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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-sa-4.0Jun 2018View details →

CENTAUR project laboratory testing data

This dataset contains results from testing carried out at a laboratory facility at the University of Sheffield (UK) as part of the <a href="https://www.sheffield.ac.uk/centaur">CENTAUR project</a>.  CENTAUR is an EC funded Horizon 2020 Innovation Action.  The project has developed a system to reduce flood risk in urban areas by utilising existing available storage capacity in urban drainage networks through the use of a gate installed in an existing manhole.  The gate is controlled by Fuzzy Logic, using data from level sensors. The laboratory facility is described in the 'CENTAUR_Lab_facility.pdf '.  Further details of the sensors and logging system are provided in 'Data_File_Column_Descriptions.csv'. The file 'Test_Record.csv' describes all tests carried out.  This dataset contains 83 csv data files in for days when good data was collected, these are zipped into 'DataFiles.zip'.  Each csv file within the .zip contains the test results for one day, the files are named with the date of testing in the format yymmdd.  The csv data files do not include column headers, but a full description of the data in each column is provided in 'Data_File_Column_Descriptions.csv'.  The csv files contain data from all sensors, but the time period of the data from each sensor (or sensor set) and timesteps are not the same, hence for each sensor / sensor set there is a separate time column.  The sampling interval for the level sensors is given in column 26 of 'Test_Record.csv', this will be correct for the test period, but outside the tests the interval was often increased and this may be seen in the data files.  The gate / FCD sampling interval is the same as the Fuzzy Logic interval in column 27 of 'Test_Record.csv', although the position is only reported when the gate / FCD is active - i.e. not fully open.  At the end of a test the FCD will return to the fully open position (100%), but this final datapoint is not recorded.  The flow rate and downstream valve position sampling interval are given in column 12 of 'Test_Record.csv'. Test numbers and fuzzy logic version ids are simplified for the journal paper 'Demonstrating a Fuzzy Logic algorithm for real-time flow control in a full-scale laboratory environment' which is currently under review with the Urban Water Journal.  A correlation between the information in the paper and in 'Test_Record.csv' can be found in 'Paper_Test_Numbers.csv'. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641931.

opencc-by-4.0Jul 2019View details →

Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory

This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork. In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections. This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4. See accompanying technical note for more Information.

opencc-by-4.0Nov 2019View details →

Laboratory simulations of benzene oxidation and formation of highly oxygenated organic molecules (HOM)

This dataset supplements the following manuscript: Garmash, O., Rissanen, M. P., Pullinen, I., Schmitt, S., Kausiala, O., Tillmann, R., Percival, C., Bannan, T. J., Priestley, M., Hallquist, Å. M., Kleist, E., Kiendler-Scharr, A., Hallquist, M., Berndt, T., McFiggans, G., Wildt, J., Mentel, T., and Ehn, M.: Multi-generation OH oxidation as a source for highly oxygenated organic molecules from aromatics, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-582, in review, 2019. It presents data from Table 1, Tables S1-S4 and Figures 5, A1 and A2, including model input data.

opencc-by-4.0Sep 2024View details →

Road-deposited sediment wash-off experiments on a large-scale laboratory

This dataset includes raw and processed data from a series of large-scale laboratory tests that were conducted to assess and study the wash-off process of RDS (Road deposited sediments) considering variations in rainfall intensity, for two scenarios: 30 mm/h and 50 mm/h; and modifying RDS loads applied on BLOCK for three scenarios: 100 g/m2, 150 g/m2, 200 g/m2. First, the hydraulic was detailly characterized including rainfall intensity maps, water flows, surface water depths and surface water velocities for both rainfall intensities tested. A synthetic granulometric of RDS was homogeneously distributed on the physical model surface and then washed-off by the simulated rainfall. A total of 31 water samples were collected at the manhole discharge per each experiment. Total RDS mass that remain on the surface and inside the gully was collected by a wet vacuum after the rainfall event. A mass balance considering the initial RDS applied and the total RDS recollected in the three samples locations, was calculated. TUR (Turbidity), EC (Conductivity), TS (Total Solids), TSS (Total suspended solids), TDS (Total dissolved solids), RDS mass by flow, and RDS mass flow variables were measured for the RDS samples recollected in the Manhole discharge. The behaviour of each RDS fraction was also analysed through laser diffraction (Beckman Coulter LS 13 320, Aqueous Liquids Module). This work is part of a Transnational Access developed by the Universidad Distrital Francisco José de Caldas (Colombia) and Universidade da Coruña (Spain) within the scope of Co-UDlabs project. Data may be used to increase knowledge on road-deposited sediment wash-off process, allowing also for calibrating, developing, and validating new and existing urban wash-off models.

opencc-by-4.0Sep 2021View details →

Research data supporting "Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment"

Research data supporting the publication: Higgins SG, Nogiwa-Valdez AA, Stevens MM, Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment, Nature Protocols, 2021. This repository contains the raw survey data of 172 current and historic electronic laboratory notebook (ELN) software packages. Main files: <ul> <li>"ELN_Review_Higgins_2021_Survey.csv"  = raw survey data in 'tidy' data format</li> <li>"ELN_Review_Higgins_2021.Rmd" = an R Markdown File (R Notebook) that takes the survey data as input and produces summary statistics and plots. This file was written using R Studio as the IDE.</li> </ul> Derived files, generated from those above: <ul> <li>"ELN_Review_Higgins_2021.nb.html" = a self-contained HTML file that is automatically generated by R Studio, based on the markdown file. This can be opened in any web browser to allow manual inspection of the code and comments without the need for specialist software. Embedded within this file is also the original markdown script (i.e. a copy of the code in "ELN_Review_Higgins_2021.Rmd")</li> <li>"ELN_Review_Higgins_2021_Lifetimes_Interactive_Figure1.html" = an HTML file generated by the script above via the plotly package. It contains an interactive version of the ELN survey data, allowing the user to hover over the timeline and explore the data.</li> <li>"ELN_Review_Higgins_2021_Timeline.pdf" = static version of ELN timeline, used to generate figure in main manuscript.</li> <li>"ELN_Review_Higgins_2021_Releases-Per-Year.pdf" = static version of number of new ELNs per year, used to generate figure in main manuscript.</li> </ul> This survey was generated from a mixture of primary and secondary sources (see references for secondary sources).

opencc-by-4.0May 2023View details →

Experimental Factors Influence Diversity Metrics of the Gut Microbiome in Laboratory Mice

Abstract Introduction Gut microbiome studies often overlook experimental factors that could influence gut microbiome diversity and could impact findings. Large-scale studies investigating these experimental factors are lacking. Thus, we aimed to determine which experimental factors influence the gut microbiome diversity in pre-clinical animal model studies. Methods We extracted DNA and sequenced the V4 region of the 16S rRNA gene of a total of 538 samples from various sections of the gastrointestinal tract of 303 young and aged male and female C57BL/6J mice of three different genotypes on five diets from three animal house facilities. As a proof-of-concept in a disease model, some mice were treated with sham or angiotensin II, a commonly studied agent used as a hypertension model. Some samples were sequenced twice as a matched-comparison group. Results Using over 17 million sequencing reads, we found that experimental factors such as animal house facility, genotype, diet, age, sex, sampling site, and technical factor (i.e., sequencing batch) affected both α- and β-diversity (weighted and unweighted UniFrac), and were associated with compositional changes in the microbiome at varying magnitude, with diet and sampling site having the largest effect. After adjustment by these factors, treatment with angiotensin II had no impact on α-diversity and was only significant in unweighted UniFrac (presence/absence of bacteria) analyses. Conclusion Our data identified several key experimental and technical factors that affect the gut microbiome in laboratory mice. Our findings support that not accounting or adjusting for these factors may lead to false-positive discoveries and non-biologically relevant findings in the gut microbiome field.

openCC (other)Jun 2023View details →

Laboratory experiments testing pH, alkalinity and particle impacts on Mn removal

Laboratory experiments were conducted to investigate impacts of pH, alkalinity, and presence of particles on Mn removal in freshwater. The dataset includes monitoring data from: 1) a 14-day experiment in Mn(II) solutions in nanopure water, 2) a 24-hour experiment in Mn(II) solutions in nanopure water, and 3) a 10-day experiment in water from two drinking water reservoirs. The 14-day pH and alkalinity laboratory experiment was conducted starting October 30, 2022 and included sample collection and pH monitoring on day 0, 1, 4, 7, 10, and 14. The 24-hour pH and alkalinity laboratory experiment was conducted starting February 20, 2023 and included sample collection and pH monitoring at 0, 1, 2, 6, 12, and 24 hours. The reservoir water laboratory experiment was conducted starting March 22, 2023 and included sample collection and pH monitoring on day 0, 1, 4, 7, and 10. This experiment tested Mn removal in water collected from the lower water column of Falling Creek Reservoir (FCR) and Carvins Cove Reservoir (CCR), located in Vinton, Virginia, USA and Roanoke, Virginia, USA respectively. Both reservoirs are owned and operated by the Western Virginia Water Authority and are managed as drinking-water sources for the city of Roanoke, VA, USA.

Measurements of Coarse Woody Debris %C and %N at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC.

Coarse woody debris (CWD) plays a critical role in nutrient retention and cycling, including the cycling and retention of carbon and nitrogen. However, comparison studies of CWD in different forest types and elevation gradients in the southern Appalachian Mountains are lacking. We measured CWD in five different forest communities/elevations at Coweeta Hydrologic Lab. A subsample of CWD in each plot was measured for percent C and percent N, as well as for cations.

Coarse Woody Debris Cations Measurements at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC.

Measurements of coarse woody debris at 10 hillslope sites at the Coweeta Hyrdological Laboratory, Macon County, North Carolina

Coarse woody debris was measured at 10 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments in Macon County, NC. The length, diameters, decay class, and species of coarse wood was measured within each of the twelve 10 x 10-m plots located within each of the 10 sites. Volume of coarse wood was then calculated. Data from a subset of the sites were used as a covariate for Aphaenogaster spp. ant occupancy rates.

Plethodon study from removal plots located at the Coweeta Hydrologic Laboratory

Recent research shows Plethodon shermani and Plethodon teyahalee within the hybrid zone at the Coweeta LTER in Otto, North Carolina forage heavily on ants (>50% of all prey items consumed; found in 94% of samples). As most vascular plants in the Southern Appalachians rely on ants for seed dispersal, this significant predation on ants, especially Aphaenogaster, reveals an intriguing and important relationship between these salamanders and the vascular plant abundance and distribution within their ecosystem. Additionally, consumption of ants increases with high temperatures and low relative humidity indicating that, with climate change, the effects of Plethodon foraging behavior on woodland biodiversity will be amplified. Using a paired design, we placed removal plots along an elevational gradient within the plethodon shermani-teyahalee hybrid zone at the Coweeta LTER to observe and quantify the effect of Plethodon foraging on ant communities, seed dispersal, and vascular plant distribution by removing the salamanders from treatment plots. Foraging rates of ants, with a focus on Aphaenogaster, were monitored at treatment and control plots using direct observation/counts of ants visiting tuna bait stations.

Terrestrial-Stream Biodiversity Litter Processing Datasets from Watershed 20 within the Coweeta Hydrologic Laboratory

Although litter decomposition is a fundamental ecological process, most of our understandings comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss -- the focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. This study was conducted at the Coweeta Hydrologic Laboratory in Watershed 20 on Ball Creek that drains into Coweeta Creek, a tributary of the Little Tennessee River. Data were analyzed using a statistical approach that first looks for additive identiy effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and/ or composition. This approach addresses questions key to understanding the potential effects of species loss on ecosystem processes. If additive effects dominate, the consequences for decomposition dynamics will be predictable based on our knowledge of individual species, but not statistically predictable if non-additive effects dominate.

opencc-by-4.0Feb 2020View details →

JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.

The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that (1) gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute (2) theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes We deliver (1) data collection and (2) numerical codes in the following files attached: (1) Data collection: <ul> <li>A PDF file named JUMP-zonal-jets-data-collection-GRL.pdf that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li>JUMP-JetsInTheLab.zip</li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li>uvData-SRS-istep-312000-nstep-50-niz-12.nc</li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter <ul> <li>uvData-JupObs-istep-0-nstep-4-niz-1.nc</li> <li>StatisticalData-JupObs.nc</li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li>IPV-QGPV-Jupiter-Saturn.zip</li> </ul> </li> </ul> (2) Numerical codes: <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul>   The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver (1) velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and (2) guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.   Acknowledgments This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.

opencc-by-4.0Jan 2021View details →

Non-structural carbohydrates and photosynthesis in boreal Scots pine and dwarf shrubs, in field and laboratory.

The manuscript entitled "Non-structural carbohydrates and photosynthesis in boreal Scots pine and dwarf shrubs" used two set of data: Field data and Laboratory data ##### 1. FIELD DATA: We measured photosynthesis and non-structural carbohydrate (NSC) content in adult Scots pine (Pinus sylvestris L.), in boreal conditions at Hyytiaälä SMEAR II station in Sourthen Finland. In the folder "Field Data", you will find automatic CO2 exchange measurements by shoot chambers, dynamic parameters for the light response of photosynthesis, and needles´ non-structural carbohydrate content (NSC) in 2008, 2009 and 2015. See the readme file in the folder for further information.   #### 2. LABORATORY DATA We measured the relationship between photosynthesis and non-structural carbohydrate (NSC) content under stable laboratory conditions in three shrubs species: i) evergreen lingonberry (Vaccinium vitis-idaea L.), ii) evergreen heather (Calluna vulgaris (L.) Hull) and iii) deciduous bilberry (Vaccinium myrtillus L.). The plants grew in chambers where we measured the CO2 gas exchange and estimated photosynthesis. After CO2 gas exhcnage measurements we sampled the leaves for NSC analyses. See the readme file in the folder for further information.

opencc-by-4.0Jan 2021View details →

Shock Ramp Compressions Measurements of Iron on the Sandia National Laboratories' Z-Machine

This data contains 1) the apparent velocity data from Velocity Interferometer System for Any Reflector (VISAR) data analyzed using the PointVISAR program for experiments Z3155 and Z3339 and 2) the equation of state results from analyzing the velocity data using a backward integration -- forward Lagrangian analysis. These experiments were performed on the Sandia National Laboratories' Z-Machine, where the iron samples were dynamically compressed via shocked compression to approximately 275 Gpa and further ramp compression to approximately 400 GPa. This covers pressure-temperature regions near the melt line as well as the interior conditions of terrestrial planets. The Z3155 data include four samples, each with two VISAR traces, and the Z3339 data include six samples, each with two or three VISAR traces. The apparent velocity can be corrected to true velocity using the latest lithium fluoride window correction for a 532 nm wavelength. PointVISAR is available as part of the Sandia Matlab AnalysiS Hierarchy (SMASH) toolbox. Details of the backward integration -- forward Lagrangian anaylsis that was used can be found in the related publication. Example data file interpretation: "Z3155_north_panel_bot_sample_01.txt" is the first VISAR trace from the bottom sample of the north panel on experiment Z3155. "Z3155_EoS_combined.txt" is the sample-averaged Equation of State result from experiment Z3155. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2020-13961 O

opencc-by-4.0Apr 2017View details →

Laboratory dataset on Self-Heating Behavior and Ignition of Shale Rock

The file attached contains a complete set of experimental data from shale rock self-heating ignition cubic basket experiments. The experiments were carried out in a thermostatically controlled oven with thermocouples for measuring the ambient and shale sample temperatures. The data is divided in two parts, one for coarse particles and one for fine particle experiments. The data reported includes the dates of experiments, volume of shale basket being tested, oven ambient temperature, fuel mass of shale, bulk density of the shale, residue mass after the experiment, percentage of residue in respect to initial mass, and if the sample ignited or not. This data is in support of the journal paper: F. Restuccia, N. Ptak, G. Rein, Self-Heating Behavior and Ignition of Shale Rock, Combustion and Flame, Vol 176, 2017, pp 213-219. doi: 10.1016/j.combustflame.2016.09.025.

opencc-by-4.0Oct 2024View details →

Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer

This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the Laboratoire de Glaciologie (GLACIOL) at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf).

opencc-by-4.0Nov 2023View details →

Rigid and hinged very large floating structure (VLFS) dataset - Kelvin Hydrodynamics Laboratory

This dataset corresponds to the measurements performed at the Kelvin Hydrodynamics Laboratory at the University of Strathclyde, in August 2022, to assess the motion performance and internal loading of a rigid and hinged very large floating structure (VLFS) under regular waves. The VLFS was constructed with three pontoons and two hinges. The hinges were replaced with aluminium steel bars to built the rigid VLFS. The dimensions of each pontoon of the VLFS were 580 mm x 580 mm x 52 mm. Each pontoon was built with 2 mm layer of carbon fibre and a 50 mm layer of PVC foam.The VLFS was tested in regular waves at two incidences: 0 degrees and 30 degrees. For 0 degree incidence, the wave frequencies tested ranged from 0.4 to1.6 Hz in intervals of 0.1 Hz. Four wave heights were tested, h=5, 10, 20 and 40 mm. For 30 degree incidence, the same range of frequencies were tested, but only one wave height, h=5 mm. Preliminary results for some of the data at 0 degrees incidence can be found in the conference paper: https://doi.org/10.36688/ewtec-2023-389.  Further analysis of this dataset and additional results are in preparation for a journal manuscript.The following files are included as part of the dataset:<ol><li>Motion files (Matlab files).</li><li>Strain gauge and wave height files (Matlab files).</li><li>Data description file - Description of files.</li><li>Test matrix - Test cases summarised with nomenclature used in files.</li><li>Matlab script to sort out position of motion spheres as depicted in Figure 1.</li><li>Video of the hinged VLFS subject to a train of regular waves at f=0.8 Hz, i.e. when the wavelength is of similar length to the length of the platform, i.e. f=0.8 Hz.</li></ol><ul><li>The motion files contain the time series information recorded for each of the motion detection spheres. Because the motion raw data is not labelled sequentially, it is necessary to run the Matlab file included in the data repository to sort out the information of the spheres.</li><li>The strain gauge files contain the raw strain gauge data (8 channels) and the wave gauge data with the file number describing the corresponding test in the test matrix.</li></ul>The VLFS was equipped with 36 motion detection spheres and 8 strain gauges. The diagram and notation of each sphere is depicted in Figure 1. Figure 1 is available in the Data description document.