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3,655 results for “Structural data”
Input data of the multi-patch geometries used in: A. Farahat, H. M. Verhelst, J. Kiendl, M. Kapl, Isogeometric analysis for multi-patch structured Kirchhoff–Love shells, Computer Methods in Applied Mechanics and Engineering 411 (2023) 116060 DOI: 10.1016/j.cma.2023.116060
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Data from: Changes in brain structure and function following exposure to oral LSD during adolescence: A multimodal MRI study
<p><em>Background</em>: LSD is a hallucinogen with complex neurobiological and behavioral effects. Underlying these effects are changes in brain neuroplasticity. This is the first study to follow the developmental changes in brain structure and function following LSD exposure in periadolescence. We hypothesized LSD given during a time of heightened neuroplasticity, particularly in the forebrain, would affect cognitive and emotional behavior and the associated underlying neuroanatomy and neurocircuitry. </p> <p><em>Methods:</em> Female and male mice were given vehicle, single, or multiple treatments of 3.3 µg of LSD by oral gavage starting on postnatal day 51. Between postnatal days 90-120 mice were imaged and tested for cognitive and motor behavior. MRI data from voxel-based morphometry, diffusion weighted imaging, and BOLD resting state functional connectivity were registered to a mouse 3D MRI atlas with 139 brain regions providing site-specific differences in global brain structure and functional connectivity between experimental groups.</p> <p><em>Results:</em> Motor behavior and cognitive performance were unaffected by periadolescent exposure to LSD. Differences across experimental groups in brain volume for any of the 139 brain areas were few in number and not focused on any specific brain region. Multiple exposures to LSD significantly altered gray matter microarchitecture across much of the brain. These changes were primary associated with the thalamus, sensory and motor cortices, and basal ganglia. The forebrain olfactory system and prefrontal cortex and hindbrain cerebellum and brainstem were unaffected. The functional connectivity between forebrain white matter tracts and sensorimotor cortices and hippocampus was reduced with multidose LSD exposure.</p> <p><em>Conclusion:</em> Does early exposure to LSD in periadolescence have lasting effects on brain development? There was no evidence of LSD having consequential effects on cognitive or motor behavior when animal were evaluated as young adults 90-120 days of age. Neither were there any differences in the volume of specific brain areas between experimental conditions. The pronounced changes in indices of anisotropy across much of the brain would suggest altered gray matter microarchitecture and neuroplasticity. The reduction in connectivity in forebrain white matter tracts with multidose LSD and consolidation around sensorimotor and hippocampal brain areas requires a battery of tests to understand the consequences of these changes on behavior.</p>
Data from: Structure and composition of a canopy-beetle community (Coleoptera) in a Neotropical lowland rainforest in southern Venezuela
<p>Species richness, community structure, and taxonomic composition are important characteristics of biodiversity. Beetle communities show distinct diversity patterns according to habitat attributes. Tropical rainforest canopies, which are well known for their richness in Coleoptera, represent such a conspicuous life zone. Here, I describe a canopy-inhabiting beetle community associated with 23 tree species in a Neotropical lowland rainforest. Adult beetles were sampled manually and in aerial traps using a large tower crane for a cumulative year. The sample revealed 6738 adult beetles, which were assigned to 862 (morpho-)species in 45 families. The most species-rich beetle families were Curculionidae (<em>n</em> = 246), Chrysomelidae (<em>n</em> = 121), and Cerambycidae (<em>n</em> = 89). The most abundant families were Curculionidae (<em>n</em> = 2746) and Chrysomelidae (<em>n</em> = 1409). Dominant beetle families were found in most assemblages. The beetle community consisted of 400 singletons (46.4%). A similar proportion was evident for assemblages of single tree species. I found that 74.5% of all beetle species were restricted in their occurrence on host trees to the phenological season and time of the day. This daily and seasonal migration causes patterns similar to mass effects, and therefore accounts for the high proportion of singletons.</p>
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
Data and appendices: Genetic structure of the European white elm (Ulmus laevis Pall., Ulmaceae) in Switzerland
<p>This dataset is linked to the following article published in Annals of Forest Science: Dermelj, L., Fragnière, Y., Jacob, G. et al. Genetic structure of the European white elm (Ulmus laevis Pall., Ulmaceae) in Switzerland. Annals of Forest Science 81, 28 (2024). <a href="https://urldefense.com/v3/__https://doi.org/10.1186/s13595-024-01245-8__;!!Dc8iu7o!yKfGPXx0qHn-nE984OtuCUPPKaVjkUX03CZMhD3un5K_DjKgv8OrJB1nXYJdoWBTDKXDNA196eOKhBbjLjqqPgOm$" target="_blank" rel="noopener noreferrer">https://urldefense.com/v3/__https://doi.org/10.1186/s13595-024-01245-8__;!!Dc8iu7o!yKfGPXx0qHn-nE984OtuCUPPKaVjkUX03CZMhD3un5K_DjKgv8OrJB1nXYJdoWBTDKXDNA196eOKhBbjLjqqPgOm$</a></p> <p> </p> <pre> </pre>
FAIRmat Tutorial 10: FAIR electronic-structure data in NOMAD
<p>The FAIRmat consortium aims to extend the current NOMAD-Lab (meta)data structure to a large variety of materials-science data. Given our strong foundation in computational data, especially DFT, we are now extending our scope. In this tutorial, we will explain the (meta)data structure for <em>ab initio</em> calculations, with an emphasis on precision and on going beyond the accuracy limits of DFT.</p> <p>This tutorial is suitable for new and experienced researchers who want to learn about the latest features in treating DFT and beyond DFT methodologies. We will give a brief introduction to the NOMAD Lab and the FAIRmat consortium, followed by a guided tutorial where we will:</p> <ol> <li>Show you how you can upload, publish, and explore <em>ab initio</em> computational data.</li> <li>Show you how to define your own complex workflows, linking between DFT and beyond DFT calculations.</li> <li>Give you examples of the post-processing capabilities of the NOMAD Lab.</li> </ol> <p>In more detail: Precision settings are now searchable, allowing for “data-quality” filtering over the NOMAD data. Using simple queries, we will show how to generate a sampling that extrapolates towards the basis set limit. For those already familiar with their code of choice, there is also the native tier quick filter that matches recommended developer settings. Moreover, for ease in navigating the density-functional space, we will be presenting a new, knowledge-based categorization system that is more refined and semantically richer than Jacob’s ladder. Finally, we will show the latest developed schemas which try to cover computational techniques that go beyond DFT and which are useful to treat excited-state and advanced many-body properties: the <em>GW</em> approximation, Bethe-Salpeter equation (BSE) solutions, tight-binding-based modeling (using Wannier projections or Slater-Koster fittings), and Dynamical Mean-Field Theory (DMFT). </p> <p><strong>Disclaimer: </strong>NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Predicting Publication of Clinical Trials Using Structured and Unstructured Data
<p>This dataset (N=76,950) links metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a bibliographic database of academic journal articles), and can be used to model whether a clinical trial will get published or not.</p>
Automated recognition of RNA structure motifs by their SHAPE data signatures
<p>Datasets, code and results supporting the manuscript:</p> <p>Radecki P., Ledda M. & Aviran S., Automated recognition of RNA structure motifs by their SHAPE data signatures</p>
Fine vertical structures at the cloud heights of Venus revealed by radio holographic analysis of Venus Express and Akatsuki radio occultation data -- dataset
<p>The data used in the figures in the paper "Fine vertical structures at the cloud heights of Venus revealed by radio holographic analysis of Venus Express and Akatsuki radio occultation data" by Imamura et al. (J. Geophys. Res)</p> <p>The description of the columns in the files are given in the header section.</p>
Geophysical data from: "A review of geophysical methods for soil structure characterization"
<p>The geophysical data presented herein was collected in the Soil Structure Observatory (see, Keller et. al. 2017) and were used to create Figures 4b, 5a, 5b, 9a and 9b for the manuscript entitled: "A review of geophysical methods for soil structure characterization" submitted to reviews of geophysics. The data set includes:<br> <br> GPR data collected from South to North in the bare soil of block C before and after compaction<br> GPR_800Mhz_BSbeforecompaction.DZW<br> GPR_800Mhz_BSbeforecompaction.DZT<br> GPR_800Mhz_BSaftercompaction.DZW<br> GPR_800Mhz_BSaftercompaction.DZT<br> <br> ERT data collected from South to North in the bare soil of block B before and after compaction<br> ERT_B2_14-03beforecompaction.bin<br> ERT_B2_14-04aftercompaction.bin<br> <br> Time-lapse ERT data collected in block A in September of 2017:<br> timelapseERT2017.zip<br> The date and time in the files names indicate the time in which the resistivimeter started collecting data plus two hours.<br> e.g. for 2017-09-11-18-01-41.bin, the first data point was collected at 14:01 on the 11th of September of 2017.<br> The first 464 data points correspond to bare soil collected from North to South<br> The following 464 data points correspond to grassy soil from South to North<br> <br> Meteorological data from 2017<br> meteodata_Reckenholz2017.xls<br> <br> Time domain reflectometry data from September of 2017 for block A (10, 20, 40 and 70cm depth). Two land covers (bare soil and grassy soil) and two compaction treatments (full compacted and non compacted) are included.<br> TDR_A1_Sept2017.xlsx</p> <p> </p> <p>Keller, T., Colombi, T., Ruiz, S., Manalili, M. P., Rek, J., Stadelmann, V., ... & Schymanski, S. (2017). Long-Term Soil Structure Observatory for Monitoring Post-Compaction Evolution of Soil Structure. <em>Vadose Zone Journal</em>, <em>16</em>(4).</p>
Data from: Crustal structure in central-eastern Greenland from Receiver Functions
<p>Data used in: </p> <p>Helene A. Kraft, Hans Thybo, Lev P. Vinnik, and Sergei Oreshin (2018). Crustal structure in central-eastern Greenland from Receiver Functions, submitted to Journal of Geophysical Research - Solid Earth (Paper #2018JB015919R).</p>
Research data supporting "Residue-Specific Solvation Directed Thermodynamic and Kinetic Control over Peptide Self-Assembly with 1D/2D Structure Selection"
<p>Experimental research raw data supporting the publication by Lin, Y. et al, 2019, "Residue-Specific Solvation Directed Thermodynamic and Kinetic Control over Peptide Self-Assembly with 1D/2D Structure Selection", ACS Nano. DOI: 10.1021/acsnano.8b08117.</p> <p>Molecular simulation data is available upon reasonable request from irene.yarovsky@rmit.edu.au.</p>
Data, code, models for "Weakly Supervised Semantic Segmentation for Joint Key Local Structure Localization and Classification of Aurora Image"
<p>Data, code and models for https://ieeexplore.ieee.org/document/8410588/</p>
Supplementary Data for "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation"
<p>DFT-optimized structures, energies, and computed physicochemical properties of metal-organic frameworks that correspond with work in "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation" (DOI: <a href="https://pubs.acs.org/doi/10.1021/acscatal.8b05178">10.1021/acscatal.8b05178</a>).</p>
Experimental data and benchmarks used in the paper "Theoretical Foundations for Structural Symmetries of Lifted PDDL Tasks"
<p>This dataset contains both benchmarks and data used in the paper.</p> <p>PDDL benchmark files can be found in the files benchmarks.tar.gz and<br> bagged-benchmarks.tar.gz. The former contains all domains from all IPCs from the<br> repository https://bitbucket.org/aibasel/downward-benchmarks, without duplicate<br> domains that have been used in multiple IPCs. The latter contains the subset of<br> these tasks for which the reformulation in the "bagged representation" from the<br> following paper succeeded:</p> <p>Riddle, P.; Douglas, J.; Barley, M.; and Franco, S. 2016. Improving<br> performance by reformulating PDDL into a bagged representation.<br> In ICAPS 2016 Workshop on Heuristics and Search for Domain-<br> independent Planning, 28–36.</p> <p>We obtained the implementation of the baggy reformluation from the authors.</p> <p>All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.2621897).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s). Some directories (not for<br> the ground experiments, where these files got too large) also contain a file<br> generators.py that contain all symmetry group generators in permutation<br> notation.</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.</p>
Data set for "Structural transitions in the RNA 7SK 5' hairpin and their effect on HEXIM binding"
<p>Raw data set for "Structural transitions in the RNA 7SK 5' hairpin and their effect on HEXIM binding"</p> <p> </p> <p>Version 1.0: Energy landscape data and MD trajectories for RNA+ARM peptide</p>
Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA Data Archive
<p>[This repository contains the source data for the workflow presented in the manuscript "<strong>Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA</strong>". The workflow can be found here: http://gitlabscottgroup.med.usherbrooke.ca/gaspard/snakemake_blockbuster ]</p> <p>The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 115 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new tRNA and snoRNA genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distort the perceived architecture of the human transcriptome.</p>
Research data supporting "Rolling Circle Transcription-Amplified Hierarchically Structured Organic-Inorganic Hybrid RNA Flowers for Enzyme Immobilization""
<p>Raw research data supporting the publication:</p> <p>Wang Y. et al., 2019, ACS Applied Materials and Interfaces, DOI: 10.1021/acsami.9b04663</p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
Data and code for: Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages
<p><strong>Data and Code for the article:</strong></p> <p><strong>Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages</strong><br> <br> <em>Jonas O. Wolff, Gustavo B. Paterno, Daniele Liprandi, Martín J. Ramírez, Federico Bosia, Arie van der Meijden, Peter Michalik, Helen M. Smith, Braxton R. Jones, Alexandra M. Ravelo, Nicola Pugno and Marie E. Herberstein</em><br> <br> Journal: <strong>Evolution</strong> <br> DOI: <a href="https://doi.org/10.1111/evo.13834">https://doi.org/10.1111/evo.13834</a> </p> <p>Github repository: https://github.com/paternogbc/Wolff_et_al_Evolution_aerial_spider_webs</p> <p><br> When using the <strong>data available</strong> in this repository, please cite the original publication. </p> <p>Contact jonas.wolff@mq.edu.au for any further information. </p> <p>Wolff, J. O., Paterno, G. B., Liprandi, D. , Ramírez, M. J., Bosia, F. , der Meijden, A. , Michalik, P. , Smith, H. M., Jones, B. R., Ravelo, A. M., Pugno, N. and Herberstein, M. E. (2019), <strong>Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages</strong>. Evolution. Accepted Author Manuscript. doi:10.1111/evo.13834</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.