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189 results for “scale development;”
CodePori: Large Scale System for Autonomous Software Development by Using Multi-Agents
<p>This dataset accompanies the paper <strong>"CodePori: A Large-Scale System for Autonomous Software Development Using Multi-Agents."</strong> The dataset is recorded in an MS Excel file, which contains the following sheets, with a brief description of each provided below:</p> <ol> <li> <p><strong>Selected Projects:</strong> Contains the descriptions of the 20 selected projects along with the GitHub URL for each.</p> </li> <li> <p><strong>Modifications:</strong> Contains details of the modifications made to the projects to ensure successful execution.</p> </li> <li> <p><strong>Outputs:</strong> Contains the output for each project.</p> </li> <li> <p><strong>Failed Projects:</strong> Contains data on the projects that failed.</p> </li> </ol>
Developing National Identity Scale: As Indonesian Case
<p><strong><em>Background: </em></strong></p> <p><em>National identity is an important concept to be developed, so it is necessary to have a measurement instrument with evidence of validity and reliability. Thus, the accuracy of the information obtained through the measurement of this instrument will be guaranteed.</em></p> <p> </p> <p><strong><em>Objective:</em></strong></p> <p><em>This article aims to test the psychometric properties of the national identity scale to fulfil the standardization of good measurement.</em></p> <p> </p> <p><strong><em>Method:</em></strong></p> <p><em>In this study, three analytical approaches were used: content validity involving eight subject matter experts (SME), confirmatory factor analysis (CFA), and reliability analysis. For the reliability and CFA approach, 300 students were used as samples taken through the convenience sampling technique.</em></p> <p> </p> <p><strong><em>Result:</em></strong></p> <p><em>The analysis results show that out of the 20 items compiled in the content validity analysis, one item is inadequate because it has a below standard score on Aiken's coefficient of validity. This one item was revised again to be included in the tryout and data collection for the following analysis stage. The results of the CFA analysis show that the model and data are fit, and there are two inadequate items. At the same time, the contribution of the three factors on the national identity scale is significant. In the third analysis, it was found that adequate coefficient reliability affected the low standard error measurement (SEM).</em></p> <p> </p> <p><strong><em>Conclusion:</em></strong></p> <p><em>Based on the analysis results, it can be concluded that the Indonesian national identity scale has fulfilled good psychometric properties. It can be proven from evidence validity based on content, confirmatory factor analysis (CFA), and reliability analysis.</em></p>
Data set for the article 'Temporal scaling in C. elegans larval development'
<p>This directory contains all analyzed data and data analysis scripts to create all figures for the article<br> Filina et al., Temporal scaling in C. elegans larval development, PNAS 2022 119:e2123110119</p>
Dissociable Multi-scale Patterns of Development in Personalized Brain Networks
<p>The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results demonstrate that the development of functional brain networks align with and refine a hierarchy linked to cognition</p>
Datasets Energy Citizenship Scale Development and Validation (Task 2.4, D2.2, D2.3)
<p>The datasets are part of the Reports on the development (D2.2) and validation (D2.3) of the energy citizenship scale. They are also part of the publication "Energy citizenship as people's perceived (collective) rights and responsibilities in a just and sustainable energy transition - scale development and validation" (https://doi.org/10.1016/j.jenvp.2024.102310). </p>
Testing Means to Scale Early Childhood Development Interventions in Rural Kenya
ClinicalTrials.gov study NCT03548558. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Developing hierarchical density-structured models to study the national-scale dynamics of an arable weed
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Comprehensive analysis of mRNA 3′ ends during early zebrafish development reveals dynamics of 3′UTR changes on a genome-wide scale
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Scaling and development of elastic mechanisms: the tiny strikes of larval mantis shrimp
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MERFISH+, a large-scale, multi-omics spatial technology resolves the transcriptomic holograms of the 3D human developing heart
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Data from: Phenotypic plasticity drives the development of laterality in the scale-eating cichlid fish <em>Perissodus microlepis</em>
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Development of a panel of SNP loci in the emblematic southern damselfly (Coenagrion mercuriale) using a hybrid method: Pitfalls and recommendations for large-scale SNP genotyping in a non-model endangered species
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Data S1 from "Global scale drivers of crop visitor diversity and the historical development of agriculture."
<p>Understanding diversity in flower visitor assemblages helps us improve pollination of crops and support better biodiversity conservation outcomes. Much recent research has focused on drivers of crop-visitor diversity operating over spatial scales from fields to landscapes, such as pesticide and habitat management, while drivers operating over larger scales of continents and biogeographic realms are virtually unknown. Flower and visitor traits influence attraction of pollinators to flowers, and evolve in the context of associations that can be ancient or recent. Plants that have been adopted into agriculture have been moved widely around the world and thereby exposed to new flower visitors. Remarkably little is known of the consequence of these historical patterns for present day crop-visiting bee diversity. We analyze data from 317 studies of 27 crops worldwide and find that crops are visited by fewer bee genera outside their region of origin and outside their family's region of origin. Thus, recent human history and the deeper evolutionary history of crops and bees appear to be important determinants of flower-visitor diversity at large scales that constrain the levels of visitor diversity that can be influenced by field- and landscape-scale interventions.</p>
Large-scale green grabbing for wind and solar PV development in Brazil
<h2>Large-scale green grabbing for wind and solar PV development in Brazil</h2><p>This repository contains the R code and parts of the data used for the analysis in the paper "Large-scale green grabbing for wind and solar PV development in Brazil" by Michael Klingler, Nadia Amelie, Jamie Rickman, and Johannes Schmidt, available as <a href="https://eartharxiv.org/repository/view/5824/">pre-print</a>.</p><p>Due to data sharing limitations, we cannot provide all data in the repository. Partly this data is not available publically at all (i.e. Bloomberg data, data by the instituto socio ambiental), partly the data has to be downloaded manually (CAR).</p><p>We still provide a repository which at least allows to understand the procedures we used during the analysis.</p><h3>Land tenure data set</h3><p>The procedures used to form our final land tenure data set can be found in land-tenure-data/processing.txt It is a mix of analyses in Python and in QGis.</p><h3>Analysis of land tenure data and park ownership/investment information</h3><p>The R-code to analyze the owernship relationships between windpark areas and investors/owners can be found in src/. All required libraries will install automatically.</p><p>The first two scripts cannot be executed due to data limitations. They create the sankey diagrams linking park areas to onwers and investors:</p><p>- 1.1-figures-results-1-wind.R</p><p>- 1.2-figures-results-1-solar.R</p><p>These three scripts are used to analyze the land tenure types prevailing on parks and comparing them to random areas. They should run with the provided data sets:</p><p>- 2-random-sampling-areas.R</p><p>- 3-intersection-parks-land-tenure.R</p><p>- 4-figures-land-tenure.R</p><p>This script validates our data against an independent data source. However, it cannot be run as it needs the proprietary Bloomberg database:</p><p>- 5-validation.R</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>
FIGURE 3. Begonia nigritarum. A. habit. B. inflorescence. C. staminate flower. D. pistillate flower. E. developing capsule. F. leaf, abaxial view. G. rhizome. Scale bars for A in Begonia ×dinglensis, a natural hybrid of Philippine Begonia section Baryandra, as evidenced by morphological, phylogenetic and cytological data
FIGURE 3. Begonia nigritarum. A. habit. B. inflorescence. C. staminate flower. D. pistillate flower. E. developing capsule. F. leaf, abaxial view. G. rhizome. Scale bars for A is 10 cm, B and G are 1 cm, C–E are 0.5 cm, F is 3 cm. [All photos from Ching-I Peng 23858 (HAST).]
FIGURE 2. Begonia camiguinensis. A. habit. B. inflorescence. C. staminate flower. D. pistillate flower. E. developing capsule. F. leaf, abaxial view. G. rhizome. Scale bars for A in Begonia ×dinglensis, a natural hybrid of Philippine Begonia section Baryandra, as evidenced by morphological, phylogenetic and cytological data
FIGURE 2. Begonia camiguinensis. A. habit. B. inflorescence. C. staminate flower. D. pistillate flower. E. developing capsule. F. leaf, abaxial view. G. rhizome. Scale bars for A is 10 cm, B and G are 1 cm, C–E are 0.5 cm, F is 3 cm. [All photos from Ching-I Peng 23853 (HAST).]
Data from: Genotyping-in-Thousands by sequencing panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales
<p>The ability to differentiate life history variants is vital for estimating fisheries management parameters, yet traditional survey methods can be inaccurate in mixed-stock fisheries. Such is the case for kokanee, the resident freshwater form of sockeye salmon (<i>Oncorhynchus nerka</i>), which exhibits various reproductive ecotypes (stream-, shore-, deep-spawning) that co-occur with each other and/or anadromous <i>O. nerka</i> in some systems across their pan-Pacific distribution. Here, we developed a multi-purpose Genotyping-in-Thousands by sequencing (GT-seq) panel of 288 targeted single nucleotide polymorphisms (SNPs) to enable accurate kokanee stock identification by geographic basin, migratory form, and reproductive ecotype across British Columbia, Canada. The GT-seq panel exhibited high self-assignment accuracy (93.3%) and perfect assignment of individuals not included in the baseline to their geographic basin, migratory form, and reproductive ecotype of origin. The GT-seq panel was subsequently applied to Wood Lake, a valuable mixed-stock fishery, revealing high concordance (>98%) with previous assignments to ecotype using microsatellites and TaqMan<span> </span>SNP genotyping assays, while improving resolution, extending a long-term time-series, and demonstrating the scalability of this approach for this system and others.</p>
Development and validation of a Community Resilience Scale for Youth (CRS-Y) Community Resilience Scale for Regional Youth
<p>The purpose of this article is to present the development and validation of a Community Resilience Scale for Youth (CRS-Y) among a Portuguese sample of nearly 4000 young people growing up in regions on the border with Spain. The scale was developed for young people to assess their perception of the resilience of regional communities in terms of positive development and purposeful experiences for young people. Resilient communities, under a social ecological approach, are those able to move forward on social change and transformation. This concept is especially remarkable in more challenging contexts such as border regions of mainland Portugal which are characterised by economic, social, educational, and cultural disadvantages while discovering possibilities of resilience through promising local dynamics.</p> <p>A multi-step approach was used to develop this scale of 12-item scale. Items were generated based on an in-depth literature review and research previously conducted with young people in these contexts. The overall sample was randomly divided into two subsamples of 1828 and 1735 young people each. Principal component analysis was performed with one of the subsamples and yielded a three-factor structure, explaining 61.5% of the total variance. Confirmatory factor analysis performed on the second showed good fit indexes. Furthermore, internal consistency of the three proposed components, gauged either by Cronbach’s alpha or McDonald’s omega, indicated good reliability. Given the results, the CRS-Y is a valid and reliable tool showing adequate psychometric properties.</p> <p>This scale will be useful for schools and policy makers at the local level. Indicators such as the promotion of opportunities to participate and be recognised, collective trust and the promotion of shared values and protection are relevant in assessing regional communities’ resilience and informing youth policies.</p> <p> </p> <p><strong>The scoring and interpretation guidelines for this scale should be as follows:</strong></p> <p>The Community Resilience Scale for Youth (CRS-Y) was developed for young people to assess their perception of the resilience of regional communities in terms of positive development and purposeful experiences for young people. The scale consists of 12 items rated on a 5-point Likert scale (1=low agreement to 5=high agreement), where participants indicate their level of agreement with each statement about their community. </p> <p>Items are grouped into a total score [MEAN(Factor1,Factor2,Factor3)] and three factors/dimensions: </p> <p>Factor 1-Promotion of opportunities and collective trust [5 items; MEAN(Item1,Item2,Item3,Item4,Item5)];</p> <p>Factor 2-Promotion of shared values and protection [4 items; MEAN(Item6,Item7,Item8,Item9)];</p> <p>Factor 3-Promotion of intercommunity trust and ties [3 items; MEAN(Item10,Item11,Item12)]. </p> <p>Higher scores indicate a greater perception of the resilience of regional communities in terms of positive development and purposeful experiences for young people.</p>
FIGURE. Seedlings, seeds, embryos, anthers, and pollen in Dicorynia. A–D. Different stages of development in seedlings of D. paraensis, First eophiles unifoliolate and opposite; E–G. Seed of D. guianensis: E. External surface; F. Endosperm of the longitudinally sectioned seed, note the slightly gelatinous upper region; G. Cotyledon and embryo of longitudinally sectioned seed; H. SEM of seed's testa in D. paraensis; I. SEM of endosperm's surface in D. guianensis (notice the presence of circular perforations); J–K. SEM of the hypocotyl-radicular axis of the seed in D. guianensis and D. paraensis; L. SEM of seed's testa in D. guianensis; M–N. SEM of plumule region in embryo of D. guianensis and D. paraensis (note the developed leaf primordia); O. Apex of anther in longer stamen of D. paraensis, showing 4 sporangia and two pores covered by an apicle; P. Apex of anther in shorter stamen of D. guianensis, at least 9 sporangia; Q. Apex of anther in longer stamen of D. guianensis, 8 sporangia; R. Pollen grains in D. paraensis. A–D: Falcão, M.J. 91; E–G, I–J, L–M: Gentry 63030; H, K, N: Berry, P.E. 7460; O: Amaral, E. 618; P, Q: Unknown collector MO1576407; Scale bar. A–D: 2cm; E–G: 3mm; H–L: 1mm; M–N: 100 μm; O-Q: 200μm; R: 5 μm. in A Taxonomic Revision of the Amazonian Genus Dicorynia (Fabaceae: Dialioideae)
FIGURE. Seedlings, seeds, embryos, anthers, and pollen in Dicorynia. A–D. Different stages of development in seedlings of D. paraensis, First eophiles unifoliolate and opposite; E–G. Seed of D. guianensis: E. External surface; F. Endosperm of the longitudinally sectioned seed, note the slightly gelatinous upper region; G. Cotyledon and embryo of longitudinally sectioned seed; H. SEM of seed's testa in D. paraensis; I. SEM of endosperm's surface in D. guianensis (notice the presence of circular perforations); J–K. SEM of the hypocotyl-radicular axis of the seed in D. guianensis and D. paraensis; L. SEM of seed's testa in D. guianensis; M–N. SEM of plumule region in embryo of D. guianensis and D. paraensis (note the developed leaf primordia); O. Apex of anther in longer stamen of D. paraensis, showing 4 sporangia and two pores covered by an apicle; P. Apex of anther in shorter stamen of D. guianensis, at least 9 sporangia; Q. Apex of anther in longer stamen of D. guianensis, 8 sporangia; R. Pollen grains in D. paraensis. A–D: Falcão, M.J. 91; E–G, I–J, L–M: Gentry 63030; H, K, N: Berry, P.E. 7460; O: Amaral, E. 618; P, Q: Unknown collector MO1576407; Scale bar. A–D: 2cm; E–G: 3mm; H–L: 1mm; M–N: 100 μm; O-Q: 200μm; R: 5 μm.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.