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51 results for “structural joints”
Database of measurements for damage detection of T-type timber structural joint by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated joint between two timber beams connected at an angle of 90⁰. Presented data related to seven different states of joints, five load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p>
Database of measurements for damage detection of panel-to-panel moment joints in timber structures by Coaxial Correlation Method
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned in two different ways on either side of the investigated panel-to-panel connection. Presented data related to ten different states of joints, two load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds with frequency range from 10 Hz to 2000 Hz). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the case of static load equal to 151.8 kg with sweep-type input signal, and T2 scheme of sensors placement is described in Kurtenoks, V.; Kurajevs, A.; Buka-Vaivade, K.; Serdjuks, D.; Lapkovskis, V.; Mironovs, V.; Podkoritovs, A.; Vilnitis, M. The Quality Assessment of Timber Structural Joints Using the Coaxial Correlation Method. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1929. https://doi.org/10.3390/buildings13081929</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>
Joint embedding of vertebrate brain single-cell RNA-Seq using sequence or structure
<p>Embeddings of single-cell RNA-Seq data from three adult vertebrate brain datasets into Orthogroup feature space or Structural cluster feature space. Orthogroups were generated using OrthoFinder v5.5.0; Structural clusters were assigned by using FoldSeek to cluster AlphaFold-v4 structural predictions.<br> <br> The three datasets used as the basis for these embeddings were:</p> <ul> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3768152">"Brain8"</a> from the <a href="https://www.frontiersin.org/articles/10.3389/fcell.2021.743421/full">Jiang et al. 2021</a> zebrafish cell atlas (files beginning with GSM3768152)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2906405">"Brain1"</a> from the <a href="https://www.sciencedirect.com/science/article/pii/S0092867418301168#sec4">Han et al. 2018</a> mouse cell atlas (files beginning with GSM2906405)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM6214268">"Xenopus_brain_COL65"</a> from the <a href="https://www.nature.com/articles/s41467-022-31949-2">Liao et al. 2022</a> Xenopus laevis adult cell atlas (files beginning with GSM6214268)</li> </ul> <p>For each dataset, we also generated a standardized cell type annotation file based on the author's originally provided cell type annotation data. The first column is the cell barcode for that species and the second column is the original study's cell type annotation for that cell.</p> <p>For the Xenopus brain data, we removed around ~18k cells that were not annotated in the original data to simplify data analyses - these are reflected in the files with the "subsampled" suffix. Subsampled versions of the data are also available for the joint embedding space (prefixed with "DrerMmusXlae").</p> <p>For the final datasets used in our analyses, we also provide features x cell matrices as .h5ad files for smaller file sizes and faster loading using Scanpy. </p> <p>For visualizing our UMAP plots of our top200 embedding space, we provide ".tsv" files with a variety of metrics and the x and y positions of each cell in the UMAP. See "DrerMmusXlae_adultbrain_FoldSeek_plotlydata.tsv" and "DrerMmusXlae_adultbrain_OrthoFinder_plotlydata.tsv"</p> <p>These data are part of the Arcadia Science Pub titled <a href="https://doi.org/10.57844/arcadia-vw5e-2670">"Comparing gene expression across species based on protein structure instead of sequence"</a>.</p>
Dataset: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Dataset used in the experiments of the publication: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data" by Bach et al.</p> <p><strong>File description:</strong></p> <ul> <li> <p>cfmid4.tar: MS² spectra simulated using <a href="https://bitbucket.org/wishartlab/cfm-id-code/src/CFM-ID_4.0.7/">CFM-ID (v4.0.7)</a> for all molecular candidate structures</p> </li> <li> <p>db_layout.png: Visualization of the SQLite database (DB) layout</p> </li> <li> <p>massbank.sqlite.gz: DB containing all needed data to (re-)run the experiments shown in the paper. Please read "DB_README.md" for further details. The database file can be unpacked using gzip.</p> </li> <li> <p>metfrag.tar: MetFrag input files and MS² scores for all candidate sets computed using the <a href="https://ipb-halle.github.io/MetFrag/projects/metfragcl/">MetFrag software</a>.</p> </li> <li> <p>sirius_scores.tar: MS² scores for all candidates and measured spectra using the <a href="https://bio.informatik.uni-jena.de/software/sirius/">SIRIUS software</a>.</p> </li> <li> <p>sirius_inputs.tar: Input (ms-files) for the SIRIUS software.</p> </li> <li> <p>DB_README.md: Description of each table in the "massbank.sqlite" SQLite DB.</p> </li> <li> <p>db_processing_scripts.tar: Scripts to re-produce the "massbank.sqlite" and a README.md providing further information on the process.</p> </li> <li> <p>massbank__2020.11__v0.6.1.sqlite: Base SQLite DB from which the "massbank.sqlite" was build up. It was created using the "<a href="https://github.com/bachi55/massbank2db">massbank2db</a>" (v0.6.1) Python package using the <a href="https://github.com/bachi55/MassBank-data/tree/2020.11-branch">MassBank release 2020.11</a>.</p> </li> <li> <p>substructure_fingerprints.tar: Pre-computed substructure counting fingerprints for all candidates related to our experiments.</p> </li> </ul> <p><strong>Instructions:</strong></p> <p>The "massbank.sqlite" can be directly used with the Structure Support Vector Machine Model (SSVM) described in the manuscript and implemented in the "<a href="https://github.com/aalto-ics-kepaco/msms_rt_ssvm">ssvm</a>" Python package.</p> <p>If desired, the database can be re-produced using the scripts provided in "db_processing_scripts.tar":</p> <ol> <li>Create a directory for all data</li> <li>Download and extract the ... <ol> <li>Processing scripts</li> <li>MS² scorer outputs (e.g. metfrag.tar)</li> <li>Pre-computed substructure fingerprints</li> </ol> </li> <li>Follow the instructions given in the "README.md" of the "db_processing_scripts.tar"</li> </ol>
Data for "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities"
<p>Data for the paper "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities" which is in minor revision in Ecology Letters (manuscript id:ELE-00589-2021.R1). A doi will be provided upon publication.</p> <p>Current citation: Danet, A., Mouchet, M., Bonnaffé, W., Thébault, E., & Fontaine, C. (In revision) Species<br> richness and food-web structure jointly drive total biomass and its temporal stability in<br> fish communities Minor revision in Ecology Letters.</p> <p>The repository constains data describing fish community monitoring across stream sections in metropolitan France over the period 1995-2018 by the French Office of Water and Aquatic Ecosystems (ONEMA) using electrofishing.</p> <p>The repository contains:</p> <ul> <li> description of fishing: fishing_protocol.csv <ul> <li>surface: sampled surface</li> <li>opcod: fishing operation code, a unique identifier for each sampling event</li> <li>station: unique identifier for each site</li> <li>nb_sp, nb_ind: number of species, number of individuals</li> </ul> </li> <li>geographical information: station_basin.csv <ul> <li>X, Y: spatial coordinates of the station, expressed in metres in Lambert93 (epsg:2154)</li> <li>basin: name of the hydrographic basin</li> </ul> </li> <li>environment: environment.csv ( _mean: mean, _med: median, _cv: coefficient of variation) <ul> <li>alt: altitude</li> <li>d_source: distance to source</li> <li>strahler: strahler order</li> <li>BOD: Biological Oxygen Demand</li> <li>temperature: water temperature</li> <li>flow: water flow</li> </ul> </li> <li>community data: community_data.csv <ul> <li>species: three digits code corresponding to a given species (see Table S1, Danet et al. in revision)</li> <li>nind: number of individuals</li> <li>biomass: biomass in gram</li> </ul> </li> <li>Length of each fish individual: fish_length.csv <ul> <li>length: length of the fish in millimeter</li> </ul> </li> <li>Inferred food-web: class_network.rda <ul> <li>data: <ul> <li>class_id: size class of a fish individual</li> </ul> </li> <li>network: these data.frame can be handled by igraph::graph_from_data_frame() <ul> <li>from, to: "to" eats "from"</li> </ul> </li> <li>composition: <ul> <li>sp_class: concatenation of species and class_id columns</li> <li>bm_std: biomass reported to the sampled surface</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Dataset presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions"
<p>Dataset (observed tippers, solar quiet global-to-local transfer functions, and global Q responses) presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions".</p>
Data used in 'The joint role of coevolutionary selection and network structure in shaping trait complementarity in mutualisms' manuscript
<p>The files in this repository correspond to the raw and processed data described in the 'The joint role of coevolutionary selection and network structure in shaping trait complementarity in mutualisms' manuscript.</p> <p>Once expanded, the zip files contains two directories and one documentation file, named data_documentation. Please refer to this file for a thorough description of the organization and contents of raw and processed data files.</p>
Result files (ONLYSTEREO): "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Result files associated with the publication: "<strong>Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data</strong>" by Bach et al.</p> <p>The following files are included in the archive:</p> <ul> <li>Raw max-marginal predictions using LC-MS²Struct for all LC-MS² experiments of the ONLYSTEREO setup</li> <li>Averaged max-marginals for the LC-MS²Struct over all SSVM models</li> <li>Ranks for the ground-truth structures predicted by Only MS² and LC-MS²Struct (molecule class analysis)</li> </ul> <p>Instructions:</p> <ul> <li>clone the repository containing the experimental scripts and analysis notebooks: <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp">https://github.com/aalto-ics-kepaco/lcms2struct_exp</a></li> <li>download the archive in this repository</li> <li>unpack the archive in the git-repository root directory</li> <li>follow the instructions given in the <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp/blob/main/README.md">README.md</a> of the git-repository to reproduce the figures, etc.</li> </ul>
Result files (ALLDATA): "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data with LC-MS²Struct"
<p>Result files associated with the publication: "<strong>Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data</strong>" by Bach et al.</p> <p>The following files are included in the archive:</p> <ul> <li>Raw max-marginal predictions using LC-MS²Struct for all LC-MS² experiments of the ALLDATA setup</li> <li>Averaged max-marginals for the LC-MS²Struct over all SSVM models</li> <li>Top-k accuracies for the comparison methods (MS²+RT, ...)</li> <li>Ranks for the ground-truth structures predicted by Only MS² and LC-MS²Struct (molecule class analysis)</li> </ul> <p>Instructions:</p> <ul> <li>clone the repository containing the experimental scripts and analysis notebooks: <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp">https://github.com/aalto-ics-kepaco/lcms2struct_exp</a></li> <li>download the archive in this repository</li> <li>unpack the archive in the git-repository root directory</li> <li>follow the instructions given in the <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp/blob/main/README.md">README.md</a> of the git-repository to reproduce the figures, etc.</li> </ul> <p>Version history:</p> <ul> <li><strong>Version 1</strong>: Experimental results for "Method comparison" and "Molecule classification analysis" where performed with <strong>2D fingerprints</strong> (<a href="https://www.biorxiv.org/content/10.1101/2022.02.11.480137v1">preprint v1</a>)</li> <li><strong>Version 2</strong> <em>(this version)</em>: Experimental results for "Method comparison" and "Molecule classification analysis" where performed with <strong>3D fingerprints</strong></li> </ul>
Dataset for the structural investigation of hybrid SLS joints
<p>The dataset contains raw data of structural tests performed on<br> pinned hybrid titanium-composite joints. Geometry is similar to ASTM D5868.</p> <p>For further context, the creators refer to the research conducted<br> at the Institute of Structural Lightweight Design,<br> Johannes Kepler University, Linz, Austria.</p> <p>This research was conducted within the SUSTAINair project.<br> This project has received funding from the European Union’s Horizon 2020<br> research and innovation program under grant agreement No. 101006952.</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>
Lithospheric structure and strength variations in Antarctica from joint modeling of elevation, geoid and seismic data
<p>These models include Moho depth, LAB depth and integrated lithospheric strength based on a 1D approach involving thermal analysis under local isostasy and a rheological method.</p>
ADHESIVELY BONDED JOINTS OF THE PRIMARY STRUCTURE WITHIN THE HAP PROJECT
<p>In the HAP (High Altitude Platform) project, the German Aerospace Center (DLR) is developing a solar-powered long-range aircraft, various payloads for Earth observation missions, the ground control station and operating concept. The mission conditions and<br> objectives pose major challenges to the aircraft structure, especially with respect to lightweight design. This presentation intrudces the design process of bonded joints for the primary structure on the example of the wing spar, as the most critical component. The wing, with an overall span of 27m, is divided in three wing sections, with the tubular wing spar being assembled from several segments for manufacturing reasons. Considering the demanding lightweight design expectations, adhesively bonded scarfed tubular joints are selected. A manufacturing concept is developed to minimize effort for joint preparation. The dimensioning is based on global (GFEM) and detailed finite element models (DFEM) to fine-tune the design (stiffness discontinuities, surface toughness) to increase the load<br> carrying capacity. This is flanked by physical tests for model validation and material characterization, such as adhesive characterization, to increase DFEM accuracy.</p>
Data files for 'Tan et al., (2023). Structural heterogeneity-controlled rupture process of the 2021 Mw 7.1 Fukushima, Japan earthquake revealed by joint inversion of seismic and geodetic data'
<p>slip model.dat: rupture model of the 2021 Mw 7.1 Fukushima earthquake</p> <p>In 'slip model.dat', each row contains the moment rate function of each sub-fault. The numbers of the sub-faults are given in the first two columns.</p>
Database of measurements for damage detection of T-type steel structural joint by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure’s response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated joint between two steel beams connected at an angle of 90⁰. Presented data related to five different states of joints, seven load levels, two placements of used electrodynamic actuators and two type of input signal (short impulse and sweep signal with duration 0.5 seconds). In the “<strong>Read_me_first.pdf</strong>” is described the experiment, the format of .csv files names and files’ structure.</p>
Effects of Tofacitinib (CP-690,550) on Magnetic Resonance Imaging (MRI)- Assessed Joint Structure In Early Rheumatoid Arthritis (RA)
ClinicalTrials.gov study NCT01164579. IPD Sharing: Not stated. Countries: 9. Publications: 14.
A Study to Assess the Effect of Tocilizumab + Methotrexate on Prevention of Structural Joint Damage in Patients With Moderate to Severe Active Rheumatoid Arthritis (RA)
ClinicalTrials.gov study NCT00106535. IPD Sharing: Not stated. Countries: 16. Publications: 8.
Data from: The 'filtering' metaphor revisited: competition and environment jointly structure invasibility and coexistence
'Filtering', or the reduction in species diversity that occurs because not all species can persist in all locations, is thought to unfold hierarchically, controlled by the environment at large scales and competition at small scales. However, the ecological effects of competition and the environment are not independent, and observational approaches preclude investigation into their interplay. We use a demographic approach with 30 plant species to experimentally test (i) the effect of competition on species persistence in two soil moisture environments, and (ii) the effect of environmental conditions on mechanisms underlying competitive coexistence. We find that competitors cause differential species persistence across environments even when effects are lacking in the absence of competition, and that the traits that determine persistence depend on the competitive environment. If our study had been observational and trait-based, we would have erroneously concluded that the environment filters species with low biomass, shallow roots, and small seeds. Changing environmental conditions generated idiosyncratic effects on coexistence outcomes, increasing competitive exclusion of some species while promoting coexistence of others. Our results highlight the importance of considering environmental filtering in light of, rather than in isolation from, competition, and challenge community assembly models and approaches to projecting future species distributions.
Positive and negative interactions jointly determine the structure of Müllerian mimetic communities
<p>Negative and positive ecological interactions have opposite effects on the structure of ecological communities, in particular in terms of ecological similarity among interacting species. In nature, species belonging to the same guild often interact in both negative and positive ways, yet the interplay between interactions of different kinds in intraguild community dynamics remains poorly understood. Müllerian mimetic communities are particularly suited for investigating this interplay because positive (mutualistic mimicry) and negative (competition for trophic resource and micro-habitat) interactions are relatively easy to identify. Empirical research has shown that the combination of competition and mutualistic mimicry does not necessarily drive convergence along all dimensions of the ecological niche, but the determinants of such mixed result are unknown. Here, we analyze the structure of Müllerian mimetic communities simulated with an agent-based model. We show that mutualistic mimicry favours ecological similarity on dimensions along which similarity favours fine-scale co-occurrence. Co-mimetic species use similar micro-habitats, but do not necessarily use similar resources. Heterogeneity of resources among micro-habitats is necessary for ecological similarity on resource use among co-mimetic species to occur. We therefore highlight the importance of fine-scale co-occurrence if we are to understand how positive and negative interactions structure ecological communities.</p> <p> </p>
ScienceDex guides
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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.