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14,239 results for “STRUCTURE”
Pseudodynamic testing of a substandard infilled steel structure
<p>Data from the pseudodynamic testing of a substandard, infilled steel building, is provided. The structure and related testing activities are described in:</p> <div> <div> <div> <p><em>"Assessment of existing steel frames: Numerical study, pseudo-dynamic testing and influence of masonry infills" (2021) Luigi Di Sarno, Fabio Freddi, Mario D’Aniello, Oh-Sung Kwon, Jing-Ren Wu, Fernando Guti ́errez-Urzúa, Raffaele Landolfo, Jamin Park, Xenofon Palios, Elias Strepelias,Journal of Constructional Steel Research, 185, https://doi.org/10.1016/j.jcsr.2021.106873</em></p> <p> </p> </div> </div> </div>
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
<h2>General</h2> <p>For more details and the most up-to-date information please consult our project page: <a href="https://kainmueller-lab.github.io/fisbe" target="_blank" rel="noopener">https://kainmueller-lab.github.io/fisbe</a>.</p> <h2>Summary</h2> <ul> <li>A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains <ul> <li>30 completely labeled (segmented) images</li> <li>71 partly labeled images</li> <li>altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)</li> </ul> </li> <li>To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects</li> <li>A set of metrics and a novel ranking score for respective meaningful method benchmarking</li> <li>An evaluation of three baseline methods in terms of the above metrics and score</li> </ul> <h2>Abstract</h2> <p>Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.</p> <h2>Dataset documentation:</h2> <p>We provide a detailed documentation of our dataset, following the <a href="https://arxiv.org/abs/1803.09010" target="_blank" rel="noopener">Datasheet for Datasets</a> questionnaire:</p> <p><em>>> <a href="https://kainmueller-lab.github.io/fisbe/datasheet" target="_blank" rel="noopener">FISBe Datasheet</a></em></p> <p>Our dataset originates from the <a href="https://www.janelia.org/project-team/flylight" target="_blank" rel="noopener">FlyLight project</a>, where the authors released a large image collection of nervous systems of ~74,000 flies, <a href="https://gen1mcfo.janelia.org/cgi-bin/gen1mcfo.cgi" target="_blank" rel="noopener">available for download</a> under CC BY 4.0 license.</p> <h2>Files</h2> <ul> <li>fisbe_v1.0_{completely,partly}.zip <ul> <li>contains the image and ground truth segmentation data; there is one <em>zarr</em> file per sample, see below for more information on how to access <em>zarr</em> files.</li> </ul> </li> <li>fisbe_v1.0_mips.zip <ul> <li>maximum intensity projections of all samples, for convenience.</li> </ul> </li> <li>sample_list_per_split.txt <ul> <li>a simple list of all samples and the subset they are in, for convenience.</li> </ul> </li> <li>view_data.py <ul> <li>a simple python script to visualize samples, see below for more information on how to use it.</li> </ul> </li> <li>dim_neurons_val_and_test_sets.json <ul> <li>a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.</li> </ul> </li> <li>Readme.md <ul> <li>general information</li> </ul> </li> </ul> <h2>How to work with the image files</h2> <p>Each sample consists of a single 3d MCFO image of neurons of the fruit fly.<br>For each image, we provide a pixel-wise instance segmentation for all separable neurons.<br>Each sample is stored as a separate <em>zarr</em> file (<a href="https://zarr.readthedocs.io" target="_blank" rel="noopener">zarr</a> is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").<br>The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.<br>The segmentation mask for each neuron is stored in a separate channel.<br>The order of dimensions is CZYX.</p> <p>We recommend to work in a virtual environment, e.g., by using conda:</p> <p><code>conda create -y -n flylight-env -c conda-forge python=3.9</code><br><code>conda activate flylight-env</code></p> <h3>How to open <em>zarr</em> files</h3> <ol> <li>Install the python zarr package: <pre><code>pip install zarr</code></pre> </li> <li>Opened a zarr file with:<br> <p><code>import zarr</code><br><code>raw = zarr.open(<path_to_zarr>, mode='r', path="volumes/raw")</code><br><code>seg = zarr.open(<path_to_zarr>, mode='r', path="volumes/gt_instances")</code></p> <p><code># optional:</code><br><code>import numpy as np</code><br><code>raw_np = np.array(raw)</code></p> </li> </ol> <p>Zarr arrays are read lazily on-demand.<br>Many functions that expect numpy arrays also work with zarr arrays.<br>Optionally, the arrays can also explicitly be converted to numpy arrays.</p> <h3>How to view <em>zarr</em> image files</h3> <p>We recommend to use <a href="https://napari.org" target="_blank" rel="noopener">napari</a> to view the image data.</p> <ol> <li>Install napari: <pre><code>pip install "napari[all]"</code></pre> </li> <li>Save the following Python script: <br> <p><code>import zarr, sys, napari</code></p> <p><code>raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")</code><br><code>gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")</code></p> <p><code>viewer = napari.Viewer(ndisplay=3)</code><br><code>for idx, gt in enumerate(gts):</code><br><code> viewer.add_labels(</code><br><code> gt, rendering='translucent', blending='additive', name=f'gt_{idx}')</code><br><code>viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')</code><br><code>viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')</code><br><code>viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')</code><br><code>napari.run()</code></p> </li> <li>Execute: <pre><code>python view_data.py <path-to-file>/R9F03-20181030_62_B5.zarr</code></pre> </li> </ol> <h2>Metrics</h2> <ul> <li>S: Average of avF1 and C</li> <li>avF1: Average F1 Score</li> <li>C: Average ground truth coverage</li> <li>clDice_TP: Average true positives clDice</li> <li>FS: Number of false splits</li> <li>FM: Number of false merges</li> <li>tp: Relative number of true positives</li> </ul> <p>For more information on our selected metrics and formal definitions please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>Baseline</h2> <p>To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely <a href="https://github.com/Kainmueller-Lab/PatchPerPix" target="_blank" rel="noopener">PatchPerPix (ppp)</a>, <a href="https://github.com/google/ffn" target="_blank" rel="noopener">Flood Filling Networks (FFN)</a> and a non-learnt application-specific <a href="https://www.biorxiv.org/content/10.1101/2020.06.07.138941v1" target="_blank" rel="noopener">color clustering from Duan et al.</a>.<br>For detailed information on the methods and the quantitative results please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>License</h2> <p>The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>.</p> <h2>Citation</h2> <p>If you use <em>FISBe</em> in your research, please use the following BibTeX entry: </p> <pre><code>@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }</code></pre> <h2>Acknowledgments</h2> <p>We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable<br>discussions.<br>P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.<br>This work was co-funded by Helmholtz Imaging.</p> <h2>Changelog</h2> <p>There have been no changes to the dataset so far.<br>All future change will be listed <a href="https://kainmueller-lab.github.io/fisbe/changelog" target="_blank" rel="noopener">on the changelog page</a>.</p> <h2>Contributing</h2> <p>If you would like to contribute, have encountered any issues or have any suggestions, please <a href="https://github.com/Kainmueller-Lab/fisbe/issues" target="_blank" rel="noopener">open an issue</a> for the FISBe dataset in the accompanying github repository.</p> <p>All contributions are welcome!</p>
Economical routes to size-specific assembly of self-closing structures
<p>This data contains images related to a publication on the self-assembly of DNA origami particles (<a href="https://www.science.org/doi/10.1126/sciadv.ado5979">https://www.science.org/doi/10.1126/sciadv.ado5979</a>). In this work, we conduct self-assembly experiments with various unique subunit types that target two different diameters of tubule structures.</p> <p>We provide image data of tubules that are associated with the probability distributions reported across several figures in the main text. Images of tubules are in the ZIP archives and show the section of tubules we analyzed to produce the probability distributions in the manuscript. Each folder of images has an associated CSV file that relates an image name to the type of tubule that the image was identified as. Tubule types have "m" and "n" values.</p> <p>We provide full tomogram reconstruction data for the multicomponent tubules that are shown in Figure 2 of the main text. In the ZIP archive, each tubule image has two files associated with it: a REC file that contains the tomogram reconstruction data and an MDOC file that contains imaging metadata. REC files can be opened with the open-source software IMOD.</p> <p>We provide raw image data of pitch- and width-controlled tubules that have been labeled with gold nanoparticles. These accompany the representative images in Figure 4 in the main text. (Pitch Controlled 4-color with GNPs.zip, Width Controlled 4-color with GNPs.zip).</p> <p>We provide raw image data of length-controlled tubules. These images accompany Figure 5 in the main text. (Length Controlled Tubule Images.zip)</p> <p><strong>Associated publication citation:</strong></p> <div> <p><span>Thomas E. Videbæk <em>et al., </em></span><span>Economical routes to size-specific assembly of self-closing structures. </span><span><em>Sci. Adv. </em></span><span><strong>10</strong>, </span><span>eado5979 </span><span>(2024). </span><span>DOI:<a href="https://doi.org/10.1126/sciadv.ado5979">10.1126/sciadv.ado5979</a></span></p> </div>
Charting nanocluster structures via convolutional neural networks
<p>The repository contains a notebook for the training of the autoencoder for the RDFs for structural classification. The notebook describes the procedure going from RDFs calculation to clustering of the reduced space. In the folder are contained Au147 structures, together with the associated pretrained AE, the 3D chart and the different clustering performed varying mean shift bandwidth.</p> <p>Files:</p> <p>- ChartAu147.ipynb: notebook</p> <p>- Configurations: directory with the dataset divided according to the CNA classification of the structures, xyz format with no headers, every 147 lines is a single structure</p> <p>- Libraries: directory with functions imported in the notebook</p> <p>- Precomputed: directory with the precomputed outputs</p> <p> - rdfs.npy: preocmputed RDFs of the data stored in configurations, npy format to load with NumPy</p> <p> - labels.npy: CNA labels of the RDFs, npy format to load with NumPy</p> <p> - model_au147.pth: pretrained model for au147</p> <p> - scaler_au147.pkl: minmax scaler of the RDFs</p> <p> - chart_3d.dat: 3d space generated via the encoder on the au147 dataset</p> <p> - ae_reconstructions.npy: reconstructions of the rdfs of the model (model_au147.pth)</p> <p> - MSscanbw: pretrained mean shift clustering with different bandwidths, the file "clus_vs_bw.dat" reports the number of clusters associated to each bandwidth</p>
The genetic population structure of Lake Tanganyika's Lates species flock, an endemic radiation of pelagic top predators
<p>Data associated with the manuscript "The genetic population structure of Lake Tanganyika’s Lates species flock, an endemic radiation of pelagic top predators," where we investigate the genetic population structure of the four endemic <em>Lates </em>species in Lake Tanganyika.</p> <p><strong>Abstract</strong>: Life history traits are important in shaping gene flow within species and can thus determine whether a species exhibits genetic homogeneity or population structure across its range. Understanding genetic connectivity plays a crucial role in species conservation decisions, and genetic connectivity is an important component of modern fisheries management in fishes exploited for human consumption. In this study, we investigated the population genetics of four endemic <em>Lates</em> species of Lake Tanganyika (<em>Lates stappersii</em>, <em>L. microlepis</em>, <em>L. mariae</em> and <em>L. angustifrons</em>), using reduced-representation genomic sequencing methods. We find the four species to be strongly differentiated from one another, with no evidence for contemporary admixture. We also find evidence for high levels of genetic structure within <em>L. mariae</em>, with the majority of individuals from the most southern sampling site forming a genetic group distinct from the individuals at other sampling sites<em>.</em> We find evidence for much weaker structure within the other three species, <em>L. stappersii,</em> <em>L. microlepis</em>, and <em>L. angustifrons</em>, although small and unbalanced sample sizes and imprecise geographic sampling locations may hinder our ability to detect weak population structure. We call for further research into the origins of the genetic differentiation that we observe in these four species, particularly that of <em>L. mariae</em>, which may be important for the conservation and management of this species.</p> <p>Code associated with the analysis of these data can be found on GitHub at <a href="https://github.com/jessicarick/lates-popgen">https://github.com/jessicarick/lates-popgen</a>.</p>
Data from: Functional structure of European forest beetle communities is enhanced by rare species
<p>From article abstract:</p> <p><a href="https://doi.org/10.1016/j.biocon.2022.109491">https://doi.org/10.1016/j.biocon.2022.109491</a></p> <p><strong>ABSTRACT</strong></p> <p>Biodiverse communities have been shown to sustain high levels of multifunctionality and thus a loss of species likely negatively impacts ecosystem functions. For most taxa, however, the roles of individual species are poorly known. Rare species, often the most likely to go extinct, may have unique traits leading to unique functional roles. Alternatively, rare species may be functionally redundant, such that their loss would not disrupt ecosystem functions. We quantified the functional role of rare species by using capture records of wood-living (saproxylic) beetle species, combined with recent databases of their morphological and ecological traits, from three regions in central and northern Europe. Using a rarity index based on species’ local abundance, geographic range, and habitat breadth, we used local and regional species removal simulations to examine the contributions of both the rarest and the most common beetle species to three measures of community functional structure: functional richness, functional specialization, and functional originality. In both regional species pools and local communities, all three of these measures declined more rapidly when rare species were removed than under common (or random) species removal scenarios. These consistent patterns across scales and among several forest types give evidence that rare species provide unique functional contributions, and that their loss may disproportionately impact ecosystem functions. This implies that conservation measures targeting rare and endangered species, such as preserving intact forests with dead wood and mature trees, can provide broader ecosystem-level benefits. Experimental research linking functional structure to ecosystem processes should be prioritized to increase our understanding of the functional consequences of species loss and to develop more effective conservation strategies.</p> <p> </p> <p><strong>DATASET DESCRIPTION</strong></p> <p>This dataset includes a) beetle capture information and b) beetle trait information from three countries: 1) Norway, 2) Finland, and 3) Germany. </p> <p> </p> <p><strong>FILES</strong></p> <p><strong>readme.txt</strong> -- this has the information from this description section</p> <p><strong>Norway_traits.csv</strong>, <strong>Finland_traits.csv</strong>, <strong>Germany_traits.csv</strong> -- these are the trait files, including all species</p> <p><strong>Norway_sites.species.csv</strong>, <strong>Finland_sites.species.csv</strong>, <strong>Germany_sites.species.csv</strong> -- this has species (rows) by sites (columns); values are the number of beetles caught (for number of traps, dates, and other site covariates, see related dataset: <a href="https://doi.org/10.5061/dryad.tmpg4f50b">https://doi.org/10.5061/dryad.tmpg4f50b</a> and manuscript: <a href="https://doi.org/10.1111/jbi.14272">https://doi.org/10.1111/jbi.14272</a>). Species names follow GBIF taxonomic backbone.</p> <p><strong>Traits_METADATA.csv</strong> -- this has information on all the fields in the trait data</p> <p> </p>
Research data for "[2.2.2.2]Paracyclophanetetraenes (PCTs): cyclic structural analogues of poly(p-phenylene vinylene)s (PPVs)"
<p>This dataset contains the underlying experimental (<sup>1</sup>H NMR, <sup>13</sup>C NMR, <sup>31</sup>P NMR, high-resolution mass spectrometry (HRMS), UV-vis absorption, photoluminescence (PL), cyclic voltammetry) and computational (molecular geometries, input/output files for Q-Chem, Gaussian, and TheoDORE) research data for the article “[2.2.2.2]Paracyclophanetetraenes (PCTs): cyclic structural analogues of poly(p-phenylene vinylene)s (PPVs)”.</p> <p>Content (names of folders and files are given in <strong>bold face</strong>):</p> <p>The folder for each molecule (<strong>Br-P2</strong>, <strong>O-3</strong>, <strong>O-P1</strong>, <strong>O-PCT</strong>, <strong>PCT</strong>, <strong>Quinine</strong>, <strong>S-2</strong>, <strong>S-3</strong>, <strong>S-P1</strong>, <strong>S-P2</strong>, <strong>S-PCT</strong>) contains the molecular structure as .mol and .cdxml file, as well as subfolders for the <strong>Experimental </strong>research data and, if available, the <strong>Computational </strong>research data.</p> <p>The <strong>Experimental </strong>research data<strong> </strong>folder for each molecule contains the measurement files. The file names are composed of the acronym of the measured molecule, the type of measurement, and further details about the measurement (if needed to distinguish the files). Measurements were performed as described in the article.</p> <p>Computations were performed on the molecules <strong>O-PCT</strong> and <strong>S-PCT</strong> as described in the article.<br> The <strong>Computational </strong>research data folder for each molecule contains subfolders for geometry optimisations of the individual electronic states and rotamers:</p> <ul> <li><strong>c2_neut</strong>: neutral singlet state for C<sub>2</sub> rotamer</li> <li><strong>c2_trip</strong>: T<sub>1</sub> state of C<sub>2</sub> rotamer</li> <li><strong>c2_2P</strong>: charged state (2+) of C<sub>2</sub> rotamer</li> <li><strong>c2_2M</strong>: charged state (2-) of C<sub>2</sub> rotamer</li> <li><strong>cs_neut</strong>: neutral singlet state for C<sub>s</sub> rotamer</li> <li>etc.</li> </ul> <p>Additional content:</p> <ul> <li><strong>TS</strong>: Full transition state optimisation</li> <li><strong>TS_constrained</strong>: constrained transition state optimisation</li> <li><strong>VIST</strong>: Data for VIST plots</li> <li><strong>FROZEN</strong>: Computations for frozen PCT structure (denoted <strong>O/S-PCT</strong>@<strong>PCT</strong> in the article)</li> </ul> <p>Each optimisation folder contains the following:</p> <ul> <li><strong>qchem.[in,out]</strong>: input/output for geometry optimisation</li> <li><strong>final.xyz</strong>: optimised geometry</li> <li><strong>SOLV/qchem.[in,out]</strong>: input/output for solvated single-point computation</li> <li><strong>tNICS/gaussian.[com,log]</strong>: input/output for NMR shielding tensors</li> </ul>
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
Three-dimensional thermal structure of East Asian continental lithosphere
<p>This data set includes 3 supplement data files and 1 readme file for 3D thermal structure of East Asian continental lithosphere. </p>
Dataset for "Numerical methods for the detection of phase defect structures in excitable media"
<p>This archive contains the numerical methods presented in the publication "Numerical methods for the detection of phase defect structures in excitable media" as well as the data sets these methods have been applied on. The Python module for Ithildin (py_ithildin.zip) contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (scripts-pdl-detection.zip). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The second variable for the optical data (optical_20200204114234_v.npy) is a delayed version of the first variable. The other files contain simulation results from several finite differences simulations of the mono-domain model. For details, see our paper.</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271351"><strong>Numerical methods for the detection of phase defect structures in excitable media</strong></a><br> Kabus D, Arno L, Leenknegt L, Panfilov AV, Dierckx H (2022) Numerical methods for the detection of phase defect structures in excitable media. PLOS ONE 17(7): e0271351. <a href="https://doi.org/10.1371/journal.pone.0271351">https://doi.org/10.1371/journal.pone.0271351</a></p>
Dataset of The latent factor structure and assessment of childbirth-related PTSD in fathers and co-parents: psychometric characteristics of the City Birth Trauma Scale – French version (partner version)
<p>Little is known about the latent factor structure of CB-PTSD symptoms in co-parents (i.e., (a non-expecting mother or father). The City Birth Trauma Scale (City BiTS) was developed to assess childbirth-related posttraumatic stress disorder following childbirth (CB-PTSD), based on the PTSD criteria of the DSM-5. Still, no validated French questionnaire exists to assess CB-PTSD symptoms in co-parents. This study aimed (1) to establish the latent factor structure of CB-PTSD, and (2) to validate the French version of the City BiTS (partner version). </p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 282 co-parents who had an infant within the last 12 months. Sociodemographic data such as age, marital status, educational level, weeks of gestation, type of delivery, history of traumatic childbirth, or history of a traumatic event is available. </p>
The Structure of Monomeric Hydroxo-CuII Species in Cu-CHA. A Quantitative Assessment.
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>m</strong>, <strong>f34</strong>,<strong> xyz</strong>, <strong>out</strong>, <strong>in</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA</strong>,<strong> spc </strong>and<strong> par.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>Periodic DFT computations with(out) filename extensions <strong>out</strong> and <strong>f34</strong> in ASCII format.</li> <li>Molecular cluster computations with filename extensions <strong>in</strong> and <strong>out</strong> in ASCII format.</li> <li>Geometry information of cluster models is stored in <strong>xyz</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>X-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>Periodic DFT computations were generated using distributed parallel version of CRYSTAL17 code.</li> <li>Molecular cluster computations were generated using the ORCA (v4.2.1 and v5.0.2) code.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP3_20220713_01_CW</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and spc/par formats.</li> <li>Files in <strong>PARACAT_WP3_20220713_02_HYSCORE</strong> folder includes HYSCORE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20220713_03_ESE</strong> folder includes ESE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20220713_04_MATLAB</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m formats.</li> <li>Files in <strong>PARACAT_WP3_20220713_05_MODELLING </strong>folder includes periodic and cluster quantum chemical computations inputs, outputs and geometries in ASCII format.</li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>ESE</strong> – Electron Spin Echo detected EPR, <strong>HYSCORE</strong> – HYperfine Sublevel CORrelation spectroscopy, <strong>DFT </strong>– Density Functional Theory, <strong>CCSD</strong> – Coupled Cluster Single Double, <strong>PBEXX</strong> – PBE functional with XX percentage of exact Hartree-Fock exchange, <strong>DSDBLYP</strong> – DSD-BLYP double-hybrid functional, <strong>B3LYP </strong>– B3LYP functional, <strong>DSDPBEP86</strong> – DSD-PBEP86 double-hybrid functional, <strong>CuOH </strong>– CuOH species in Chabazite, zeolite topology, <strong>CuCHA_X </strong>– name of the Copper-exchanged Chabazite sample, <strong>supercell</strong> – indicates the supercell periodic model, <strong>O<sub>2</sub>act </strong>– activation of the Chabazite sample in O<sub>2</sub> atmosphere.</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT), Hartree (Ha), cm<sup>-1</sup> (ν), megahertz (MHz), nanometers (nm)</strong>.</li> </ul> </li> <li>abbreviations: <strong>8mr </strong>is the Cu docking sites; <strong>4c </strong>and <strong>3c</strong> are the different coordination geometry; <strong>OHos</strong> and <strong>OHss</strong> indicate the orientation of –OH group (same side or opposite side with respect to the Al ion). <strong>all </strong>indicates the full periodic frequency calculation of the model. <strong>cluster </strong>indicates the geometry of the cluster model extracted from the relaxed periodic ones. Periodic DFT computations with filename extension <strong>.f34</strong> include structural/symmetry information of optimized structure. Periodic DFT computations without extension contain the input commands. Molecular cluster computations with filename extension <strong>.in</strong>/<strong>.out</strong>/<strong>.xyz</strong> are inputs, outputs, and structure of cluster models.</li> </ul> </li> </ul>
Protein structure files for the paper "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.
<p>This archive contains models of HRAS, KRAS, and NRAS homo- and heterodimers with various mutations discussed in the paper, "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.<br> as well as crystallographic dimers of these proteins as identified by the ProtCAD database, http://dunbrack2.fccc.edu/ProtCAD/Results/PfamArchClusterInfo.aspx?GroupId=8 (cluster 5). Several of the models are shown in Supp. Figure 11b and the crystallographic dimers of RAS that provide evidence for the possible biological relevance of these models are shown in Supp. Figure 11a.</p> <p>The crystallographic dimers were identified by clustering all possible interfaces generated by symmetry operators in crystals of HRAS, KRAS, and NRAS as described in the paper: Xu, Q., Dunbrack, R.L. ProtCID: a data resource for structural information on protein interactions. <em>Nat Commun</em> <strong>11</strong>, 711 (2020). https://doi.org/10.1038/s41467-020-14301-4.</p> <p>The models were created by superposing monomers of HRAS, KRAS, or NRAS onto the alpha4-alpha5 dimer present in the crystal of PDB entry 3k8y. Mutations were made in PyMOL. The structures were relaxed with the FastRelax protocol and the Ref2015 scoring function in the program Rosetta, which uses the backbone-dependent rotamer library of Shapovalov and Dunbrack to repack side chains.</p> <p>The crystallographic dimers are contained in a zipped PyMOL session. The mmCIF format for all the structures is present in a zip file, Tang_et_al_crystallographic_and_modeled_RAS_dimer_ciffiles.zip. The PyMOL session and zip file contains 87 HRAS dimers, 14 KRAS dimers, and 1 NRAS dimer, all having the interface consisting of the alpha4 and alpha5 helices. The PyMOL session also contains the modeled structures. Only Mg ions and GTP/GNP/GDP ligands are shown. Others are present but hidden and may be displayed by PyMOL ("show sticks, het").</p> <p> </p>
Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions
<p>Dataset from the publication: <strong>Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions</strong>. DOI: <a href="https://doi.org/10.5194/egusphere-2022-230">10.5194/egusphere-2022-230</a></p> <p> </p> <p>Includes: <em><strong>EGFs, Dispersion Curves measurements, RFs, Vs3dmodel and Moho depths</strong></em></p> <p> </p>
Zeolite Templated Carbon Materials - DFTB Structural Database
<p>Zeolite-templated carbon (ZTC) is a unique porous carbonaceous material in that its structure is ordered at the nanometre scale, enabling a representative periodic description at the atomistic level. A structural library for ZTC of varying compositions was created using density functional tight binding (DFTB) potentials parameterized for materials science applications (matsci-0-3). We provide here quantum chemical-refined structures of models with CH, CHO, CHON, CHOB, and CHOBN compositions with various degrees of heteroatom substitution. The "initial ZTC structure" files correspond to the initial model used in our work that was developed using molecular mechanics, empirical force fields. These structural models comprise the characteristic morphological features of highly porous carbon materials, such as open-blade surfaces, edges, saddles, and closed-strut formations, spanning a range of curvatures and characteristic sizes. The optimized structures in CIF and native DFTB file formats are organized in the "stationary structure" file based on the optimization pathways that lead to the stationary structures.</p> <p>Secondly, we carried out alternating compression and expansion of the CHO model unit cell to determine the lowest energy structure as well as to obtain the bulk modulus. The file "bulk modulus" contains two data sets that describe the deformational energy landscape of pure faujasite zeolite, Na-substituted zeolite, and the ZTC model structure.</p> <p>The file "analysis tools" is a representative compilation of utilities for file format conversion, fractional vs. Cartesian crystal coordinates, and structural analysis spreadsheets.</p> <p>The agreement between experimental measurements and the computational model is remarkable that demonstrates the power of approximate density functional theory as a cost-effective computational tool with chemical accuracy for the investigation of structure/property relationships in real-world carbon-based solids.</p>
The pan-genome of Aspergillus fumigatus provides a high-resolution view of its population structure revealing high-levels of lineage-specific diversity driven by recombination
<p><em>Aspergillus fumigatus </em>is a deadly agent of human fungal disease, where virulence heterogeneity is thought to be at least partially structured by genetic variation between strains. While population genomic analyses based on reference genome alignments offer valuable insights into how gene variants are distributed across populations, these approaches fail to capture intraspecific variation in genes absent from the reference genome. Pan-genomic analyses based on <em>de novo</em> assemblies offer a promising alternative to reference-based genomics, with the potential to address the full genetic repertoire of a species. Here, we use a combination of population genomics, phylogenomics, and pan-genomics to assess population structure and recombination frequency, phylogenetically structured gene presence-absence variation, evidence for metabolic specificity, and the distribution of putative antifungal resistance genes in <em>A. fumigatus</em>. We provide evidence for three distinct populations of <em>A. fumigatus</em>, structured by both gene variation (SNPs and indels) and distinct gene presence-absence variation with unique suites of accessory genes present exclusively in each clade. Accessory genes displayed functional enrichment for nitrogen and carbohydrate metabolism, hinting that populations may be stratified by environmental niche specialization. Similarly, the distribution of antifungal resistance genes and resistance alleles were often structured by phylogeny. Despite low levels of outcrossing, <em>A. fumigatus</em> demonstrated a large pan-genome including many genes unrepresented in the Af293 reference genome. These results highlight the inadequacy of relying on a single-reference based approach for evaluating intraspecific variation, and the power of combined genomic approaches to elucidate population structure, genetic diversity, and the putative ecological drivers of clinically relevant fungi.</p> <p>Accompanying manuscript is available as preprint at <a href="https://dx.doi.org/10.1101/2021.12.12.472145">https://dx.doi.org/10.1101/2021.12.12.472145</a> </p> <p>Lotus A. Lofgren, Brandon S. Ross, Robert A. Cramer, Jason E. Stajich. Combined Pan-, Population-, and Phylo-Genomic Analysis of <em>Aspergillus fumigatus</em> Reveals Population Structure and Lineage-Specific Diversity bioRxiv 2021.12.12.472145; doi: https://doi.org/10.1101/2021.12.12.472145</p>
Networking nutrients: how nutrition determines the structure of ecological networks - Dataset
<p>Raw sequencing data and other metadata files are associated with Cuff et al. (2021a, 2022a), available at https://doi.org/10.5281/zenodo.4708418</p> <p>Data/code relating to the macronutrient contents and delineation of tropho-species clusters are associated with Cuff et al. (2021b, 2022b), available at https://doi.org/10.5281/zenodo.5738016</p> <p>Cuff, Jordan P. (2021a). A molecular analysis of the diet and biocontrol potential of spiders in cereal crops - Dataset. <em>Zenodo</em>. doi: 10.5281/zenodo.4708419</p> <p>Cuff, Jordan Patrick, Tercel, M. P., Vaughan, I. P., Drake, L. E., Wilder, S. M., Bell, J. R., … Symondson, W. O. (2021b). Evidence for nutrient-specific foraging of predators under field conditions. <em>Zenodo</em>. doi: 10.5281/zenodo.5738015</p> <p>Cuff, Jordan P., Tercel, M. P. T. G., Drake, L. E., Vaughan, I. P., Bell, J. R., Orozco-terWengel, P., … Symondson, W. O. C. (2022a). Density-independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops. <em>Environmental DNA</em>, in press. doi: 10.1002/edn3.272</p> <p>Cuff, Jordan P., Tercel, M. P. T. G., Vaughan, I. P., Drake, L. E., Wilder, S. M., Bell, J. R., … Symondson, W. O. C. (2022b). Evidence for nutrient-specific foraging of predators under field conditions. <em>Authorea</em>. doi: 10.22541/au.164908092.21266343/v1</p>
Tectonic evolution and deep mantle structure of the eastern Tethys since the latest Jurassic
<p>Uploaded by Sabin Zahirovic (sabin.zahirovic@sydney.edu.au)<br>6 August 2018</p> <p>Notes:</p> <p>Plate reconstructions can be downloaded from: <br><a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Zahirovic_etal_ESR_EasternTethys_Supplement.zip" target="_blank" rel="noopener">https://www.earthbyte.org/webdav/ftp/Data_Collections/Zahirovic_etal_ESR_EasternTethys_Supplement.zip</a></p> <p>The relevant seafloor paleo-agegrid can be downloaded from:<br><a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Zahirovic_etal_2016_ESR_AgeGrid/" target="_blank" rel="noopener">https://www.earthbyte.org/webdav/ftp/Data_Collections/Zahirovic_etal_2016_ESR_AgeGrid/</a> </p> <p>This is internal revision 888 of the 2015_v2 seafloor age-grid. </p> <p>Citation:<br>Zahirovic, S., Matthews, K. J., Flament, N., Müller, R. D., Hill, K. C., Seton, M., and Gurnis, M., 2016, Tectonic evolution and deep mantle structure of the eastern Tethys since the latest Jurassic. Earth Science Reviews, v. 162, p. 293-337."<br><a href="https://www.sciencedirect.com/science/article/pii/S0012825216302872%22" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S0012825216302872%22</a> </p>
Testing absolute plate reference frames and the implications for the generation of geodynamic mantle heterogeneity structure
<div>Description of Resources - Shephard et al. (2012)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Shephard, G. E., Bunge, H. P., Schuberth, B. S., Müller, R. D., Talsma, A. S., Moder, C., & Landgrebe, T. C. W. (2012). Testing absolute plate reference frames and the implications for the generation of geodynamic mantle heterogeneity structure. Earth and Planetary Science Letters, 317, 204-217. doi: <a href="https://doi.org/10.1016/j.epsl.2011.11.027" target="_blank" rel="noopener">10.1016/j.epsl.2011.11.027</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div>This data collection includes both the rotations and topologically closed polygons* for each of the 5 absolute reference frames that were tested in the publication. They are to be loaded in GPlates (<a href="http://www.gplates.org" target="_blank" rel="noopener">http://www.gplates.org</a>).</div> <div> </div> <div>*Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are valid at 1 Myr intervals. The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation files. </div> <div> </div> <div>The files associated with this data collection include:</div> <div>• <strong>Hybrid hotspot model (Moving and Fixed hotspots) (HHS)</strong></div> <div>* Caltech_Global_20110311HHS.gpml (37 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412HHS.rot (287 KB)- global rotation model</div> <div> </div> <div>• <strong>Fixed hotspot model (FHS)</strong></div> <div>* Caltech_Global_20110311FHS.gpml (36.8 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412FHS.rot (291 KB) - global rotation model</div> <div> </div> <div>•<strong> Hybrid hotspot and palaeomagnetic model (PMG)</strong></div> <div>* Caltech_Global_20110311PMG.gpml (35.9 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412PMG.rot (287 KB) - global rotation model</div> <div> </div> <div>• <strong>Subduction reference frame model (SUB)</strong></div> <div>* Caltech_Global_20110311SUB.gpml (36 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412SUB.rot (287 KB) - global rotation model</div> <div> </div> <div>• <strong>Hybrid hotspot and TPW-corrected palaeomagnetic model (TPW)</strong></div> <div>* Caltech_Global_20110311TPW.gpml (36.8 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412TPW.rot (287 KB)- global rotation model</div> <div> </div> <div>Project files (.gproj) are included for each .gpml/.rot pair.</div> <div> </div> <div>This article has additional supplementary data available with the online publication.</div> <div> </div> <div> </div> <div>Additional notes:</div> <div>*.rot contains the rotations for all plates and topological polygons.</div> <div>Each model is specific according to the African Plate (Plate ID 701) rotations. The rotations for all other plates are the same across each of the five models with the exception of cross-overs involving Pacific/Panthalassa plates for times earlier than 83.5Ma; these must be absolute reference frame specific and were re-calculated for each model. Programs used to calculate the new finite rotations include "adder" and "seaflow" </div> <div> </div> <div>*.gpml and .shp files contain continuously closing plate polygons i.e. from plate boundaries, from 140 Ma to present-day in 1 million year increments. </div> <div>These files differ slightly from those used in the paper, but are the most up-to-date version (as at May 2011) and are based on an updated model, Seton et al. (2012).</div> <div>They are specific to each of the five absolute reference frames. </div> <div> </div> <div>Note on velocity calculations in GPlates:</div> <div>GPlates calculates the velocity within each plate based on the stage rotation for that time period and averages for that respective period. For this reason, the velocities of a plate do not change incrementally within the time period and then abruptly change according to the next time period/stage rotation. </div> <div>This is also why there appears to be a "jump" in velocity magnitude and direction between 140 and 139 Ma.</div>
The tectonic evolution of the Arctic since Pangea breakup: Integrating constraints from surface geology and geophysics with mantle structure
<div>Description of Resources - Shephard et al. (2013)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Shephard, G. E., Müller, R. D., & Seton, M. (2013). The tectonic evolution of the Arctic since Pangea breakup: Integrating constraints from surface geology and geophysics with mantle structure. Earth-Science Reviews, 124(0), 148-183. doi: <a href="https://doi.org/10.1016/j.earscirev.2013.05.012" target="_blank" rel="noopener">10.1016/j.earscirev.2013.05.012</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div>Note: This paper is based on a global model (Seton et al., 2012), which should also be referenced if looking globally or regions other than the Arctic or northern Panthalassa.</div> <div> </div> <div>The files that make up the tectonic reconstruction model include:</div> <div>• <strong>Rotations </strong>- This is a global rotation model (based on Seton et al., 2012) that includes the new rotations for the Arctic.</div> <div>* Shephard_etal_ESR2013.rot (373 KB)</div> <div> </div> <div>• <strong>Coastlines </strong>- These are present day coastlines that have been assigned plate reconstruction ids to allow them to be reconstructed using the rotation file.</div> <div>* Shephard_etal_ESR2013_Coastlines.gpml (34.1 MB)</div> <div>* Shephard_etal_ESR2013_Coastlines.txt (3.2 MB)</div> <div>* Shephard_etal_ESR2013_Coastlinesc.kml (6.3 MB; datum - WGS 1984)</div> <div>* Shephard_etal_ESR2013_Coastlines.shp (3.2 MB inc auxiliary files; datum - WGS 1984)</div> <div> </div> <div>• <strong>Static polygons </strong>- These are closed polygons that split present day Earth's surface into regions that can be assigned to a given plate id, and therefore reconstructed back through time using the rotation file. These polygons can be used to cookie-cut and assign plate ids to geometry and raster data (for more information on this feature please visit http://gplates.org or http://earthbyte.org).</div> <div>* Shephard_etal_ESR2013_staticpolygons.gpml (19.4 MB)</div> <div>* Shephard_etal_ESR2013_staticpolygons.txt (2.7 MB)</div> <div>* Shephard_etal_ESR2013_staticpolygons.kml (4.4 MB; datum - WGS 1984)</div> <div>* Shephard_etal_ESR2013_staticpolygons.shp (2.3 MB inc auxiliary files; datum - WGS 1984)</div> <div> </div> <div>• <strong>Plate boundary geometries and resolved topologies</strong> – Resolved topologies comprise ridges, transforms, subduction zones and other plate boundary geometries. These boundaries intersect to form closed plate polygons ('resolved topologies') that are valid at 1 Myr intervals (0-200 Ma). The plate boundary geometries and plate polygons have been assigned plate reconstruction ids to allow them to be reconstructed using the rotation file.</div> <div>* Shephard_etal_ESR2013_platebounds.gpml (27.7 MB) - contains both plate boundaries and resolved topological plate polygons</div> <div>* Resolved topologies:</div> <div>- topology_*.00Ma.txt (20.6 MB)</div> <div>- topology_*.00Ma.shp (12.5 MB inc auxiliary files; datum - WGS 1984)</div> <div> </div> <div> </div> <div>References</div> <div> </div> <div>M. Seton, R.D. Müller, S. Zahirovic, C. Gaina, T.H. Torsvik, G. Shephard, A. Talsma, M. Gurnis, M. Turner, S. Maus, M. Chandler, (2012). Global continental and ocean basin reconstructions since 200 Ma. Earth-Science Reviews, 113(3–4), 212-270. doi:<a href="https://doi.org/10.1016/j.earscirev.2012.03.002" target="_blank" rel="noopener">10.1016/j.earscirev.2012.03.002</a></div>
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.