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538 results for “Vector data”
Data from "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad"
<p>This upload includes data shown in the figures of the Paper "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad".</p>
Supplementary Data: Global fits if simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator".</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi. Plotting scripts (*.pip) are designed to work with either the original version of pippi 2.1 or the forked unreleased version. The provided scripts do not reproduce all the figures in the paper exactly.</p> <p>To save storage space, all samples have been compressed using <code>tar</code>. To inflate each dataset after downloading run <code>tar -zxvf <samples>.hdf5.gz</code>.</p> <p>To facilitate uploading to Zenodo, several of the data files have been thinned to only include enough points to reproduce plots.</p>
Compilation of Land Use Data in 21 and 37 Category Classifications - Ipswich and Parker River Watersheds - 1971, 1985, 1991, and 1999 - Vector Shapefile.
The MassGIS Land Use datalayer has 37 land use classifications interpreted from 1:25,000 aerial photography. This layer contains data for 21 and 37 category classifications for the years of 1971, 1985, 1991, and 1999. Coverage is complete for all towns that fall partially or completely within the Ipswich River and/or Parker River watersheds. Data compiled for 1971, 1985, 1991, and 1999.
Data from: Transformation of measurement uncertainties into low-dimensional feature vector space
<p>Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for post-processing, or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose global ocean temperature data. The uses of the method range from supporting decision making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterisation of the El Niño Southern Oscillation. </p>
Vector sequences in early WIV SRA sequencing data of SARS-CoV-2 inform on a potential large-scale security breach at the beginning of the COVID-19 pandemic
<p>DESCRIPTION</p> <p>Sequences identified as Influenza A virus, Spodoptera frugiperda rhabdovirus and Nipah henipavirus have been previously identified within the early HiSeq 1000 and HiSeq 3000 sequencing data of SARS-CoV-2, SRR11092059,SRR11092060,SRR11092061 and SRR11092062, and were being used to support the hypothesis that a "simultaneous outbreak of multiple zoonotic viruses" have happened in the Huanan Seafood market. https://doi.org/10.31219/osf.io/s4td6</p> <p>However, a closer examination of these sequences revealed that they were not sequences of actual wild viruses, but were in stead fragments left behind from PCR products and cloning vectors harboring both cDNA clones and infectious clones of such viruses, with evidence of viral sequences being joined directly to DNA sequences of vector and non-human origin within the same short reads.</p> <p>Here are the vector sequences and PCR product-like sequences recovered from the earliest WIV SRA sequencing data of Human SARS-CoV-2 from dataset SRR11092059,SRR11092060,SRR11092061,SRR11092062.</p> <p>Sequences associated with Vectors and PCR products from 3 distinct viral species have been obtained: The 3'-end of a Nipah Henipahvirus with fusion to a Hepatitis D virus Ribozyme, a T7 terminator and a Tetracycline resistance gene, The 5'-end of the same Nipah Henipahvirus with fusion to sequences found in diverse vectors, A complete vector genome encoding the HA gene of Influenza A virus subtype H7N9 under a CMV promoter and a bgH polyA terminator, and 221 Contiguous sequences corresponding to the Spodoptera frugiperda rhabdovirus reference genome fused to sequences that were homologous to multiple Plastid sequences and Notably Mitochondrial sequences of Rodents.</p> <p>As sequences corresponding to a rescued infectious clone of a BSL-4 organism (Nipah Henipahvirus) were found in sample sequences that supposedy represents patient samples that were obtained from Hospital ICU and sequenced in a pathogen diagnosis laboratory (which is separate from the Virology Research laboratory which is implied by the context of an Infectious Clone of such an organism, evident by the 3'-HDV ribozyme and T7 terminator fused directly to the 3'-terminus of the Nipah Henipahvirus reads), The discovery of artifact-containing sequences of at least 3 different pathogen species that are phylogenetically and methodologically distinct from each other in samples that were supposedly submitted by a laboratory that is Separate from the virological research laboratories that could have hosted such clone sequences imply extensive crosstalk and cross-contamination between the various laboratories within the Wuhan Institute of Virology, which includes at least one BSL-4 laboratory with evidence of containment breach of a BSL-4 organism and it's subsequent introduction into RNA-seq samples that were processed by a laboratory of distinct and separate purposes than the basic virological research evidenced by the Infectious Clone of the Hipah Henipahvirus.</p> <p>Such a discovery therefore likely imply a major security breach happening within the Wuhan institute of Virology at the time when the first sequences of SARS-CoV-2 was sampled and sequenced, which have important implications on the origins of the SARS-CoV-2 virus itself.</p> <p>METHODS</p> <p>The metagenomic sequencing datasets, SRR11092059,SRR11092060,SRR11092061 and SRR11092062 were first analyzed using the NCBI phylogenetic analysis tool, which identified viral sequences that is not related to SARS-CoV-2 itself. These include Influenza A virus (IAV, subtype H7N9), Spodoptera frugiperda rhabdovirus and Nipah Henipahvirus.</p> <p>The datasets were then subjected to BLAST search using MEGABLAST against the reference sequences of such viruses to verify the existence of the viral sequences and determine the exact sybtype of such viruses and the closest sequences on GenBank that corresponds to the reads. There seuqences are MH926031.1 for the Spodoptera frugiperda rhabdovirus, KY199425.1 for the Influenza A virus and AY988601.1 for the Nipah Henipahvirus.</p> <p>A second round BLAST analysis with these identified sequences were then performed, which unexpectedly revealed numerous reads corresponding to Cloning vectors and non-human Mitochondrial and Plastid sequences being fused directly to the sequences of the identified viral species. Reads were then downloaded and subjected to assembly using the CAP3 sequence assembly program and the EGASSEMBLER tool. Contig sequences were then queried against the NCBI nr/nt database which unanimously identified the original sample sequences as viral sequences inserted into cloning vectors.</p> <p>The complete sequence of the Influenza A virus Haemagluttinin (HA) gene clone was obtained from SRR11092061,SRR11092062 using multiple rounds of BLAST search and sequence assembly expansion on the existing vector-virus junction contigs, and a partial sequence corresponding the 3'-end of Nipah Henipahvirus AY988601.1 fused to a 3'-HDV ribozyme, T7 terminator and a Tet resistance gene was obtained from SRR11092059. In addition, 221 Contig sequences corresponding to the Rhabdovirus MH926031.1 fused to Chloroplast sequence MN524635.1 and Rodent Mitochondrial sequence MT241668.1 have been recovered from SRR11092061.</p> <p>We then performed a BLAST search using the identified vector sequences on SRR11092059,SRR11092060,SRR11092061 and SRR11092062, which confirms the existence of these two vetor sequences in all 4 datasets.</p>
Data from: Virus infection and host plant suitability affect feeding behaviors of cannabis aphid (Hemiptera: Aphididae), a newly described vector of potato virus Y
<p>Aphids are the most prolific vectors of plant viruses resulting in significant yield losses to crops worldwide. P<span>otato virus Y (PVY) </span>is transmitted in a non-persistent manner by 65 species of aphids. <span>With the increasing acreage of hemp </span>(<i>Cannabis sativa</i> L.) (Rosales: Cannabaceae) <span>in the U.S, we were interested to know if the cannabis aphid (<i>Phorodon cannabis</i> Passerini) </span><span>(Hemiptera: Aphididae) </span><span>is a potential vector of PVY.</span> Here, we conduct transmission assays and utilize the electrical penetration graph (EPG) technique to determine whether cannabis aphids can transmit PVY to hemp (host) and potato (non-host) (<i>Solanum tuberosum</i> L.) (Solanales: Solanaceace). We show for the first time that the cannabis aphid is an efficient vector of PVY to hemp (96%) and potato (91%) using cohorts of aphids. In contrast, individual aphids transmitted the virus more efficiently to hemp (63%) compared to potato (19%). During the initial 15 minutes of EPG recordings, aphids demonstrated lower number and time spent performing intracellular punctures on potato compared to hemp, which may in part explain low virus transmission to potato using individual aphids. During the entire 8-hour recording, viruliferous aphids spent less time ingesting phloem compared to non-viruliferous aphids on hemp. This reduced host suitability could potentially cause aphids to disperse to more suitable hosts thereby increasing virus transmission. Overall, our study shows that cannabis aphid is an efficient vector of PVY, and that virus infection and host plant suitability affect feeding behaviors of the cannabis aphid in ways which may increase virus transmission.</p>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database
<p> Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.</p>
Simulation Results Data for 'Modelling new insecticide-treated bed nets for malaria-vector control: How to strategically manage resistance?'
<p>GENERAL INFORMATION</p> <p>1. Title of Dataset: Simulation Results Data for 'Modelling new insecticide-treated bed-nets for malaria-vector control: How to strategically manage resistance?'</p> <p>2. Author Information<br> A. Investigator Contact Information<br> Name: Philip G. Madgwick<br> Institution: Syngenta <br> Address: Jealott’s Hill International Research Centre, Bracknell, RG42 6EY, UK<br> Email: philip.madgwick@syngenta.com</p> <p> B. Investigator Contact Information<br> Name: Ricardo Kanitz<br> Institution: Syngenta <br> Address: Syngenta Crop Protection, Rosentalstrasse 67, CH-4058 Basel, Switzerland<br> Email: ricardo.kanitz@syngenta.com</p> <p><br> 3. Date of data collection (single date, range, approximate date): 2021-01-13 to 2021-02-01 </p> <p>4. Geographic location of data collection: UK </p> <p>5. Information about funding sources that supported the collection of the data: </p> <p>This work was conducted during a postdoctoral research position for PGM funded by the Innovative Vector Control Consortium (IVCC).</p> <p><br> SHARING/ACCESS INFORMATION</p> <p>1. Licenses/restrictions placed on the data: NA</p> <p>2. Links to publications that cite or use the data: [UPDATE]</p> <p>3. Links to other publicly accessible locations of the data: NA</p> <p>4. Links/relationships to ancillary data sets: NA</p> <p>5. Was data derived from another source? No</p> <p>6. Recommended citation for this dataset: [UPDATE]</p> <p><br> DATA & FILE OVERVIEW</p> <p>1. File List: <br> PSData_random6.csv - 10^6 random samples of each of the 17 parameters in the model, where rows are samples and columns are parameters (with column names corresponding to the parameters identified in the rows of Table 1 of the manuscript; see also DATA-SPECIFIC INFORMATION)<br> Data_random6_maxpsMixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_maxpsMixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_maxpsMixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mosaic_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revmm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_revmn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revnn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloA_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloB_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance </p> <p>2. Relationship between files, if important: </p> <p>Files are named in accordance with the variables that describe each simulation setup, as described above. Each simulation dataset has 10^6 runs that correspond to the 10^6 random samples of each of the 17 parameters in the model in 'PSData_random6.csv'.</p> <p>3. Additional related data collected that was not included in the current data package: NA</p> <p>4. Are there multiple versions of the dataset? No</p> <p><br> METHODOLOGICAL INFORMATION</p> <p>1. Description of methods used for collection/generation of data: Data were collected using Simulator.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>2. Methods for processing the data: Data were processed using Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>3. Instrument- or software-specific information needed to interpret the data: Analysis was conducted in R version 4.0.3 (2020-10-10), using R packages identified in Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>4. Standards and calibration information, if appropriate: NA</p> <p>5. Environmental/experimental conditions: NA</p> <p>6. Describe any quality-assurance procedures performed on the data: NA</p> <p>7. People involved with sample collection, processing, analysis and/or submission: NA </p> <p><br> DATA-SPECIFIC INFORMATION FOR: PSData_random6.csv</p> <p>1. Number of variables: </p> <p>17 variables with column names that have the following parameter meanings (see Table 1 in the manuscript): <br> Population Size = N = starting population size (and carrying capacity in logistic model); random sample range on log-scale: 10^2 - 10^9<br> Intrinsic Birth Rate = b = % population growth rate (in logistic model); random sample following a standard log-normal distribution with mean=0 and sd=1<br> Intrinsic Death Rate = d = % breeding mosquitoes that die into next generation; random sample range: 0 - 1<br> Female Exposure = x_[female-symbol] = % female mosquitoes that receive a dose; random sample range: 0 - 1<br> Male Exposure x_[male-symbol] = % male mosquitoes that receive a dose; random sample range: 0 - 1<br> Initial Frequency A = f_0,A = starting frequency of allele A; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 1 = m_1 = % dosed mosquitoes that die from insecticide 1; random sample range: 0 - 1<br> Resistance Restoration A = r_A = % return to baseline fitness with resistance allele A; random sample range: 0 - 1<br> Dominance of Resistance Restoration A = h^r_A = % resistance restoration in heterozygote with allele A; random sample range: 0 - 1<br> Resistance Cost A = c_A = % non-dosed mosquitoes that die from carrying allele A; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost A = h^c_A = % resistance cost in heterozygote with allele A; random sample range: 0 - 1<br> Initial Frequency B = f_0,B = starting frequency of allele B; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 2 = m_2 = % dosed mosquitoes that die from insecticide 2; random sample range: 0 - 1<br> Resistance Restoration B = r_B = % return to baseline fitness with resistance allele B; random sample range: 0 - 1<br> Dominance of Resistance Restoration B = h^r_B = % resistance restoration in heterozygote with allele B; random sample range: 0 - 1<br> Resistance Cost B = c_B = % non-dosed mosquitoes that die from carrying allele B; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost B = h^c_B = % resistance cost in heterozygote with allele B; random sample range: 0 - 1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA </p> <p>4. Missing data codes: NA</p> <p>5. Specialized formats or other abbreviations used: NA</p> <p><br> DATA-SPECIFIC INFORMATION FOR: all other dataset files (e.g. Data_random6_maxpsMixture_Fixed_mm.csv) </p> <p>1. Number of variables: </p> <p>10 variables with column names that have the following meanings:<br> A_t_50% = the recorded number of generations that it takes for resistance allele A to reach >50% frequency; 0 means that resistance allele A never reaches >50% frequency <br> A_f_250 = the frequency of resistance allele A at the 250th generation <br> A_f_bar = the mean frequency of resistance allele A over the first 250 generations <br> B_t_50% = the recorded number of generations that it takes for resistance allele B to reach >50% frequency; 0 means that resistance allele B never reaches >50% frequency <br> B_f_250 = the frequency of resistance allele B at the 250th generation <br> B_f_bar = the mean frequency of resistance allele B over the first 250 generations <br> nf_80% = the recorded number of generations that it takes for the female population size to recover to >80% of its original size in the 0th generation; 0 means that the female population size never reaches >80% recovery; 1 means that the female population size never drops below 80% of its original size in the 1st generation<br> nf_250 = the female population size at the 250th generation<br> nf_bar = the mean female population size over the first 250 generations <br> nf_ext = the recorded number of generations that it takes for the female population size to drop below 1 (i.e. population extinction); 0 means that female population size never reaches <1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA</p> <p>4. Missing data codes: all missing data is recorded as 0 </p> <p>5. Specialized formats or other abbreviations used: NA</p>
Images of the data brushes generated for the ApPEARS deliverable D5.1 Database of vector based brush strokes and sample prints that demonstrate the range of printed materials
<p>These images are appendices of ApPEARS deliverable D5.1 Database of vector based brush strokes and sample prints that demonstrate the range of printed materials. They show the generated data brushes.</p>
Data and code associated with the paper 'Mode-Specific Coupling of Nanoparticle-on-Mirror Cavities with Cylindrical Vector Beams'
<p>Data and code associated with the following paper: <a href="https://doi.org/10.1021/acs.nanolett.3c00561">V. Vento et al, Nano Lett. 2023</a></p> <p>A thorough explanation of the experiment performed is available there.</p> <p>The name of each sub-folder and file in <strong>Maps_data_code.zip</strong> indicates the corresponding figure number ("FIG #") and the type of content ("raw_data", "data", "plot", "analysis", "calculation", "simulation").</p> <p>The Raman maps data are analyzed through the script <em>Raman_maps_analysis.m</em>. The photoluminescence maps data in the supplementary information are analyzed through the script <em>PL_maps_analysis.m.</em> </p> <p>Used softwares: Matlab R2021a, Python 3.9, Comsol Multiphysics 5.6</p> <p> </p>
All data for the preprint Population genetics of Glossina palpalis gambiensis in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease
<p>Data set for the paper titled "Population genetics of <em>Glossina palpalis gambiensis</em> in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease"</p>
Data from: Virus infection and host plant suitability affect feeding behaviors of cannabis aphid (Hemiptera: Aphididae), a newly described vector of potato virus Y
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Data from: Transformation of measurement uncertainties into low-dimensional feature vector space
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Data from: Double-strand break repair pathways differentially affect processing and transduction by dual AAV vectors
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Data from: Genetic reconstruction of a bullfrog invasion to elucidate vectors of introduction and secondary spread
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Data from: Transinfection of Wolbachia wAlbB into Culex quinquefasciatus mosquitoes does not alter vector competence for Hawaiian avian malaria (Plasmodium relictum GRW4)
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Data from: Present and future suitability of invasive and urban vectors through an environmentally-driven mosquito reproduction number
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GIS vector data for sample locations and plots associated with the Hillslope Study in Macon County, NC
The Hillslope Study sites represent a gradient of landscapes, including forested, valley agriculture, and mountain housing developments. These locations and plots were used to collect samples of various matrices for numerous analyses at differing intervals. The data set consists of Open Office spreadsheet and other files that document all the Hillslope Study locations.
Data from: Age influences the thermal suitability of Plasmodium falciparum transmission in the Asian malaria vector Anopheles stephensi
<p><span>Models predicting disease transmission are vital tools for long-term planning of malaria reduction efforts, particularly for mitigating impacts of climate change. We compared temperature-dependent malaria transmission models when mosquito life history traits were estimated from a truncated portion of the lifespan (a common practice) to traits measured across the full lifespan. We conducted an experiment on adult female <i>Anopheles stephensi, </i>the Asian urban malaria mosquito, to generate daily per capita values for mortality, egg production, and biting rate at six constant temperatures. Both temperature and age significantly affected trait values. Further, we found quantitative and qualitative differences between temperature-trait relationships estimated from truncated data versus observed lifetime values. Incorporating these temperature-trait relationships into an expression governing the thermal suitability of transmission, relative <i>R<sub>0</sub></i></span><span>(</span><i><span>T</span></i><span>)<i>,</i> resulted in minor differences in the breadth of suitable temperatures for <i>Plasmodium falciparum</i> transmission between the two models constructed from only <i>An. stephensi</i> trait data. However, we found a substantial increase in thermal niche breadth compared to a previously published model consisting of trait data from multiple <i>Anopheles</i> mosquito species. Overall, this work highlights the importance of considering how mosquito trait values vary with mosquito age and mosquito species when generating temperature-based suitability predictions of transmission.</span></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.