RP spectra
<p>This XML file contains the basis function configuration adopted for the internally calibrated BP and RP spectra published in Gaia Data Release 3. The same file is included in the GaiaXPy (https://gaia-dpci.github.io/GaiaXPy-website/index.html) python package offering some useful functions to use the spectra.</p> <p>The content of the file and its basic usage are described in detail in Appendix C in the paper "Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data", De Angeli, F. et al. A&A (2022).</p>
NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>The purpose of this data set is to enable users working with NASA’s ATM airborne lidar waveform data to estimate their own range calibration if using range tracking methods different from the centroid estimate included in the ATM data files (Studinger <em>et al.</em>, 2022; <a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>). The airborne data are freely available from the National Snow and Ice Data Center (<a href="https://nsidc.org/home">NSIDC</a>) at the links listed in the table below. </p> <p>In pressurized aircraft the transmitted laser pulse travels through the aircraft’s optical window close to the scan mirror. Backscatter from both the scan mirror and the aircraft’s optical window in the fuselage are close in time to the transmitted laser pulse and partially overlap with the transmit waveform recorded by the ATM’s optical receiver. To record a “clean” transmit waveform the transmit pulse is sampled from behind a translucent beam splitter and subsequently injected into a multimode fiber-optic cable to provide a fixed optical delay that results in temporal separation between the recorded transmit pulse and contamination from backscattered photons from the scan mirror and the aircraft’s optical window. The delay due to the optical fiber and other system components introduce a laser time-of-flight range bias. The signal strength can also affect the calculated range in a way that depends on the waveform tracking algorithm. This variable influence, known as range walk, and the bias are determined from ground calibration measurements in which ATM data is collected from a stationary target at known range (true range) while varying the return intensity from signal extinction to detector saturation. The resulting signal-dependent deviation of measured range from the true range combine the system delay and range walk correction and are subtracted from the uncalibrated ranges to yield the calibrated range estimates "/laser/calrng" in the airborne waveform files (Studinger <em>et al.</em>, 2022; Appendix B3; <a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>).</p> <p>This data set includes ATM ground test waveform data from the wide and narrow scanner, the true ranges, as well as the range calibration tables (caltables) used for processing the airborne data products. A MATLAB® function to read the ground test waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6248436">https://doi.org/10.5281/zenodo.6248436</a></p> <p> </p> <table> <tbody> <tr> <td> <p><strong><strong>ATM Data Product ID at NSIDC</strong></strong></p> </td> <td> <p><strong><strong>Temporal Coverage</strong></strong></p> </td> </tr> <tr> <td> <p><strong><a href="https://nsidc.org/data/ilatmw1b">https://nsidc.org/data/ilatmw1b</a></strong></p> </td> <td> <p><strong>17 July 2017 - 20 November 2019</strong></p> </td> </tr> <tr> <td> <p><strong><a href="https://nsidc.org/data/ilnsaw1b">https://nsidc.org/data/ilnsaw1b</a></strong></p> </td> <td> <p><strong>29 October 2017 - 20 November 2019</strong></p> </td> </tr> </tbody> </table> <p>The file name of the groundtest calibration table that was used to process the airborne data is stored in the field "/ancillary_data/documentation/header_text" of each airborne data file. The corresponding groundtest waveform file has the same time tag and data set identifier as the calibration table file. E.g., the corresponding ground test waveform file for the calibration table “caltable_20170628_193814.atm6AT5.binned_data.txt” is “ILATMW1B_20170628_193814.atm6AT5.h5”. The table below lists the ground test waveform files that should be used for each campaign and instrument:</p> <table> <tbody> <tr> <td> <p><strong><strong>Year</strong></strong></p> </td> <td> <p><strong><strong>Campaign</strong></strong></p> </td> <td> <p><strong><strong>Data Set</strong></strong></p> </td> <td> <p><strong><strong>Groundtest Waveform Data</strong></strong></p> </td> </tr> <tr> <td> <p><strong><strong>2017</strong></strong></p> </td> <td> <p><strong>17-JUL-2017 25-JUL-2017</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20170628_193814.atm6AT5.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>29-OCT-2017 25-NOV-2017</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20171017_141954.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>29-OCT-2017 25-NOV-2017</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20171208_122808.atm6BT7.h5</strong></p> </td> </tr> <tr> <td> <p><strong><strong>2018</strong></strong></p> </td> <td> <p><strong>22-MAR-2018 01-MAY-2018</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20180309_110944.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>10-OCT-2018 16-NOV-2018</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20181002_151220.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>22-MAR-2018 01-MAY-2018</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20180301_115659.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>10-OCT-2018 16-NOV-2018</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20181002_160127.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> <p><strong><strong>2019</strong></strong></p> </td> <td> <p><strong>03-APR-2019 16-MAY-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20190321_102239.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-SEP-2019 16-SEP-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20190817_132201.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>23-OCT-2019 20-NOV-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20191017_121209.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-APR-2019 16-MAY-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20190327_105730.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-SEP-2019 16-SEP-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20190817_131026.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>23-OCT-2019 20-NOV-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20191017_122059.atm6DT7.h5</strong></p> </td> </tr> </tbody> </table> <p> </p> <p><strong><strong>See also:</strong> </strong></p> <p>User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></p> <p>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></p> <p>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p>
Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference grade performances - dataset
<p>This repository contains the data used for the analysis of the paper "Characterisation and calibration of PM sensors at high temporal resolution to reference grade performances" submitted to Heliyon and available as a pre-print:</p> <p>Bulot, Florentin M. J. and Ossont, Steven J. and Morris, Andrew and Basford, Philip J. and Easton, Natasha H. C. and Mitchell, Hazel L. and Foster, Gavin L. and Cox, Simon J. and Loxham, Matthew, Characterisation and Calibration of Low-Cost Pm Sensors at High Temporal Resolution to Reference-Grade Performance. Available at SSRN: <a href="https://ssrn.com/abstract=4360707">https://ssrn.com/abstract=4360707</a> or <a href="http://dx.doi.org/10.2139/ssrn.4360707">http://dx.doi.org/10.2139/ssrn.4360707</a></p> <p> </p> <p>The code used to conduct the data analysis is available at <a href="https://doi.org/10.5281/zenodo.7261417">https://doi.org/10.5281/zenodo.7261417</a></p> <p> </p> <p>.</p> <p> </p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p>Description of the files.</p> <p>202007_to_202107_nocs - contains the data from the low-cost sensors</p> <p>It contains the following headers:<br> - "sensor" - sensor id<br> - "site" - name of the air quality monitor hosting the sensor<br> - "median_PM1" - PM1 mass concentration (ug/m3)<br> - "median_PM10" - PM10 mass concentration (ug/m3)<br> - "median_PM25" - PM25 mass concentration (ug/m3)<br> - "median_PM4" - PM4 mass concentration (ug/m3) (only available for SPS30)<br> - "median_n05" - particle number concentration (SPS30) of particles between 0.3um and 0.5um<br> - "median_n1" - particle number concentration (SPS30) of particles between 0.3um and 1um<br> - "median_n10" - particle number concentration (SPS30) of particles between 0.3um and 10um<br> - "median_n25" - particle number concentration (SPS30) of particles between 0.3um and 2.5um<br> - "median_n4" - particle number concentration (SPS30) of particles between 0.3um and 4um<br> - "median_gr03um" - particle number concentration (PMS5003) of particles >0.3um<br> - "median_gr05um" - particle number concentration (PMS5003) of particles >0.5um<br> - "median_gr100um" - particle number concentration (PMS5003) of particles >10um<br> - "median_gr10um" - particle number concentration (PMS5003) of particles >1um<br> - "median_gr25um" - particle number concentration (PMS5003) of particles >2.5um<br> - "median_gr50um" - particle number concentration (PMS5003) of particles >5um<br> - "median_pm100_cf1" - PM10 mass concentration with cf1 calibration for PMS5003<br> - "median_pm10_cf1" - PM1 mass concentration with cf1 calibration for PMS5003<br> - "median_pm25_cf1" - PM25 mass concentration with cf1 calibration for PMS5003<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_pm_2min - contains the PM mass concentration data from the Fidas 200S.</p> <p>It contains the following headers:<br> - "PM2.5" - PM2.5 mass concentration (ug/m3) Fidas 200S<br> - "PM10" - PM10 mass concentration (ug/m3) Fidas 200S<br> - "PMtot" - PM total mass concentration (ug/m3) Fidas 200S<br> - "PM1" - PM1 mass concentration (ug/m3) Fidas 200S<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_weather_2min - contains the weather data from the Fidas 200S</p> <p>It contains the following headers:<br> - "rh" - relative humidity (%)<br> - "dew_point_temperature" - dew point temperature (Celsius)<br> - "air_pressure" - Air pressure (hPa)<br> - "temperature" - temperature (Celsius)<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"</p>
Dataset for the comparison of performance of two leak detectors using hydrogen reference leaks (supplement to paper "Advancing Hydrogen Leak Detection: Design and Calibration of Reference Leaks")
<p>Excel file containing some measurements made in December 2023, using three hydrogen reference leaks, to assess the performance of two distinct leak detectors, one portable and made specifically for hydrogen and one MSLD in hydrogen-mode.</p>
caseysaenger/ForamMgCa_PSM: files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data".
<p>files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data". Saenger, C. and M. N. Evans. Resubmitted to Paleoceanography and Paleoclimatology, May 3, 2019.</p>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
Research Data/Code for "Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification"
<p>This repository contains research data and code for supplementing the manuscript <br>"Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification" <br>by L. Gossel, E. Corbean, S. Dübal, P. Brand, M. Fricke, H. Nicolai, C. Hasse, S. Hartl, S. Ulbrich, and D. Bothe. </p> <p>There is a corresponding preprint available on Arxiv: https://doi.org/10.48550/arXiv.2404.13092</p> <p><br>Users are referred to the manuscript for background information. This repository shall enable reproduction of the reported results and does not stand alone. </p> <p>Please read important information in the README in the top-level directory. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Dataset for: In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier
<p>Dataset for manuscript "In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier", <a href="https://arxiv.org/abs/2406.03063">arXiv:2406.03063</a></p> <p>Containing the uncalibrated raw measurement data and the calibrated dataset after applying the 8-term error model.</p>
Mars 2020 Perseverance SHERLOC WATSON camera pre-delivery characterization and calibration image data
<p>The data presented here include images acquired by the WATSON (Wide Angle Topographic Sensor for Operations and eNgineering) camera during pre-delivery characterization and calibration testing at Malin Space Science Systems (MSSS, San Diego, California, USA) in September and October 2019. They also include video documentation of the camera’s dust cover motion. WATSON is one of two imaging subsystems of the SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instrument onboard NASA’s Mars 2020 Perseverance rover which landed in Jezero crater, Mars, in February 2021.</p> <p>These data accompany the instrument calibration and characterization report by Edgett et al. (2019) and the WATSON characteristics reported by Bhartia et al. (2021). The image data presented here are listed and described in the Appendix to Edgett et al. (2019), which is also available here with the data.</p> <p>References cited:</p> <p>Bhartia, R., L. W. Beegle, L. DeFlores, W. Abbey, J. Razzell Hollis, K. Uckert, B. Monacelli, K. S. Edgett, M. R. Kennedy, M. Sylvia, D. Aldrich, M. Anderson, S. A. Asher, Z. Bailey, K. Boyd, A. S. Burton, M. Caffrey, M. J. Calaway, R. Calvet, B. Cameron, M. A. Caplinger, B. L. Carrier, N. Chen, A. Chen, M. J. Clark, S. Clegg, P. G. Conrad, M. Cooper, K. N. Davis, B. Ehlmann, L. Facto, M. D. Fries, D. H. Garrison, D. Gasway, F. T. Ghaemi, T. G. Graff, K. P. Hand, C. Harris, J. D. Hein, N. Heinz, H. Herzog, E. Hochberg, A. Houck, W. F. Hug, E. H. Jensen, L. C. Kah, J. Kennedy, R. Krylo, J. Lam, M. Lindeman, J. McGlown, J. Michel, E. Miller, Z. Mills, M. E. Minitti, F. Mok, J. Moore, K. H. Nealson, A. Nelson, R. Newell, B. E. Nixon, D. A. Nordman, D. Nuding, S. Orellana, M. Pauken, G. Peterson, R. Pollock, H. Quinn, C. Quinto, M. A. Ravine, R. D. Reid, J. Riendeau, A. J. Ross, J. Sackos, J. A. Schaffner, M. Schwochert, M. O Shelton, R. Simon, C. L. Smith, P. Sobron, K. Steadman, A. Steele, D. Thiessen, V. D. Tran, T. Tsai, M. Tuite, E. Tung, R. Wehbe, R. Weinberg, R. H. Weiner, R. C. Wiens, K. Williford, C. Wollonciej, Y.-H. Wu, R. A. Yingst, J. Zan (2021) Perseverance’s Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) investigation, Space Science Reviews 217, 58. https://doi.org/10.1007/s11214-021-00812-z</p> <p>Edgett, K. S., M. A. Caplinger, M. A. Ravine (2019) Mars 2020 Perseverance SHERLOC WATSON Camera Pre-delivery Characterization and Calibration Report, Malin Space Science Systems, San Diego, California. https://doi.org/10.13140/RG.2.2.18447.00165</p>
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.