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7,438 results for “surface”
Small Mammal Exclosure Study (SMES) Surface Soil Disturbance in the Chihuahuan Desert Grassland and Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (1995-2005)
The purpose of this study is to determine whether or not the activities of small mammals regulate plant community structure, plant species diversity, and spatial vegetation patterns in Chihuahuan Desert shrublands and grasslands. What role if any do indigenous small mammal consumers have in maintaining desertified landscapes in the Chihuahuan Desert? Additionally, how do the effects of small mammals interact with changing climate to affect vegetation patterns over time? This is data for animal created soil surface disturbance measured from each of the SMES study plots. Soil surface disturbance was measured from each of the 36 one-meter2 quadrats twice each year when vegetation was measured.
Sediment temperature at three depths below sediment surface in intertidal mudflats in Virginia, 2013-2014
We logged the sediment temperature at three intertidal mudflat sites between September, 2013 and September, 2014. At each site, three submersible temperature loggers (HOBO Pendant UA-002; Onset Computer Corporation) were buried at 3 cm, 10 cm and 20 cm below the sediment surface. Temperature was recorded in ten minute intervals. Each site was unvegetated with muddy sediment. For sites 1 and 2, data was recorded in two time series. Time series 1 from 09/02/2013 - 12/16/2013 and time series 2 from 12/19/2013 - 09/18/2014. At site 2, 20 cm depth, no data was recorded after 12/16/2013. At site 3, data was recorded at all depths continuously from 09/2/2013 to 08/28/2014. Date and time was recorded as GMT offset by -5 hours (Eastern Standard Time).
Nutrient, phytoplankton and zooplankton data, 20 year surface biomass from Darwin
<p>Nutrient, phytoplankton and zooplankton data to accompany Sonnewald et al. : Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces.</p> <p>Note: We discard the areas (gridcells) with biomass <10-4</p> <p> </p>
CO Emissions inferred from Surface CO Observations over China in December 2013 and 2017
<p><strong>CO_obs.rar</strong> includes assimilation observations for 2013 and 2017, independent verification observations for 2014, 2017 and 2018. NCP, YRD, and PRD represent the North China Plain, the Yangtze River Delta, and the Pearl River Delta, respectively.</p> <p><strong>emission_36km_2012.nc</strong> and <strong>emission_36km_2016.nc</strong> are prior emissions, <strong>emission_36km_2013.nc</strong> and <strong>emission_36km_2017.nc</strong> are posterior emissions inferred with default 40% uncertainty setting. <strong>emission_36km_20.nc</strong> and <strong>emission_36km_60.nc</strong> are posterior emissions inferred with 20% and 60% uncertainty setting, respectively, which are used for sensitivity test. <strong>emission_36km_nosuper.nc</strong> is posterior emissions inferred without ‘super observation’ method. These files have dimensions of 39 VAR×123 RAW×163 COL and the third variable is CO.</p>
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)
<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018). </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>
AFM Surface Coating
<p><strong>AFM Surface Coating - AFMBioMed Summer School 2020</strong></p> <p>This 7 minutes video tutorial shows how to properly label, handle, incubate (coat), rinse and store AFM surfaces (cantilevers and coverslips) during a multi-stage coating process.</p>
Datasets For "Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data", Blake et al. (2020), JGR
<p>Datasets and sample Python codes for the 2020 paper <em>"Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data"</em>, by Blake et al., submitted to the Journal of Gephysical Research, Space Physics. </p> <p>Up-to-date Python codes can be found at <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p>The complete SWMF simulation folders (including parameter and log files etc.) can be requested from <a href="https://ccmc.gsfc.nasa.gov/index.php">NASA's Community Coordinated Modeling Center</a>.</p> <p>#########</p> <p><strong>Data/ </strong>contains the following:</p> <p><strong>Data/HIST_DATA.txt </strong>contains the minimum Dst values and calculated maximum extents of the auroral equatorward boundaries for 25 years of INTERMAGNET data (1991-2016). The fourth column is the standard deviation of the calculated auroral boundary in degrees. </p> <p><strong>Data/Boundary_Fits.csv </strong>contains the calculated minimum Dst values, and calculated auroral boundaries using Method 1 and Method 2 (see main paper's ttext), for each of the 15 SWMF simulations. Also included are the uncertainties for each calculation.</p> <p><strong>Data/SWMF_outputs/ </strong>contains 15<strong> </strong>.txt files,<strong> </strong>each of which correspond to an SWMF simulation of the same name given in Table 1 in the main text. These data are for the magnetic longitude, magnetic latitude and maximum calculated <em>E<sub>H</sub> </em>(V/km) for each simulation.</p> <p>#########</p> <p><strong>Codes/ </strong>contains two python scripts, and some sample data. These scripts correspond to Section 2 in the main text:</p> <p>1) <strong>Boundary_Calc.py</strong> calculates the extent of the auroral boundary using magnetic latitudes and maximum calculated <em>E<sub>H</sub></em> values from multiple INTERMAGNET sites. </p> <p>2) <strong>Efield_Calc.py </strong>calculates the E-field for a single INTERMAGNET site using the Quebec 1-D resistivity model.</p> <p>A more detailed description of these codes can be found here: <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p> </p>
Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions
<p>This repository contains raw surface Electromyography signals termed surface Electromyograms (<a href="https://en.wikipedia.org/wiki/Electromyography">sEMG</a>) recorded with 8 circular surface Ag/AgCl pairs of electrodes placed circumferentially around the forearm of the dominant arm in 10 able-bodied individuals (5 Females and 5 Males). The proposed method for processing sEMG data with subjects' characteristics and protocol can be found in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>For each subject, sEMG was recorded from <strong>three recording electrode array positions</strong> termed P1, P2, and P3 for 9 hand movements. We provide a compressed .7z folder with 10 sub-folders for each subject named by <strong>subject ID</strong> (ID1, ID2, ... ID10). Each sub-folder contains 27 .txt data files (for 9 movements × 3 electrode array positions), except for subject ID7 (there are 24 .txt records, since three records for wrist extension EX in P1, P2, and P3 positions got corrupted in subject ID7). Average size of 10 sub-folders is 167.50 ± 27.02 MB with maximum of 194 MB and minimum of 117 MB.</p> <p>The subjects performed following hand movements from the reference resting position –relaxation, R (explained in-detail in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>): (1) spherical power grasp, PS, (2) three finger sphere grasp, 3F, (3) two finger prismatic grasp, PP, (4) wrist flexion, FL, (5) wrist extension, EX, (6) radial deviation, RD, (7) ulnar deviation, UD, and then forearm rotation i.e. (8) pronation, PR, and (9) supination, SU. PS, 3F, PP, FL, EX, RD, UD, PR, and SU correspond to <strong>type of hand movement</strong> in naming convention for .txt data files.</p> <p><a href="https://www.youtube.com/playlist?list=PLI3SYeiSufnBo6UDAZt9NJO9ecb-InJqb">Hand movements YoutTube playlist</a> contains explanatory videos for 9 hand movements recorded in this study, and we also provide corresponding .wmv here in the "movies hand movements.7z". Naming convention for .wmv files is <strong>type of hand movement</strong> with both full name and abbreviation for the movement (for example "radialDeviation-RD.wmv").</p> <p>Naming convention for .txt data files within 10 sub-folders is: <strong>subjects ID _ type of hand movement _ recording electrode array position</strong> (for example: "ID1_3F_P1.txt" in sub-folder ID1, "ID9_RD_P3.txt" in sub-folder ID9).</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/record/4039550/files/EMG%20dataset.7z?download=1">EMG dataset.7z</a>, 267 .txt data files, text format</li> <li><a href="https://zenodo.org/record/4039550/files/movies%20hand%20movements.7z?download=1">movies hand movements.7z</a>, 9 .wmv files, explanatory hand movement videos (also available on <a href="https://www.youtube.com/playlist?list=PLI3SYeiSufnBo6UDAZt9NJO9ecb-InJqb">YouTube</a>)</li> <li><a href="https://zenodo.org/record/4039550/files/README.txt?download=1">README.txt</a>, metadata for data files, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point* according to the following structure</strong></p> <ol> <li>column - CH1** (recorded samples from channel 1)</li> <li>column - CH2** (recorded samples from channel 2)</li> <li>column - CH3** (recorded samples from channel 3)</li> <li>column - CH4** (recorded samples from channel 4)</li> <li>column - CH5** (recorded samples from channel 5)</li> <li>column - CH6** (recorded samples from channel 6)</li> <li>column - CH7** (recorded samples from channel 7)</li> <li>column - CH8** (recorded samples from channel 8)</li> </ol> <p>* For subjects ID1 and ID2 three decimal places are provided, while for other subjects 6 decimal places in .txt data files are provided.</p> <p>** Each data file contains at least 10 repetitions of the corresponding movement. In cases where file contains >10 repetitions (overall 162 .txt data files), we used the first or the last ten for the analysis (except for two files where short and strong artifact appeared during the measurement procedure, and corresponding movement repetitions were discarded) presented in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>Sample rate was set at 1000 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. For more in-detail explanations of electrode array assemble and positioning for sEMG channels CH1, CH2, ... CH8, please refer to <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant preprint and dataset as:</p> <ol> <li> <p>Miljković, N. and Isaković, M.S., 2021. Effect of the sEMG electrode (re) placement and feature set size on the hand movement recognition. <em>Biomedical signal processing and control</em>, 64:102292. <em><a href="https://doi.org/10.1016/j.bspc.2020.102292">10.1016/j.bspc.2020.102292</a></em></p> </li> <li> <p>Miljković, N. and Isaković, M.S., 2020. Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions. [Data set]. <em>Zenodo</em> <em><a href="https://zenodo.org/record/4039550">10.5281/zenodo.4039550</a></em>.</p> </li> </ol> <p><strong>ACKNOWLEDGEMENTS</strong> (from <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>): "Special appreciation the authors owe to Professor Mirjana B. Popović from the University of Belgrade for her kind support,precious guidance, and advice regarding this research which significantly improved the manuscript. Also, the authors would like to thank Dr Matija Štrbac from Tecnalia Serbia Ltd. for providing advice throughout the study.The authors thank all volunteers for their participation."</p>
KOK1606_Gradients1_Surface_O2Ar_NCP
<p>Data were collected between April 23<sup>rd</sup> and May 4<sup>th</sup>, 2016 using an equilibrated inlet mass spectrometer (EIMS, Cassar et al., 2009) connected to the surface uncontaminated seawater supply of the Research Vessel <em>Kaimikai o’ Kanaloa</em> on Cruise ID KOK1606. Data were collected near-continuously over the cruise track which departed and returned from Honolulu, HI and sampled waters of the North Pacific subtropical gyre and transition zone.</p>
Raw and analyzed data for manuscript: "Wood surface ablation and nanostructuring using a femtosecond laser"
<p><strong>Abstract</strong></p> <p>The processing of Norway spruce and European beech wood specimens by means of femtosecond laser pulses was investigated on conditioned natural samples as well as on samples coated with beeswax or a water-borne stain. Depending on laser pulse energies and processing times, this allowed for different modes of surface modification. At low laser intensities, an etching almost without thermal impact was detected, whereas higher laser intensities led to the generation of hierarchical micro and nanostructures. The usage of argon or atmospheric air as cover gases during the laser processing had only minor effects on the surface structures. Observed differences in the etching or functionalization of the wooden surfaces mostly originated in the chemical structure of the surface finish and the physical properties of the wood substrates, such as the density or moisture content.</p>
Uncovering the Triplet Ground State of Triangular Graphene Nanoflakes Engineered with Atomic Precision on a Metal Surface
<p>OPEN DATA related to the research publication:</p> <p>J. Li, S. Sanz, J. Castro-Esteban, M. Vilas-Varela, N. Friedrich, T. Frederiksen, D. Peña, and J. I. Pascual, <em>Uncovering the triplet ground state of triangular graphene nanoflakes engineered with atomic precision on a metal surface</em>, Phys. Rev. Lett. <strong>124</strong>, 177201 (2020) [arXiv:1912.08298]</p> <p>Abstract: Graphene can develop large magnetic moments in custom-crafted open-shell nanostructures such as triangulene, a triangular piece of graphene with zigzag edges. Current methods of engineering graphene nanosystems on surfaces succeeded in producing atomically precise open-shell structures, but demonstration of their net spin remains elusive to date. Here, we fabricate triangulenelike graphene systems and demonstrate that they possess a spin S=1 ground state. Scanning tunneling spectroscopy identifies the fingerprint of an underscreened S=1 Kondo state on these flakes at low temperatures, signaling the dominant ferromagnetic interactions between two spins. Combined with simulations based on the meanfield Hubbard model, we show that this S=1 π paramagnetism is robust and can be turned into an S=1/2 state by additional H atoms attached to the radical sites. Our results demonstrate that π paramagnetism of high-spin graphene flakes can survive on surfaces, opening the door to study the quantum behavior of interacting π spins in graphene systems.</p>
Meteorology, environment and surface flux data for grassland sites in Germany
<p>Observation and model data for locations Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg), in conjunction with selected journal publications. These data have primarily been used for investigation of surface carbon fluxes (Net Ecosystem Exchange, Gross Primary Productivity), seasonal climatic trends and land management. </p> <p>The sites are part of TERENO, a network of observatories in Germany. The TERENO Data Portal should provide other and more up-to-date information. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples. </p>
Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019)
<p>Argo-based mixed layer depth profiles derived from the CORA product as described in Whitt Nicholson Carranza. A binned 2-degree climatology was published previously:</p> <p>https://github.com/danielwhitt/globalimpacts_2019_whittetal/blob/master/MonthlyClimatology_ARGO_MLDbmax_TEOS10_Copernicus_PF_2000-2017_all_jun252019_nc.nc</p> <p>with:</p> <p>Whitt, D. B., Nicholson, S. A., & Carranza, M. M. (2019). Global Impacts of Subseasonal (< 60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth. <em>Journal of Geophysical Research: Oceans</em>, <em>124</em>(12), 8798-8831</p> <p>Contact the authors with questions. </p> <p>The chosen mixed layer depth definition is the same as "HMXL", a standard output of the Community Earth System Model (CESM) ocean component.</p>
Carbonyl Sulfide (OCS/COS) and Carbon Disulfide (CS2): global modelled marine surface concentrations and emissions, 2000-2019
<p>This dataset contains a global ocean emission inventory of the sulfur-containing trace gases carbonyl sulfide (OCS/COS) and carbon disulfide (CS2). It covers the period 2000-2019, and includes a monthly average and an average diel cycle for each month for sea surface concentrations and emissions to the atmosphere. The spatial resolution is 2.8° x 2.8° at the equator (T42 grid), the depth extends from the surface to the mixed layer depth.</p> <p>Carbonyl sulfide (OCS) is the most abundant, long-lived sulphur gas in the atmosphere and a major supplier of sulfur to the stratospheric sulfate aerosol layer. The short-lived gas carbon disulfide (CS<sub>2</sub>) is oxidized to OCS and constitutes a major indirect source to the atmospheric budget of OCS. We encourage the use of the data provided here as input for atmospheric modelling studies to further assess the atmospheric OCS budget and the role of OCS in climate.</p>
ICESat-2 sea ice ancillary data - Mean Sea Surface Height Grids
<p>File format: NetCDF</p> <p>Mean Sea Surface (MSS) Height data grids used for the production of ICESat-2 sea ice data products (ATL07, ATL10, ATL20, ATL21). Blended data from CryoSat-2 and DTU13.</p>
GulfDrifters: A consolidated surface drifter dataset for the Gulf of Mexico
<p>This dataset consists of all publicly available surface drifter trajectories from the Gulf of Mexico, subjected to a uniform quality control and processing methodology and interpolated onto hourly resolution. Full details as to the datasets and processing may be found in</p> <p>Lilly, J. M. and P. Pérez-Brunius (2021). A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements. <em>Earth System Science Data</em>, 13: 645–669. https://doi.org/10.5194/essd-13-645-2021.</p> <p>A related dataset is the GulfFlow space/time gridded velocity product, comprised of these data together with three proprietary experiments. GulfFlow is available at https://zenodo.org/record/3978793 for noncommercial use. </p> <p>One of those proprietary experiments is the Deep Water Dispersion Experiment (DWDE). This is available for noncommercial use at https://zenodo.org/record/3979964, together with a version of the GulfDrifters dataset, GulfDriftersDWDE, that also incorporates the DWDE data.</p> <p> </p> <p> </p> <p> </p>
Eectrochemical immunosensor for the quantification of S100B at clinically relevant levels using a cysteamine modified surface
<p>Datasets analyzed during the work titled "An electrochemical immunosensor for the quantification of S100B at clinically relevant levels using a cysteamine modified surface".</p>
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
Raw and analyzed data for manuscript: "An open-source surface barrier discharge plasma pretreatment for reduced cracking of outdoor wood coatings"
<p><strong>Highlights:</strong></p> <ul> <li>Surface barrier discharges are an affordable and available plasma technology for industrial, laboratory and home-workshop applications.</li> <li>Plasma pretreatments had no impact on the appearance of different protective wood coating for outdoor usage.</li> <li>The weathering performance of outdoor wood coatings improved by plasma, showing less cracks and less biotic factors.</li> </ul>
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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.