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538 results for “ECS”
Eddy covariance (EC) vertical carbon fluxes from a Georgia tidal salt marsh from 2014 to 2024
We present our methodology and data for science ready vertical carbon fluxes from a Spartina alterniflora tidal salt marsh as part of the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site on Sapelo Island, Georgia, USA. Vertical carbon fluxes were measured through the eddy covariance (EC) method from 2014 to 2024. The EC flux tower was located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. The proportional influence of marsh habitats on the flux measurements were 4% tall, 38% short, and 58% medium height form Spartina alterniflora. We present the net ecosystem exchange (NEE), ecosystem respiration (ER), and gross primary production (GPP) at 30-minute fluxes (μmol CO2 m-2 s-1), daily averages (μmol CO2 m-2 s-1) and totals (g C m-2 day-1), and annual (g C m-2 year-1) quantities. We provide estimated uncertainty for each flux at each integrated timescale as 95% confidence intervals. Providing open access to 10-year carbon flux datasets can facilitate collaboration for advancing regional and global blue carbon synthesis and scale-up studies.
Software for processing data from a fast-responding RINKO EC oxygen/temperature sensor (JFE Advantech Co, Ltd)
This dataset describes how data from a fast-responding JFE Advantech RINKO EC ARO-EC-CM sensor connected to a Nortek Vector is processed to obtain accurate aquatic eddy covariance measurements. The code and documentation are stored in a .zip file. It consists of a manual, Fortran source code, a definition file and a complied executable suitable for running on Microsoft Windows. The software development was supported by NSF funding to PI Berg (OCE-1824144, OCE-2223204).
Mapping of FoodEx2 Exposure Hierarchy with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives
<p>FoodEx2 is a comprehensive food classification and description system aimed at covering the need to describe food in data collections across different food safety domains. All foods, including beverages and food supplements reported in the EFSA Comprehensive Food Consumption Database are coded with the FoodEx2 Exposure Hierarchy. EFSA developed a mapping of all FoodEx2 basic terms reported in the Comprehensive Database with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives in order to facilitate the assessment of exposure to food additives. In particular, this mapping has been used in the Food Additives Intake Model 2.0 (FAIM), which allows the estimation of chronic dietary exposure to food additives based on use levels proposed for food categories as presented in Annex II (part D) of Regulation (EC) No 1333/2008 on food additives.</p> <p>Some of the food categories, restrictions and/or exceptions presented in the Regulation could have not been mapped with FoodEx2 basic terms and the original food descriptors and/or FoodEx2 facets might have been used to correctly map all eating events. This information might not be available in this table.</p>
Parental Engagement and Relationships (PEAR) in Early Childhood (EC). Impact study: Parents' responses
<p>Dataset with parents' quantitative responses collected within the impact study of the project Parental Engagement and Relationships (PEAR) in Early Childhood (EC).</p> <p>The file (.sav) can be opened using IBM SPSS Software. The file is named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925.</p>
MAR-EC-Earth3 HIST (1961-2014) and SSP245 European Alps (2015-2100)
<p>This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the EC-EARTH3 GCM (CMIP6 version)<br> The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x)<br> Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The realization used is r25i1p1f1 and simulation was done for the historial (1961-2014), SSP245 scenario (2015-2100). For information about the EC-EARTH3 simulation, contact Eduardo Moreno-Chamarro (eduardo.moreno@bsc.es).<br> Data are available at the daily frequency, with one variable per file (10 years of data per file)</p> <p><strong>CC:</strong> Cloud cover, [0-1]<br> <strong>MB:</strong> Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)<br> <strong>QQz: </strong> Near-surface specific humidity at constant height, [g/kg] (2)</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]<br> SP: Surface pressure, [hPa]<br> ST: Surface temperature, [C]<br> TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>LWD: Surface downward longwave radiation, [W/m2]<br> LWU: Surface upward longwave radiation, [W/m2]</p> <p>SWD: Surface downward shortwave radiation, [W/m2]<br> SWU: Surface upward shortwave radiation, [W/m2]</p> <p>SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUe.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479<br> (2) For variables TTz, QQz constant height level are : 2m<br> (3) For variables UUz, VVz constant height level are : 50m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respective fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City<br> </p>
Installing a EC Sensor on a remote location.
<p><strong>1.Introduction</strong></p> <p>The objective of this document is to provide a description of the dataset entitled “Installing an EC Sensor on a remote location”.</p> <p>The guidelines on how to use the files are included in this document.</p> <p>The dataset is part of the deliverables D9.4 (First data management plan) and D9.5 (Final data management plan).</p> <p><strong>2.Description of the data</strong></p> <p><strong>2.1.Origin</strong></p> <p>This dataset includes data collected from the experiments related to the task “Installing a EC Sensor on a remote location” as part of the WP8 “validation in the industrial scenario”.</p> <p><strong>2.2.Type</strong></p> <p>The data consists of Eddy Current (EC) measurements.</p> <p><strong>2.3.Formats</strong></p> <p>The acquired data are available in several formats.</p> <p>2.3.1.*.sidata files</p> <p>These files are proprietary format that can be opened with the software “UPecView” supplied by Sensima Inspection (http://www.sensimainsp.com).<br> This software provides an interface familiar to what expected by eddy-current inspectors.</p> <p>Each file includes all the relevant information that may be used for analysis: the measurements and the instrument configuration (ex. Excitation frequency of the probe) is contained in this file.</p> <p>2.3.2.*.csv files</p> <p>The csv files contain an export of the measurements only (without instrument settings); a comma separator is used. Each row is composed of the following variables: Time (s), Signal (in-phase), Signal (out-of-phase), Channel/state, Extra signal (ADC), Encoder coordinate 1 (x), Encoder coordinate 2 (y), Encoder coordinate 3 (z), Encoder error status.</p> <p> </p> <p> </p> <p><strong>3.Measurements indexing</strong></p> <p>Folder</p> <p>Filename</p> <p>Creation date</p> <p>Description</p> <p>Target</p> <p>Location</p> <p> </p> <p>EXP001</p> <p>0001A</p> <p>12.03.2019</p> <p>Calibration block scan</p> <p>Calibration block</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0001B</p> <p>12.03.2019</p> <p>Manual reference scan on weld pipe</p> <p>Calibration block</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002A</p> <p>12.03.2019</p> <p>Drone overall scan inspection and sensor deployment</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002B</p> <p>12.03.2019</p> <p>Deployed sensor</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002C</p> <p>12.03.2019</p> <p>Deployed sensor</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002D</p> <p>12.03.2019</p> <p>Permanent sensor removal</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
EC 5th Framework ENPOWER austenitic edge welded beam contour cut metrology for assessing residual stress
<p>Data from contour method cut surfaces collected from an autogenously edge-welded AISI 316H stainless steel beam produced as part of ENPOWER. Surfaces were generated as part of a slitting experiment and then subsequently measured with a coordinate measurement machine. This dataset forms the basis for <a href="https://doi.org/10.1115/1.4004626">"<em>Slitting and Contour Method Residual Stress Measurements in an Edge Welded Beam</em>" Hosseinzadeh et al. (2012)</a>, and further information on the specimen background and diffraction based results can be found in <a href="https://doi.org/10.1115/PVP2008-61339">"<em>A statistical framework for analysing weld residual stresses for structural integrity assessment</em>" Nadri et al. (2008)</a>.</p> <p>Datasets are in the form of lists of x,y.z coordinates, with one point per line, whitespace delimited in millimeters. The *Perimeter1.txt file coincides with *Surface1.txt, with the former an outline identifying the cut surface periphery, and the latter points lying on the surface. The same format is employed for the other side of the cut.</p>
Global mean TAS and net TOA flux in CMIP5 piControl and abrupt4xCO2 experiments using EC-Earth model
<p>Near surface air temperature (tas) and net heat flux at top of the atmosphere (NetTOA) from CMIP5 piControl and abrupt4xCO2 experiments with EC-Earth2.3. Each file contains the annual global mean from one experiment for the respective variable, calculated using cdo.</p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA high-resolution model part 2 (of 2)
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA standard-resolution model
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA high-resolution model part 1 (of 2)
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 v3.3.1 model.
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Idealized precession GCM simulations with EC-Earth-2-2
<p>This repository contains output from the EC-Earth-2-2 extreme precession simulations (Prec-min / Pmin and Prec-max / Pmax). The experiment details are described in the papers given as references. In this repository, monthly output of the last 50 years of simulation is given for precipitation (in m/s), zonal wind (east-west, in m/s, parameter 165), meridional wind (north-south, in m/s, parameter 166), mixed layer depth (MLD, in m, based on a σ0 difference of 0.01 with the surface), sea surface salinity (in PSU) and thermocline depth (TCD, in m).</p> <p>Furthermore, climatological mean monthly output (averaged over the last 50 years of simulation) is given for surface air temperature (parameter 167), surface sensible heat flux (146), surface latent heat flux (147), surface solar radiation downwards (169), surface thermal radiation downward (175), net surface solar radiation (176), net surface thermal radiation (177), net top solar radiation (at top of atmosphere, 178), and net top thermal radiation (at top of atmosphere, 179). Furthermore the following 3D variables are given on pressure levels: geopotential height (129), specific humidity q (133), zonal wind (east-west, 131), meridional wind (north-south, 132), vertical velocity (135). Please see the ECMWF GRIB parameter database for units and more details on these parameters: https://apps.ecmwf.int/codes/grib/param-db.</p> <p>More output from these simulations can be found through the repository:<br> Bosmans, Joyce. (2019). Idealized orbital extreme GCM simulations with EC-Earth-2-2 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3268528</p> <p>References:</p> <p>Bosmans, J. H. C. (2014). A model perspective on orbital forcing of monsoons and Mediterranean climate using EC-Earth. UU Depts. of Physical Geography and Earth Sciences.<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., & Lourens, L. J. (2015). Response of the North African summer monsoon to precession and obliquity forcings in the EC-Earth GCM. Climate dynamics, 44(1-2), 279-297.<br> Bosmans, J. H. C., Hilgen, F. J., Tuenter, E., & Lourens, L. J. (2015). Obliquity forcing of low-latitude climate. Climate of the Past, 11(10), 1335-1346.<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., Lourens, L. J., & Rohling, E. J. (2015). Precession and obliquity forcing of the freshwater budget over the Mediterranean. Quaternary Science Reviews, 123, 16-30.<br> Bosmans, J. H. C., Erb, M. P., Dolan, A. M., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., Edge, D., Pope, J.O. & Lourens, L. J. (2018). Response of the Asian summer monsoons to idealized precession and obliquity forcing in a set of GCMs. Quaternary Science Reviews, 188, 121-135.<br> Chetankumar, J., Bosmans, J.H.C., Srinivasan, J. & Chakraborty, A. (2019): The response of tropical precipitation to Earth's precesion: the role of energy fluxes and vertical stability. Climate of the Past, 15, 449-462.</p> <p> </p> <p>Contact:<br> Joyce.Bosmans@ru.nl</p> <p>Acknowledgements:<br> These model simulations were performed during Joyce Bosmans' PhD research, funded by an Utrecht University `Focus en Massa' grant and partly performed at the Royal Netherlands Meteorological Institute (KNMI), with support from ECMWF.</p>
D2.2 IPSP Dataset_under EC review
<p>This dataset is part of the DIAMAS project deliverable 'IPSP Database'. The dataset contains a section of the DIAMAS survey where respondents authorised the DIAMAS project to include that information. After data cleaning and manual checking, this dataset contains data for 651 IPSPs. Data from Turkey will be added at a later stage. IPSP names are rendered in various languages. All IPSP URLs were tested with a Google Apps script, resulting in changes to 149 URLs, and the removal of three that did not resolve. IPSPs were further subdivided into ERA regions: Eastern Europe, Northern Europe, Southern Europe, Western Europe, Northern Africa, and Southwest Asia. IPSP responses from outside the ERA are out of scope for the report but included in the dataset in the region 'rest of the world'.</p> <p>The dataset provides the groundwork for the IPSP Registry, due to be delivered in June 2025 as part of DIAMAS Work Package 4: Building capacity through knowledge-sharing.</p> <p>All data are available for reuse under a CC0 license.</p>
Data archive for the journal article: "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites"
<p>Data archive accompanying the peer-reviewed journal article "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites". In January 2021 this article was accepted for publication in the journal <em>Atmospheric Measurement </em><em>Techniques</em>. Data are uploaded in the form of Igor 8.0 graphics source files (.pxp) and data exported to Excel spreadsheet (.xlsx).</p>
Data for: Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid-ammonia nucleation
<p>Model dataset variables produced from the IFS and TM5 modules in EC-Earth3 version 3.3.4. which contains the control case and three experiments with the NPF lookup table. This paper is published at EGUshpere by journal: Geoscientific Model Development.</p> <p>The files contain:</p> <p>Compressed tar file of NetCDF data from IFS output for all four simulations. All IFS data have been averaged to monthly means from 6-hourly grib datasets. The post-process bash script which contains the function for the CDN and cloud effective radius weighted average towards cloud_time is found in the supplemented zendo link.</p> <p>NetCDF files from TM5 general output for each simulation. </p>
The Q-TOF proteomics data for the identification of mammalian L-fucose dehydrogenase (EC 1.1.1.122)
<p>The enclosed zip file contains data files (RAW format) from MS^E experiment. The experiment was performed with the use of Acquity nanoUPLC coupled with a Synapt G2 HDMS Q-TOF mass spectrometer (Waters) fitted with a nanospray source. It aimed at the identification of proteins present in the gel bands S1-S9 and the gel band Z1. The bands have come from SDS-PAGE and zymography analyses, respectively, of the most active enzyme fraction from the Reactive Red Agarose 120 purification step.</p>
РИС. 1. Схематичное иЗображение глаЗа наЗемного лёгочного моллюска. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела; L abs – абсолютное расстоЯние между Зрачком и наружной поверхностью хрусталика; D l – продольный диаметр хрусталика; А – абсолютный диаметр Зрачabs ка; D – поперечный диаметр глаЗа. FIG. 1. Schematic drawing of the eye of a terrestrial pulmonate mollusk. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body; L abs – the absolute distance between the pupil and the outer surface of the lens; D l – the longitudinal diameter of the lens; А abs – the absolute diameter of the pupil; D e – the transverse diameter of the eye. in Зрачок камерных глаЗ наЗемных брюхоногих моллюсков (Heterobranchia, Stylommatophora)
РИС. 1. Схематичное иЗображение глаЗа наЗемного лёгочного моллюска. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела; L abs – абсолютное расстоЯние между Зрачком и наружной поверхностью хрусталика; D l – продольный диаметр хрусталика; А – абсолютный диаметр Зрачabs ка; D – поперечный диаметр глаЗа. FIG. 1. Schematic drawing of the eye of a terrestrial pulmonate mollusk. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body; L abs – the absolute distance between the pupil and the outer surface of the lens; D l – the longitudinal diameter of the lens; А abs – the absolute diameter of the pupil; D e – the transverse diameter of the eye.
РИС. 2. ГлаЗ Monachoides incarnata. A. ФотографиЯ препарата иЗолированного глаЗа. B. ФотографиЯ продольного полутонкого среЗа глаЗа. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела. FIG. 2. The eye of Monachoides incarnata. A. Photograph of the preparation of the isolated eye. B. Photograph of the longitudinal semithin section of the eye. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body. in Зрачок камерных глаЗ наЗемных брюхоногих моллюсков (Heterobranchia, Stylommatophora)
РИС. 2. ГлаЗ Monachoides incarnata. A. ФотографиЯ препарата иЗолированного глаЗа. B. ФотографиЯ продольного полутонкого среЗа глаЗа. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела. FIG. 2. The eye of Monachoides incarnata. A. Photograph of the preparation of the isolated eye. B. Photograph of the longitudinal semithin section of the eye. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body.
The Secret Life of Writing at the EC European Researchers' Night 2020
<p><a href="https://www.youtube.com/channel/UCGT6GmyssibzkpfDX9xkbXA">La Noche de los Investigadores en Castilla y León</a></p> <p>En este RINCÓN EUROPEO nuestros investigadores te cuentan en primera persona los proyectos de I+D+i en los que están trabajando a nivel continental.</p> <p>LOS DATOS 🔎</p> <p>Investigadora: Ainoa Castro Correa</p> <p>Entidad: Universidad de Salamanca</p> <p>Financiación: Horizon 2020 - ERC - Starting Grant Proyecto: PeopleAndWriting: The Secret Life of Writing: People, Script and Ideas in the Iberian Peninsula (c. 900-1200) / GenteYEscritura: la vida secreta de la escritura: gente, guion e ideas en la Península Ibérica (c. 900-1200)</p> <p> </p> <p> </p> <p>) <a href="https://youtu.be/EaYj4ZEjfMk">https://youtu.be/EaYj4ZEjfMk</a><strong> </strong></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)
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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.