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1,045 results for “Generated Data”
Massively parallel sequencing data of the HIV-1 pol region generated from the plasma of therapy-naïve chronically infected Brazilian blood donors
<p>The submitted massively parallel sequencing (MPS) data were partial data from the pol region of HIV-1 plasma viruses. Samples were obtained from 18 therapy-naive HIV-1 Brazilian blood donors with longstanding infection. Illumina ultra-deep sequencing technology (MiSeq platform) was used to generate the sequences. </p>
Next-generation Sequencing Data Associated with "Genome Editing Outcomes Reveal Mycobacterial NucS Participates in a Short-Patch Repair of DNA Mismatches"
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DNA sequence data generated using non-invasive feather and eggshell samples from the Grenada Dove for two gene regions: Cyt b and ND2
<p>As an island endemic with a decreasing population, the Critically Endangered Grenada Dove <em>Leptotila wellsi</em> is threatened by accelerated loss of genetic diversity resulting from ongoing habitat fragmentation. Small, threatened populations are difficult to sample directly but advances in molecular methods mean that non-invasive samples can be used. We performed the first assessment of genetic diversity of populations of Grenada Dove by a) assessing mtDNA genetic diversity in the only two areas of occupancy on Grenada, b) defining the number of haplotypes present at each site and c) evaluating evidence of isolation between sites. We used non-invasively collected samples from two locations: Mt Hartman (n=18) and Perseverance (n=12). DNA extraction and PCR were used to amplify 1,751 bps of mtDNA from two mitochondrial markers: NADH dehydrogenase 2 (<em>ND2</em>) and Cytochrome b (<em>Cyt b</em>). Haplotype diversity (<em>h</em>) of 0.4, a nucleotide diversity (π) of 0.00023 and two unique haplotypes were identified within the <em>ND2</em> sequences; a single haplotype was identified within the <em>Cyt b </em>sequences. Of the two haplotypes identified; the most common haplotype (haplotype A = 73.9%) was observed at both sites and the other (haplotype B = 26.1%) was unique to Perseverance. Our results show low mitochondrial genetic diversity and clear evidence for genetically isolated populations. The Grenada Dove needs urgent conservation action, including habitat protection and potential augmentation of gene flow by translocation in order to increase genetic resilience and diversity with the ultimate aim of securing the long-term survival of this Critically Endangered species. </p>
Relaxed-rate data generated and analysed for Lambert, Valente & Etienne 2023
<p>This repository contains the data for accompanying the PhD research by Lambert et al. (2023), investigating the variation of biodiversity dynamics on islands. The data upload contains two folders. The logs folder contains are plain text logs from running the DAISIE models on the University of Groningen Hábrók High Performance Computing Cluster (HPCC). There is one log per HPCC job. The results folder contains folders for each archipelago included in the study and within each of those folders are model output from the DAISIE R package. Each result is stored as an .rds file. Data was generated and analysed using the relaxedDAISIE and DAISIE R packages. The code for these packages is version controlled on GitHub and is freely available in open-source repositories. See the Related Identifiers section for links to relevant archived versions of both these packages.</p>
Data from: Evidence for hybridisation-driven heteroplasmy maintained across generations in a ricefish endemic to a Wallacean ancient lake
<p><span>Heteroplasmy, </span><span>the presence of multiple mitochondrial DNA (mtDNA) haplotypes within cells of an individual,</span><span> is caused by mutation or paternal leakage. However, heteroplasmy is usually resolved to homoplasmy within a few generations because of germ-line bottlenecks; therefore, instances of heteroplasmy are limited in nature. Here, we report </span><span>heteroplasmy in the ricefish species <em>Oryzias matanensis</em>, endemic to Lake Matano, an ancient lake in Sulawesi Island, in which one individual was known to have many heterozygous sites in the <span class="shorttext">mitochondrial NADH dehydrogenase subunit 2 (ND2) gene</span>. </span><span>In this study, </span><span>we cloned the ND2 gene for some additional individuals with heterozygous sites and demonstrated that they are truly heteroplasmic. Phylogenetic analysis revealed that the extra haplotype within the heteroplasmic <em>O. matanensis</em> individuals clustered with haplotypes of </span><em><span>O. marmoratus</span></em><span>, a congeneric species inhabiting adjacent lakes. This indicated that the heteroplasmy originated from paternal leakage due to interspecific hybridisation. </span><span>The extra haplotype was unique and contained </span><span>t</span><span>wo</span><span> nonsynonymous substitutions. </span><span>These findings demonstrate that this hybridisation-driven heteroplasmy was maintained across generations for a long time to the extent that the extra mitochondria evolved within the new host.</span></p>
Automated bio-AFM generation of large mechanome data set and their analysis by machine learning to classify prostatic cell lines_Training base 100 PC3-GFP
Open the record for dataset details and reuse information.
Subnanosecond-electromagnetic-pulse-generated-by-a-long-spark-discharge:-Lightning-implication-data
<p><strong>Data description</strong></p><p>The data is used in the paper "Subnanosecond electromagnetic pulse generated by a long spark discharge: Lightning implication" (M. Gushchin et. al.) submitted in December 2023 in Geophysical Research Letters. Two types of files are presented. First are photos stored in "png" format. Second are waveforms stored in text files. First column is time and second is value. The delimiter is ";".</p><p><strong>Data is used in second figure</strong></p><p>Figure_2a.png -- Photo of the the appearance and growth of leaders with their streamer zones from the upper (HV) electrode</p><p>Figure_2b.png – First flash on the lower (grounded) electrode.</p><p>Figure_2c.png -- Common streamer zone formation after the upward leader starts.</p><p>Figure_2d.png -- Current increase in downward and upward leader channels, reduction in the size of the common streamer zone.</p><p>Figure_2e.png -- Discharge main stage.</p><p>Figure_2f_curve_1.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2a.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_2.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2b.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_3.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2c.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_4.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2e.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_5.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2f.png" photo. Time unit is mks, value unit is a.u.</p><p><strong>Data is used in third figure</strong></p><p>Figure_3b_curve1.dat -- The power waveform from RF analyzer f0 = 6 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve2.dat -- The power waveform from RF analyzer f0 = 5.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve3.dat -- The power waveform from RF analyzer f0 = 4.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve4.dat -- The power waveform from RF analyzer f0 = 3.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve5.dat -- The power waveform from RF analyzer f0 = 2 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve6.dat -- The power waveform from RF analyzer f0 = 1 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3a_curve1.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve1.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve2.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve2.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve3.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve3.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve4.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve4.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve5.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve5.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve6.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve6.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3c.dat -- Waveform obtained using TEMH. Time unit is mks, value unit is V/m.</p><p>Figure_3d.dat – Detailed waveform obtained using TEMH. Time unit is ns, value unit is V/m.</p><p> </p><p><strong>Data is used in fourth figure</strong></p><p>Figure_4b_curve_1.dat – TPMP waveform obtained during calibration. Time unit is ns, value unit is A/m.</p><p>Figure_4b_curve_2.dat – IPPL waveform obtained during calibration. Time unit is ns, value unit is E/m.</p><p>Figure_4c_curve_1.dat – TEMH waveform obtained in shot #104 at 12-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4c_curve_2.dat – IPPL waveform obtained in shot #104 at 12-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4d_curve_1.dat – TEMH waveform obtained in shot #19 at 13-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4d_curve_2.dat – IPPL waveform obtained in shot #19 at 13-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4e_curve_1.dat – TPMP waveform obtained in shot #19 at 25-sept-23. Time unit is ns, value unit is A/m.</p><p>Figure_4e_curve_2.dat – IPPL waveform obtained in shot #19 at 25-sept-23. Time unit is ns, value unit is E/m.</p><p>Figure_4f_curve_1.dat – TPMP waveform obtained in shot #2 at 27-sept-23. Time unit is ns, value unit is A/m.</p><p>Figure_4f_curve_2.dat – IPPL waveform obtained in shot #2 at 27-sept-23. Time unit is ns, value unit is E/m.</p><p><strong>Data is used in fifth figure.</strong></p><p>Figure_5a.png – The photo of negative discharge</p><p>Figure_5b.dat -- Waveform from a capacitive probe. Time units is mks, value units is a.u.</p><p>Figure_5c.dat -- The power waveform from RF analyzer f0 = 0.98 GHz, df = 40 MHz obtained simultaneously with Figure_5b.dat. Time unit is mks, value unit is dB. </p><p>Figure_5d.dat -- UWB EMP waveform obtained using TEMH obtained simultaneously with Figure_5c.dat. Time unit is ns, value unit is V/m.</p>
Data from: Multi-generation genetic contributions of immigrants reveal cryptic elevated and sex-biased effective gene flow within a natural meta-population
<p>Impacts of immigration on micro-evolution and population dynamics fundamentally depend on net rates and forms of resulting gene flow into recipient populations. Yet, the degrees to which observed rates and sex ratios of physical immigration translate into multi-generational genetic legacies have not been explicitly quantified in natural meta-populations, precluding inference on how movements translate into effective gene flow and eco-evolutionary outcomes. Our analyses of three decades of complete song sparrow (<em>Melospiza melodia</em>) pedigree data show that multi-generational genetic contributions from regular natural immigrants substantially exceeded those from contemporary natives, consistent with heterosis-enhanced introgression. Further, while contributions from female immigrants exceeded those from female natives by up to three-fold, male immigrants' lineages typically went locally extinct soon after arriving. Both the overall magnitude, and the degree of female bias, of effective gene flow therefore greatly exceeded those which would be inferred from observed physical arrivals, reshaping the eco-evolutionary implications of immigration.</p>
Data generated from calculations in "Gauge Field Dynamics in a Multilayer Kitaev Spin Liquid"
<p>The Kitaev honeycomb model hosts a quantum spin liquid in its ground state, where the excitations are gapless Majorana fermions and static <span><span><span><span><span><span><span>Z</span></span></span><span>2</span></span></span></span></span> gauge fluxes called visons. We consider Kitaev models stacked on top of each other, weakly coupled by Heisenberg interaction linear in <span><span><span><span><span>J</span><span>⊥</span></span></span></span></span>. This inter-layer coupling breaks the integrability of the model and makes the gauge fields dynamic. While single visons stay static in this model, an inter-layer pair of visons can hop with a hopping amplitude linear in <span><span><span><span><span>J</span><span><span><span>⊥</span></span></span></span></span></span></span>, but remains confined to a single plane. An intra-layer vison-pair, in contrast, is constrained to move along the stacking direction only. Depending on the anisotropy of the Kitaev couplings <span><span><span><span><span>K</span><span>x</span></span><span>,</span><span><span>K</span><span>y</span></span><span>,</span><span><span>K</span><span>z</span></span></span></span></span>, the intra-layer vison pairs show completely different dynamical behaviours. While coherent intra-layer tunnelling is possible for sufficiently strong anisotropies, only incoherent processes are possible in the isotropic case. When a magnetic field opens a gap for Majorana fermions, one can identify two types of intra-layer vison pairs, one bosonic and one fermionic. Only the bosonic pair obtains a hopping rate linear in <span><span><span><span><span>J</span><span>⊥</span></span></span></span></span>. We argue that our results can be used to identify leading instabilities of the Kitaev phase induced by the inter-layer coupling.</p>
Data and Software of "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids"
<h1><strong>Id: Data of following publication</strong></h1> <p>title = "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids",<br>journal = "<span>Composite Structures</span>",<br>volume =" 348",<br>pages = "118503",<br>year = "2025",<br>doi = "<a href="https://doi.org/10.1016/j.compstruct.2024.118503" target="_blank" rel="noopener">10.1016/j.compstruct.2024.118503</a>",<br>author = "José Rothkegel, Benjamin Renson, Michael Bruyneel, Ludovic Noels"</p> <p>Data doi on 10.5281/zenodo.10829042</p> <h1>pyRVE</h1> <h2><em>Python Code for Geometrical Generator for Braided Composites RVE</em></h2> <p>pyRVE is a code written in <em>Python</em> using the <em>GMSH API</em> that generates the Representative Unit Cell (RUC) of braided composites. It allows the generation of the RUC of triaxial braided for <em>Diamond</em> and <em>Regular</em> patterns.</p> <h2>Requirements</h2> <p>To run, it requires:</p> <ul> <li>The GMSH Python API, which must be built with OpenCascade support. <ul> <li>Choose a local installation directory; <code>CMAKE_INSTALL_PREFIX=$HOME/local/gmsh</code>, and <code>GMSHPY_INSTALL_DIRECTORY=$HOME/local/gmsh</code> e.g.;</li> <li>Make that directory part of your <code>export PYTHONPATH=$HOME/local/gmsh/lib:$PYTHONPATH</code>.</li> <li>After compiling use <code>make install</code>.</li> </ul> </li> <li>The CM3 app dG3D if the final RVE homogenized solution is needed (<a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a>).</li> <li>Make sure that the latest version of OpenCascade (OCCT) is used. Current used version in occt-V7.8.0.</li> </ul> <h2>Usage</h2> <h3>File Structure</h3> <p>A typical run case must have a file structure, where:</p> <ul> <li><code>brd</code>: the files <code>.brd</code> and <code>.brep</code> are located here. The <code>.brd</code> is a backup of the <code>braidClass</code> instance used in the model saved using <code>pickle</code>, the <code>.brep</code> is the Boundary Representation file that can be opened with <em>GMSH</em>.</li> <li><code>csv</code>: the <code>.csv</code> file saved here is the initial output of the code. It contains the actually used dimensions and the final cover factor of the braid.</li> <li><code>data</code>: It contains <code>.csv</code> files with the material properties and the dimensions of the tows. The original model dimensions are read from here.</li> <li><code>dir</code>: In the case of running the RVE homogenization, the directions of the tow fibers are stored here. They are saved for post processing.</li> <li><code>msh</code>: the mesh file <code>.msh</code> obtained after the geometry geneartion is stores here.</li> <li><code>png</code>: in the case of automatic post processing, png files are stored here.</li> <li><code>res</code>: this folder is used to store the homogenization results. They have to be moved here.</li> <li><code>stp</code>: if acitvated, a <code>.stp</code> file of the geometry is stored here</li> <li><code>svg</code>: the projection of the geometry on the <em>x-y</em> plane is stored here.</li> <li><code>vtk</code>: A copy of the mesh file without the matrix mesh is sotred here as a `.vtk`` file.</li> </ul> <h3>How to Run</h3> <p>We will consider the current file structure to run the example in 000_Base. To run the code, it can be called from the command prompt as</p> <div> <pre><code>python3 ../../source/mainRVE.py --name <i> --pattern <pattern></code></pre> </div> <p>In this case, the <code>--name</code> refers to the index that will be given to the model, where <code><i></code> must be changed to an integer and <code>--pattern</code> refers to the wanted pattern to be used, where <code><pattern></code> must be changed to either <code>dia</code> or <code>reg</code>.</p> <blockquote> <p>Note: <code><code>--name</code>cat</code> can also be used to reproduce the regular pattern benchmark of the paper. In that case, the volume fraction of fiber in the tows is hard coded as the provided value in the reference (i.e. 0.86). For other cases, the volume fraction is evaluated from the tow cross-sections.</p> <p>Note: <code>mainRVE.py</code> must be accesible from the directory where the case is being run. This example shows the usage of the current file structure.</p> </blockquote> <h3>All Command Line Options</h3> <p>The code can be run using further options that serve different purpouses, some serving pre processing needs and other serving run administration. The different command line options are:</p> <ul> <li>Required: <ul> <li><code>--name</code> : it gives a suffix to the run model. It is usually an integer.</li> <li><code>--pattern</code> : indicates the type of pattern to be used to build the geometry. The two current options are <code>dia</code> for diamond and <code>reg</code> for regular.</li> </ul> </li> <li>Optional <ul> <li><code>-dG3D</code>: it indicates that the homogenization of the generated RUC is to be perfomed.</li> <li><code>-GMSH</code> : it indicates that GMSH must be open upon competion of the generation of the mesh.</li> <li><code>-loadModel</code> : it will try to load a premade model. It will ignore <code>--pattern</code>.</li> <li><code>--rndPrm</code> : it will generate randomized geometrical parameters. It can be used to generate batches of results. It takes an argument that can be <code>2</code>, <code>4</code> or <code>6</code>. Currently, <code>2</code> gives a random value for <code>s_axial</code> and <code>theta</code>, <code>4</code> randomizes the same as <code>2</code> and adds <code>h_axial</code> and <code>h_bias</code>, and <code>6</code> randomizes the same as <code>4</code> and adds <code>w_axial</code> and <code>w_bias</code>.</li> </ul> </li> <li>Pre-Processing <ul> <li><code>-refCF</code>: it tells the code to generate a grid of values for <code>s_axial</code> and <code>theta</code> where only the cover factor is obtained. It is meant for posterior graphing purposes.</li> </ul> </li> </ul> <h3>Examples</h3> <p>Following the run options, a few examples are indicated</p> <ul> <li>A basic mesh generation run for the basic data, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg</code></pre> </div> <ul> <li>The generation of the cover factor data and export, considering a <strong>regular pattern</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --pattern reg -refCF</code></pre> </div> <ul> <li>A run for the modified basic data, where the <strong>2</strong> parameters are modified <em>randomly</em>, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg --rndPrm 2</code></pre> </div> <ul> <li>A run, where model <strong>2</strong> already exists in <code>brd</code> folder but not the <code>.msh</code> and <code>.vtk</code> files:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 -loadModel </code></pre> </div> <h2>Code Structure</h2> <p>The code is implemented into Python files, where <code>mainRVE.py</code> runs the whole code. The files are:</p> <ul> <li>Braid: <ul> <li><code>braidClass.py</code> :</li> <li><code>bzrPairClass.py</code> :</li> </ul> </li> <li>Geometry <ul> <li><code>bezrClass.py</code> :</li> <li><code>bilnClass.py</code> :</li> <li><code>patchClass.py</code> :</li> <li><code>pntSetClass.py</code> :</li> <li><code>pointClass.py</code> :</li> <li><code>sctnClass.py</code> :</li> <li><code>stripeClass.py</code> :</li> <li><code>surfClass.py</code> :</li> <li><code>surfOffClass.py</code> :</li> </ul> </li> <li>Material: <ul> <li><code>chamis.py</code> :</li> </ul> </li> <li>Tools: <ul> <li><code>dataIO.py</code> :</li> <li><code>postDirection.py</code> :</li> <li><code>tool.py</code> :</li> <li><code>toolData.py</code> :</li> </ul> </li> <li><code>curveClass.py</code> :*</li> </ul> <p> </p> <p> </p>
Data from: Self-amplifying RNA generated with the modified nucleotides 5-methylcytidine and 5-methyluridine mediate strong expression and immunogenicity in vivo
<p>When utilized in therapeutic applications, synthetic self-amplifying RNA can lead to higher and more sustained expression than standard messenger RNA. This feature is particularly important for gene replacement therapy applications where prolonged expression could reduce the dose and frequency of treatments. The inclusion of modified nucleotides in synthetic non-amplifying mRNA has been shown to increase RNA stability, reduce immune activation and enhance gene expression. Preclinical and clinical studies with self-amplifying RNA (saRNA) have so far exclusively relied on RNA containing the canonical nucleotides adenosine, cytidine, guanosine and uridine. For the first time, we show that non-canonical nucleotides, such as m5C and m5U, are sufficiently compatible with a replicon derived from Venezuelan equine encephalitis alphavirus mediating protein translation <em>in vitro</em>, while those containing m1ψ in place of uridine show no detectable expression. When administered <em>in vivo</em>, saRNA generated with m5C or m5U mediate sustained gene expression of the luciferase reporter gene with those incorporating m5U appearing to lead to more prolonged expression. Finally, distinct antigen-specific humoral and cellular immune responses were induced by modified saRNA encoding the model antigen ovalbumin. The use of modified nucleotides with saRNA-based platforms could enhance their potential to be used effectively in a variety of applications.</p>
Automatic User Story Generation: A Comprehensive Systematic Literature Review - Data Extraction
<p>This document presents the data extraction performed for the Systematic Literature Review in Automatic User Story Generation.</p>
Data on soil variables (with plot IDs) and grassland species traits used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. Journal of Vegetation Science, 35, e13259. Available from: https://doi.org/10.1111/jvs.13259
<p>File <a href="../api/records/10983049/draft/files/Plot_IDs_990ua.txt/content" target="_blank" rel="noopener noreferrer">Plot_IDs_990ua.txt</a> contains the IDs of the 1-m2 plots used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. The plot data are stored in the sPlot database (PPBio South Brazilian Grassland Database).</p> <p>File <a href="../api/records/10983049/draft/files/E_990ua_21SoilVar.txt/content" target="_blank" rel="noopener noreferrer">E_990ua_21SoilVar.txt</a> contains data on soil variables evaluated in the 250 m transects, but here expanded to the 990 1-m2 plots (each transect was sampled using 10 1-m2 pots).</p> <p>File <a href="../api/records/10983049/draft/files/B_769spp_4t.txt/content" target="_blank" rel="noopener noreferrer">B_769spp_4t.txt</a> is the species trait database collected in the framework of several research projects in the Quantitative Ecology Lab (EcoQua) and Grassland Vegetation Studies Lab (LevCamp) of Universidade Federal do Rio Grande do Sul (UFRGS). Data gaps were filled by compiled from the TRY database and data imputation.</p> <p> </p> <p> </p>
MetaVision3D: Automated Framework for the Generation of Spatial Metabolome Atlas in 3D | MALDI Data
<p>This repository contains MALDI data related to the Ma et al. study "<strong>MetaVision3D: Automated Framework for the Generation of Spatial Metabolome Atlas in 3D</strong>". Processed MALDI pixel-by-pixel .csv files for both metabolomics and lipidomics for two Wild-type samples, one 5xFAD sample and one GAA sample. If you use this dataset in your research, please consider citing the above study.</p> <p>The content of the files are:<br>wt.zip - pixel-by-pixel .csv files of metabolomics and lipidomics for wild-type sample.</p> <p>5x.zip - pixel-by-pixel .csv files of metabolomics and lipidomics for 5xFAD sample.</p> <p>gaa.zip - pixel-by-pixel .csv files of metabolomics and lipidomics for GAA sample.</p> <p>wt2.zip - pixel-by-pixel .csv files of metabolomics and lipidomics for wild-type2 sample.</p>
Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.
<p>ML_AB_H: Datasets of 64 water molecules and a single proton with 64 water molecules.</p> <p>ML_AB_VP2: Datasets of a single V^2+ ion with 64 water molecules.</p> <p>ML_AB_VP3: Datasets of a single V^3+ ion with 64 water molecules.</p> <p>ML_AB_FeP2: Datasets of a single Fe^2+ ion with 64 water molecules.</p> <p>ML_AB_FeP3: Datasets of a single Fe^3+ ion with 64 water molecules.</p> <p>ML_AB_CuP1: Datasets of a single Cu^+ ion with 64 water molecules.</p> <p>ML_AB_CuP2: Datasets of a single Cu^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP2: Datasets of a single Ru^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP3: Datasets of a single Ru^3+ ion with 64 water molecules.</p> <p>ML_AB_AgP1: Datasets of a single Ag^+ ion with 64 water molecules.</p> <p>ML_AB_AgP2: Datasets of a single Ag^2+ ion with 64 water molecules.</p> <p>ML_AB_O2: Datasets of a single O2 ion with 64 water molecules.</p> <p>ML_AB_O2N1: Datasets of a single O2^- ion with 64 water molecules.</p> <p>ML_AB_water: Datasets of 64 water molecules presenting bulk water and 64 water molecules representing water slab.</p> <p>All datasets were generated by VASP using PAW, plane wave basis sets with cutoff energy of 520 eV and RPBE+D3 exchange-correlation functional with zero-damping. All ab initio calculations were done on extended systems with periodic boundary conditions. See details in <a href="https://doi.org/10.48550/arXiv.2409.11000">https://doi.org/10.48550/arXiv.2409.11000</a>.</p>
Code and data for Porting the Meso-NH Atmospheric Model on Different GPU Architectures for the Next Generation of Supercomputers (version MESONH-v55-OpenACC)
<p>GeometricMG.pdf (source: https://bitbucket.org/em459/tensorproductmultigrid/src/master/Documentation/)<br>MESONH_Bench_HECTOR_ADASTRA_LEONARDO.tar.gz: code and data for Meso-NH bench<br>Performance.zip: code and data for figures related to performance<br>WeatherApplications.zip: namelists for running weather applications<br>OASIS3_WW3.tar.gz: OASIS and WW3 codes for running the Meso-NH WWW3 coupled simulation</p>
Data for: Parallel recolonisations generate distinct genomic sectors in kelp following high magnitude earthquake disturbance
<p>Large-scale disturbance events have the potential to drastically reshape biodiversity patterns. Notably, newly vacant habitat space cleared by disturbance can be colonised by multiple lineages, which can lead to the evolution of distinct spatial 'sectors' of genetic diversity within a species. We test for disturbance-driven sectoring of genetic diversity in intertidal southern bull kelp, <i>Durvillaea antarctica</i> (Chamisso) Hariot following the high-magnitude 1855 Wairarapa earthquake in New Zealand. Specifically, we use genotyping-by-sequencing (GBS) to analyse fine-scale population structure across the uplift zone to assess the fit of alternative recolonisaton models. Our analysis reveals that specimens from the uplift zone carry genomic signatures distinct from populations in other regions, consistent with recolonisation after the 1855 earthquake. Crucially, our analysis identifies two parapatric spatial-genomic sectors of <i>D. antarctica</i> at Turakirae Head, which experienced the most dramatic uplift. We infer that bull kelp in the Wellington region survived moderate uplift and recolonised the devastated Turakirae Head coastline through two parallel, eastward recolonisation events. By identifying multiple parapatric genotypic sectors within a recently recolonised coastal region, the current study confirms that competing lineage expansions can generate striking spatial structuring of genetic diversity, even in highly dispersive taxa.</p>
Data accompanying "Integrated Dual-Laser Photonic Chip for High-Purity Carrier Generation Enabling Ultrafast Terahertz Wireless Communications"
<p>This dataset contains measurement data for the results presented in "Integrated Dual-Laser Photonic Chip for High-Purity Carrier Generation Enabling Ultrafast Terahertz Wireless Communications".</p>
Data generated for study of simultaneous design of wind turbines and cable layout in offshore wind
<p>This set of files contains the results of the models proposed in the manuscript: "Pérez-Rúa, J.-A. and Cutululis, N. A.: A Framework for Simultaneous Design of Wind Turbines and Cable Layout in Offshore Wind, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-47, in review, 2021."</p>
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling: Generated data files and figures
<p>All data files and figures generated for the paper "Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling".</p> <p>The code used to generate this is archived at <a href="https://doi.org/10.5281/zenodo.6123254">zenodo.org/record/6123254</a></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.