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2,208 results for “Coupling”
Ramped Pyrolysis Oxidation (RPO) coupled radiocarbon (14C-DOC) and stable carbon (13C-DOC), high-resolution molecular composition (FT-ICR MS), and biodegradable dissolved organic carbon (BDOC) of groundwater, river water, and lagoon water in northeast Alaska, 2017
Supra-permafrost groundwater (SPGW), river water, and lagoon water were sampled near Kaktovik, AK to assess the reactivity and origin of dissolved organic matter (DOM) across interconnected hydrologic systems during late summer. Water samples were collected on August 17th 2017 from SPGW along the beach of Jago Lagoon (Jago GW), surface water from the Jago River’s main channel above tidal influence (Jago R), and from the water column of Kaktovik Lagoon at 2–3 m depth (KA LW). Measurements were made from grab samples for river and lagoon water, and from a composite sample for SPGW gathered from 10 individual shoreline locations. Data include dissolved organic carbon concentration (DOC, mg C L-1), Ramped Pyrolysis Oxidation (RPO) derived fraction compositions of 13C-DOC (δ13C ‰), 14C-DOC (in fraction modern), and method/instrumental error in the 14C and 13C results, and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) molecular composition and summarized compound classes. Biodegradable DOC (BDOC) bottle experiments were performed using all three sample types, where DOC concentration was subsequently measured at 2, 7, 14, and 28 days. FT-ICR MS composition was subsequently measured at the 28-day timepoint to track changes in molecular formulae and compound class relative abundance following biodegradation. Data from RPO serial thermal oxidation include temperature and normalized CO2 profiles for each background sample. Thermal-oxidation profiles of CO2 were transformed into non-parametric activation energy (E) distributions using an inverse model. Model output includes C mass of oxidized CO2 (µg C), Tmax (K), Emax (kJ mol-1), Emean (kJ mol-1), Estd (kJ mol-1), and p(0,E)max of user-defined sample fractions. FT-ICR MS results include a summary table of the relative abundance of compound classes (e.g., unsaturated phenolic, polyphenolic, aliphatic, condensed aromatics, peptide-like) and elemental groupings (e.g., CHO-type, CHON-type, CHOS-type, CHON
Global eutrophication and antibiotic resistance genes dataset for "Coupling mechanisms between cyanobacteria and antibiotic resistance genes in freshwater ecosystems"
This dataset compiles global records of cyanobacteria, antibiotic resistance genes (ARGs), and associated water quality parameters to support research on freshwater ecosystem dynamics. It includes 990 metagenomes, 16,648 chlorophyll-a (Chl-a) records, and over 90 documented cases of ARGs–cyanobacteria co-occurrence under comparable spatiotemporal conditions. The dataset covers the years 2000–2024 and provides both raw measurements and harmonized tables for cross-study comparisons. Data were extracted from previously published literature and public repositories, with references to source publications included. This archive is intended to facilitate reproducible analyses, enable large-scale meta-studies, and support further exploration of microbial interactions in freshwater systems.
Consensus-seeking and conflict-resolving: an fMRI study on college couples’ shopping interaction
Open the record for dataset details and reuse information.
Coupling between Sediment and Water Column Populations of Ammonia Oxidizing Thaumarchaeota in the Duplin River near Sapelo Island, Georgia
Populations of nitrifying organisms in the water column at Marsh Landing display a midsummer peak in the abundance of ammonia oxidizing Archaea (AOA) at the site, coinciding with a peak in nitrite concentration. Marsh Landing is at the mouth of the Duplin River, a dead-end tidal channel that drains an extensive area of salt marsh. While the lower Duplin River at Marsh Landing exchanges tidally with Doboy Sound and thus South Atlantic Bight (SAB) coastal waters, water in its upper reaches has a residence time of weeks. The work reported here had two goals: 1) test the hypothesis that the surrounding salt marsh is the source of nitrifiers seen in water samples taken at Marsh Landing; and 2) compare the seasonal dynamics of nitrifiers in surficial sediments with those in the water column. We sampled 6 stations along the ~20 km length of the Duplin River. We collected surface water samples (~0.20 m) at low- to mid-tide, monthly from April-December 2014. Sediment samples (top 1 cm) were collected at the same time from unvegetated creek bank at 2 locations on the Duplin River and from 4 locations spanning the creek bank-to-upland gradient of the saltmarsh accessible from the Teal Boardwalk. The abundance of ammonia oxidizing Archaea, Marine Group 1 Archaea (Thaumarchaeota), ammonia oxidizing Betaproteobacteria (AOB), Bacteria and Nitrospina, a nitrite oxidizing bacterium, were determined by quantitative PCR (qPCR) of DNA extracted from the samples. This data set contains the abundance estimates from April to December 2014 for sediment and water column samples, with corresponding water quality measurements (temperature, salinity and nitrogenous nutrient concentrations).
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis
<p>This data repository is associated with the paper:</p> <p>Morris,J., A. Sokolov, J. Reilly, A. Libardoni, C. Forest, S. Paltsev, A Schlosser, R. Prinn and H. Jacoby (2025). Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis. <em>Nature Communications </em><strong>16</strong>, 2703. https://doi.org/10.1038/s41467-025-57897-1</p> <p>This paper quantifies key socio-economic and climate uncertainties using the MIT Integrated Global System Model. </p>
Attention-based frontal-posterior coupling for visual consciousness in the human brain
<ol> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.m</li> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.mat</li> <li>DataCode_FigS2_Attentional_Capture_Image_Detecability.mat <ul> <li>.m Code (1) using .mat Data (2 and 3) illustrate main behavioral findings in our manuscript. Panel figures shown in Figure.1 and Figure.S2 could be well replicated using these materials.<br><br></li> </ul> </li> <li>Au_Step06_0601_unit_2C.m</li> <li>Au_Step06_0601_unit_mC.m</li> <li>Train_DSVM_xilei.m</li> <li>Classify_DSVM.m</li> <li>svmclassify.m</li> <li>svmtrain_xilei.m <ul> <li>.m Code (4) and .m code (5) using child .m functions (6, 7, 8 and 9) illustrate core codes used to discriminate neural pattern differences on a 2-class issue (image presence versus image absence) or a 3-class issue (animal, object or face), respectively. </li> </ul> </li> <li>Note_Location_activeChannels_distanceTest.m</li> <li>Note_Location_activeChannels_distanceTest.mat</li> <li>Note_Location_activeChannels.mat <ul> <li>.m Code (10) using .mat Data (11 and 12) illustrate our method used to calculate distance between responsive contacts. Based on that, we also made a statistical inference against a chance-level distribution. Panel figure shown in Figure.2F could be well replicated using these materials.<br><br></li> </ul> </li> <li>easy_ImgC.m <ul> <li>.m Code (13) illustrate our method used to calculate imaginary coherence between responsive contacts. A Rayleigh Z correction was also performed and outputed.<br><br></li> </ul> </li> <li>easy_visibility.m <ul> <li>.m Code (14) illustrate our method used to calculate an index of visibility from which measures of interest tied to an invisible image was subtracted from that of a visible image. </li> </ul> </li> </ol> <p> </p>
Coupled atmosphere-wave-ocean simulation of Hurricane Dorian (2019)
<p><strong>Description</strong></p> <p>This dataset provides the output of the coupled atmosphere-wave-ocean simulation of Hurricane Dorian from August 29 to September 7, 2019. The simulation is a composite of two separate simulations:</p> <ol> <li>From 00 UTC August 29 to 00 UTC September 1, 2019</li> <li>From 00 UTC September 1 to 00 UTC September 7, 2019</li> </ol> <p>The first simulation serves as "spin-up" for the hurricane and its environment prior to landfall. The second simulation is initialized from the output of the first simulation, while relocating the Dorian vortex to its correct position on September 1. Due to the size of the dataset only the surface fields are made available.</p> <p><strong>Model configuration</strong></p> <ul> <li><strong>Atmosphere</strong>: Weather Research and Forecasting (WRF, https://github.com/wrf-model/WRF) model v4.2.2, with the Advanced Research WRF (ARW) dynamical core. The model has a 3-km resolution grid over the parent domain and a 1-km resolution nest over the Bahamas region (September 1-7 only), both with 45 vertical layers. Initial and boundary conditions are based on 6-hourly ERA-5 dataset.</li> <li><strong>Ocean Waves</strong>: University of Miami Wave Model (UMWM, https://umwm.org). The model is configured at the same 3-km as the atmosphere model, and has 36 directional bins and 37 frequency bins that are logarithmically spaced from 0.0313 to 2 Hz.</li> <li><strong>Ocean Circulation</strong>: HYbrid Coordinate Ocean Model (HYCOM, https://github.com/HYCOM) v2.3.01, configured at 0.01 degree resolution and 41 vertical layers. Initial and boundary conditions are based on daily GOFS 3.1 41-layer HYCOM + NCODA Global 1/12° Analysis, daily. K-Profile Parameterization for vertical mixing.</li> <li><strong>Coupling</strong>: Earth System Modeling Framework (ESMF, https://github.com/esmf-org/esmf) v8.0.1</li> </ul> <p><strong>File Description</strong></p> <ul> <li>blkdat.input - HYCOM (ocean circulation) configuration file</li> <li>dorian2019_atmosphere_1km_2019090100.nc - Atmosphere at 1-km resolution dataset</li> <li>dorian2019_atmosphere_waves_3km_2019082900.nc - Atmosphere and waves at 3-km resolution dataset, Aug 29 - Sep 1.</li> <li>dorian2019_atmosphere_waves_3km_2019090100.nc - Atmosphere and waves at 3-km resolution dataset, Sep 1-7</li> <li>dorian2019_ocean_1km_2019082900.nc - Ocean circulation at 1-km resolution dataset</li> <li>main.nml - UMWM (waves) configuration file</li> <li>namelist.input - WRF (atmosphere) configuration file</li> <li>regional.depth.[ab] - HYCOM (ocean circulation) bathymetry files</li> <li>regional.grid.[ab] - HYCOM (ocean circulation) grid files</li> <li>umwm.gridtopo - UMWM (waves) grid and bathymetry file</li> <li>wrfbdy_d01 - WRF (atmosphere) boundary conditions file</li> <li>wrfinput_d01.2019082900 - WRF (atmosphere) initial conditions file for parent domain on Aug 29</li> <li>wrfinput_d01.2019090100 - WRF (atmosphere) initial conditions file for parent domain on Sep 1</li> <li>wrfinput_d02.2019090100 - WRF (atmosphere) initial conditions file for inner nest on Sep 1</li> </ul> <p><strong>Coupled model source code</strong></p> <p>The model source code has not yet been released. We plan to open source it upon publication of the paper describing the simulation. When the source code is released, we will add the link to this repository.</p>
Assessing the environmental benefit of palladium-based single-atom heterogeneous catalysts for Sonogashira coupling
<p>Dataset supporting the article "Assessing the environmental benefit of palladium-based single-atom heterogeneous catalysts for Sonogashira coupling" by D. Faust Akl, D. Poier, S. C. D’Angelo, T. P. Araújo, V. Tulus, O. V. Safonova, S. Mitchell, R. Marti, G. Guillén-Gosálbez, and J. Pérez-Ramírez<em>.</em></p>
Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac–Coulomb(–Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states: Figures
<p>This entry contains the figures included in the paper titled "Equation-of-Motion Coupled-Cluster Theory based on the 4-component Dirac--Coulomb(--Gaunt) Hamiltonian. Energies for single electron detachment, attachment and electronically excited states", by Avijit Shee, Trond Saue, Lucas Visscher and Andre Severo Pereira Gomes.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1320320</p> <p>There are three figures that use the (original) png files included in <a href="https://zenodo.org/api/files/7bda2e2b-ac69-41aa-a21e-821e88bfb973/original-figures.tar.bz2">original-figures.tar.bz2 </a>:</p> <p>figure 1: Potential energy curves of the spin-orbit split X<sup>2</sup>Π and A<sup>2</sup>Π states of the XO molecules, obtained with EOM-IP and the <sup>2</sup>DCG<sup>M</sup> Hamiltonian.</p> <p>figure 2: Internuclear distances (in Angstrom), harmonic vibrational frequencies (in cm<sup>−1</sup>) and the vertical Ω = 3/2 − 1/2 energy difference (in eV) for the X<sup>2</sup>Π and A<sup>2</sup>Π states of the XO molecules, obtained with EOM-IP and the <sup>2</sup>DCG<sup>M</sup> Hamiltonian.</p> <p>figure 3: SO-ZORA/QZ4P/Hartree-Fock (ADF) spinor magnetization plots (isosurfaces at 0.03 a.u.) and energies (in Eh) for the valence spinors of the XO<sup>−</sup> species (from left to right: X = Cl, Br, I, At, Ts).</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Figures
<p>This entry contains the sources for the figures included in the body of the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes, as well as those found in the supplementary information.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1477004</p> <p> </p> <p> </p>
Dataset _ Seasonal variation of biogas upgrading coupled with digestate treatment in an outdoors pilot scale algal-bacterial photobioreactor
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Seasonal variation of biogas upgrading coupled with digestate treatment in an outdoors pilot scale algal-bacterial photobioreactor</strong><strong>”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
Readout of an antiferromagnetic spintronics system by strong exchange coupling of Mn2Au and Permalloy
<p>Data corresponding to figures 2 and 3 of <a href="https://arxiv.org/abs/2106.02333">arXiv:2106.02333</a>.</p> <p>Manuscript accepted by Nature Communications.</p>
Dataset for the published article "Quantum version of the integral equation theory based dielectric scheme for strongly coupled electron liquids"
<p>The data contained in the zip file constitute the main research data of the article entitled as "<em>Quantum version of the integral equation theory based dielectric scheme for strongly coupled electron liquids</em>", published in the Journal of Chemical Physics as a Communication. In this article, a novel dielectric scheme is proposed for strongly coupled electron liquids that handles quantum mechanical effects beyond the random phase approximation level and treats electronic correlations within the integral equation theory of classical liquids. This self-consistent scheme features a complicated dynamic local field correction functional and yields unprecedently accurate results for the static structure factor without featuring any adjustable or empirical parameters.</p> <p>In particular, the datasets contain the static structure factors of the paramagnetic electron liquid as computed by four schemes of the self-consistent dielectric formalism and as extracted from state-of-the-art path integral Monte Carlo (PIMC) simulations. The dielectric schemes of interest are all tailor-made for the strongly coupled regime of the finite temperature uniform electron fluid (UEF; also known as jellium or quantum one-component plasma). These are the newly proposed quantum version of the integral equation theory based scheme (qIET), the newly proposed quantum version of the hypernetted-chain based scheme (qHNC), the integral equation theory based scheme (IET) [1,2] and the hypernetted-chain based scheme (HNC) [3,4].</p> <p>The static structure factors are provided for 20 paramagnetic UEF state points defined by (r<sub>s</sub>,Θ)={(50,0.50),(60,0.50),(70,0.50),(80,0.50),(90,0.50),(100,0.50),(100,0.75),(100,1.00),(100,2.00),(100,4.00),(110,0.50),(125,0.50),(125,0.75),(125,1.00),(125,1.50),(125,2.00),(150,0.50),(150,1.00),(200,0.50),(200,1.00)} where r<sub>s</sub> is the quantum coupling parameter and Θ is the degeneracy parameter. </p> <p>In the qIET, qHNC, IET and HNC datasets; the first column corresponds to the wavenumber normalized to the Fermi wavenumber and the second column corresponds to the static structure factor value. In the PIMC datasets, the first column corresponds to the wavenumber multiplied by the first Bohr radius, the second column corresponds to the static structure factor value and the third column corresponds to the associated error bars.</p> <p>[1] P. Tolias, F. Lucco Castello and T. Dornheim, J. Chem. Phys. 155, 134115 (2021).<br> [2] F. Lucco Castello, P. Tolias and T. Dornheim, EPL 138, 44003 (2022).<br> [3] S. Tanaka, J. Chem. Phys. 145, 214104 (2016).<br> [4] T. Dornheim, T. Sjostrom, S. Tanaka and J. Vorberger, Phys. Rev. B 101, 045129 (2020).</p>
Coupled climate-glacier modelling of the last glaciation in the Alps: modelling data
<p>This dataset contains key distributed 2D variables resulting from the modelling of the Alpine Ice Field over the last glacial cycle from Jouvet and al. (2023, 10.1017/jog.2023.74), including basal surface topography, ice thickness, pressure-adjusted basal temperature, basal and surface ice flow speeds. The results are given on a raster grid in UTM system of coordinate with a spatial resolution of 2 km and a temporal resolution of 100 year. The data are compiled in netCDF.</p> <p>As explained in the paper, the model was designed to match LGM evidence. Modelled results related to intermediate states and to the Holocene must be interpreted with caution considering the relative coarse resolution (2 km). Small ice caps aside the main Alpine Icefield (except the Jura) were excluded.</p>
MCR LTER: Coral Reef: Coupled Natural-Human Systems: Survey of fish being sold on the roadside 2020-2021
This dataset includes the results of a survey on fish sold by the roadside in Moorea, French Polynesia. During 2020-2022, more than 7000 fish were identified and sized from photographs taken during the market surveys. These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396). This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Evolutionary coupling range varies widely among enzymes: data and code
<p>Data and code needed to reproduce result of the paper "Evolutionary coupling range varies widely among enzymes", by J. Echave.</p> <p>biorxiv: https://doi.org/10.1101/2020.12.19.423588</p>
Next generation global ice-ocean-biogeochemistry coupled model with 13C-cycling (GFDL MOM5-BLING13C)
<p> </p> <p>======= DESCRIPTION =======</p> <p>This is the model output supporting our paper <em>A next generation ocean carbon isotope model for climate studies I: Steady state controls on ocean <sup>13</sup>C</em> (2021 Global Biogeochemical Cycles).</p> <p>This model output simulates the transient response of ocean carbon biogeochemistry to anthropogenic CO<sub>2</sub> and <sup>13</sup>CO<sub>2</sub> atmospheric emissions with a nominal lateral resolution of 1° and 50 vertical levels. The model uses the NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) MOM5 coupled to the NOAA-GFDL Biogeochemistry with Light Iron Nutrients and Gas (BLING) with <sup>13</sup>C-cycling. Atmospheric forcing is prescribed using the repeating annual cycle of the Common Ocean Reference Experiment version 2 normal year forcing dataset (COREv2-NYF). The implementation of <sup>13</sup>C-cycling applies isotopic fractionations during air-sea gas exchange, photosynthetic production of organic matter, and formation of calcium carbonate. The sensitivity of dissolved inorganic <sup>13</sup>C in the ocean to the CO<sub>2</sub> gas exchange rate is explored by repeating the simulation twice, once using the latest OMIP-CMIP6 protocol for the k-U<sub>10 </sub>parameterization (standard) and once using the previous OCMIP2 protocol (fast-gas-exchange).</p> <p> </p> <p>Files information:</p> <ul> <li><strong>ocean_static.nc</strong>: Static fields (longitude, latitude, area).</li> <li><strong>1990-2002.ocean_month.nc</strong>: Monthly output between 1990 and 2002 of ocean physical variables (temperature, salinity, averaged mixed layer depth, maximum mixed layer depth).</li> <li><strong>1990-2002.ocean_bling_trc_month_CMIP6.nc</strong>: Monthly output between 1990 and 2002 of biogeochemical variables* for the simulation using the OMIP-CMIP6 air-sea gas exchange protocol.</li> <li><strong>1970_1989_d13c_org_mldave_CMIP6.nc</strong>: Monthly output between 1970 and 1989 of d<sup>13</sup>C of organic matter averaged over the mixed layer.</li> <li><strong>1990-2002.ocean_bling_trc_month_OCMIP2.nc</strong>: Monthly output between 1990 and 2002 of biogeochemical variables* for the simulation using the OCMIP2 air-sea gas exchange protocol.</li> </ul> <p>* Biogeochemical variables are dissolved inorganic carbon, dissolved inorganic carbon-13, oxygen, and dissolved inorganic phosphate.</p> <p> </p> <p>======= HOW TO CITE =======</p> <p>This model output can be freely distributed, but please cite it using the following paper:</p> <p>Claret, M., Sonnerup, R. E., & Quay, P. D. (2021). A next generation ocean carbon isotope model for climate studies I: Steady state controls on ocean <sup>13</sup>C. <em>Global Biogeochemical Cycles</em>, 35, e2020GB006757. <a href="https://doi.org/10.1029/2020GB006757">https://doi.org/10.1029/2020GB006757</a></p> <p> </p> <p>======= ACKNOWLEDGEMENTS =======</p> <p>This work was funded by the National Science Foundation (NSF-OCE 1356756 and NSF-OCE 1829796). We would also like to acknowledge high-performance computing support from Cheyenne (<a href="https://doi.org/10.5065/D6RX99HX">doi:10.5065/D6RX99HX</a>) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the NSF.</p> <p> </p> <p>======= QUESTIONS AND REQUESTS? =======</p> <p>Please contact Mariona Claret (mclaret@uw.edu) or Rolf Sonnerup (rolf@uw.edu).</p> <p> </p>
Supplementary data for "The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ"
<p>µCT-scans of the upper tibial regions of the foreleg (T1) and the midleg (T2) of <em>Ramulus artemis</em> (Westwood, 1859), <em>Carausius morosus</em> (Sinéty, 1901), and <em>Sipyloidea sipylus</em> (Westwood, 1859). For use of scans, please cite the following publication:</p> <p>Strauß, J., Moritz, L. & Rühr, P.T. (<strong>2021</strong>): The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ. <em>Frontiers in Ecology and Evolution (Research Topic “Evolutionary Biomechanics of Sound Production and Reception”)</em>. doi: <a href="https://doi.org/10.3389/fevo.2021.632493">10.3389/fevo.2021.632493</a>.</p> <p>All scans were performed with a commercial μCT desktop system (Skyscan 1272, Bruker microCT, Kontich, Belgium) at the Zoological Research Museum Alexander Koenig, Leibniz Institute for Animal Biodiversity, Bonn, Germany.</p> <p><strong>µCT scan settings of all samples:</strong></p> <p><em>Ramulus artemis:</em></p> <ul> <li>tube voltage = 30 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1980 ms</li> <li>binning = 1x1</li> <li>averaging = 8</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Ramulus_artemis_T1.tif; Ramulus_artemis_T2.tif</li> </ul> <p><em>Carausius morosus:</em></p> <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 5</li> <li>random movement = 15 px</li> <li>voxel size = 1.0 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Carausius_morosus_T1.tif; Carausius_morosus_T2.tif</li> </ul> <p><em>Sipyloidea sipylus:</em></p> <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 7</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Sipyloidea_sipylus_T1.tif; Sipyloidea_sipylus_T2.tif</li> </ul>
Sondni, Madhya Pradesh, India. Vidyādhara couple, detail, early sixth century.
<p>Sondni, Madhya Pradesh, India. Vidyādhara couple, detail, early sixth century. Now National Museum of India. Photograph 1980; digitisation 2016.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.