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288 results for “Method development”
Data from: Novel methods to define invasive procedures at the end-of-life were developed to improve quality of end of life care research: A population-based cohort study in colorectal cancer
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Data for: Information accessibility, accounting manipulation, and sustainable development of digital enterprises: Based on double moderating effect model and panel PSM-DID method
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Data for: Development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection
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Data from: What smells? Developing in-field methods to characterize the chemical composition of wild mammalian scent cues
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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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Online Resource 1 – Maximal hoop stress developed in the lining of a tunnel excavated with a single shield TBM at the state of equilibrium (comparison between different calculation methods)
<p>The maximal hoop stress developed in the lining of a tunnel at the state of equilibrium calculated with the various ConVergence-ConFinement (CV-CF) methods is compared with the results obtained with a 3D numerical model of a tunnel excavation. A sensibility analysis is performed in order to compare the performance of the CV-CF approaches. The choice of the values of the mechanical parameters of the ground and of the lining is carried out in an attempt to cover the large range of situations encountered within single shield TBM. The total set of results from the sensibility analysis is shown in this work. Results obtained from some empirical formula proposed by the authors are also included. <strong>A version 2 of the document with some minor corrections has been published. </strong></p>
Data from: Development and validation of rapid environmental DNA (eDNA) detection methods for bog turtle (Glyptemys muhlenbergii)
Bog turtles Glyptemys muhlenbergii are listed as Species of Greatest Conservation Need (SGCN) for wildlife action plans in every state it occurs and multi-state efforts are underway to better characterize extant populations and prioritize restoration efforts. However, traditional sampling methods can be ineffective due to the turtle's wetland habitat, small size, and burrowing nature. Molecular methods, such as qPCR, provide the ability to overcome this challenge by effectively quantifying minute amounts of turtle DNA left behind in its environment (eDNA). Developing such methods for bog turtles has proved difficult partly because of the high sequence similarity between bog turtles and closely-related, cohabitating species, most often wood turtles ( Glyptemys insculpta ). Additionally, substrates containing bog turtle eDNA are often rich in organics or other substances that frequently inhibit both DNA extraction and qPCR amplification. Here, we describe the development and validation of a qPCR assay, BT3, targeting the mitochondrial cytochrome oxidase I gene that correctly identifies bog turtles with 100% specificity and sensitivity when tested on 201 blood samples collected from six species over a wide geographic range. We also developed a full-process internal control employing a genetically modified strain of Caenorhabditis elegans to improve DNA extraction methods, limit false negative results due to qPCR inhibition, and measure total DNA recovery from each sample. Using the internal control, we found that DNA recovery varied by over an order of magnitude between samples and likely explains the lack of bog turtle detection in some cases. Methods presented herein are highly-specific and may offer a more cost effective, non-invasive tool to supplement bog turtle population assessments in the eastern United States. Poor or differential DNA recovery, which remains unmeasured in the vast majority of eDNA studies, significantly reduced the ability to detect bog turtle in their natural environment.
Supplementary Material for The Impact of COVID-19 on Open Source Development Activities: A Multi-Method Study
<p><strong>Context:</strong> The social isolation measures resulting from the COVID-19 outbreak changed work practices in various sectors, especially with the shift to working from home. However, it is still unclear the implications of the pandemic on the maintenance and evolution of open-source software (OSS). Objective: In this study, we analyze the effects of COVID-19 on the development activity of OSS and how social isolation changed the productivity and emotional state of OSS contributors. <strong>Method:</strong><br>To investigate this issue, we have conducted a multi-method study. We first quantitatively investigated how the COVID-19 outbreak impacted the development activity of open-source projects by mining the development history of 155 open-source projects. We then qualitatively investigated the perceptions of open-source core developers about the impact of COVID-19 on their activities, using a survey as an instrument. <strong>Results:</strong> Our results indicate that the pandemic led to some effect on the development<br>activity of OSS repositories, especially in the early days of the outbreak. For instance although we observe an increase in the number of pull requests accepted, our results also indicate a sudden increase in the turnover rate of core developers. However, a few months after the declaration of the current pandemic, our results suggest stabilization in these metrics. Other effects were more far-reaching, such as a decrease in newcomers throughout the observed period after the pandemic was declared. Some findings are supported by the results of our qualitative study, whose results indicate that most of the respondents of our survey consider that COVID-19 did not change their productivity substantially. <strong>Conclusions:</strong> Our findings can help practitioners and researchers to better understand the effect of COVID-19 on the software engineering field.</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>
Raw data for Urine DNA (uDNA) as a non-lethal method for endoparasite biomonitoring: development and validation
<p>Changes in environmental conditions alter host-parasite interactions, raising the need for effective epidemiological surveillance. Developing operational, accurate, and cost-effective methods to assess individual infection status and potential for pathogen spread is a prerequisite to anticipate future disease outbreaks in wild populations. For endoparasites, effective detection of infections usually relies on host-lethal approaches, which are barely compatible with wildlife conservation objectives. Here, we used the brown trout (<i>Salmo trutta</i>) - <i>Tetracapsuloides bryosalmonae</i> host-parasite system to develop a non-lethal method for endoparasite infection detection, hereafter called "uDNA" for urine DNA. The uDNA diagnostic test is based on the amplification of endoparasite DNA from host urine. We sampled wild fish (N = 111) from eight sites, let them excrete in individual buckets filled with mineral water and performed parasite DNA amplification from water filtration. We compared the results of the uDNA diagnostic test for host infection status and parasite load to those from kidney samples (the current standard method). uDNA was sensitive in determining host infection status (even for infected hosts showing no sign of the disease), since up to 90% of fish individuals were correctly assigned to their infection status. The quantity of uDNA detected from the hosts depended on the sampling sites, suggesting a spatial variation in the parasite spread. uDNA was positively, but weakly correlated with parasite load in the kidney. This correlation depended on the severity of macroscopic lesions caused by the disease, and was negative in fish with severely damaged kidney, likely due to impaired urine excretion. The uDNA approach provides novel avenues to non-lethally infer infection parameters from wildlife populations at large spatial scales. By targeting parasite transmission stage, uDNA is also valuable to get insights on the parasite fitness and the ecological and evolutionary dynamics of this host-parasite interaction.</p>
Validation data Set: Development and validation of a quantitative method for 15 antiviral drugs in poultry muscle using liquid chromatography coupled to tandem mass spectrometry
<p>Validation dataset for paper published in the Journal of Chromatography A.</p> <p> </p> <p>Clément Douillet, Mary Moloney, Melissa Di Rocco, Christopher Elliott, Martin Danaher,<br> Development and validation of a quantitative method for 15 antiviral drugs in poultry muscle using liquid chromatography coupled to tandem mass spectrometry, Journal of Chromatography A, Volume 1665, 2022, 462793, ISSN 0021-9673,</p> <p><br> Abstract:</p> <p>The objective of this work was to develop a quantitative multi-residue method for analysing antiviral drug residues and their metabolites in poultry meat samples. Antiviral drugs are not licensed for the treatment of influenza in food producing animals. However, there have been some reports indicating their illegal use in poultry. In this study, a method was developed for the analysis of 15 antiviral drug residues in poultry muscle (chicken, duck, quail and turkey) using liquid chromatography coupled to tandem mass spectrometry. This included 13 drugs against influenza and associated metabolites, but also two drugs employed for the treatment of herpes (acyclovir and ganciclovir). The method required the development of a novel chromatographic separation using a hydrophilic interaction chromatographic (HILIC) BEH amide column, which was necessary to retain the highly polar compounds. The analytes were detected using a triple quadrupole mass spectrometer operating in positive electrospray ionization mode. A range of different sample preparation protocols suitable for polar compounds were evaluated. The most effective procedure was based on a simple acetonitrile-based protein precipitation step followed by a further dilution in a methanol/water solution. The confirmatory method was validated according to the EU 2021/808 guidelines on different species including chicken, duck, turkey and quail. The validation was performed using various calibration curves ranging from 0.1 µg kg−1to 200 µg kg−1, according to the analyte. Depending on the analyte sensitivity, decision limits achieved ranged from 0.12 µg kg−1 for arbidol to 34.7 µg kg−1 for ribavirin. Overall, the reproducibility precision values ranged from 2.8% to 22.7% and the recoveries from 84% to 127%. The method was applied to 120 commercial poultry samples from the Irish market, which were all found to be residue-free.<br> Keywords: Antiviral drug residues; Influenza; HILIC; LC-MS/MS; Poultry muscle</p>
The dataset of the paper titled "Investigating End-Users' Values for Agriculture Mobile Applications Development: A Mixed-Methods Empirical Study on Bangladeshi Female Farmers"
<p>This package includes a survey questionnaire, demographic questions, focus groups questionnaire, interview questionnaire, and 10 main values with corresponding attributes and the referred names used in this paper submitted to IST.</p>
Development of a mass production method for the smut fungus Doassansia niesslii , a potential biological control agent of the invasive weed Butomus umbellatus (IC_MScEA_Final project data)
<p>Data of my MSc final project (MSc Ecological Applications, Imperial College London). Student ID: 02175995.</p>
Quantifying Winter Forage Resources for Reindeer: Developing a Method to Estimate Ground Lichen Cover and Biomass at a Local Scale
<p>Data and R scripts used in the paper Quantifying Winter Forage Resources for Reindeer: Developing a Method to Estimate Ground Lichen Cover and Biomass at a Local Scale (https://doi.org/10.1016/j.tfp.2024.100768).</p>
Data from: Rearing and sampling methods for estimating spruce budworm development rates at constant temperatures
<p>We describe an experimental protocol for measuring the response of spruce budworm post-diapause larval development to temperature. This protocol is specifically designed to include measurements of development near their upper and lower thermal thresholds. The application of this protocol to a laboratory colony allowed for the first experimental evidence that spruce budworm larval development occurs at temperatures as low as 5 ºC and as high as 35 ºC and provides data to estimate development rates at temperatures from 5–35 ºC in 5 ºC increments. Our protocol is also designed to minimize mortality near the thermal development thresholds thus allowing for multi-generational studies. We observed developmental plasticity in larvae reared at constant temperatures, particularly the occurrence of up to 42% of some individuals requiring only five instars to complete development, compared to the expected six instars. An occurrence that exhibited no clear relation to temperature. While this protocol is specifically designed for spruce budworm, it provides a template for the study of other species' developmental responses to temperature.</p>
FIG. 3. Hibernation habitat study methods developed for a in Hoplodactylus tohu Scarsbrook & Walton & Rawlence & Hitchmough 2023, n. sp.
FIG. 3. Hibernation habitat study methods developed for a partially mined peatland ecosystem inhabited by a Massasauga population located in southern Ontario, Canada. Life-zone methods include a grid of groundwater wells paired with a frost tube in each study area (Mined and Not Mined). Life zone (LZ) equals the groundwater level (GWL) minus frost depth (FD). All measurements are relative to the ground surface.
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
Developing Advanced MRI Methods for Detecting the Impact of Nutrients on Infant Brain Development
ClinicalTrials.gov study NCT02058225. IPD Sharing: NO. Countries: 1. Publications: 3.
Development a Method to Extract Antibiotic Concentration From Interstitial Lung and Epithelial Lining Fluid.
ClinicalTrials.gov study NCT03970265. IPD Sharing: NO. Countries: 1. Publications: 1.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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