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12,662 results for “linking”
Accretion in the recurrent nova T CrB: Linking the superactive state to the predicted outburst
<p>UBV photometry of the recurrent nova T CrB </p>
18th hole, Old Course, St Andrews Links
Source: Objaverse 1.0 / Sketchfab
18th hole tee stone, Old Course St Andrews Links
Source: Objaverse 1.0 / Sketchfab
Data for: Template-Directed Synthesis of Strained meso-meso-Linked Porphyrin Nanorings
<p>Calculated molecular xyz coordinates from molecular dynamics simulations, StrainViz calculations and screening of ligand designs.</p>
Linked Papers With Code: Knowledge Graph Embeddings
<p>We provide <strong>knowledge graph embeddings</strong> for the <strong>Linked Papers With Code</strong> Knowledge Graph. More information at <a href="https://linkedpaperswithcode.com/">https://linkedpaperswithcode.com/</a></p>
Complete Dataset links and GPS timestamps and locations of our recordings at the Morton Arboretum.
<p>This repository contains all the data to reproduce the experiments conducted in the research article:<br><br>"Acoustic fingerprints in nature: A self-supervised learning approach for ecosystem activity monitoring"</p> <p>The complete dataset for reproducing the experiments conducted in this research are available at<a href="https://web.lcrc.anl.gov/public/waggle/datasets/morton_arb.tar"> Complete Morton Arboretum Data</a>.</p> <p>For a sub-sample of the complete version of the dataset, go to the following link: <a href="https://web.lcrc.anl.gov/public/waggle/datasets/morton_arb_small.tar">2021/06/30 study</a></p> <p>This repository also contains GPS timestamps, and GPS locations of our recordings at the Morton Arboretum, an internationally recognized tree-focused botanical garden and research center in Lisle, IL.</p> <p> </p> <p> </p>
Basal force fluctuations and granular rheology: Linking macroscopic descriptions of granular flows to bed forces with implications for monitoring signals
<p>Herein lies user defined functions, submission scripts, and intitial particle data necessary to build a discrete element solver (using MFiX-DEM version 21.1.1: https://mfix.netl.doe.gov/products/mfix/) and run a monodisperse pure granular flow with 5mm beads in a shear cell configuration. Contained within this dataset are particle velocity and force data for the entire collection of particles ('VELOCITY<em>'* </em>and 'FORCES*.DAT', respectively). This dataset also contains particle-force data recorded at the bottom force plate, writen at 10 kHz. Two MATLAB scripts are included for post processing the force plate data. </p>
Dataset and Code used in ApJ publication: "A novel approach to understanding the link between supermassive black holes and host galaxies"
<p>All the data of this paper is found under BH_M-sigma_compilation/Data</p> <p>The main data tables are found the at BH_M-sigma_compilation/Data/BHcompilation_updated.csv</p> <p>The SDSS data galSpecInfo-dr8.fits and galSpecLine-dr8.fits can be found at: <a href="https://www.sdss3.org/dr8/spectro/spectro_access.php" rel="nofollow">https://www.sdss3.org/dr8/spectro/spectro_access.php</a></p> <p>The main code for this paper is at BH_M-sigma_compilation/Code/Mixture_Upper_Limits and consists of the 3 files for the Stan model, R code and Python code respectively: model.stan stan_fit.r hurdle_model.ipynb</p> <p>The figures generated for the paper can be found at BH_M-sigma_compilation/Figures</p> <p>A novel approach to understanding the link between supermassive black holes and host galaxies © 2024 by Gabriel Sasseville is licensed under CC BY 4.0. To view a copy of this license, visit <a href="https://creativecommons.org/licenses/by/4.0/" rel="nofollow">https://creativecommons.org/licenses/by/4.0/</a></p>
Reproducibility Package for "ExpectAll: A BDD Based Approach for Link Failure Resilience in Elastic Optical Networks"
<p>This is a reproducibility package for the VMCAI'25 paper "ExpectAll: A BDD Based Approach for Link Failure Resilience in Elastic Optical Networks".</p> <p>Constantly growing demands on higher bandwidth and quality of service in modern communication networks motivate the introduction of fully optical network technologies that can eliminate the bottlenecks of optical to digital signal conversions. Recent advances in elastic optical networks enable fine-grained resource allocation technologies for traffic demands, which introduces the Routing and Spectrum Allocation (RSA) problem. In order to improve network resilience for multiple link failures while avoiding double light-spectrum allocation, we present Ex- pectAll---a novel approach and a tool for resilience and path/spectrum allocation based on binary decision diagrams (BDDs). Our method efficiently computes and stores all solutions to the RSA problem in the BDD data structure, facilitating optimal and fast failover protection for failure scenarios even with multiple failing links. ExpectAll surpasses the state- of-the-art methods in both the speed of finding a single optimal solution for a currently occurring failure scenario as well as in the preparation time required to precompute all optimal route and spectrum assignments.</p> <p> </p> <p> </p>
Pesodu-periodic changes in the crust thinning at the Eastern Asia link to global surface environment evolution and biological distinction
Open the record for dataset details and reuse information.
Pesodu-periodic changes in the crust thinning at the Eastern Asia link to global surface environment evolution and biological distinction
Open the record for dataset details and reuse information.
Supplementary Data for Crelier et al. (2025) entitled "Mobility of South America's Transcontinental Drainage Divide and Shrinkage of the Paraná River Basin Linked to Lithologic and Geodynamic Controls"
<p>Supplementary Data for Crelier et al. (2025) entitled “Mobility of South America’s Transcontinental Drainage Divide and Shrinkage of the Paraná River Basin Linked to Lithologic and Geodynamic Controls” and published in Springer Nature's Scientific Reports.</p>
Defensive symbiont genotype distributions are linked to parasitoid attack networks - Dataset and scripts
<p>This R project includes data and scripts for paper - Defensive symbiont genotype distributions are linked to parasitoid attack networks</p> <p>Script for analysis:</p> <p> Run all the scripts in order, to get all analyses and results in this paper. </p> <p> Script_0_package_install_load.R: <br> Installs and loads the necessary R packages required for all subsequent scripts.<br> This R project</p> <p> Script_1_Extract_from_Table_S2.R<br> Extracts information from:<br> Table_S2.csv (located in /Rawdata/) Data_4_Aphid_sequences.fas<br> Generates 10 files for further analyses:<br> Aphid relatedness distance:<br> Aphid_phylogenetic_relatedness_16species.csv<br> Aphid_phylogenetic_relatedness_22species.csv<br> Aphid_phylogenetic_relatedness_31species.csv<br> Hamiltonella - Aphid Matrices:<br> Hamiltonella_Aphid_matrix_16species.csv<br> Hamiltonella_Aphid_matrix_22species.csv<br> Hamiltonella_Aphid_matrix_31species.csv<br> Parasitoid - Aphid Matrices:<br> Para_Aphid_matrix_16species.csv<br> Para_Aphid_matrix_22species.csv<br> Plant - Aphid Matrices:<br> Plant_Aphid_matrix_16species.csv<br> Plant_Aphid_matrix_31species.csv</p> <p><br> Script_2_BarPlot_Fig1.R: <br> Generates Fig. 1 using: <br> Data_11_Parasitoid_genus_aphid_22_species_Fig.1.csv Data_12_Plant_genus_aphid_31_species_Fig.1.csv<br> The bar order in Fig. 1 is arranged by proportion, which is not directly supported in the ggplot2 package in R. Therefore, we need manually reordered if using the first plot code; or, manually colour it using the second plot code. </p> <p><br> Script_3_MMRR_analysis.R<br> Uses 10 distance/distribution matrices of Parasitoid, Plant, Hamiltonella, and Aphid relationships to compute matrix correlations with the MMRR (Multiple Matrix Regression with Randomization) model.</p> <p><br> Script_4_Species_linkage_Fig2_FigS4.R<br> Uses 7 distribution matrices of Parasitoid, Plant, and Hamiltonella relationships with Aphids (excluding Aphid genetic distance matrices) to generate the species linkage diagrams for Fig. 2 and Fig. S4, For aesthetic purposes, unconnected sample points have been moved.</p> <p><br> Script_5_MMRR_Fig3_FigS5.R<br> Plots the MMRR correlations using the 10 distance/distribution matrices for Parasitoid, Plant, Hamiltonella, and Aphid relationships (Fig. 3 & Fig. S5). </p> <p> Script_6_parasitoid_specialization_Fig4.R<br> Computes specialization levels using the H2 Index for:<br> Parasitoid-Aphid<br> Aphid-Hamiltonella<br> Parasitoid-Hamiltonella relationships <br> Generates Figure 4.</p> <p> Script_7_Ecologicial_indices&plots_Table1_FigS3.R<br> Uses 7 distribution matrices of Parasitoid, Plant, and Hamiltonella relationships with Aphids (excluding Aphid genetic distance matrices) to:<br> Calculate ecological indices (Richness, Shannon Index, Simpson Index).<br> Use linear models to analyze the relationships between Parasitoid/Plant-Aphid and Aphid-Hamiltonella communities. <br> Outputs results for Table 1 and Figure S3.</p> <p><br> Script_8_Bubble_plot_FigS2.R<br> This script using <br> Hamiltonella_Aphid_matrix_31species.csv <br> and two phylogeny trees <br> Data_8_Aphid_species_phylogeny.txt & <br> Data_9_Hamiltonella_phylogeny.txt<br> To create a Cophylogeny tree of Hamiltonella strains and aphid species that we identified in this study and previously known strains. (Fig. S2)</p> <p> Script_9_DADA2_pipeline.R<br> This script is the DADA2 pipeline used to processing the raw COI sequencing data into ASV (Amplicon Sequence Variant) count files. After processing, the ASV files required manual curation to link each ASV with a distinct parasitoid and aphid species name based on BLAST results. In our updated version, we have provided both the raw data (BioProject PRJNA1139364) as well as the manually curated files (Data_1 and Data_2) which includes the ASV species names. All subsequent analyses can be reproduced using the curated datasets (Data_1 and Data_2), ensuring clarity and replicability for users.<br> We have incorporated the SRA Toolkit to streamline the process. This allows users to download raw SRR sequencing files directly and convert them into FASTQ files, which can then be input into the DADA2 pipeline. The pipeline produces two ASV count files, one for each sequencing run.</p> <p> </p> <p>Raw data and inofrmation:</p> <p> Table_S2.csv<br> The Dataset from Supplementary Table S2: After manual pooling the Parasitoid/Aphid species based on the Data_1,2 and 6.<br> No: the number of this sample<br> City: The city that collected this sample<br> Location: the location of sample collection<br> Sample category: Aphid mummies or alive aphids<br> Sample code: the name of sample (self defined)<br> Collection year: when this sample was collected<br> Aphid taxa / Wasp taxa / Plant species /Hamiltonella strain: The Aphid/Parasitoid/Plant/Hamiltonella species <br> Sequencing method: Illumina or Sanger sequencing <br> Sequencing date: the date of sending this sample to sequencing<br> Data source: From this study or from Wu et al. 2022: Local adaptation to hosts and parasitoids shape Hamiltonella defensa genotypes across aphid species</p> <p> Data_1_Deep_sequencing_data_block_1.xlsx & Data_2_Deep_sequencing_data_block_2.xlsx <br> Sample: The sample name from Illumina sequencing, which can later be found on GenBank (BioProject PRJNA1139364).<br> Aphid: The detected aphid species for each sample.<br> Parasitoid: The detected parasitoid species.<br> The remaining columns contain ASV (Amplicon Sequence Variant) data derived from high-throughput barcoding sequencing.<br> Data1 and Data_2 are the manually curated versions of the BioProject PRJNA1139364 with added species names for each ASV. </p> <p> Data_3_Parasitoid sequences.fas & <br> Data_4_Aphid sequences.fas & <br> Data_5_Hamiltonella sequences.fas,<br> FASTA files for:<br> Parasitoid species<br> Aphid species<br> Hamiltonella strains<br> These sequences were used for phylogenetic reconstruction and subsequent analyses.</p> <p> Data_6_Parasitoid_Aphid_pooled_table.xlsx <br> Contains the OTU (Operational Taxonomic Unit) manual pooling results for Parasitoid and Aphid species based on 99% (4 base pair) sequence similarity:<br> Parasitoid pooling together group & Aphid pooling together group: Original names from Illumina sequencing.<br> Original name: Representative sequences for each group.<br> Pooled species name: Final species names used in all analyses.<br> Pooled sequences: Final representative sequences used in all analyses.<br> The result of manual pooling see the Table_S2.csv</p> <p> Data_7_Parasitoid_species_phylogeny.txt & Data_8_Aphid_species_phylogeny.txt & Data_9_Hamiltonella_phylogeny.txt <br> Phylogenetic trees for:<br> Parasitoid species<br> Aphid species<br> Hamiltonella strains<br> These trees were generated using the PhyML tool on the ATGC Montpellier platform, original fasta file was Data_3, 4 & 5.</p> <p> Data_10_aphid_host_info.csv <br> Provides aphid host information for generating Figure 2 and Figure S4:<br> Aphid: Names of aphid species included in this study.<br> Host: Host categories:<br> 1: Herb aphids<br> 2: Grass aphids<br> 3: Tree aphids<br> Host_category: Detailed descriptions of host categories.</p> <p> Data_11_Parasitoid_genus_aphid_22_species_Fig.1.csv & Data_12_Plant_genus_aphid_31_species_Fig.1.csv <br> Reduced matrices used for Figure 1, created by merging data from:<br> OTUs associated with the same parasitoid species.<br> Plant species belonging to the same genus.</p> <p> 7 Distribution Matrices (from Script_1_Extract_from_Table_S2.R)<br> Hamiltonella-Aphid Matrices:<br> Hamiltonella_Aphid_matrix_16species.csv<br> Hamiltonella_Aphid_matrix_22species.csv<br> Hamiltonella_Aphid_matrix_31species.csv<br> Parasitoid-Aphid Matrices:<br> Para_Aphid_matrix_16species.csv<br> Para_Aphid_matrix_22species.csv<br> Plant-Aphid Matrices:<br> Plant_Aphid_matrix_16species.csv<br> Plant_Aphid_matrix_31species.csv<br> Structure:<br> Rows represent aphid species.<br> Columns represent Hamiltonella strains, parasitoid species, or host plant species linked to each aphid species.<br> Usage: These matrices were used to generate Figures 2, 3, 4, Figures S2, S3, S4, S5, and for MMRR tests, ecological indices, and H2 index calculations.</p> <p> 3 Aphid phylogenetic relatedness matrices derived from Script_1_Extract_from_Table_S2.R<br> Aphid_phylogenetic_relatedness_16species.csv<br> Aphid_phylogenetic_relatedness_22species.csv<br> Aphid_phylogenetic_relatedness_31species.csv<br> These matrices provide phylogenetic distance information, showing pairwise genetic distances between the 31 aphid species included in this study.</p> <p> SraRunTable.csv<br> The BioProject (PRJNA1139364) information downloaded directly from Genbank. </p>
Formal-Specification-to-Code Trace Links Recovery
<p>The experiment data and the code used in our experiments to evaluate the performance of the proposed formal-specification-to-code trace links establishment method, and the other exsiting text-based trace links recovery methods, including Latent Semantic Indexing, Vector Space Model, Word2Vec embeddings, and LLM-based embeddings, are here. It originate from three projects. The first one is an open source VDM-SL specification of an AccountSys and its Java implementation. It comes from the book “Formal Software Development: From VDM to Java” written by Quentin Charatan and Aaron Kans. Both the VDM-SL formal specification and the Java implementation of the AccountSys were developed and provided by Prof. Aaron Kans from University of East London. It illustrates how to model bank accounts and transactions made on these as a series of deposits and withdrawals. Bank.java file implementing the GUI of the system in the source code is not used since it contains syntactical error and cannot build AST. The second project involves the SOFL specification and the Java implementation of an ATM system. The operations on current accounts of an ATM, such as deposit, withdraw, show balance, print out transaction records are specified by using SOFL formal language and implemented by using Java langauge. The third project involves an open source VDM-SL specification of a hotel system along with its C implementation. The specification describes a hotel management system where guests can check in, enter rooms, and manage cards associated with rooms and keys. The VDM-SL specification, created by Daniel Jackson and presented in his book “Software Abstractions: Logic, Language, and Analysis”. </p>
Raw images for bead aggregation assays - Heterophilic and homophilic cadherin interactions in intestinal intermicrovillar links are species dependent
<p>Intermicrovillar links are formed by two non-classical members of the cadherin superfamily of calcium-dependent cell adhesion proteins: protocadherin-24 (PCDH24, also known as CDHR2) and the mucin-like protocadherin (CDHR5). Images in this data set are for bead aggregations assays used to identify the PCDH24 and CDHR5 domains involved in both heterophilic and homophilic adhesion for human and mouse proteins.</p>
Disentangling the links between habitat complexity and biodiversity in a kelp‐dominated subantarctic community
<p>Habitat complexity is one of the most important factors modulating species diversity. This feature comprises several interrelated attributes, such as number, size, and spatial arrangement of complexity-forming elements. However, the separate and joint effects of these attributes on diversity and community structure are still not well understood. Here, we assess the relationships between several structural-complexity attributes of the subantarctic kelp <i>Lessonia flavicans </i>and species richness, total abundance, and structure of kelp-associated macrobenthic communities. We predicted that longer thalli and larger holdfasts favour greater species richness and total abundance of invertebrate organisms. To test the prediction, an observational sampling program was established in two sites of the Strait of Magellan. Uni- and multivariate analyses revealed both positive and negative effects of kelp structural-complexity attributes on diversity. Holdfast diameter and maximum frond length, followed by thallus wet weight, had the strongest positive fits to species richness and total abundance; the number of stipes, on the other hand, was negatively associated with both response variables. Longer fronds were associated with greater abundances of spirorbid polychaetes. Larger holdfasts supported larger abundances of Nereididae and Terebelidae polychaetes and the limpet <i>Nacella mytilina</i>. Contrarily, kelps with longer fronds and more stipes supported fewer amphipods. In this way, we demonstrate that different dimensions of habitat complexity can have contrasting effects on diversity and community structure, highlighting the fundamental role of multiple dimensions of kelp habitat complexity for local biodiversity.</p>
Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity
<p>The processes that give rise to species richness gradients are not well understood, but may be linked to resource-based limits on the number of species a region can support. Ecological limits placed on regional species richness should also affect population demography, suggesting that these processes could also generate genetic diversity gradients. If true, we might better understand how broad-scale biodiversity patterns are formed by identifying the common causes of genetic diversity and species richness. We develop a hypothetical framework based on the consequences of regional variation in ecological limits set by resource availability and heterogeneity to simultaneously explain spatial patterns of species richness and neutral genetic diversity. Repurposing raw genotypic data spanning 38 mammal species sampled across 801 sites in North America, we show that estimates of genome-wide genetic diversity and species richness share spatial structure. Notably, species richness hotspots tend to harbor lower levels of within-species genetic variation. A structural equation model encompassing eco-evolutionary processes related to resource availability, habitat heterogeneity, and contemporary human disturbance supports the spatial patterns we detect. These results suggest broad-scale patterns of species richness and genetic diversity could both partly be caused by intraspecific demographic and evolutionary processes acting simultaneously across species.</p>
Burst timescales and luminosities as links between young pulsars and fast radio bursts Dataset
<p>The dataset to reproduce the results and plots in Nimmo et al. 2021b (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210511446N/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210511446N/abstract</a>). The scripts to produce the plots can be found here: <a href="https://github.com/KenzieNimmo/FRB20200120E_timescales">https://github.com/KenzieNimmo/FRB20200120E_timescales</a></p> <p>The software used to make the data products are:</p> <ul> <li>SFXC (Keimpema et al. 2015;<a href="https://github.com/aardk/sfxc/"> https://github.com/aardk/sfxc/</a>) </li> <li>DSPSR (van Straten & Bailes 2011; <a href="http://dspsr.sourceforge.net/">http://dspsr.sourceforge.net/</a>)</li> <li>PSRCHIVE (Hotan et al. 2004; <a href="http://psrchive.sourceforge.net">http://psrchive.sourceforge.net</a>)</li> <li>numpy (Harris et al. 2020; <a href="https://numpy.org/install/">https://numpy.org/install/</a>)</li> </ul> <p>A complete list of the data products:</p> <ul> <li>8us/125kHz full polarisation archive files (dspsr) for all 5 bursts presented in the work (coherently and incoherently dedispersed to 87.75pc/cc). Created from filterbank data made using SFXC. Use load_file.load_archive from the above github link to load data into python as a numpy array. <ul> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr158a_corr_no0017_8us_125kHz_FullPol_FullDedisp_dm87.75.ar.calib</li> </ul> </li> <li>31.25ns/16MHz Stokes I filterbank data for B2, B3 and B4. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using SFXC. <ul> <li>pr141a_corr_no0069_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor2_Ef.fil</li> <li>pr143a_corr_no0015_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> <li>pr143a_corr_no0057_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> </ul> </li> <li>1us/500kHz Stokes I filterbank data for B2, B3 and B4. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using SFXC. <ul> <li>pr141a_corr_no0069_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor2_Ef.fil</li> <li>pr143a_corr_no0015_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> <li>pr143a_corr_no0057_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> </ul> </li> <li>125ns/4MHz full pol archive file of burst B3. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using dspsr from a filterbank file made by SFXC. <ul> <li>pr143a_corr_no0015_125ns_4000kHz_FullPol_FullDedisp_dm87.7527_SFXC.cor_Ef.calib</li> </ul> </li> </ul> <p>We also provide a number of numpy files containing analysis products for ease of reproducing the figures. </p> <ul> <li>2D autocorrelation functions of the dynamic spectra of all 5 bursts. Additionally we give the 2D Gaussian fits to the ACFs and the Lorentzian fits to the frequency ACFs (for measuring the scintillation bandwidth) <ul> <li>ACF_b*_8us_f8.npy</li> <li>fitACF_b*_8us_f8.npy</li> </ul> </li> <li>Peak signal-to-noise ratio (S/N) of burst B3 profile at 1us resolution as a function of dispersion measure (DM) with a Gaussian fit to the result <ul> <li>pr143a_corr_no0015_500ns_1000kHz_StokesI_FullDedisp_dm87.7527_DS.npy (500ns dynamic spectrum)</li> <li>DM_vs_peakSN_sfxc_IF1-11.npy</li> <li>DM_vs_peakSN_sfxc_IF1-11_fit.npy</li> </ul> </li> <li>Power spectrum (PS) of 31.25ns profile of B2, B3 and B4 with the power law (PL) fits and power law+lorentzian (PL_lor) fit for B3 <ul> <li>PS_B2_IF4678_31.25ns.npy</li> <li>PS_B3_IF3568_31.25ns.f8.npy (downsampled by a factor of 8)</li> <li>PS_B3_IF3568_31.25ns.npy</li> <li>PS_B4_IF67910_31.25ns.npy</li> <li>PL_fit_B2.npy</li> <li>PL_fit_B3.npy</li> <li>PL_fit_B4.npy</li> <li>PL_lor_fit_B3.npy</li> </ul> </li> <li>Faraday spectra of B1, B2, B3, B4 <ul> <li>no0015_8us_125kHz_faradayspec.npy</li> <li>no0057_8us_125kHz_faradayspec.npy</li> <li>no0069_8us_125kHz_faradayspec.npy</li> <li>no0069_8us_125kHz_faradayspec_2.npy</li> </ul> </li> <li>Stokes Q and U spectra for B1, B2, B3, B4 and the corresponding joint QU fits <ul> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib_QUdata.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib_QUfit.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> </ul> </li> </ul> <p> </p> <p> </p>
FIGURE 6 in The Imaginal Characters of Cincticostella gosei (Allen, 1975) linking the genus Cincticostella Allen, 1971 to Ephacerella Paclt, 1994 (Ephemeroptera: Ephemerellidae)
FIGURE 6. Egg morphology of Cincticostella gosei (SEM photos): A. Whole egg; B. Egg surface enlarged.
FIGURE 4 in The Imaginal Characters of Cincticostella gosei (Allen, 1975) linking the genus Cincticostella Allen, 1971 to Ephacerella Paclt, 1994 (Ephemeroptera: Ephemerellidae)
FIGURE 4. Male and female structures of Cincticostella gosei (digital photos): A. Forewing; B. Hindwing; C. Male head and thorax (ventral view); D. Female abdominal terga (ventral view); E. Genitalia (dorsal view); F. Genitalia (ventral view); G. Genitalia (lateral view). Scale bar: A–C. 1.0 mm; D–G. 0.5 mm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.