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2,984 results for “Raw data”
Amoxicillin degradation pathways and mass spectra raw data (using LC-MS orbitrap)
<p>The link provides five documents namely:</p> <p>File No.1 (Proposed Chemical Structures-tabulated)</p> <p>File No.2 (MS and MS2 images) support for File no.1</p> <p>File No.3 Transformation Products Pathway</p> <p>File No.4 Explanation + Justification of proposed chemical structures</p> <p>Raw Data obtained from compound discoverer</p>
Raw luminescence data for samples from Abri 122/1200 (Vârghiș Gorges, Romania)
<p>The files contain the raw luminescence data used to calculate the equivalent doses and ages cited in the study by Schmidt et al.: Evidence for the oldest Middle Palaeolithic cave occupation in the Romanian Carpathians.</p> <p>The .seq files contain the measurement parameters, while the .binx files contain the results (.binx files can be read by the Analyst software thta can be downloaded for free here: https://users.aber.ac.uk/ggd/).</p> <p>DRT: Dose recovery test</p> <p>PHP: Preheat plateau test</p> <p> </p> <p>The .csv files contain all parameters used to calculate the final ages, which were derived by using the software DRAC (Durcan et al., 2015). The two scenarios considering the shielding of the cave overburden for calculation of the cosmic dose rate refer to the two different .csv files, according to their name.</p> <p> </p>
Raw data for crystal structure of the flavoprotein monooxygenase TrlE from Streptomyces cyaneofuscatus Soc7, PDB ID: 8RQH
<p>X-Ray raw data for crystal structure of the flavoprotein monooxygenase TrlE from Streptomyces cyaneofuscatus Soc7 (PDB ID: 8RQH). The data have been collected at the Swiss Light Source (2022-11-20) at the X06SA beamline at a wavelength of 1.00003A.</p>
Raw data (RF) provided for : Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans
<p><strong>Abstract :</strong> Estimation of glomerular function is a key element in the diagnosis of kidney disease. However, the study of glomeruli in the clinic remains indirect through urine and blood tests. Recent imaging technique called Ultrasound Localization Microscopy (ULM) originated from the ability to record continuous movements of individual microbubbles in the bloodstream. Although it improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.</p> <p>We acquired ultrasound sequences from living humans and rats and then applied filtering dividing the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize this new population of microbubbles with singular behaviors: we called this technique “sensing ULM” (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.</p> <p>In this study, we report the observation of microbubbles flowing in capillaries bundles, i.e. the glomeruli, in the kidney in living humans and rats. We introduce a set of analysis tools dedicated to extracting quantitative information from individual microbubbles, like the remanence time or the normalized distance.</p> <p>As glomeruli play a key role in kidney function, their observation could yield a deeper understanding of kidney diseases and provide a diagnostic tool for patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is one of the keys to understanding most diseases, like cancers, diabetes, or kidney failures. </p> <p><strong>Academic reference to be cited : </strong>Denis, Bodard, Hingot, Chavignon, Battaglia, Renault, Lager, Aissani, Hélénon, Correas, and Couture. <em>Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans,</em> eBioMedicine, 2023.</p> <p><strong>Article</strong> : <a href="https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext">https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext</a></p> <p><strong>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>Beamformed dataset</strong> : <a href="../record/6811910#.ZA9dV3bMLid">https://zenodo.org/record/6811910#.ZA9dV3bMLid</a></p> <p><strong>Corresponding authors : </strong></p> <ul> <li>Article : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Sylvain Bodard, <a href="mailto:sylvain.bodard@aphp.fr">sylvain.bodard@aphp.fr</a></li> <li>Scripts, and codes : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Jacques Battaglia, <a href="mailto:jacques.battaglia@sorbonne-universite.fr">jacques.battaglia@sorbonne-universite.fr</a></li> <li>Materials, collaborations, rights and others: Olivier Couture, <a href="mailto:olivier.couture@sorbonne-universite.fr">olivier.couture@sorbonne-universite.fr</a></li> </ul>
Raw Data for the Protocol: Antibody-Assisted Selective Isolation of Purkinje Cell Nuclei
<p>Sun1/sfGFP+, Pcp2-Cre+ and Sun1/sfGFP+, Pcp2-Cre- cryosectioned cerebella immunostained for the Myc tag (files 3037, 3046), which is fused to the GFP protein, Calbindin (files 3038, 3047) and Hoechst (files 3036, 3045). </p> <p> </p> <p>Original uncropped images from western blot analysis of TOM20, Histone H3, and GAPDH.</p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
Raw and processed autofluorescence neuroimaging data
<p>Raw and processed <em>in vivo</em> mouse whole-cortex autofluorescence imaging data.</p> <p>This release accompanies the pub <a href="https://doi.org/10.57844/arcadia-b963-15ac">"Label-free neuroimaging in mice captures sensory activity in response to tactile stimuli and acute pain</a>.</p> <p>The raw data is split into five zipfiles because it is quite large (70GB total). Each of these five zipfiles corresponds to one of the five subdirectories in the single zipfile of processed data.</p>
Raw data of the study: Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a northern Andean city
<p>This datasheet contains raw data on bird count records made from 2016 and 2019. Data were taken in urban and adjacent non-urban areas of Medellín, Colombia. It was part of a collaborative sampling effort during environmental assessments and personal research, summarizing systematic information on 139 sampling points (124 within the city and 15 in adjacent non-urban areas). All points were sampled under the same protocol in order to facilited data for research; in all cases, sampling was in charge of ornithologist with at least 4 years of previous experience in bird surveys. This protocol consisted in sampling during 10 minutes, four times per point (i.e., repetitions), using a fixed radius of 25 m. </p> <p>Information on bird surveys (Count_Data within the corresponding datasheet tab) contains the ID of each site; whether corresponded to a urban or non-urban site; in what category of urban development the site was located, based on 1000, 500 and 200 m buffers (from the observer during bird counts: moderate, low or high); the taxonomic information of each species (order, family, scientific name); the number of recorded individuals; the repetition or number of the visit (1, 2, 3, or 4); the name of the project; the name of the observer, and the date of sampling. </p> <p>Information on categorization of bird species (Categorization within the corresponding datasheet tab) represents additional information on altitudinal ranges, trophic guilds, distribution, and others. In addition, information on frequency for each bird species is given, according to the location of each sampling site and the way it was grouped. This information was the base for categorizing bird species as urban avoider, utilizer, or dweller, under the calculations and decision rules that are also given within the corresponding cells of the datasheet.</p> <p>Any further information or questions about this data could be ask directly, writing to the e-mails: jgarizabal@unal.edu.co or njmacer@unal.edu.co.</p> <p> </p>
Raw metabolomics data of the paper: New findings in the metabolism of the saffron apocarotenoids, crocins and crocetin, by the human gut microbiota
<p>Raw dataset of the metabolomicas data of the study : New findings in the metabolism of the saffron apocarotenoids, crocins and crocetin, by the human gut microbiota.</p> <p>The csv file contain the raw data matrix exported from MS-DIAL software after total ions aligment across all study samples.</p>
Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software
<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: <strong>- Dynamic_PEFC_data.h5</strong> # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z <strong>- liquid_simulation.h5</strong> # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). <strong>- phasetran_simulation.h5</strong> # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: <strong>- astra_nonrigid.zip</strong> # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. <strong>- reconstruction_PEFC.py</strong> # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * <strong>!!!</strong> Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. <strong>- reconstruction_simulation.py</strong> # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</p>
N2O raw data from static greenhouse gas chamber measurements
<p>This dataset contains N<sub>2</sub>O concentration measurements of a 2 years measurement campaign for greenhouse gas fluxes from agricultural soils.</p> <p>The format of the data is ready to be fed into the gasfluxes R package on CRAN to calculate fluxes for each individual chamber measurement (identical IDs are referred to one single measurement, the ID contains the measurement day, treatment and replicate).</p> <p>The data is originally published in Krauss et al. 2017 and further used for improvements of the flux calculation procedure in Hüppi et a. 2018 (see references)</p>
Applications and raw data for SciPipe genomics and transcriptomics case studies
<p>Accompanying applications and raw data for the genomics and transcriptomics (RNA-Seq) case studies for SciPipe [1] available at https://github.com/pharmbio/scipipe-demo </p> <p>[1] http://scipipe.org</p>
NQRS raw data to 10.1103/PhysRevX.8.021076 TABLE VII
<p>These are the raw data files to the NQR measurements presented in https://link.aps.org/doi/10.1103/PhysRevX.8.021076. T1 and T2 measurements were performed using the sequences explained in APPENDIX B. Fitting models (B1), (B2) and (B3) have been applied.</p> <p>The best-fit parameters for T1, T2 and FWHM can be found in APPENDIX C, TABLE VI.</p> <p>The names of the files follow the labelling: sampleNr_transitionNr_temperture_sequence type.txt</p> <p>the organization of the txt-files is as follows:</p> <p><strong>header: </strong>file source, number of frequency-points (Starting Point, End Point) and number of records (Starting 2D Record, Ending Record) = number of parameter sweeps (e.g. inversion time, or echo time)</p> <p><strong>Data set</strong>: records stacke on top of each other:</p> <p><strong> real part [a.u.] / imaginary part [a.u.] / frequency shift [kHz]</strong></p> <p>the frequency is given in difference from the central frequency (Obeserve Freq.), which can be found in the footer</p> <p><strong>footer</strong>: contains information on the used sequence and its paramteres. eg.g the "Obeserve Freq" of the spectrometer during the sequence, (excitiation and detection frequency)</p> <p>also: filter bandwidth, dwell time, delay time, pulse length ....</p> <p>the paramter "step size" of the sweep parameter (echo time or inversion time, respectively) is available on request: christian.goesweiner@tugraz.at</p> <p> </p> <p> </p>
GATEMAN project, GNSS raw data in presence of spoofing
<p>GNSS raw data generated during the in-lab validation activities of spoofing detection and localization performed in the frame of the <strong>GATEMAN</strong> project. These files are grouped for each type of validation scenario defined. A word file describing the test setup is included.</p>
AOSLO Single Cell Blood Flow - Raw Data (eLife paper: Joseph et al. 2019)
<p>Raw AOSLO videos of eLife paper Joseph et al. 2019 - 'Imaging single-cell blood flow in the smallest to largest vessels in the living retina'. <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.7554_eLife.45077&d=DwMFaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=EdsTL7DuEvOHun7eVBmBd9sxUuPhDEmdFDf0tlkKUO4&m=56MyNrE_d-v6PSU7Go9NePVOrIpAvWHBaC8wVvgs3_k&s=G7SkWT-fSV9d3SJmpWEUUGp2a6mGotbG-uAwNZftpIo&e=">https://doi.org/10.7554/eLife.45077</a> . For additional data or questions, please contact author Aby Joseph (aby.joseph@rochester.edu, dreamworks1991@gmail.com)</p>
raw data
<p>Spectral images of the cis to trans transition of FK11 triggered by a diode and follow up until reversal.</p>
Raw data supporting Identifying invertebrates from pitfall, flight interception traps and hand collecting
<p>Raw data supporting identifying invertebrates from pitfall, flight interception traps and hand collecting: comparing metabarcoding with traditional methods.</p> <p>Two step PCRs were performed on each sample replicate using modified primers mICOIintF and jgHCO2198 followed by the Nextera XT index kit v2 Set A (Illumina).</p> <p>The pool was loaded onto an illumina MiSeq using a MiSeq Reagent Kit v2 500 cycle kit (Illumina), with 10% Phi-X to generate 250-bp paired-end reads.</p>
The terrestrial carnivorous plant Utricularia reniformis sheds light on environmental and life-form genome plasticity: Annotation, Gene Ontology and raw data
<p><strong>Description:</strong> In this work, we deeply sequenced (genome and transcriptome of different organs), assembled, and analyzed the 311-Mbp genome of the terrestrial carnivorous plant <em>U. reniformis</em> (Lentibulariaceae). This project presents great importance to the understanding of genomic, evolutive and functional aspects of<em> U. reniformis</em>, which may, with the next-generation sequencing and computational biology approaches shed light to a better understanding not only for the biology and evolution of <em>Utricularia</em> genus, but also for other genera and lineages of the Lentibulariaceae family. Here we present all the raw data generated, including annotation and gene ontology files.</p> <p><strong>External Information</strong></p> <p><a href="https://genomevolution.org/coge/GenomeInfo.pl?gid=54799">Genome Browser</a> avaliable at CoGe Portal (https://genomevolution.org/coge/GenomeInfo.pl?gid=54799)</p> <p><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">GenBank </a><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">Bioproject</a> (https://www.ncbi.nlm.nih.gov/bioproject/290588) for raw genomic and transcriptomic reads</p> <p><a href="https://bv.fapesp.br/en/auxilios/84264/genomics-and-transcriptomics-of-utricularia-reniformis-lentibulariaceae-an-evolutive-and-function/">FAPESP grant website</a> contaning the project abstract and other information.</p> <p><strong>Papers published related to <em>Utricularia reniformis</em> genome</strong></p> <pre><strong>[1]</strong> Silva SR, Diaz YC, Penha HA, Pinheiro DG, Fernandes CC, Miranda VF, MichaelTP, Varani AM. <strong>The Chloroplast Genome of Utricularia reniformis Sheds Light on the Evolution of the ndh Gene Complex of Terrestrial Carnivorous Plants from the Lentibulariaceae Family</strong>. PLoS One. 2016 Oct 20;11(10):e0165176. doi:<strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/27764252">10.1371/journal.pone.0165176</a></strong>. </pre> <pre><strong>[2] </strong>Silva SR, Alvarenga DO, Aranguren Y, Penha HA, Fernandes CC, Pinheiro DG, Oliveira MT, Michael TP, Miranda VFO, Varani AM. <strong>The mitochondrial genome of the terrestrial carnivorous plant Utricularia reniformis (Lentibulariaceae): Structure, comparative analysis and evolutionary landmarks.</strong> PLoS One. 2017 Jul19;12(7):e0180484. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/28723946">10.1371/journal.pone.0180484</a></strong>.</pre> <pre><strong>[3] </strong>Silva SR, Moraes AP, Penha HA, Julião MHM, Domingues DS, Michael TP, Miranda VFO, Varani AM. <strong>The Terrestrial Carnivorous Plant Utricularia reniformis Sheds Light on Environmental and Life-Form Genome Plasticity.</strong> Int J Mol Sci. 2019 Dec 18;21(1). pii: E3. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/31861318">10.3390/ijms21010003</a></strong>.</pre> <p><strong>Acknowledgements</strong></p> <p>This work was supported by Sao Paulo Research Foundation FAPESP, Grant ID: [1325164-6]</p> <p> </p> <p><strong>---------------------------------------------------------</strong><br> <strong>FILES DESCRIPTION</strong><br> <strong>---------------------------------------------------------</strong><br> <br> ----------------<br> <strong>ANNOT-vFinal.sql: </strong>MySQL database containing all integrated annotation information of Urenif and Ugibba<br> ----------------<br> <strong>TABLE fields description</strong><br> gene_name gene name generated by EVidence Modeler + PASA<br> length gene lenght<br> status duplicate_gene_classifier status (0:singleton, 1:dispersed, 2:proximal, 3: tandem, 4:WGD)<br> product gene product <br> GOterms Blast2GO/OmicsBox GOterms<br> GO_mapping Blast2GO/OmicsBox GOterms derived from direct mapping (UniProt)<br> GO_annotation Blast2GO/OmicsBox annotated GOterms<br> GO_interpro Blast2GO/OmicsBox derived from InterProScan<br> EC Blast2GO/OmicsBox EC number<br> EC_name Blast2GO/OmicsBox enzyme name<br> NOG_annot EggNOG annotation description<br> NOG_EC EggNOG EC number<br> NOG_GO EggNOG GOterms<br> NOG_class EggNOG COG/KOG classfication<br> KEGG_Pathway EggNOG KEGG pathyways<br> KEGG_ko EggNOG KEGG ko<br> CAZy EggNOG CAZy enzymes<br> TAIR_gene Closest A. thaliana gene name (homologous) TAIR database lasted version<br> TAIR_annot Closest A. thaliana gene product (homologous) TAIR database lasted version <br> ortho MCL clustering among Vvinifera, Athaliana, and Slycopersicum (S:singleton, C: clustered, Y: shared)<br> ortho_two MCL clustering among Urenif and Ugibba (S:singleton, C: clustered, Y: shared)<br> -<br> -<br> ----------------<br> <strong>CEGs.zip </strong> 336 shared and concatenated CEGs from Urenif, U. gibba, Genlisea nigrocaulis, G. hispidula, G. aurea, G. pygmaea, and G. repens.<br> ----------------</p> <p><strong>ProcessRepeats_mod</strong> Modified version of RepeatMasker, ProcessRepeats script for detection of plant evolutionary lineages<br> ----------------</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia gibba</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Ugibba</strong><strong>-no-masked.fa </strong> Ugibba genome excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ugibba-softmasked.fa</strong> Ugibba genome RepeatMasker softmasked and excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ug.collinearity </strong> MCScanX collinearity file<br> <strong>Ug-duplicates.txt</strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ug.gene_type </strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ug.tandem </strong> Ugibba tandem genes generated by MCScanX tool<br> <strong>Ugibba_annot.annot </strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim) <strong>Ugibba_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong><strong> </strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Ugibba</strong><strong>.cDNA</strong> Ugibba cDNAs fasta file<br> <strong>Ugibba</strong><strong>.CDS </strong> Ugibba CDSs fasta file<br> <strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Ugibba GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Ugibba GFF3 file fully annotated (genes only)<br> <strong>Ugibba_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Ugibba_fasta.fasta</strong> Blast2GO/OmicsBox Ugibba fasta proteins containg annotation (product and GO terms)<br> <strong>ugibba_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>ugibba_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>ugibba_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)</p> <p><strong>Ugibba_GAF.txt</strong> GAF file<br> <strong>Ugibba</strong><strong>.gene</strong> Ugibba gene fasta file<br> <strong>Ugibba_GOstat.txt </strong> GOstat file<br> <strong>Ugibba</strong><strong>-PASA-assemblies.fasta </strong> Ugibba PASA assemblies<br> <strong>Ugibba</strong><strong>-PASA.stats </strong> Ugibba annotation STATS<br> <strong>Ugibba</strong><strong>.</strong><strong>prot</strong><strong> </strong> Ugibba protein fasta file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff </strong> Ugibba RepeatMasker gff file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff3 </strong> Ugibba RepeatMasker gff3 file<br> <strong>Ugibba</strong><strong>-RepeatMasker.tbl </strong> Ugibba RepeatMasker results<br> <strong>Ugibba</strong><strong>-RepeatMasker-v2.gff3</strong> Ugibba RepeatMasker gff3 second version file<br> <strong>Ugibba</strong><strong>-RNAseq-assembled.fasta </strong> Ugibba RNAseq assembled transcriptome (Trinity)<br> <strong>Ugibba_TEs_DANTE_2019.fa </strong> Ugibba TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Ugibba_WEGO.txt </strong> WEGO file</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia reniformis</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Urenif</strong><strong>-no-masked.fa </strong> Urenif genome excluding organellar genomes<br> <strong>Urenif</strong><strong>-</strong><strong>softmasked</strong><strong>.fa</strong> Urenif genome RepeatMasker softmasked and excluding organellar genomes<br> <strong>Ur.collinearity </strong> MCScanX collinearity file<br> <strong>Ur-duplicates.txt </strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ur.gene_type</strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ur.tandem</strong> Urenif tandem genes generated by MCScanX tool<br> <strong>Urenif_annot.annot</strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim)<br> <strong>Urenif_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Urenif</strong><strong>.cDNA</strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>.CDS </strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Urenif GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Urenif GFF3 file fully annotated (genes only)<br> <strong>Urenif_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Urenif_fasta.fasta</strong> Blast2GO/OmicsBox Urenif fasta proteins containg annotation (product and GO terms)<br> <strong>urenif_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>urenif_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>urenif_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)<br> <strong>Urenif_GAF.txt </strong> GAF file<br> <strong>Urenif</strong><strong>.gene</strong> Urenif gene fasta file<br> <strong>Urenif_GOStat.txt </strong> GOstat file<br> <strong>Urenif</strong><strong>-PASA-assemblies.fasta</strong> Urenif PASA assemblies<br> <strong>Urenif</strong><strong>-PASA.stats </strong> Urenif annotation STATS<br> <strong>Urenif</strong><strong>.</strong><strong>prot</strong><strong> </strong> Urenif protein fasta file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff </strong> Urenif RepeatMasker gff file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff3 </strong> Urenif RepeatMasker gff3 file<br> <strong>Urenif</strong><strong>-RepeatMasker.tbl </strong> Urenif RepeatMasker results<br> <strong>Urenif</strong><strong>-RepeatMasker-v2.gff3 </strong> Urenif RepeatMasker gff3 second version file<br> <strong>Urenif</strong><strong>-RNAseq-assembled.fasta </strong> Urenif RNAseq assembled transcriptome (Trinity)<br> <strong>Urenif_TEs_DANTE_2019.fa </strong> Urenif TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Urenif_WEGO.txt </strong> WEGO file<br> <strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong></p>
Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness"
<p><strong>Raw data for the article:</strong> Bestion, E, Soriano-Redondo, A, Cucherousset, J, Jacob, S, White, J, Zinger, L, Fourtune, L, Di Gesu, L, Teyssier, A, Cote, J. Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness. Proceedings of the Royal Society: B. 2019. 286:20192227. https://doi.org/10.1098/rspb.2019.2227</p> <p><strong>This data should be cited as</strong>: Bestion, E, Soriano-Redondo, A, Cucherousset, J, Jacob, S, White, J, Zinger, L, Fourtune, L, Di Gesu, L, Teyssier, A, Cote, J (2019). Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness", Bestion et al 2019 Proceedings B. (Version 1). Zenodo. https://doi.org/10.5281/zenodo.3475402</p> <p><strong>This data is composed of</strong> one dataset with 21 columns and a README file</p> <p>Composition of the Bestion_2019_isotopy_dataset_for_zenodo.csv dataset</p> <p>- Individual: numerical index corresponding to each of the 327 individuals in the dataset<br> - Age: age class, J = juvenile (<1 year old), A = adult (1 and 2+ year old)<br> - Sex: F (female) or M (male)<br> - Climate: Present-day climate or Warm climate<br> - Enclosure: enclosure number (10 enclosures, 5 per climatic treatment)<br> - delta13C_september: stable isotope values for delta13C in september<br> - delta15N_september: stable isotope values for delta15N in september<br> - delta13C_september_corrected: stable isotope values for delta13C in september corrected for the stable isotope value of the three invertebrate prey categories<br> - delta15N_september_corrected: stable isotope values for delta15N in september corrected for the stable isotope value of the three invertebrate prey categories<br> - Prop_predator_eaten: proportion of predatory invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Prop_phytophagous_eaten: proportion of phytophagous invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Prop_detritivorous_eaten: proportion of detritivorous invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Levins_diet_index: levins' dietary index corresponding to lizard diet specialization (with 3 = completely generalist and 1 = completely specialist lizard)<br> - Body_Size_september: lizard body size (snout-vent length in mm)<br> - Body_Mass_september: lizard body mass (in g)<br> - Body_Condition_september: lizard body condition (residuals of body mass by body size)<br> - Microbiota_shannon_index: shannon index representing gut microbial bacteria community diversity<br> - Survival_winter: survival during the winter (1 = survived, 0 = died)<br> - Abundance_predator_enclosure: abundance of predatory invertebrates within the enclosure<br> - Abundance_phytophagous_enclosure: abundance of phytophagous invertebrates within the enclosure<br> - Abundance_detitivorous_enclosure: abundance of detritivorous invertebrates within the enclosure</p> <p> </p> <p> </p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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