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Figure 1 in Small-scale spatial distribution of ghost shrimp and macrobenthic fauna in an Amazon macrotidal dissipative sandy beach
Figure 1. Map of Algodoal-Maiandeua island showing the study beach (Fortalezinha) and the sample areas (Area 1: muddy sediment – next to a tidal channel, Area 2: sandy sediment).
Figure 3 in Small-scale spatial distribution of ghost shrimp and macrobenthic fauna in an Amazon macrotidal dissipative sandy beach
Figure 3. Mean density (± standard error) of the Lepidophtalmus siriboia burrows in the intertidal zones (HT: high intertidal; MT: mid intertidal; LT: low intertidal) of the study areas (Area 1: muddy sediment – next to a tidal channel, Area 2: sandy sediment). Different letters indicate significant differences (p <0.05); letters indicate the results of Tukey tests for the comparisons between areas (uppercase letters) and among intertidal zones (lowercase letters).
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
<p>Hydrological data measured in Lompolonjängänoja (LJO) catchment and used in Nousu et al.</p> <p> </p> <p>ET_fluxes.csv<br>- Eddy-covariance based, daily evapotranspiration (ET) fluxes [mm/d] at Kenttärova (NFOR) and Lompolojänkkä (NWET) stations</p> <p>GW_levels.csv<br>- Observed groundwater levels [m] relative to the ground surface measured around the LJO catchment</p> <p>Q_runoff.csv<br>- Observed specific discharge [mm/d] at the LJO catchment outlet</p> <p>THETA_kenttarova.csv<br>- Automatically measured soil moisture (i.e. volumetric water content [m3/m3]) around Kenttärova stations</p> <p>THETA_spatial.csv<br>- Manually measured soil moisture (i.e. volumetric water content [m3/m3]) around the LJO catchment</p>
Animation, MESA inlists, and post-processing package for "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants"
<p>Videos, post-processing package for mock ALMA observations, MESA (r23.05.1 with mesasdk-x86_64-linux-23.7.3) inlists and history files, Jupyter notebook and data to reproduce figures of the paper "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants".</p>
Immune Gene Repertoire of Soft Scale Insects (Hemiptera: Coccidae)
<p>Dataset (fasta sequences and alignments) referred to the manuscript "<span>Immune Gene Repertoire of Soft Scale Insects (Hemiptera: Coccidae)"</span></p> <p><strong><span>Abstract: </span></strong><span>Insects possess an effective immune system which has been extensively characterized in several model species, revealing a plethora of conserved genes involved in recognition, signaling and response to pathogens and parasites. However, some taxonomic groups, characterized by peculiar trophic niches, such as plant-sap feeders, which are often important pests of crops and forestry ecosystems, have been largely overlooked regarding their immune gene repertoire. Here we annotated the immune genes of soft scale insects (Hemiptera: Coccidae), for which omics data are publicly available. By using immune genes of aphids and <em>Drosophila</em> to query the genome of <em>Ericerus pela</em>, as well as the transcriptomes of <em>Ceroplastes cirripediformis</em> and <em>Coccus</em> sp., we pointed out the lack of peptidoglycan recognition proteins, galectins, thaumatins and antimicrobial peptides in Coccidae</span><span>. This work contributes to expand our knowledge about the evolutionary trajectories of immune genes and offers a list of promising candidates for developing new control strategies based on the suppression of pests’ immunity through RNAi technologies.</span></p>
A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration
Open the record for dataset details and reuse information.
Global-scale greenhouse cultivation areas (Version 1)
<p>This data set provides spatially explicit estimates of the area at 3 meters resolution directly used for greenhouse cultivation on a global scale. It was derived using PlanetScope data and Sentinel-2 data for the year 2019. This data set does not cover all existing greenhouses across the globe. The data can be viewed here: <a title="Follow link" href="https://rs-cph.projects.earthengine.app/view/greenhouse" target="_blank" rel="nofollow noopener">https://rs-cph.projects.earthengine.app/view/greenhouse. </a><a target="_blank">Please contact xito@ign.ku.dk for data and inform us if you identify areas that require an update.</a></p> <p>Any usage must be solely for Noncommercial education or scientific research purposes, and publication in academic or scientific research journals. Licensee agrees that all such publications must include an attribution that clearly and conspicuously identifies Planet Labs PBC.</p>
Large-scale simulation of thermal conductivity in CaSiO3 perovskite with neuroevolution potential
<p>The dataset contains the thermal conductivity of CaSiO3 perovskit, MgSiO3 perovskite, periclase, as well as the heat flux across the core-mantle boundary.</p>
Data and Software for Developing a General Comprehensive Evaluation Method for Cross-Scale Precipitation Forecasts
<div> </div> <div>Readme for GCEM Data and Code</div> <div> </div> <div>1 The data and code of two typical cases in Section 4.1</div> <div>1.1 Data</div> <div>1.1.1 Observed Precipitation Data</div> <div>1.1.1.1 /1_Two_Typical_Processes_data/1_1Observed_precipitation_data/2019071612</div> <div>(in the 1_Two_Typical_Processes_data.rar file)</div> <div>Hourly precipitation from 00:00 to 12:00 UTC on July 16, 2019 for Case 1</div> <div>surfr01h.nc</div> <div> surfr02h.nc</div> <div> surfr03h.nc</div> <div> surfr04h.nc</div> <div> surfr05h.nc</div> <div> surfr06h.nc</div> <div> surfr07h.nc</div> <div> surfr08h.nc</div> <div> surfr09h.nc</div> <div> surfr10h.nc</div> <div> surfr11h.nc</div> <div>surfr12h.nc</div> <div>1.1.1.2 /1_Two_Typical_Processes_data/1_1Observed_precipitation_data/2020061312</div> <div>(in the 1_Two_Typical_Processes_data.rar file)</div> <div>Hourly precipitation from 00:00 to 12:00 UTC on June 13,2020 for Case 2</div> <div>surfr01h.nc</div> <div> surfr02h.nc</div> <div> surfr03h.nc</div> <div> surfr04h.nc</div> <div> surfr05h.nc</div> <div> surfr06h.nc</div> <div> surfr07h.nc</div> <div> surfr08h.nc</div> <div> surfr09h.nc</div> <div> surfr10h.nc</div> <div> surfr11h.nc</div> <div>surfr12h.nc</div> <div>1.1.2 Forecasted Precipitation Data</div> <div>1.1.2.1 /1_Two_Typical_Processes_data/1_2Forecasted_precipitation_data/2019071612</div> <div>(in the 1_Two_Typical_Processes_data.rar file)</div> <div> Data for Case 1 during 00:00-12:00 UTC on July 16, 2019</div> <div>WRF3.2019071600000.nc (initial field at 12:00 UTC on July 16, 2019)</div> <div>WRF3.2019071600012.nc (12-hour accumulated precipitation during 00:00–12:00 UTC on July 16, 2019)</div> <div>1.1.2.2 /1_Two_Typical_Processes_data/1_2Forecasted_precipitation_data/2020061312</div> <div>(in the 1_Two_Typical_Processes_data.rar file)</div> <div> Data for Case 2 during 00:00-12:00 UTC on June 13,2020</div> <div>WRF3.2020061300000.nc (initial field at 12:00 UTC on June 13,2020)</div> <div>WRF3.2020061300012.nc (12-hour accumulated precipitation during 00:00–12:00 UTC on June 13,2020)</div> <div> </div> <div>1.2 Code and Configuration Files</div> <div>1.2.1 GCEM of Software and Configuration</div> <div> (/Code/1_Two_Typical_Processes/1Software_Configuration_of_GCEM in the Code.rar file)</div> <div>pastonc6hd2.f90 Main program, reads observed and forecasted precipitation data, performs GCEM verification, and outputs result files.</div> <div> module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file</div> <div> module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div> mod_uxpasid2.f90 Subprogram, module for used to perform GCEM verification on forecasted data</div> <div> module_outnc.f90 Subprogram, module for outputting the verification results in netCDF file format</div> <div> compilePAS10mmd2.sh Used to compile source files to generate executable file under Linux</div> <div> r12hfile.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div> pastonc6hd2.exe Executable file</div> <div>1.2.2 TS of Software and Configuration</div> <div> (/Code/1_Two_Typical_Processes/2Software_Configuration_of_TS-Score in the Code.rar file)</div> <div>tsmain01.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div> module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div> module_uxtsiTure.f90 Subprogram, module for used to perform TS verification on forecasted data</div> <div> compileTS.sh Used to compile source files to generate executable file under Linux</div> <div> r12hfile.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div> tsmain01.exe Executable file</div> <div> </div> <div>1.3 Output files</div> <div>1.3.1 GCEM verification results</div> <div> (/1_Two_Typical_Processes_data/1_3Results/1_3_1Results_GCEM in the 1_Two_Typical_Processes_data.rar file)</div> <div> rainverd2019071612012.nc Result file in netCDF format</div> <div> rainverd2019071612012.nc.txt Result explanation file in netCDF format</div> <div> outnc12hd22019071612.txt GCEM result file in text format</div> <div> rainverd2020061312012.nc Result file in netCDF format </div> <div> rainverd2020061312012.nc.txt Result explanation file in netCDF format</div> <div> outnc12hd22020061312.txt GCEM result file in text format</div> <div> </div> <div>1.3.2 TS verification results</div> <div> (/1_Two_Typical_Processes_data/1_3Results/1_3_2Results_TS in the 1_Two_Typical_Processes_data.rar file)</div> <div> ts12h2019071612.txt TS result file in text format</div> <div> ts12h2020061312.txt TS result file in text format</div> <div> </div> <div>1.4 Compiling Environment</div> <div> The verification program runs in a UNIX environment and requires the intel compiler (v2017) and the netCDF (v4.6.1) support library</div> <div> UNIX Environment Settings</div> <div># .bashrc</div> <div>module load intel/intel-compiler-2017.5.239</div> <div>module load intelmpi/2019.6.154</div> <div>export F90=ifort</div> <div>export NETCDF=/public/software/mathlib/netcdf/4.6.1_intel-2017_mpi-2017_hdf5-1.8.20-intel2017</div> <div>export NETCDF_LIB=$NETCDF/lib</div> <div>export NETCDF_INC=$NETCDF/include</div> <div>export PATH=$NETCDF/bin:$PATH</div> <div>export LD_LIBRARY_PATH=$NETCDF/lib:$LD_LIBRARY_PATH</div> <div> </div> <div>1.5 Compiling and Running Steps</div> <div>1.5.1 The steps for case 1 during 00:00–12:00 UTC on July 16, 2019</div> <div> 1. Creating an installation and running sub-directory</div> <div> mkdir p2019</div> <div> 2. Copying data sources, code files and configuration files to this directory</div> <div> 3. Running in this directory</div> <div> ./compilePAS10mmd2.sh Compile to generate executable file (pastonc6hd2.exe)</div> <div>./compileTS.sh Compile to generate executable file (tsmain01.exe)</div> <div> 4. Modifying the configuration file (r12hfile.txt)</div> <div> 5. Run the executable files </div> <div> ./pastonc6hd2.exe > outnc12hd22019071612.txt</div> <div>Creating the GCEM result file (rainverd2019071612012.nc), procedure file (outnc12hd22019071612.txt)</div> <div>./tsmain01.exe >ts12h2019071612.txt</div> <div>Creating the TS result and procedure file (ts12h2019071612.txt)</div> <div> </div> <div>1.5.2 The steps for case 2 during 00:00–12:00 UTC on June 13,2020</div> <div> 1. Creating an installation and running sub-directory</div> <div> mkdir p2020</div> <div> 2. Copying data sources, code files and configuration files to this directory</div> <div> 3. Running in this directory</div> <div> ./compilePAS10mmd2.sh Compile to generate executable file (pastonc6hd2.exe)</div> <div>./compileTS.sh Compile to generate executable file (tsmain01.exe)</div> <div> 4. Modifying the configuration file (r12hfile.txt)</div> <div> 5. Run the executable files</div> <div> ./pastonc6hd2.exe > outnc12hd22020061312.txt</div> <div>Creating the GCEM result file (rainverd2020061312012.nc), procedure file (outnc12hd22020061312.txt)</div> <div>./tsmain01.exe >ts12h2020061312.txt</div> <div>Creating the TS result and procedure file (ts12h2019071612.txt)</div> <div> </div> <div>1.6 Module code main interface description</div> <div>1.6.1 skinput()</div> <div>subroutine skinput(skfile,skfilenum,rain,gridskx,gridsky,longitude,latitude)</div> <div> integer,intent(in) :: skfilenum</div> <div> character(len=200),dimension(skfilenum),intent(in) :: skfile</div> <div> real,dimension(:,:),allocatable,intent(out) :: rain</div> <div> integer,intent(out) :: gridskx,gridsky</div> <div> </div> <div>usage: Read a set of observed precipitation data files and output grid accumulated precipitation</div> <div>skfile, A set of filenames that are arrays of strings (input)</div> <div>skfilenum, Number of files (input)</div> <div>rain, Accumulated precipitation, rain(nx,ny) (output)</div> <div>gridskx, grid points, nx (output)</div> <div>gridsky, grid points, ny (output)</div> <div>gridlon, Longitude array, gridlon(nx) (output)</div> <div>gridlat, Latitude array, gridlat(ny) (output)</div> <div> </div> <div>1.6.2 ybinput()</div> <div>subroutine ybinput(ybfile,apcp,gridybx,gridyby,gridyblon,gridyblat)</div> <div> character(len=200),intent(in) :: ybfile</div> <div> real,dimension(:,:),allocatable,intent(out) :: apcp,gridyblat,gridyblon</div> <div> integer,intent(out) :: gridybx,gridyby</div> <div> </div> <div>usage: Read a set of forecasted precipitation data files and output forecast grid precipitation</div> <div>ybfile, Forecast file (input)</div> <div>apcp, forecasted precipitation array, apcp(nx, ny) (output)</div> <div>gridybx, Number of grid points for forecast data, nx (output)</div> <div>gridyby, Number of grid points for forecast data, ny (output)</div> <div>gridyblon, Longitude of forecast data, gridyblon(nx, ny) (output)</div> <div>gridyblat, Latitude of forecast data, gridyblat(nx, ny) (output)</div> <div> </div> <div>1.6.3 uxpasid2()</div> <div>subroutine uxpasid2(ui,xi,level,pas,iTure,iclass,ieps)</div> <div> real,intent(in) :: ui,xi,level</div> <div> real,intent(out) :: pas,ieps</div> <div> integer,intent(out) :: iTure,iclass</div> <div> </div> <div>usage: Read in the observed and forecasted precipitation, and output the PAS score result</div> <div>rainsk, Observed precipitation (input)</div> <div>rainyb, Forecasted precipitation (input)</div> <div>level, Specifing magnitude (input)</div> <div>ipas, Pas score value (0-1) or correct value of no precipitation forecast (1)</div> <div>iTure, 0 indicates that the rating is correct for a no precipitation forecast;</div> <div>1 indicates a PAS score of ≥ the specified magnitude;</div> <div>9 indicates that it is not in the no precipitation test, nor is it the verification the specified magnitude;</div> <div>-999 indicates default.</div> <div>iclass, 0 indicates the category (no precipitation forecast is correct)</div> <div>1 indicates the category of insufficient precipitation forecast (observation u<10mm)</div> <div>2 indicates the category of excessive(or equal) precipitation forecast(observation u<10mm)</div> <div>3 indicates the category of insufficient precipitation forecast (observation u≥10mm)</div> <div>4 indicates the category of excessive(or equal) precipitation forecast(observation u≥10mm)</div> <div>-999 indicates default.</div> <div>ieps, 0 indicates the forecasted and observed precipitation are equal</div> <div><0 indicates insufficient precipitation forecast</div> <div>>0 indicates excessive precipitation forecast</div> <div>-999 indicates default.</div> <div> </div> <div>1.6.4 outpasnc()</div> <div>subroutine outpasnc(title,vtime,vhour,gridncx,gridncy,gridnclon,gridnclat,rainncsk,rainncyb,&</div> <div> pasc,pas01,pas10,pas25,pas50, pas2p5,pas5,pas15, pascnc,pasnc01,pasnc10,&</div> <div> pasnc25,pasnc50,pasnc2p5,pasnc5,pasnc15,ipsnc,epsnc,iepsnc,ips,eps,ieps)</div> <div> character(len=10),intent(in) :: vtime,title</div> <div> integer,intent(in) :: vhour,gridncx,gridncy</div> <div> character(len=10) :: chour</div> <div> real,dimension(gridncx),intent(in) :: gridnclon</div> <div> real,dimension(gridncy),intent(in) :: gridnclat</div> <div> real,dimension(gridncx,gridncy),intent(in) :: rainncsk,rainncyb,pascnc,pasnc01</div> <div> real,dimension(gridncx,gridncy),intent(in) :: pasnc10,pasnc25,pasnc50,ipsnc,epsnc,iepsnc</div> <div> real,dimension(gridncx,gridncy),intent(in) ::pasnc2p5,pasnc5,pasnc15</div> <div> real,intent(in) :: pasc,pas01,pas10,pas25,pas50,ips,eps,ieps,pas2p5,pas5,pas15</div> <div> </div> <div>usage: Output data to a netCDF format file</div> <div>title, File name tag (d)</div> <div>vtime, time string (yyyymmdddhh, eg. 2019071612)</div> <div>vhour, Accumulated precipitation duration (12)</div> <div>gridncx, x grid points (240)</div> <div>gridncy, y grid points (200)</div> <div>gridnclon, x grid points longitude array </div> <div>gridnclat, y grid points latitude array</div> <div>rainncsk, Observed precipitation</div> <div>rainncyb, Forecasted precipitation</div> <div>pasc, PASC</div> <div>pas01, ≥0.1mm PAS</div> <div>pas10, ≥10mm PAS</div> <div>pas25, ≥25mm PAS</div> <div>pas50, ≥50mm PAS</div> <div>pas2p5, ≥2.5mm PAS</div> <div>pas5, ≥5mm PAS</div> <div>pas15, ≥15mm PAS</div> <div>pascnc, PASC array</div> <div>pasnc01, ≥0.1mm PAS array</div> <div>pasnc10, ≥10mm PAS array</div> <div>pasnc25, ≥25mm PAS array</div> <div>pasnc50, ≥50mm PAS array</div> <div>pasnc2p5, ≥2.5mm PAS array</div> <div>pasnc5, ≥5mm PAS array</div> <div>pasnc15, ≥15mm PAS array</div> <div>ipsnc, IPS array</div> <div>epsnc, EPS array</div> <div>iepsnc, IEPS array</div> <div>ips, IPS</div> <div>eps, EPS</div> <div>ieps IEPS</div> <div> </div> <div>2 The data and code of extreme rainfall event in Section 4.2</div> <div>2.1 Data</div> <div>2.1.1 Observed Precipitation Data</div> <div> (/2_1Observed_precipitation_data in the 2_1Observed_precipitation_data.rar file)</div> <div>CMPAday-2021072100UTC.nc</div> <div>CMPAday-2021072200UTC.nc</div> <div>2.1.2 Forecasted Precipitation Data</div> <div>2.1.2.1 PWAFS data</div> <div> (in the wrfout_2021071812012.rar, wrfout_2021071812036.rar, wrfout_2021071912012.rar, wrfout_2021071912036.rar, wrfout_2021072012012.rar and wrfout_2021072012036.rar files)</div> <div>wrfout_2021071812012.nc (initial field at 12:00 UTC on 18 July 2021, with forecast time of 12 hours)</div> <div>wrfout_2021071812036.nc (initial field at 12:00 UTC on 18 July 2021, with forecast time of 36 hours)</div> <div>wrfout_2021071912012.nc (initial field at 12:00 UTC on 19 July 2021, with forecast time of 12 hours)</div> <div>wrfout_2021071912036.nc (initial field at 12:00 UTC on 19 July 2021, with forecast time of 36 hours)</div> <div>wrfout_2021072012012.nc (initial field at 12:00 UTC on 20 July 2021, with forecast time of 12 hours)</div> <div>wrfout_2021072012036.nc (initial field at 12:00 UTC on 20 July 2021, with forecast time of 36 hours)</div> <div>2.1.2.2 GRAPES data</div> <div> (/2_2_2Forecasted_02_data in the 2_2_2Forecasted_02_data.rar file)</div> <div>GRAPES2021071812012.nc (initial field at 12:00 UTC on 18 July 2021, with forecast time of 12 hours)</div> <div>GRAPES2021071812036.nc (initial field at 12:00 UTC on 18 July 2021, with forecast time of 36 hours)</div> <div>GRAPES2021071912012.nc (initial field at 12:00 UTC on 19 July 2021, with forecast time of 12 hours)</div> <div>GRAPES2021071912036.nc (initial field at 12:00 UTC on 19 July 2021, with forecast time of 36 hours)</div> <div>GRAPES2021072012012.nc (initial field at 12:00 UTC on 20 July 2021, with forecast time of 12 hours)</div> <div>GRAPES2021072012036.nc (initial field at 12:00 UTC on 20 July 2021, with forecast time of 36 hours)</div> <div> </div> <div>2.2 Code and Configuration Files</div> <div>2.2.1 PAS of Software and Configuration</div> <div> (/Code/2_extreme_rainfall_event/1Software_Configuration_of_PAS in the Code.rar file)</div> <div>pasmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs GCEM verification, and outputs result files.</div> <div>pasgmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs GCEM verification, and outputs result files.</div> <div> module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file</div> <div> module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div> module_ybinputGrapes.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div> mod_uxpasid2.f90 Subprogram, module for used to perform GCEM verification on forecasted data</div> <div> module_outnc.f90 Subprogram, module for outputting the verification results in netCDF file format</div> <div> compilePAS250mm.sh Used to compile source files to generate executable file under Linux</div> <div> compileGrapesPAS250mm.sh Used to compile source files to generate executable file under Linux</div> <div>r12hfilePWAFS2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>pasmain250mm.exe Executable file</div> <div>pasgmain250mm.exe Executable file</div> <div>2.2.2 TS of Software and Configuration</div> <div> (/Code/2_extreme_rainfall_event/2Software_Configuration_of_TS in the Code.rar file)</div> <div>tsmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div>tsgmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div>module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file</div> <div>module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div>module_ybinputGrapes.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div>module_uxtsiTure.f90 Subprogram, module for used to perform TS verification on forecasted data</div> <div>compileTS250mm.sh Used to compile source files to generate executable file under Linux</div> <div>compileGrapesTS250mm.sh Used to compile source files to generate executable file under Linux</div> <div>r12hfilePWAFS2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>tsmain250mm.exe Executable file</div> <div>tsgmain250mm.exe Executable file</div> <div>2.2.3 FSS of Software and Configuration</div> <div> (/Code/2_extreme_rainfall_event/3Software_Configuration_of_FSS in the Code.rar file)</div> <div>fssmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div>fssgmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div>module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file</div> <div>module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div>module_ybinputGrapes.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div>module_FSSom.f90 Subprogram, module for used to perform FSS verification on forecasted data</div> <div>compilefss250mm.sh Used to compile source files to generate executable file under Linux</div> <div>compileGrapesfss250mm.sh Used to compile source files to generate executable file under Linux</div> <div>r12hfilePWAFS2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>fssmain250mm.exe Executable file</div> <div>fssgmain250mm.exe Executable file</div> <div> </div> <div>2.3 Output files</div> <div>2.3.1 GCEM verification results</div> <div> (/2_3Results/2_3_1Results_PAS in the 2_3Results.rar file)</div> <div> rainverd2021072000001.nc Result file in netCDF format</div> <div> rainverd2021072000001.nc.txt Result explanation file in netCDF format</div> <div> rainverd2021072100001.nc Result file in netCDF format</div> <div> rainverd2021072100001.nc.txt Result explanation file in netCDF format</div> <div> rainverd2021072200001.nc Result file in netCDF format</div> <div> rainverd2021072200001.nc.txt Result explanation file in netCDF format</div> <div> outnc22021072000_250mm.txt GCEM result file in text format</div> <div> outnc22021072100_250mm.txt GCEM result file in text format</div> <div> outnc22021072200_250mm.txt GCEM result file in text format</div> <div> rainverg2021072000001.nc Result file in netCDF format</div> <div> rainverg2021072000001.nc.txt Result explanation file in netCDF format</div> <div> rainverg2021072100001.nc Result file in netCDF format</div> <div> rainverg2021072100001.nc.txt Result explanation file in netCDF format</div> <div> rainverg2021072200001.nc Result file in netCDF format</div> <div> rainverg2021072200001.nc.txt Result explanation file in netCDF format</div> <div> outgnc22021072000_250mm.txt GCEM result file in text format</div> <div> outgnc22021072100_250mm.txt GCEM result file in text format</div> <div> outgnc22021072200_250mm.txt GCEM result file in text format</div> <div>2.3.2 TS verification results</div> <div> (/2_3Results/2_3_2Results_TS in the 2_3Results.rar file)</div> <div> ts2021072000_24h250mm.txt TS result file in text format</div> <div> ts2021072100_24h250mm.txt TS result file in text format</div> <div> ts2021072200_24h250mm.txt TS result file in text format</div> <div> tsg2021072000_24h250mm.txt TS result file in text format</div> <div> tsg2021072100_24h250mm.txt TS result file in text format</div> <div> tsg2021072200_24h250mm.txt TS result file in text format</div> <div>2.3.3 FSS verification results</div> <div> (/2_3Results/2_3_3Results_FSS in the 2_3Results.rar file)</div> <div> fss2021072000_24h250mm.txt TS result file in text format</div> <div> fss2021072100_24h250mm.txt TS result file in text format</div> <div> fss2021072200_24h250mm.txt TS result file in text format</div> <div> fssg2021072000_24h250mm.txt TS result file in text format</div> <div> fssg2021072100_24h250mm.txt TS result file in text format</div> <div> fssg2021072200_24h250mm.txt TS result file in text format</div> <div> </div> <div>2.4 Compiling Environment</div> <div> The verification program runs in a UNIX environment and requires the intel compiler (v2017) and the netCDF (v4.6.1) support library</div> <div> UNIX Environment Settings</div> <div># .bashrc</div> <div>module load intel/intel-compiler-2017.5.239</div> <div>module load intelmpi/2019.6.154</div> <div>export F90=ifort</div> <div>export NETCDF=/public/software/mathlib/netcdf/4.6.1_intel-2017_mpi-2017_hdf5-1.8.20-intel2017</div> <div>export NETCDF_LIB=$NETCDF/lib</div> <div>export NETCDF_INC=$NETCDF/include</div> <div>export PATH=$NETCDF/bin:$PATH</div> <div>export LD_LIBRARY_PATH=$NETCDF/lib:$LD_LIBRARY_PATH</div> <div> </div> <div>2.5 Compiling and Running Steps</div> <div>2.5.1 Creating an installation and running sub-directory</div> <div> mkdir zzby</div> <div>2.5.2 Copying data sources, code files and configuration files to this directory</div> <div>2.5.3 Running in this directory</div> <div>./compilePAS250mm.sh Compile to generate executable file (pasmain250mm.exe)</div> <div>./compileGrapesPAS250mm.sh Compile to generate executable file (pasgmain250mm.exe)</div> <div>./compileTS250mm.sh Compile to generate executable file (tsmain250mm.exe)</div> <div>./compileGrapesTS250mm.sh Compile to generate executable file (tsgmain250mm.exe)</div> <div>./compilefss250mm.sh Compile to generate executable file (fssmain250mm.exe)</div> <div>./compileGrapesfss250mm.sh Compile to generate executable file (fssgmain250mm.exe)</div> <div>2.5.4 Modifying the configuration file (r12hfile.txt)</div> <div>Link Configuration file to r12hfile.txt</div> <div>Mainly modifying the data sources</div> <div>2.5.5 Run the executable files</div> <div>Link different configuration files, and run executable files, then output different results.</div> <div>Creating the GCEM result and procedure files</div> <div>./pasmain250mm.exe > outnc22021072000_250mm.txt</div> <div>./pasmain250mm.exe > outnc22021072100_250mm.txt</div> <div>./pasmain250mm.exe > outnc22021072200_250mm.txt</div> <div>./pasgmain250mm.exe > outgnc22021072000_250mm.txt</div> <div>./pasgmain250mm.exe > outgnc22021072100_250mm.txt</div> <div>./pasgmain250mm.exe > outgnc22021072200_250mm.txt</div> <div>Creating the TS result and procedure files</div> <div>./tsmain250mm.exe > ts2021072000_24h250mm.txt</div> <div>./tsmain250mm.exe > ts2021072100_24h250mm.txt</div> <div>./tsmain250mm.exe > ts2021072200_24h250mm.txt</div> <div>./tsgmain250mm.exe > tsg2021072000_24h250mm.txt</div> <div>./tsgmain250mm.exe > tsg2021072100_24h250mm.txt</div> <div>./tsgmain250mm.exe > tsg2021072200_24h250mm.txt</div> <div>Creating the FSS result and procedure files</div> <div>./fssmain250mm.exe >fss2021072000_24h250mm.txt</div> <div>./fssmain250mm.exe >fss2021072100_24h250mm.txt</div> <div>./fssmain250mm.exe >fss2021072200_24h250mm.txt</div> <div>./fssgmain250mm.exe >fssg2021072000_24h250mm.txt</div> <div>./fssgmain250mm.exe >fssg2021072100_24h250mm.txt</div> <div>./fssgmain250mm.exe >fssg2021072200_24h250mm.txt</div> <div> </div> <div>3 PAS mini-program</div> <div>3.1 Code for PAS mini-program</div> <div>(/Code/3_Software_of_PAS in the Code.rar file)</div> <div> pas10ux.f90 Main program, used to perform PAS verification on single point precipitation forecast</div> <div> mod_uxpasid2.f90 Subprogram, Module for PAS of single point forecast</div> <div> compilePAS10ux.sh Used to compile source files to generate executable file under Linux</div> <div> pas10ux.exe Executable file</div> <div> </div> <div>3.2 The steps for PAS mini-program</div> <div> 1. Creating an installation and running sub-directory</div> <div> mkdir pas</div> <div> 2. Copying code files and configuration files to this directory</div> <div> 3. Running in this directory</div> <div> ./compilePAS10ux.sh Compile to generate executable file (pastonc6hd2.exe)</div> <div> 4. linking the executable file as pas</div> <div> ln -sf pas10ux.exe pas</div> <div> 5. Running the PAS mini-program</div> <div> </div> <div>for example: ./pas 15 20</div> <div>Parameter 1: 15 represents observed precipitation</div> <div>Parameter 2: 20 represents forecasted precipitation</div> <div>Output: 0.895 1</div> <div> </div> <div> The following instructions for specific usage:</div> <div>pas rainsk rainyb [level]</div> <div> Input parameters</div> <div> Parameter 1 (rainsk): observed precipitation (mm)</div> <div> Parameter 2 (rainyb): forecasted precipitation (mm)</div> <div> Parameter 3 (level): Specifing magnitude (Optional, default to ≥0.1 mm)</div> <div> Onput parameters</div> <div>Parameter 1 (ipas): Pas score value (0-1) or correct value of no precipitation forecast (1);</div> <div>-999.000 represents default.</div> <div>Parameter 2 (iTure):</div> <div>0 indicates that the rating is correct for a no precipitation forecast;</div> <div>1 indicates a PAS score of ≥ the specified magnitude;</div> <div>9 indicates that it is not in the no precipitation test, nor is it the verification the specified magnitude;</div> <div>-999 indicates default.</div> <div> </div> <div> </div>
Processed KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
<p>The original public dataset is published in https://zenodo.org/records/10439422, we processed the KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework.</p>
Quality assessment of the included papers to assess the pooled prevalence of iodine deficiency among school-age children in Ethiopia, 2023 by using Newcastle-Ottawa Scale adapted for cross-sectional studies.
<p>This is a quality assessment data of the studies included to assess the pooled prevalence of iodine deficiency and associated factors among school-age children in Ethiopia</p>
raw data - Association between Bristol Stool Form Scale and the risk of colorectal polyp recurrence: A retrospective cohort study
Open the record for dataset details and reuse information.
Source data of the MultiSTAAR manuscript "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies".
<p>This dataset serves as the source data for Figures 2-3 and Extended Data Figures 1-2 of the MultiSTAAR manuscript titled "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies". MultiSTAAR is a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies.<br><br><strong>Figure 2.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and ncRNA multi-trait analysis of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerids (TG) using TOPMed data (<em>n</em> = 61,838).<br><br><strong>Figure 3.</strong> TOPMed genetic region (2-kb sliding window) unconditional multi-trait analysis results of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) using TOPMed data (<em>n</em> = 61,838).<br><br><strong>Extended Data Figure 1.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of fasting glucose (FG) and fasting insulin (FI) using TOPMed data (<em>n</em> = 21,731).<br><br><strong>Extended Data Figure 2.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of C-reactive protein (CRP), interleukin 6 (IL-6), lipoprotein-associated phospholipase A2 (Lp-PLA2) activity, and lipoprotein-associated phospholipase A2 (Lp-PLA2) mass using TOPMed data (<em>n</em> = 9,380).</p>
Super sample covariance and the volume scaling of galaxy survey covariance matrices
<p>Simulation data used in Super sample covariance and the volume scaling of galaxy survey covariance matrices.</p>
Generation Of Quasi-periodic Dayside Medium Scale Traveling Ionospheric Disturbances (MSTIDs) By Intermittent Lobe Reconnection
<p>We acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China,France, Italy, Japan, Norway, South Africa, United Kingdom, and the United States of America. We acknowledge the use of DMSP/SSUSI data provided by the Johns Hopkins University Applied Physics Laboratory (https://cdaweb.gsfc.nasa.gov). Additionally, the OMNI data set is available from the OMNIWeb service online of NASA/GSFC's Space Physics Data Facility' s(https://spdf.gsfc.nasa.gov/pub/data/omni/).The ground-based magnetic field measurements are accessible via the IMAGE Magnetometer network (https://space.fmi.fi/image/).We would like to thank N.A. Frissell for providing the complete code for the MUSIC (MUltiple SIgnal Classification) algorithm (https://github.com/HamSCI/pyDARNmusic).</p>
Technological frames scales used in SEM analysis on AI adoption
<h3><span>Paper title this data set accompanies (submitted for peer review) </span></h3> <p><strong><span>Cognitive frames that drive AI adoption</span></strong></p> <h3><span>Paper Abstract</span></h3> <p><span>We investigate the role of cognitive frames in the adoption of artificial intelligence in the workplace. We propose a theoretical model of a three-level hierarchy of AI adoption that distinguishes between acceptance, collaboration, and co-creation. Each level represents higher levels of sophistication in human involvement in working with AI, ranging from passive utilitarian acceptance to active collaboration and co-creation. Using reasoning from technological frames of reference theory, we tested a structural model connecting individual cognitive frames to AI adoption. We developed a structural equation model that analyzed data collected from a sample of 305 professionals in Europe who have a high usage of technology in the workplace. Our results show the model exhibits high goodness of fit and predictive relevance. The work contributes to a greater understanding of the role of cognitive frames in driving the behavioural intention of adopting increasingly sophisticated AI technologies.</span></p> <h3><span>Grant Information</span></h3> <p><span>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101023024 for <em>Augmented-Humans</em>. </span></p>
The metre-scale roughness of asteroid (101955) Bennu from the OSIRIS-REx Laser Altimeter: Processed OLA v21 pointclouds for RMS deviation (L = 0.2 m to L = 20.0 m) [Dataset]
<p>Pointclouds:</p> <ul> <li>Pointcloud (.pcd) files for OLA RMS deviation calculation with fields: <br> <ul> <li>'Location': node points (XYZ points on Bennu's surface)</li> <li>'Intensity': height delta between the node and a neighbouring point at distance L from the node. 'Height' is defined as height above an orthogonally regressed plane fit to points within 2L of the node. </li> </ul> </li> </ul> <p> Tables:</p> <ul> <li>2020_06_bt_final_ma.xlsx: <ul> <li>Crater catalog from Bierhaus et al (2022)</li> </ul> </li> <li>bierhaus2023_craters_gt5m_lt80m_olav21_roi_flags.xlsx: <ul> <li>Subset of catalog from Bierhaus et al. (2022) with craters between 5 m - 80 m diameter used for interior-exterior roughness ratio in Bierhaus et al. (2023) and Rossmann et al. (2024).</li> </ul> </li> </ul> <p> </p>
A cavity-microscope for micrometer-scale control of atom-photon interactions
<p>Data corresponding to the article "A cavity-microscope for micrometer-scale control of atom-photon interactions".</p>
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)
<p><strong>This page only provides the </strong><strong>drone-view image</strong><strong> dataset. </strong></p> <ul> <li><strong>For the ground-level image dataset, please visit <a href="https://zenodo.org/records/13570934" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)</em></a>.</strong></li> <li><strong>For the point clouds, please visit <em><a href="https://zenodo.org/records/14228467" target="_blank" rel="noopener">SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)</a>.</em></strong></li> </ul> <p>The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a <code>color_palette.xlsx</code> file, which contains the RGB_id and corresponding RGB values.</p> <p>All files follow the naming convention: <code>{central_tree_id}_{timestamp}</code>, where <code>{central_tree_id}</code> represents the ID of the tree centered in the image, which is typically in a prominent position, and <code>timestamp</code> indicates the time when the data was collected.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>rgb</strong>: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: <code>Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36</code>.</p> </li> <li> <p><strong>depth_pfm</strong>: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.</p> </li> <li> <p><strong>instance_segmentation</strong>: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps <code>tree_id</code> to <code>RGB_id</code>. The <code>tree_id</code> can be used to look up detailed information about each tree in <code>obj_info_final.xlsx</code>, while the <code>RGB_id</code> can be matched to the corresponding RGB values in <code>color_palette.xlsx</code>. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.</p> </li> <li> <p><strong>obj_info_final.xlsx</strong>: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).</p> </li> <li> <p><strong>landscape_info.txt</strong>: This file contains the ground location information within the scene, sampled every 0.5 meters.</p> </li> </ul> <p>For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets:</p> <ul> <li><strong>{name}_coco.json</strong>: This file contains the annotation of each tree in the scene.</li> <li><strong>{name}_filtered.json</strong>: This file is derived from the previous one, but filtering is applied to rule out overlapping instances.</li> </ul> <p>⚠️: 7z files that begin with "<strong>!</strong>" indicate that the RGB values in the images within the <code>instance_segmentation</code> folder cannot be found in <code>color_palette.xlsx</code>. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in <a href="https://github.com/microsoft/AirSim/issues/3423" target="_blank" rel="noopener">link1</a> and <a href="https://github.com/microsoft/AirSim/issues/1852" target="_blank" rel="noopener">link2</a>.</p>
Data for Numerical Simulation of Tornado-like Vortices Induced by Small-Scale Cyclostrophic Wind Perturbations
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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.