Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

Laboratory-measured and X-ray CT-derived volumetric composition of a permafrost core

This dataset contains data on the volumetric composition of a permafrost core which has been drilled in a Yedoma upland in northeast Siberia (72.36613 N, 126.27272 E) in September 2017. This dataset supplements a research article to be submitted to the scientific journal The Cryosphere. It contains the following files: volumetric_contents_sampleRes_lab+CT.csv Contains the volumetric contents of total ice, organic, and mineral measured in the laboratory at AWI Potsdam at a coarse resolution. It further contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle, downsampled to the resolution of the laboratory samples. volumetric_contents_highRes_CT.csv Contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle at the original resolution of 50µm. regression analysis_paper.py This pyhton script uses the above listed input files to perform and evaluate a regression analysis of the CT data against the laboratory data. The regression result is the composition of the CT-derived sediment phases (A,B) in terms of pore ice, organic, and mineral. The script furthermore computes evaluation metrics of the lab-CT comparison, and computes volumetric contents of pore ice, total ice, organic, and mineral at the high resolution of the original CT data. volumetric_contents_sampleRes_all.csv This file can be reproduced by the files listed above and contains, in addition to the data contained in volumetric_contents_sampleRes_lab+CT.csv, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (coarse) resolution as the laboratory samples. volumetric_contents_highRes_all.csv This file can be reproduced by the files listed above and contains, in addition to the data contained in volumetric_contents_highRes_CT.csv, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (high) resolution as the original CT data. More details can be found in the article describing the study.

Reconfiguration Time Measurements for Evolving Hardware

The dataset contains the raw measurements of the time necessary to reconfigure Lattice iCE40 FPGAs. The main goal is to see the effect of two approaches of implementing the reconfiguration process in software for Evolvable Hardware. One approach is to use existing open-source software externally, the other is to integrate the functionality in the software for Evolvable Hardware. The conditions for the reconfiguration measurements were altered in four dimensions: <ol> <li>The FPGA board <ul> <li>iCEstick <ul> <li>Lattice iCE40 HX1K FPGA</li> <li>Sample bitstream created by <a href="https://github.com/evolvablehardware/BitstreamEvolution">Bitstream Evolution Software</a></li> <li>No direct reconfiguration possible</li> </ul> </li> <li>iCE40 breakout board (ICE40HX8K-B-EVN) <ul> <li>Lattice iCE40 HX8K FPGA</li> <li>Sample bitstream created by CoBEA</li> </ul> </li> </ul> </li> <li>Write Target <ul> <li>Flash <ul> <li>Bitstream first written to an external EEPROM, then pulled by the FPGA from the EEPROM</li> </ul> </li> <li>Direct <ul> <li>Bitstream written directly to the FPGA</li> </ul> </li> </ul> </li> <li>Reconfiguration approach <ul> <li>Icestorm <ul> <li>Use external tools from Project Icestorm</li> </ul> </li> <li>CoBEA <ul> <li>Use integrated function of CoBEA reconfiguration module</li> </ul> </li> </ul> </li> <li>Bitstream size <ul> <li>Full <ul> <li>No size reduction</li> </ul> </li> <li>Compact <ul> <li>Size reduced as much as allowed by write approach <ul> <li>Icestorm: skip BRAM</li> <li>CoBEA: skip BRAM, skip comment, full compaction (level 4)</li> </ul> </li> </ul> </li> </ul> </li> </ol>   This upload contains three groups of files: <ul> <li>2 bitstreams <ul> <li>One for each used FPGA board</li> </ul> </li> <li>12 measurements <ul> <li>Comma-separated values</li> <li>File name contains the contiditions of the measurement</li> <li>Only 12 out of 16 possible combinations of conditions as "iCEstick" cannot be combined with "Direct"</li> </ul> </li> <li>1 aggregated statistic <ul> <li>Comma-separated values</li> <li>Minimum, mean, maximum, mean-minimum, maximum-mean for every measurement</li> </ul> </li> </ul>

Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018

The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.

CROSSBOW HLU2-UC1-TC1 measurements: Substation measurements gathered during curtailment activation

The data comprises the measurements read at the Konjsko substation before, during and after a curtailment in Pometeno Brno plant. The measurements gathered from the SCADA are: <ul> <li>P, Q and V of the 110, 220 and 400 Kv buses at the substation</li> </ul> P, Q and V of the line connecting the substation with the plant

opencc-by-4.0May 2022View details →

Measurements of benzene and toluene in underway surface seawater and ambient air in the Atlantic sector of the Southern Ocean on cruise ANDREXII/JR18005 between February and April 2019.

Benzene and toluene cycling iin the unpolluted marine environment s poorly understood. Due to a paucity of measurements, the role of the ocean in the atmospheric budgets of atmospheric benzene and toluene is unknown. In order to quantify the air-sea fluxes of these gases and obtain insights to their biogeochemical cycling, we measured their seawater concentrations (surface and depth profiles) and air mixing ratios in the Atlantic sector of the Southern Ocean, along a ~11000 km long transect at approximately 60o S in Feb-Apr 2019. The measurements were made using a Proton Transfer Reaction Mass Spectrometer coupled to a Segmented Flow Coil Equilibrator. Concentrations, oceanic saturations and calculated fluxes benzene and toluene are presented here.    The data is further presented and discussed in a manuscript:  Marine biogenic benzene and toluene emissions and their impact on secondary organic aerosol in the polar regions. Charel Wohl, Qinyi Li, Carlos A. Cuevas, Rafael P. Fernandez, Mingxi Yang, Alfonso Saiz-Lopez, Rafel Simó, Submitted to Atmospheric Atmospheric Chemistry and Physics, 2022   Computation of the air-sea gas fluxes is explained in detail in the linked manuscript about benzene and toluene.  Positive values indicate oceanic outgassing, thus sea to air flux.   Definitions of acronyms, site abbreviations, or other project-specific designations: deg = degree  SW = seawater concentration ATM= atmosphere SAT = saturation flux= air-sea flux in (micro)umol_m^(2)_d^(-1) nM = nano Molar seawater concentration defined as nmol dm^(-3) LAT, LONG = Latitude, Longitude. (negative indicates west and south) The timestamp indicates sampling time in UTC, expressed as  DD/MM/YYYY_HH:MM Empty data cells/points are listed as an impossible number of -999. Interruptions in the measurements are due to calibrations and other instrument maintenance.Interruptions in the calculated flux are due to missing auxiliary data at those sampling points e.g. no wind speed or underway auxiliary data. Fluxes and saturations computed using the interpolated air mixing ratio (see linked manuscript) are indicated with the suffix "_2"   Negative values correspond to readings below the blank and detection limit.  They are effectively zero and are included here as the computed negative concentration to avoid skewing the mean.   Data last modified 06.05.2022. Version 1 uploaded on that date. No further maintenance planned. This is the final data.

Supporting Information for 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'

Supporting Information of 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces' This dataset contains the Supporting Information of the publication  Rühr PT & Blanke A (2022): 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'. doi: <a href="https://doi.org/10.1111/2041-210X.13909">10.1111/2041-210X.13909</a>. It includes <ul> <li>validation measurements the forceX setups (1 Ruehr Blanke 2022 validation measurements.zip)</li> <li>all CAD files to build the forceX setup (3D-printed or metal-turned) (2 Ruehr Blanke 2022 forceX CAD files.zip)</li> <li>forceX assembly instructions in HTML format, including schematics of custom electronics (3 Ruehr Blanke 2022 forceX Assembly instructions.html)</li> <li>forceX assembly instructions as video (4 Ruehr Blanke 2022 forceX assembly video 03.mp4)</li> <li>R code that produced all validation-related figures used in the original publication and that functions as a forceR v.1.0.13 example workflow (5 Ruehr Blanke 2022 forceR_workflow_example.R)</li> <li>Python code to take videos of force measurements using the forceX camera module (6 Ruehr Blanke 2022 forceX_RPi_camera_code.py)</li> <li>bundled version of forceR v.1.0.15 (forceR_1.0.15.tar.gz)</li> </ul> The CAD files and assembly instructions are also available on <a href="https://www.thingiverse.com/thing:4961834">Thingiverse</a>. The forceR package is available on <a href="https://cran.r-project.org/web/packages/forceR/index.html">CRAN</a> (stable version) and <a href="https://github.com/Peter-T-Ruehr/forceR">GitHub</a> (development version).

opencc-by-4.0Jul 2022View details →

Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef

Dataset includes in-situ (n = 144,912) and laboratory-dispersed (n = 64) particle size measurements collected using laser diffractometry from nine rivers discharging along 800 km of Great Barrier Reef, Queensland Australia coastline. Two field campaigns (24 February to 5 March 2021 and 24th to 31st of April 2021) were undertaken to collect vertical profiles of in-situ particle size, water velocity, turbidity, and salinity. Water samples were collected for analysis of laboratory-dispersed particle size and suspended-sediment concentration. Water samples were collected using US-P61 or Van-Dorn samplers deployed alongside a LISST 200x laser diffractometer and an EXO2 YSI multiparameter water quality sonde. Total depth and water velocity were measured using a Nortek Signature ADCP and Teledyne RiverRay ADCP during the first and second field campaigns, respectively. During both campaigns, measurements were undertaken during relatively high discharge events when discharge exceeded the 90th percentile of 2020/2021 gauged wet season flows. Data are provided in three csv files. "In_situ_data.csv" contains in situ measurements of particle size, turbidity, salinity, and depth along with estimates of shear rate. Shear rate is estimated from theory and measurements of ADCP-measured total depth and depth-averaged flow (see equation 2 of Livsey et al., 2022). "Lab_data_this_study.csv" contains particle size measurements of suspended-sediment following laboratory dispersion along with coeval measurements of in-situ particle size, turbidity, salinity, and shear rate averaged over the filling time of the US-P61 sampler. "Lab_data_DES_WQI.csv" contains laboratory dispersed particle size measurements collected by the Department of Environment and Science Water Quality Investigation Unit of Queensland Australia (Turner et al., 2013) and compared to data in  "Lab_data_this_study.csv" in Livsey et al (2022).  Further details of the data collection effort and interpretation of the data are published in Livsey et al (2022) at https://doi.org/10.1029/2021JC017988.   Additional data from the 24 February to 5 March 2021 field campaign, funded by CSIRO Oceans and Atmosphere, are available from Crosswell et al (2022) at https://doi.org/10.25919/2vbh-cx08. References: Crosswell, Joey; Carlin, Geoff; Daniel, Livsey; Hillyer, Katie; Steven, Andy (2022): FNQ_2021_V01 Voyage dataset: Feb - March 2021; Biogeochemical and hydrodynamic obervations along the river-reef continuum of estuaries in eastern Cape York, Australia. v1. CSIRO. Data Collection. 10.25919/2vbh-cx08 Livsey, D. L., Crosswell, J. R., Turner, R. R., Steven, A. D. L., & Grace, P. R. (2022) Flocculation of riverine sediment draining to the Great Barrier Reef, implications for monitoring and modelling of sediment dispersal across continental shelves. Journal of Geophysical Research: Oceans. https://doi.org/10.1029/2021JC017988 Turner. R, Huggins. R, Wallace. R, Smith. R, Vardy. S, Warne. M St. J. (2013). Total suspended solids, nutrient, and pesticide loads (2010-2011) for rivers that discharge to the Great Barrier Reef Great Barrier Reef Catchment Loads Monitoring 2010-2011 Department of Science, Information Technology, Innovation and the Arts, Brisbane.

opencc-by-4.0Jul 2022View details →

Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland

Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland ================================= README version 1.3, 21/07/2022 ================================== Contact info: Paul Leahy, University College Cork paul.leahy@ucc.ie | +353 21 4902017 ================================   Contents 1. Measurement location and time period 2. What is measured (brief description) 3. Instrumentation 4. CSV file detailed descriptions ================================   1. Measurement location and time period: North roof of Kane Building, University College Cork (UCC), Ireland. Lat 51 d 53 m 34 s N. Long 8 d 29 m 39 s W. Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building). The measurements were taken over a time period of several months in the years 2013 / 2014. ================================= 2. What is measured (brief description): * LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5  altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level. * Air temperature, atmospheric pressure, relative humidity. * Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level). * 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided. See 'CSV file detailed description' below for detailed information. * Diagnostic information. ================================= 2.1 Surrounding terrain: Surrounding area is urban/suburban. The aspect is northerly. To the West: 2-5 storey buildings, open spaces, suburban. To the South: 2-3 storey buildings, open spaces, trees, river. To the East: 2-3 storey buildings, open spaces. To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban. ================================= 3. Instrumentation: ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS. ================================= 4. CSV files detailed description: 4.1 Data on 10-minute averages: Filename 05092013-03122013_10min_res.csv contains: 10 minute averaged data from 05/09/2013 to 03/12/2013. Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.   Filename 03122013-07082014_10min_res.csv contains: 10 minute averaged data from:  03/12/2013 to 07/08/2014. Measurement altitudes:  148 m, 90 m,  50 m, 35 m,  15 m above instrument level. Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).   The first two rows contain header information. Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds. Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West. Row 2 contains the data column headers including units.   Wind speeds at each altitude are recorded: No of Packets (= number of scan units averaged over) [] Wind direction (mean) [deg] Horizontal wind speed (mean) & standard deviation [m/s] Vertical wind speed (mean) & standard deviation [m/s] Horizontal variance [m^2/s^2]                       Horizontal min [m/s]                               Horizontal max [m/s]                              TI (turbulence intensity) []   Other meteorological data: Air temperature [oC] Pressure [mbar] Rel. Humidity [%] Rain indicator  [unitless] Higher values indicate more rain during the averaging interval. Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer) Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).   Other housekeeping and diagnostic data: Instrument tilt [deg] Instrument bearing [deg] GPS data [degrees N, degrees W] Battery voltage [V]  Optics, electronics and battery temperature [oC]   =====================================================   4.2 Data with high time resolution (~23 s):   Filename 05092013-11112013_23s_res.csv contains: High resolution data from 05/09/2013 to 11/11/2013 Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.   Note on time resolution: The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete. The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.   Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds. Row 2 contains the data column headers including units.   Wind speeds at each altitude are recorded: No of Packets (= scan units averaged over) [] Wind direction (mean) [deg] Horizontal wind speed (mean) & standard deviation [m/s] Vertical wind speed (mean) & standard deviation [m/s] Horizontal variance [m^2/s^2]    not defined as measurement interval is too short. Horizontal min [m/s]                    not defined as measurement interval is too short.                             Horizontal max [m/s]                    not defined as measurement interval is too short.                              TI (turbulence intensity) []           not defined as measurement interval is too short.   Other meteorological data: Air temperature [oC] Pressure [mbar] Rel. Humidity [%] Rain indicator  [unitless] Higher values indicate more rain during the scanning interval. Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer) Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.   Other housekeeping and diagnostic data: Instrument tilt [deg] Instrument bearing [deg] GPS data [degrees N, degrees W] Battery voltage [V]  Optics, electronics and battery temperature [oC]   ===================================================== 4.3 Quality control indicators:   9998                    atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog 9999                    high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path Status Flag         'Green' => good =======================================================

opencc-by-4.0Aug 2022View details →

Calibration Dataset of Device for Measuring Forces and Torques in Flexible Connection Joints for Parabolic Trough Collector

This dataset corresponds with the calibration tests of device for measuring forces and torques in flexible connection joints for parabolic trough collector. This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 823802 (SFERA-III), and it is related with the milestone number MS29 of task 10.1.B - Enhancement of sensor monitoring/calibration and measurement accuracy of laboratory test benches of RI.

opencc-by-4.0Jan 2022View details →

Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars

Dataset of the paper " Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1]. " published in Wind Energy Science [1]. [1] Brugger, P., Markfort, C., and Porté-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, 2022.

Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2018-11-05 to 2018-12-31 [RAW]

Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. The phenocam "hartheim2" was put into operation on November 5, 2018. There are no phenocam images before that date at this site. Phenocam "hartheim2" shows the view from the main tower at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.

Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2020-01-01 to 2020-12-31 [RAW]

Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2020.  Phenocam "hartheim2" shows the view from the main tower at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.

Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2021-01-01 to 2021-12-31 [RAW]

<div> Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2021.  Phenocam "hartheim2" shows the view from the main tower at 7m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosystem Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation. </div>

Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2022-01-01 to 2022-12-31 [RAW]

<div> Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022.  Phenocam "hartheim2" shows the view from the main tower at at 8.4 m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosystem Site DE-Har, Germany</a> recording the phenology and state of in-canopy vegetation.   </div>

opencc-by-nc-4.0Oct 2023View details →

MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees

We present a one-year-long Multi-Sensor dataset with Phenotypic trait measurements from honey Bees (MSPB). Data were continuously collected between April-2020 and April-2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), Varroa destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis on honey bees, such as bee acoustics analysis, multi-modal hive monitoring, queen presence detection, Varroa infection detection, hive population estimation, biological analysis of bees, etc. <h3>Related Info</h3> The data collection process, feature pre-processing, preliminary data analysis, and usage notes can be found in our paper <a href="https://arxiv.org/abs/2311.10876">https://arxiv.org/abs/2311.10876</a> Check the project webpage (<a href="https://zhu00121.github.io/MSPB-webpage/">https://zhu00121.github.io/MSPB-webpage/</a>) and Github repo (<a href="https://github.com/MuSAELab/MSPB">https://github.com/MuSAELab/MSPB</a>) for more information. <h3>Citation</h3> Kindly cite the following paper: @misc{zhu2023mspb,      title={MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees},        author={Yi Zhu and Mahsa Abdollahi and Ségolène Maucourt and Nico Coallier and Heitor R. Guimarães and Pierre Giovenazzo and Tiago H. Falk},      year={2023},      eprint={2311.10876},      archivePrefix={arXiv},      primaryClass={eess.AS} } <h3>Contact</h3> You can contact us at Yi.Zhu@inrs.ca, if you encounter any questions accessing the data.

opencc-by-4.0Mar 2024View details →

Measurements and model simulations of iodine monoxide (IO) radical, water vapor (H2O), nitrogen dioxide (NO2) radical, formaldehyde (HCHO), gaseous elemental mercury (Hg0), and oxidized mercury (HgII) at Storm Peak Laboratory, Colorado, during April 2022

This dataset was compiled to accompany the manuscript Lee et al., titled "Elevated Tropospheric Iodine over the Central Continental United States: Is Iodine a Major Oxidant of Atmospheric Mercury?", submitted to AGU Geophysical Research Letters.   file01 contains two example spectral proofs for iodine monoxide (IO) radical measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Storm Peak Laboratory, CO (SPL; 3220 meters above sea level; 40.455 degrees North; 106.745 degrees West) during April 2022. file02 contains oxygen collision-induced absorption (O2-O2) slant column densities (SCDs) measured in a spectral fit window from 350 to 388 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file03 contains O2-O2 SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file04 contains IO SCDs measured in a spectral fit window from 417.5 to 438 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file05 contains water vapor (H2O) SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file06 contains nitrogen dioxide (NO2) radical SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file07 contains formaldehyde (HCHO) SCDs measured in a spectral fit window from 328,5 to 359 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file08 contains the profiles of pressure, temperature, O2-O2, ozone (O3), NO2, and H2O derived from ECMWF CAMS reanalysis (April 2022 at SPL) and used in the radiative transfer model McArtim3 to calculate weighting functions for the trace gas profile inversions of IO, H2O, NO2, and HCHO. file09 contains the a priori profiles used for the IO profile inversions during April 2022 at SPL. One profile assumes a "flat" profile shape with a constant volume mixing ratio of 0.10 pptv throughout the atmosphere. The other profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. file10 contains the a priori profile used for the H2O profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. file11 contains the a priori profile used for the NO2 profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. file12 contains the a priori profile used for the HCHO profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. file13 contains the IO tropospheric vertical column densities (VCDtrop; surface to 12 km), volume mixing ratios near instrument altitude (VMRinstr), and degrees of freedom (DoF) measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file14 contains the H2O VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file15 contains the NO2 VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file16 contains the HCHO VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file17 contains GEOS-Chem simulated temperature, relative humidity, IO VCDtrop & VMRinstr, H2O VCDtrop & VMRinstr, NO2 VCDtrop & VMRinstr, HCHO VCDtrop & VMRinstr, and bromine monoxide (BrO) radical VCDtrop & VMRinstr at SPL from April 1 to April 30, 2022. file18 contains the gaseous elemental mercury (Hg0) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022. file19 contains the oxidized mercury (HgII) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022. file20 contains the GEOS-Chem simulated Hg0 and HgII at SPL from April 1 to April 30, 2022. file21 contains the profiles of pressure, temperature, relative humidity, BrO, bromine atom (Br), methane (CH4), chlorine monoxide (ClO) radical, chlorine atom (Cl), carbon monoxide (CO), Hg0, peroxy radical (HO2), IO, iodine atom (I), NO2, hydroxyl radical (OH), and O3 used as constraints for the gas-phase mercury box model. All profiles except IO and I are adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. The IO profile was calculated by scaling the GEOS-Chem April 2022 daytime (SZA < 85) average below 12 km by the average observed IO VCDtrop during April 2022. The I atom profile was calculated by multiplying the scaled IO profile by the ratio of unscaled I / unscaled IO profiles from GEOS-Chem.   file22 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file23 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file24 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file25 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file26 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file27 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file28 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file29 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file30 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file31 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file32 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file33 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file34 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file35 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file36 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file37 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file38 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file39 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient.   file40 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file41 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file42 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file43 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file44 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file45 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file46 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file47 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file48 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file49 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file50 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file51 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file52 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file53 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file54 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file55 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file56 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file57 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient.

opencc-by-4.0Jul 2024View details →

Measurement of the bound-state beta decay of 205Tl(81+): intermediate and result data

The data presented here is the intermediate and result data from the measurement of the bound-state beta decay of 205Tl(81+), experiment G-20-0E121, which was performed at the Experimental Storage Ring (ESR) at the GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt (Germany) in the frame of FAIR Phase-0. The experimental measurement was done from the 26th March 2020 to 6th April 2020. Intermediate Data: During the experiment, the ESR monitored the beam via three main detectors: the non-destructive 245 MHz Schottky resonator, the DC Current Transformer (DCCT), and a Multi-Wire Proportional Chamber (MWPC). In particular: <ol> <li>Schottky data: the integrated Schottky noise power density for the 205Tl(81+) peak and the 205Pb(82+) peak is provided for each storage measurement. The full Schottky spectrum can be made available on request. As described in the related works below, the Schottky data became saturated above a certain threshold due to a mismatched amplifier in the NTCAP DAQ. This results in non-exponential decay of Schottky peaks for high intensities. </li> <li>DCCT data: the entire beam current in the ring was monitored using the DCCT. The DCCT was recorded using a scalar counter and thus has a non-zero offset value, which we determined to be 28.199 µA from a period with no beam. The DCCT is intended to be used as a diagnostic tool, and is not as precise as other, purpose-built detectors.</li> <li>MWPC data: for most storage measurements, a MWPC detector was placed downstream of the gas target on the outside of the ring to detect electron recombination products. The provided data is the detection rate on the anode.</li> <li>The gas target density, as recorder by a scaler counter, is also included.</li> </ol> The intermediate data on all three of these detectors plus the gas target density is provided in the tar.gz repository, with individual ROOT files for each storage time. The ROOT files have the naming format "Storage time_MMDD_HH.root". Each file contains 5 TGraph objects. Result Data: The result data provides all the necessary individual measurement and correction values to extract a bound-state beta-decay rate from the corrected ratios. It is provided in two forms: <ol> <li>BSBD_205Tl-result_data.ods is an ODS table for easy visualisation.</li> <li>BSBD_205Tl-final_vals.txt is a text file used by the Monte Carlo analysis script provided in <a href="https://doi.org/10.5281/zenodo.11560338" target="_blank" rel="noopener">DOI 10.5281/zenodo.11560338</a>.</li> </ol> The "Ratio" column is the corrected 205Pb/205Tl decay ratio for each storage, following Equation (1) of <a href="https://www.nature.com/articles/s41586-024-08130-4" target="_blank" rel="noopener">Leckenby et al. (2024) Nature 635:321–326</a>. 1 sigma error bars, both including and not-including the estimated contamination variation, are provided. Please refer to the related works below or contact the authors for more details.

opencc-by-4.0Jun 2024View details →

SoA of existing barriers against the measuring of H2NG mixtures or pure H2

Deliverable D1.2 of THOTH2 project aims to investigate technical barriers and limitations of hydrogen injection in existing gas grids with a focus on measuring devices. Injecting hydrogen into existing gas networks requires several actions to assess the content of hydrogen in the H2NG mixture that can be transported through gas pipelines. One of the areas that needs to be evaluated is the field of gas quality and quantity measurements. The Task 1.2 "Technical and performance barriers and limitations" aimed to identify barriers and gaps related to measurement devices such as gas meters, volume converters, pressure and temperature transducers, process gas chromatographs, dew point transducers, and leak detectors. By defining this measurement area, it allows identifying gaps and barriers concerning custody transfer measurements (gas volume and heating value). Due to the different physicochemical properties of hydrogen and methane, the identification of barriers and considerations includes not only the measurement capabilities of devices for H2NG mixtures but also the resistance of design solutions to the effects of hydrogen. The assessment of the state-of-the-art technology was conducted based on a literature review, including the outcomes of projects such as "HyDeploy 2 Project" and "HyWay 27: hydrogen transmission using the existing natural gas grid?". The literature review was complemented by feedback gathered from surveys sent to manufacturers of measurement devices, selected based on the results of Task 1.1. The analyses conducted allowed identifying the following gaps and barriers: <ul> <li>In the area of gas composition analyzers: there are solutions available that allow measuring the composition of H2NG mixtures with up to 100% hydrogen content. However, these solutions are not currently utilized by Transmission System Operators (TSOs) and Distribution System Operators (DSOs), since they entered in the market recently.</li> <li>In the area of dew point transducers: there are solutions resistant to hydrogen up to 20% content, and they are partially used by TSOs and DSOs.</li> <li>In the area of leak detectors: there are solutions that can be used by operational services on gas networks transporting H2NG mixtures. These detectors allow measuring hydrogen content in the air from 0 to 100% of the lower explosive limit. This range is sufficient to ensure safe network operation. However, such leak detectors are not currently employed by TSOs and DSOs.</li> </ul>

opencc-by-4.0Jun 2024View details →

SoA of measuring devices installed in NG transmission and distribution networks

Deliverable D1.1 aims to design the state of the art of measuring devices in natural gas transmission and distribution networks.  Transporting green hydrogen into existing gas assets requires carefully assessing its effect on the existing components. Since several projects have already been completed or have planned research activities to answer still-existing technical questions, the THOTH2 project focuses on the existing measuring devices. Specifically, the focus of the project regards the identification of the existing gaps in normative standards and the suggestions for solutions to cover them (if any). To contribute the hydrogen readiness of the existing gas transport and distribution infrastructures, new methodologies and protocols have to be developed to perform validated tests for metering devices. Suggestions on the need to change the standards or develop new ones will be based on the results of these experimental tests. Despite the simplicity of the methodological approach, it would be very critical when applying it to measuring devices. Several technologies are available in the market to measure gas properties. Furthermore, the operators can select more than one configuration based on the expected field conditions. Since limited resources are available, testing all the possible configurations would be impossible. Prioritization is required. Task 1.1 aims to collect all the information to provide a clear overview of the measuring devices installed in the existing gas assets. Specifically, this document includes the state of the art of measuring devices installed in gas assets. Different technologies are available to measure gas parameters. For example, turbine, rotary piston, ultrasonic, diaphragm, thermal mass, orifice, and Coriolis meters are available to measure flow rate. These technologies differ not only for the operating principle but also for the material used, the size available on the market, and the effect that different conditions could have on the metrological performances like, for example, overload conditions, flow rate pulsations, leakages through the clearance and pressure drops. Furthermore, different maintenance activities are usually expected, resulting in different operative costs throughout the lifetime. To date, turbine, rotary piston gas, and ultrasonic meters are used for fiscal gas metering in transmission networks. Specifically, based on the data collected, turbine gas meters are the most installed technologies for medium to high flow rate, followed by rotary piston and ultrasonic (for high flow rate). Few cases of use of Coriolis meters have been found. Regarding distribution, a different situation results. Despite the fact that few answers have been received to date, and only from Italy, it appears that diaphragm gas meters are the prevailing technology installed, even if a greater penetration is expected for thermal mass meters. THOTH2 also includes other measurements like gas quality by chromatographs, pressure and temperature, and trace water dew point. Regarding temperature, it was assumed that since the sensor is not in contact with the fluid but is protected by the thermowell, it can be assumed that no problem would arise. However, further investigation should be performed to investigate if any effect of hydrogen on response time exists. Regarding pressure measurement, many models are commercially available, but attention should be given to the effect of hydrogen on the material with which the fluid is in contact. Specifically, identifying critical materials that can be affected by hydrogen among those available in commercial products should be the next step to identifying the products to be tested. Gas chromatographs are also present in different models and configurations in the existing networks. Usually, different columns are used based on the specific analysis to be performed. Even if the range of the concentration allowed for each molecule is usually known for each model, more details about the configuration of each gas chromatograph are needed to complete the analysis and check the capability to handle hydrogen. Only some models of trace water sensors have been identified in the investigated networks. Specifically, impedance sensors result in the most implemented devices. Other devices are also typically used in the networks. Electronic Volume Converters and Flow Computers convert measurements into standardized gas volumes for fiscal purposes. The main issues to be investigated are the implemented algorithms and their capability to consider hydrogen. The main algorithms are AGA8, SGERG, and AGA-NX19, and the Operators can check the hydrogen limits. The main issue is that many different models are installed in gas transmission and distribution networks. Furthermore, based on the conclusion about pressure and temperature sensors, the potential effects of hydrogen on the metrological performances of those devices that have these sensors integrated have to be carefully assessed not to overcome the limits on errors provided by the standards. Last, leak detection is essential to detect fugitive emissions to the atmosphere and to minimize the risk of failures or accidents . To date, many devices are supplied to the technicians on the field to verify the presence of hazardous substances. Since different sensors can be implemented in the same devices to measure different quantities, attention should be given in Task 2.1 to selecting those sensors that, on the current knowledge, appear to be most critical when being in contact with hydrogen.