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Rings in Clinical Trials and Drugs: Present and Future - Datasets
<p>"Rings in Clinical Trials and Drugs: Present and Future" - Datasets from publication in Journal of Medicinal Chemistry</p>
A Comprehensive Solution for Securing Connected and Autonomous Vehicles (presentation video)
<p>Video recording of the online presentation for the publication M. Kamal et al., "A Comprehensive Solution for Securing Connected and Autonomous Vehicles," 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022, pp. 790-795, doi: 10.23919/DATE54114.2022.9774594.</p>
Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution)
<pre>About Dataset Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution). Authors: Francisco J. Perez-Invernon, Francisco J. Gordillo-Vazquez, Patrick Joeckel and Heidi Huntrieser Description of the data PaR_T: Lightning parameterization based on cloud top height. PaR_L: Lightning parameterization based on cloud top height and modified over the oceans. Grewe: Lightning parameterization based on updraft velocity. AaP_P: Lightning parameterization based on convective precipitation. A AaP_M: Lightning parameterization based on Updraft strength at 440~hPa. PRaAP: Lightning parameterization based on cloud top height and updraft velocity. FinIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa. extIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa an isotherm. File format: netcdf Example: netcdf </pre> <p>2009_10h_______20090701_0000_mmlb_PRaAP.nc<br> netcdf \2009_10h_______20090701_0000_mmlb_PRaAP {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2009-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2009-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point" ;</p> <p>// global attributes:<br> :MESSy = "MESSy version d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200, http://www.messy-interface.org" ;<br> :MESSy_switch = "version 1.0" ;<br> :MESSy_channel = "version 2.4.3" ;<br> :MESSy_tracer = "version 2.6" ;<br> :MESSy_timer = "version 0.1" ;<br> :MESSy_qtimer = "version 3.0" ;<br> :MESSy_import = "version 1.0" ;<br> :MESSy_grid = "version v1.5" ;<br> :MESSy_rnd = "version 1.1" ;<br> :MESSy_aeropt = "version 2.0.2" ;<br> :MESSy_cloud = "version 2.2" ;<br> :MESSy_cloudopt = "version 2.1b" ;<br> :MESSy_convect = "version 2.0" ;<br> :MESSy_gwave = "version 1.0" ;<br> :MESSy_lnox = "version 3.0" ;<br> :MESSy_orbit = "version 0.9" ;<br> :MESSy_orogw = "version 1.1" ;<br> :MESSy_rad = "version 2.2" ;<br> :MESSy_e5vdiff = "version 1.2" ;<br> :MESSy_surface = "version 1.2" ;<br> :MESSy_tropop = "version 2.1" ;<br> :MESSy_viso = "version 2.3" ;<br> :MESSy_experiment = "2009_10h" ;<br> :EXEC_CHECKSUM = "45e5ddd17ae5992f931a9ba4bab4a921 bin/echam5.exe (md5sum)" ;<br> :GCM = "ECHAM5 version 5.3.02, Max-Planck Institute for Meteorology, Hamburg" ;<br> :GCM_spherical_trunc_n = 42 ;<br> :GCM_spherical_trunc_m = 42 ;<br> :GCM_spherical_trunc_k = 42 ;<br> :GCM_vertical_mode = "middle atmosphere (MA)" ;<br> :GCM_horizontal_mode = "global" ;<br> :GCM_advection = "Lin&Rood" ;<br> :GCM_start_date_time = "20090101 000000" ;<br> :GCM_timestep = 900.f ;<br> :F95_COMPILER_VERSION = "ifort (IFORT) 17.0.2 20170213" ;<br> :F95_COMPILER_CALL = "/opt/mpi/bullxmpi_mlx/1.2.9.2/bin/mpif90" ;<br> :F95_COMPILER_FLAGS = "-sox -fpp -g -O2 -xCORE-AVX2 -fp-model strict -align all -save-temps -DBULL -I/sw/rhel6-x64/sys/bullxlib-1.0.0/include -L/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -Wl,-rpath,/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -lbullxMATH -no-wrap-margin" ;<br> :F95_PREPROC_DEFINITIONS = "-DMESSY -DLITTLE_ENDIAN -D_LINUX64 -DHAVE_PNETCDF -DPNCREGRID -DMPIOM_13B -D_VCSREV_=\'d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200\'" ;<br> :F95_COMPILER_INCLUDES = "-I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/parallel_netcdf-1.6.0-bullxmpi-intel14/include" ;<br> :operating_date_time = "20210615 081833" ;<br> :operating_system = "Linux 2.6.32-754.33.1.el6.x86_64 on x86_64" ;<br> :operating_host = "mlogin102" ;<br> :operating_user = "Francisco-Javier Perez-Invernon (b309171)" ;<br> :channel_io_pe = 172 ;<br> :channel_time_slo = 5182200.f ;<br> :channel_name = "mmlb_PRaAP" ;<br> :channel_file_type = "output" ;<br> :channel_file_name = "2009_10h_______20090701_0000_mmlb_PRaAP.nc" ;<br> :channel_netcdf_lib = "4.3.2 of May 5 2015 13:21:25 $" ;</p>
Data for Analyses presented in associated publication: Investigating trends in those who experience menstrual bleeding changes after SARS CoV-2 vaccination
<p>This is the data used for analyses presented in our associated publication (details to be provided later). There are three datasets, because we analyzed by specific, biologically relevant subgroups (pre-menopausal people with predictable menstrual cycles, pre-menopausal people who usually do not menstruate, and post-menopausal people). The data provided here has been cleaned and aggregated as described in the associated publication. Specifically,</p> <ul> <li>here we have only included the variables that are included in our analyses</li> <li>we collapsed specific identities (gender, race, ethnicity) into binarized categories (as used in the publication) in order to reduce the likelihood someone could be re-identified from this de-identified data</li> <li>this dataset also does not include unique identifiers for individuals, but instead has row numbers for each dataset (starting at 1 for each data subset). There are no repeated individuals between datasets, as these are mutually exclusive subgroups</li> <li>Additional information regarding the survey and data processes can be found at https://osf.io/6rvxk/?view_only=f91f1247658f49e3bbf59b2f6cfd3898</li> </ul>
Global terrestrial water storage reconstruction using cyclostationary empirical orthogonal functions (1979 - present)
<p>The dataset consists of a 1979-present reconstruction of Terrestrial Water Storage (TWS) anomalies from Gravity Recovery and Climate Experiment (GRACE) using common modes of variability between TWS and terrestrial precipitation and land surface temperatures. </p>
Present-day and future changes in the hydrology of the Bhagirathi Basin
<p>This repository contains the daily outputs (Jan 1, 1991 to Dec 31 2020) produced in the project SDC project. The folder 'Final_full_30yrs_baseline.rar' contains all the historical outputs generated from the SPHY model. The folder contains data in the different formats (spatial and non spatial) '.map','.csv' and '.tss'</p> <p>The folder 'Climate_change.rar' contains climate runs from (Jan 1, 2021 to Dec 31 2100) for 4 GCM-RCM and ssp combinations.</p>
Analysed data for poster presentation "Dielectric barrier discharge plasma deoxidation of copper and iron surfaces"
<p>Removal of oxygen contaminations from metal surfaces during industrial fabrication processes is an important cleaning step mitigating the negative impact of native oxide layers on the performance of compounds. Different plasma deoxidation techniques were developed to remove such oxide layers. Although a large number of studies were focused on of low-pressure plasma techniques, there is the lack of information on the deoxidation effect of plasmas that operate in the atmospheric pressure range.</p>
Alec Davis ICOTOM19 conference presentation
<p>Simulation and texture investigation of a novel recrystallisation mechanism in Ti-6Al-4V during wire-arc AM with inter-pass deformation. High deposition rate Wire-Arc Additive Manufacture (WAAM) of Ti-6Al-4V is attractive to industry for near-net shape production of large-scale components. However, WAAM leads to the development of undesirable coarse columnar β grains which, as a consequence of the larger heat source, grow epitaxially throughout components. To break up the β-grain structure, a cold deformation step can be introduced, between deposition passes, to induce recrystallisation on heating during the α→β transformation. This inter-pass deformation process produces small, equiaxed, β grains, even when low strains are employed. In single β crystal simulations of the thermomechanical history, starting with a coarse conventional α microstructure, the more expected behaviour occurred, with limited β recrystallisation produced largely by lattice rotation. In contrast, with a fine AM starting transformation microstructure, as well as a high level of recrystallisation, a unique texture was observed, which was consistent with the deformation-induced annealing twinning produced during the α→β transformation.</p>
Data presented in Multi-trial analysis of HIV-1 envelope gp41-reactive antibodies among global recipients of candidate HIV-1 vaccines.
<p>This folder contains datasets analyzed in the manuscript:</p> <p>Multi-trial analysis of HIV-1 envelope gp41-reactive antibodies among global recipients of candidate HIV-1 vaccines.</p> <p>Frontiers in Immunology<br> Sec. Vaccines and Molecular Therapeutics<br> doi: 10.3389/fimmu.2022.983313</p>
Text-fig. 3. CT slices on Block 3. Invertebrate moulds (a, c) and remains of their hard skeletons (a, b). Large areas of limestone matrix hold either only a few scattered invertebrates or no fossil at all (b, c). Ring artefacts seen close to the isocentre of the scan (b, c) are a well-known phenomenon caused by the X-ray beams traversing the block at an insufficient radiation dose (as expected in such a large block of dense material), and are not part of any physical structure present therein (Triche et al. 2019). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner
Text-fig. 3. CT slices on Block 3. Invertebrate moulds (a, c) and remains of their hard skeletons (a, b). Large areas of limestone matrix hold either only a few scattered invertebrates or no fossil at all (b, c). Ring artefacts seen close to the isocentre of the scan (b, c) are a well-known phenomenon caused by the X-ray beams traversing the block at an insufficient radiation dose (as expected in such a large block of dense material), and are not part of any physical structure present therein (Triche et al. 2019).
Text-fig. 11. Permian ichthyofaunas from the French Massif Central. Preliminary comparisons based on total accounts of individuals (see text for explanations). Sharks in green, Acanthodes sp. in yellow, Actinopterygians in blue, Dipnoi in white (not visible but present at Autun; see Tab. 1 for details). Each circle is proportional to the total number of specimens recovered. Permian outcrops in black. Hercynian basement indicated by vertical lines. Map modified from Gand and Durand (2006). in New Actinopterygians From The Permian Of The Brive Basin, And The Ichthyofaunas Of The French Massif Central
Text-fig. 11. Permian ichthyofaunas from the French Massif Central. Preliminary comparisons based on total accounts of individuals (see text for explanations). Sharks in green, Acanthodes sp. in yellow, Actinopterygians in blue, Dipnoi in white (not visible but present at Autun; see Tab. 1 for details). Each circle is proportional to the total number of specimens recovered. Permian outcrops in black. Hercynian basement indicated by vertical lines. Map modified from Gand and Durand (2006).
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Reference atmospheres, surface and instrument data used in the test experiments presented in Ridolfi et al. 2022 (doi.org/10.5194/amt-2022-82)
<p>The supplied archive includes a set of ASCII files defining the reference atmospheric and surface states that are the basis of the simulation experiments presented in the paper of Ridolfi et al. 2022 (doi.org/10.5194/amt-2022-82). Along with atmospheric and surface data, we also supply the seasonal variability and mismatch errors used in the test experiments presented in that paper, the noise error covariance matrices anticipated for FORUM and IASI-NG spectra, and some details of the retrieval setup. The archive includes a README file summarizing the contents of the various files provided.</p>
WORCC-PMOD/WRC quality assured aerosol optical depth and Ångström exponent for Ny-Ålesund GAW station (2002- present)
<p>WORCC-PMOD/WRC quality assured aerosol optical depth and Ångström exponent for Ny-Ålesund GAW station (2002- present)</p> <p>Aerosol optical depth (AOD) measurements have been performed within the frame of Global Atmospheric Watch Precision Filter Radiometer (GAW-PFR) network in Ny-Ålesund (79N,11E) since 2002. The measurements are performed from March to October, with PFR (PrecisionFilterRadiometer) instruments provided by Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center (PMOD/WRC). The solar tracker and infrastructure are provided by provided by NILU(Norsk institutt for luftforskning) in collaboration with the Norwegian Polar Institute.</p> <p>PFR manufactured by PMOD/WRC is a temperature stabilized instrument at 20<sup>o</sup> C equipped with four narrow band interference filters with nominal centroid wavelengths 368 nm, 412 nm, 500 nm and 862 nm and bandpass (fullwidth half maximum) 4 nm for 368 nm channel and 5 nm for the rest. The instrument is calibrated in yearly bases against the WMO-AOD reference at PMOD/WRC during the polar winter. The operation is done remotely by PMOD/WRC with the installation and onsite maintenance (cleaning, alignment adjustments) done by personnel of Norwegian Polar Institute and NILU in collaboration with PMOD/WRC.</p> <p>The processing and quality assurance of the data is done following the protocols of World Optical depth Research and Calibration Center (WORCC, PMOD/WRC) (2018), and the data are submitted to the WDCA database (EBAS-NILU) as hourly mean AOD and AE values. This dataset contains the high-resolution data (1 min) cloud screened and quality assured since 2002. The provided Ångström exponent is retrieved from the 4 wavelengths.</p> <p> </p> <p> </p> <p>SUN_PFR_AOD_AE_V1.0.dat : AOD and AE for the period 2002-2021</p> <p>Lunar_PFR_AOD_AE_V1.0.dat : AOD and AE for the period 2018-2021</p> <p> </p> <p> </p> <p>KN: Calibration, operation, and processing since 2014, WORCC quality assurance protocols, quality assurance of the presented dataset</p> <p>KS: , WORCC quality assurance protocols, consulting on quality assurance of the presented dataset</p> <p>WC: calibration, operation, and processing 2002- 2014</p> <p>NS: operation and processing 2005- 2012</p> <p>HGH: principal investigator of hosting institute NILU</p> <p>SK: principal investigator of hosting institute NILU</p> <p>Acknowledgement: Special thanks the personnel of the Norwegian Polar Institute at Ny-Ålesund for all valuable the technical support for the solar and lunar measurements.</p> <p> </p> <p> </p> <p>1. Kazadzis, S., Kouremeti, N., Nyeki, S.<em>, et al.</em> (2018) The World Optical Depth Research and Calibration Center (WORCC) quality assurance and quality control of GAW-PFR AOD measurements 10.5194/gi-7-39-2018 <a href="https://gi.copernicus.org/articles/7/39/2018/">https://gi.copernicus.org/articles/7/39/2018/</a></p> <p> </p>
Data presented in "A buffer gas beam source for short, intense and slow molecular pulses"
<p>Data presented in our paper "A buffer gas beam source for short, intense and slow molecular pulses". The files give the data shown in figures 4 and 5 of the paper.</p>
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters).
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs).
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings).
Рис. 3. Коррелограммы покаЗателей обилиЯ наЗемного моллюска B. cylindrica раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок №5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 3. Spatial correlogram of the land snail B. cylindrica age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 3. Коррелограммы покаЗателей обилиЯ наЗемного моллюска B. cylindrica раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок №5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 3. Spatial correlogram of the land snail B. cylindrica age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled signs).
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters).
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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