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11,177 results for “identify”
FIGURE 6 in Morphology, songs and genetics identify two new cicada species from Morocco: Tettigettalna afroamissa sp. nov. and Berberigetta dimelodica gen. nov. & sp. nov. (Hemiptera: Cicadettini)
FIGURE 6. Body and male genitalia morphology of Berberigetta dimelodica. A—Designated male holotype of B. dimelodica. Scale bar equals 10 mm; B—Designated female paratype of B. dimelodica. Scale bar equals 10 mm; C, D—Male paratypes' pygophore overview in posterior and lateral views, respectively. Scale bars equal 500 µm. E, F—Aedeagus in upper and lateral views, respectively. Scale bars equal 200 µm. Pygophore and aedeagus photos were taken of material preserved in Kaiser gelatin. Note that the tip of the left pseudoparamer is broken.
FIGURE 2 in Morphology, songs and genetics identify two new cicada species from Morocco: Tettigettalna afroamissa sp. nov. and Berberigetta dimelodica gen. nov. & sp. nov. (Hemiptera: Cicadettini)
FIGURE 2. Body and male genitalia morphology of Tettigettalna afroamissa. A,B—Designated male holotype of T. afroamissa in dorsal and ventral views, respectively. Scale bar equals 10 mm; C, D—Designated female paratype of T. afroamissa in dorsal and ventral views, respectively. Scale bar equals 10 mm; E, F—Male paratype's pygophore in in lateral and posterior views, respectively. Scale bar equals 500 µm. Photos taken on dry specimens; G, H—Aedeagus in upper and lateral views, respectively. Scale bars equal 200 µm. Photos taken of material preserved in Kaiser gelatin.
Data and code for "Large language models identify causal genes in complex trait GWAS"
<p><span>This file contains the data and scripts for the preprint "Large language models identify causal genes in complex trait GWAS"</span></p>
Phasor Identifier Test Dataset
<p>The folder contains three sub-folders used in the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli. The data serve for specific examples on Phasor-FLIM customary analysis within the framework of the Python Notebook ran in Google Colab, extensively documented and available at: <a href="https://github.com/Mariochem92/PhasorIdentifier">https://github.com/Mariochem92/PhasorIdentifier</a></p><p>Here you can find precompiled versions for the specific analysis of cellular and encapsulated drug analysis:</p><ul><li><a href="https://github.com/Mariochem92/PhasorIdentifier/blob/main/Script%20v1/PhasorIdentifier_v1_0_3_Cellular_data.ipynb">PhasorIdentifier_v1_0_3_Cellular_data.ipynb</a></li><li><a href="https://github.com/Mariochem92/PhasorIdentifier/blob/main/Script%20v1/PhasorIdentifier_v1_0_3_encapsulated_drug(ref%20format).ipynb">PhasorIdentifier_v1_0_3_encapsulated_drug(ref format).ipynb</a></li></ul><p> </p><p>Those both work for any other analysis as well as a version of the code which is on ongoing development but more dispersive for newcomers, called PhasorIdentifier_v1_0_3_AllFunctionalities.ipynb</p><p> </p><p>All the data was acquired at laser repetition frequency 80 MHz</p><p> </p><p><strong>Evolution of phasor-FLIM signals (DFD-FLIM acquisition)</strong></p><p>Folders " <strong>Encapsulated irinotecan </strong>" and "<strong>Irinotecan and metabolite SN-38 at different pH</strong>" house files in .R64 format. Within, you'll find encapsulated irinotecan, irinotecan and metabolite SN-38 samples files. In this experiments irinotecan is encapsulated in the commercial nano formulation Onivyde in saline solution.</p><p>This data is represented in Fig 3 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.</p><p> </p><p><strong>Cellular Metabolism-Morphological and statistical analysis (DFD-FLIM acquisition)</strong></p><p>The folder " <strong>INS1E cytokine stimulation and control </strong>" houses files in .R64 format. Within, you'll find INS1E control and INS1E cytokines, which respectively stand for INS1E samples and INS1E samples upon cytokines administration.</p><p>This data is represented in Fig 4 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.</p><p> </p><p><strong>Intensity and molar Fractions (TD-FLIM acquisition converted via Fourier Transform)</strong></p><p>The folder "<strong>Doxoves Storage Files</strong>" houses a subset of Doxoves files in .ref format. Within, you'll find Doxoves4 and Doxoves37, which respectively stand for Doxoves samples stored at 4°C and 37°C. The numerical value following "Doxoves4/Doxoves37" indicates the duration of storage in days.</p><p>This data is represented in Fig 5 of the work Phasor Identifier: A Cloud-based Analysis of Phasor-FLIM Data on Python Notebooks by M.Bernardi and F.Cardarelli.</p><p> </p><p> </p>
Identified Potential Vorticity Structures associated with African Easterly Waves
<p>This data set contains identified and tracked 3-D Potential Vorticity (PV) structures associated with African Easterly Waves (AEWs), based on the ERA-5 reanalysis of the years 2002-2022 from June to October.</p><p>First the AEW troughs are identified as outlined and separately submitted in <a href="https://zenodo.org/records/8403744">this dataset.</a> To extract the 3-D PV structures in these waves, a Hilbert transform is employed to compute the wave phase at every point in the domain. This yields the trough area of the waves. Within these areas, PV is collected and processed, and assigned a low-dimensional description, including a best-fit ellipsoid for statistical analyses. More details on the methodology can be found in a soon to be submitted publication.</p><p>The JSON files within this dataset provide a description of the PV features, structured as follows:</p><ol><li>Each file contains one year of data, with one set each.</li><li>Each set includes:<ul><li>A list of timestamps, where each timestamp contains the identified troughs at a specific time. Each time entry contains a list of objects (PV features), with each containing a set-unique identifier and properties describing the feature, such as the ellipsoid and a bounding box.</li><li>The data graph, consisting of a list of edges, wherein each edge connects one parent object to multiple child objects, forming the tracking graph. A connection between a parent and child indicates that they represent the same feature entity, with the child being the next manifestation of the parent entity along the time line.</li><li>A list of tracks, with each track represented as a data graph as well.</li></ul></li></ol><p> </p>
Identifying candidate host plants for trap cropping against Drosophila suzukii in vineyards
<p>Raw data for the identification of candidate trap crop species against <i>Drosophila suzukii </i>in vineyards. Overall, oviposition attractiveness and suitability for larval development was assessed for the fruits of 60 different plant species in no-choice and multiple-choice preference test conducted in 2018 in relation to fruit traits such as skin hardness, skin elasticity, acidity and sugar content. </p>
FIGURE 3 in Molecular approach to identify sibling species of the Ceriodaphnia cornuta complex (Cladocera: Daphniidae) from Australia with notes on the continental endemism of this group
FIGURE 3. Maximum likelihood analysis of COI gene for Ceriodaphnia cf. cornuta complex. Numbers above branches are Maximum likelihood (100 replicates) and numbers in bold are from Bayesian Analysis.
FIGURE 1 in Molecular approach to identify sibling species of the Ceriodaphnia cornuta complex (Cladocera: Daphniidae) from Australia with notes on the continental endemism of this group
FIGURE 1. Phylogenetic trees inferred from concatenated mtDNA gene sequences for Ceriodaphnia cf. cornuta complex within Australia. Numbers above branches are Maximum likelihood (100 replicates) and numbers in bold are from Bayesian Analysis. Legends: = Species A; = Species B; = Species C.
Code and Data Repository for "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments"
<p>The repository includes data and R code necessary to reproduce all figures and analyses presented in the study titled "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments".</p>
Towards Identifying Python Proficiency to Foster Software Maintenance and Evolution
Open the record for dataset details and reuse information.
SNPs and InDels identified in 390 peanut accessions
<p>SNPs and InDels identified in 390 peanut accessions</p>
The code and software to identify putative trace makers of horizontal trace fossils: Palaeontology
<p>These are the code ("CurveGithub.m") and the free software ("Curvesoftware.exe") that help you identify putative trace makers of horizontal trace fossils with frequency spectrum and autocorrelation function. You can also use it to study the frequency and size of self-repeating patterns in a horizontal trace. You can use the metrics to quantify ichnotaxonomy as well. An example of the coordinates of a horizontal trace is "A curve-Github.txt", which is output from Getdata Graph Digitizer, with the first describing sentence in the output file removed, leaving cooridnates only. Zero-value is not allowed in the coordinates. The paper is published in Palaeontology, with doi: 10.1111/pala.12686, <a title="Quantitative Ichnology" href="https://onlinelibrary.wiley.com/doi/abs/10.1111/pala.12686">https://onlinelibrary.wiley.com/doi/abs/10.1111/pala.12686</a> </p> <p>IMPORTANT NOTICE !!!!!</p> <p>MATLAB code Line 41:</p> <p>"plot(ss,YY(M:M+99)/max(YY(M:M+99)),'r-'); " means plotting the autocorrelation over sampling steps;</p> <p>If you would like to plot the autocorrelation over normalized length (as in both the manuscript and the free software), it should be</p> <p>"plot(ss/Fs,YY(M:M+99)/max(YY(M:M+99)),'r-'); "</p>
Data for Figures and Tables in: "A Large Sample of Extremely Metal-poor Galaxies at z<1 Identified from the DESI Early Data"
<p>The dataset include the data used for generating the figures and forming the tables in this paper. It include 35 data files, where most of them are in FITS binary table format and six of them are in JPG format. Each file have a detailed description as presented in the ReadMe file (please refer it for more details).</p>
FIGURES 20–23 in Giffenia koreana sp. nov. (Bacillariophyta): A newly identified epipelic diatom in the tidal flat sediments of Suncheonman Bay, extant for over 1,400 Years
FIGURES 20–23. SEM images of Giffenia koreana sp. nov. using type materials. Figs 20, 21: Valve face displaying the terminal valve apex with a hyaline area. Fig. 22: The apex of the epi/hypo-valve featuring a terminal hooked raphe and numerous alveoli. Fig. 23: The mantle showing numerous nail-shaped pores clusters positioned below the raphe slit (arrow). Scale bars: 5 µm for Fig. 21; 10 µm for Figs 20, 22, 23.
FIGURES 15–19 in Giffenia koreana sp. nov. (Bacillariophyta): A newly identified epipelic diatom in the tidal flat sediments of Suncheonman Bay, extant for over 1,400 Years
FIGURES 15–19. SEM images of Giffenia koreana sp. nov. on the type materials. Fig. 15: A whole cell displaying the central thin lipshaped sternum (arrow), numerous alveoli, and raphe slit. Figs 16–19: A hyaline area between the raphe slit and the alveoli (arrow). Figs 17–19: Valve apex featuring a downturned raphe apex, and with curved alveoli (arrow). Scale bars: 5 µm for Figs 18, 19; 10 µm for Figs 15–17.
FIGURES 3–8 in Giffenia koreana sp. nov. (Bacillariophyta): A newly identified epipelic diatom in the tidal flat sediments of Suncheonman Bay, extant for over 1,400 Years
FIGURES 3–8. LM images of the undulating external valve view of Giffenia koreana sp. nov. on the holotype (Figs 3–5) and isotype (Figs 6–8). Fig. 3, 4: The valve face focused on the slightly convex side. Fig. 5: The valve face focused on the slightly concave side, with an uneven central sternum positioned along the apical axis. Figs 6–8: The valve face observed on various focuses. Scale bar: 10 µm.
FIGURES 1, 2 in Giffenia koreana sp. nov. (Bacillariophyta): A newly identified epipelic diatom in the tidal flat sediments of Suncheonman Bay, extant for over 1,400 Years
FIGURES 1, 2. Map of the study area. Fig. 1: A satellite map of the Republic of Korea. Fig. 2: Location of the study area, SCW03.
A massive proteogenomic screen identifies thousands of novel human protein coding sequences
<p>Accurate annotation of genes in the human genome is fundamental for biomedical research and genomic data interpretation. The Ensembl, RefSeq, and GENCODE consortiums continuously update the human genome annotations based on new computational and experimental evidence, and new proteins were identified constantly. The Genotype-Tissue Expression (GTEx) project has generated more than 15,000 RNA sequencing dataset from multiple-tissues of more than 800 donors which allows to model almost all transcripts and proteins in the human genome. Using proteins translated from the GTEx transcript model, more than 21 million in-silico trypsin-digested peptides were generated. To identify high-confidence novel proteins with proteomic support, we screened more than 2,000 proteomic projects in the PRIDE database and selected more than 50,000 mass spectrometry (MS) runs from 923 projects. These MS data were used to validate the predicted novel peptides. With a stringent standard, we identified almost 20,000 novel peptides. </p> <p>This dataset include files used in the the above analysis. More details can be found in the GitHub page (https://github.com/ATPs/human_novo_protein_2022). </p>
Demo datasets for the protocol to identify shared transcriptional risks between diseases and compounds predicted to result in mutual benefit
<p>We present a computational protocol (https://github.com/ghbore/protocol-cancer-cvd-similarity), implemented as a Snakemake workflow, that was used in previous works (Gao et al., 2022; Baylis et al., 2023). This protocol allows researchers to identify shared transcriptional processes that drive disease and to screen existing compounds for mutual benefit. The protocol also includes a description of the pharmacovigilance study design used to validate the effect of novel compounds using electronic health records, where applicable. This repository bundles the datasets used in previous works as an example to run through the Snakemake workflow. These datasets include the TCGA cancer dataset, the STARNET and BiKE CVD datasets, and other dependent resources.</p>
R workspace: A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances
<p>R workspace and associated code used in the the manuscript '<span>A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances'</span></p>
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.