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3,655 results for “Structural data”
HDX-MS raw data for "Structural elucidation of full-length Pfs48/45 in complex with potent mAbs isolated from a naturally exposed individual"
<p>These are the raw files of the HDX-MS data for the paper "Structural elucidation of full-length Pfs48/45 in complex with potent mAbs isolated from a naturally exposed individual."</p><p>Peptide identification files are provided in MGF/MZID, as well as in CSV format. </p>
DrugMol3D: An Expanded Collection of Molecular Data for Optimized Drug Structures and Descriptive Properties
Open the record for dataset details and reuse information.
MD data for the article "Structure comparison of beta amyloid peptide Aβ 1-42 isoforms. Molecular dynamics modeling" by Anna P. Tolstova, Alexander A. Makarov, Alexei A. Adzhubei.
<p>There are CMD and REMD trajectories for Aβ isoforms discussed in the paper together with final coordinate files for these trajectories. The resulting dataset of modeled structures includes wild type Aβ42, isoD7, pS8, D7H and H6R-Aβ42, and wild type Aβ16, isoD7, pS8, D7H and H6R-Aβ16.</p>
Fig. 6 in Species authentication of Tor spp. (family Cyprinidae) in Indonesia based on osteocranium structure and biometric data
Fig. 6. The morphology of the neurocranii bone seen from the posterior view. A: T. tambroides; B: T. tambra; C: T. douronensis; and D: T. soro. BO: basioccipital bone; EPO: epotic bone; EXO: exoccipital bone; FM: magnum foramen bone; FOL: lateral occipital foramen bone; PPTR: pterotic processus bone; PTR: pterotic bone; SOC: supraoccipital bone. Scale bar: 0.5 cm.
Fig. 8 in Species authentication of Tor spp. (family Cyprinidae) in Indonesia based on osteocranium structure and biometric data
Fig. 8. Morphology of the mandibular arch (suspensory) and the opercular apparatus seen from the lateral view. A: T. tambroides; B: T. tambra; C: T. douronensis; and D: T. soro. AN: angulo-articular bone; PL: palatinum bone; CM: coronomeckeli bone; DN: dental bone; ECT: ectopterygoid bone; END: endopterygoid bone; HY: hyomandibular bone; IOP: interoperculum bone; MTP: metapterygoid bone; OP: operculum bone; OPJ: opercular joint bone; PCR: coronoideus processus bone; PO: opercular processus bone; POP: preoperculum bone; QD: quadratum bone; RA: retroarticular bone; SOP: suboperculum bone; SYM: symplectic bone. Scale bar: 0.5 cm.
Fig. 7 in Species authentication of Tor spp. (family Cyprinidae) in Indonesia based on osteocranium structure and biometric data
Fig. 7. The morphology of the infraorbital bone seen from the lateral view. A: T. tambroides; B: T. tambra; C: T. douronensis; and D: T. soro. IO 1: infraorbital bone 1st; IO 2: infraorbital bone 2nd; IO 3: infraorbital bone 3rd; IO 4: infraorbital bone 4th; IO 5: infraorbital bone 5th; IO 6: infraorbital bone 6th. Scale bar: 0.5 cm.
Fig. 5 in Species authentication of Tor spp. (family Cyprinidae) in Indonesia based on osteocranium structure and biometric data
Fig. 5. The morphology of the neurocranii bone seen from the ventral view. A: T. tambroides; B: T. tambra; C: T. douronensis; and D: T. soro. BO: basioccipital bone; ETL: lateral ethmoid bone; EXO: exoccipital bone; FR: frontal bone; FST: subtemparal foramen bone; OS: orbitosphenoid bone; PETL: lateral ethmoid processus bone; PM: masticatori processus bone; PPTR: pterotic processus bone; PRO: prootic bone; PS: parasphenoid bone; PSPL: lateral sphenotic processus bone; PTR: pterotic bone; PTS: pterosphenoid bone; SO: supraorbital bone; SP: sphenotic bone; VO: vomer bone. Scale bar: 1 cm.
Fig. 4 in Species authentication of Tor spp. (family Cyprinidae) in Indonesia based on osteocranium structure and biometric data
Fig. 4. The morphology of the neurocranii bone seen from the lateral view. A: T. tambroides; B: T. tambra; C: T. douronensis; and D: T. soro. BO: basioccipital bone; EPO: epiotic bone; EXO: exoccipital bone; FR: frontal bone; MET: mesethmoid bone; NAS: nasal bone; OS: orbitosphenoid bone; PET: preethmoid bone; PETL: lateral ethmoid processus bone; PM: masticatori processus bone; PR: pariental bone; PRO: prootic bone; PS: parasphenoid bone; PTR: pterotic bone; PTS: pterosphenoid bone; SO: supraorbital bone; VO: vomer bone. Scale bar: 1 cm.
Fig. 1 in Species authentication of Tor spp. (family Cyprinidae) in Indonesia based on osteocranium structure and biometric data
Fig. 1. Osteocranium schematic measurement of the Tor genus, where the measurement designations are described in Table 1.
Fig. 2. Fish samples from the Tor genus. A in Species authentication of Tor spp. (family Cyprinidae) in Indonesia based on osteocranium structure and biometric data
Fig. 2. Fish samples from the Tor genus. A: T. tambroides (Bleeker 1854), B: T. tambra (Valenciennes 1842), C: T. douronensis (Valenciennes 1842), and D: T. soro (Valenciennes 1842). Scale bar: 3 cm.
Data and structures for "How accurately can we predict binding poses with AlphaFold models?
<p>Contains structures generated by AlphaFold, models from GPCRdb, and structures of proteins from PDB. </p> <p>Additionally computed rmsds for pockets, backbone, and poses, and scripts to create figures.</p> <p> </p> <p> </p>
Data for "Unveiling the effect of Ni on the formation and structure of Earth's inner core"
<p>The source data for the figures in the manuscript "Unveiling the effect of Ni on the formation and structure of Earth’s inner core".</p> <p>The .xlsx files can be read via Microsoft Excel.</p> <p> </p>
Potential and training data for 'Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential'
<p>Fitted potential, training and testing data.</p>
Does Diglossia impact brain structure? Data from Swiss Ger-man early diglossic speakers
<p>This repository contrains the raw MRI images and the analysis files used in the following paper : <br>Berger Lea, Mouthon Michael, Jost Lea, Schwab Sandra, Aybek Selma and Annoni Jean-Marie (2024), <br>Does Diglossia impact brain structure? Data from Swiss Ger-man early diglossic speakers<br>Bain Sciences</p> <p>Note: The raw MRI images include in this repository are partially publish in two other dataset (https://doi.org/10.5281/zenodo.4761370, https://doi.org/10.5281/zenodo.7031880)</p>
Data from: Prebiotic membrane structures mimic the morphology of purported early traces of life on Earth
<p>Original microscopy files used in figures for publication.</p>
public-transport-structure-analysis-data
Open the record for dataset details and reuse information.
Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists
<p><strong>### Update 03/02/2025: the most up to date version of the processing pipeline presented here, also working on Linux, is available on github: https://github.com/fischer-fjd/GCA/tree/main, and a worked example with open data from the Dutch AHN surveys is available on Zenodo: https://zenodo.org/records/14722001 ###<br></strong></p> <p>This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It accompanies the article "Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists" (accepted in Methods in Ecology and Evolution on 25/08/2024). </p> <p>In the article, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to point cloud degradation (pulse density thinning, large scan angles, loss of higher-order returns) and how uncertainties and biases propagate to commonly used forest structure metrics. In addition, we provide a standardized processing pipeline in R to convert point clouds into CHMs. </p> <p>The main data source for this study are ALS point clouds from nine Australian research sites belonging to the Terrestrial Ecosystem Research Network (TERN, 5 km x 5 km extent each). The underlying data can be found here: https://portal.tern.org.au/metadata/TERN/4ff0b4c9-cfa0-4d09-9520-b5402adc583f. For one site (Robson Creek), we also used field data to assess the sensitivity of aboveground biomass estimates to ALS point cloud characteristics. Data are available here: https://portal.tern.org.au/metadata/supersite.174. </p> <p>To characterize climatic/environmental differences between sites, we used climatic data from the CHELSA/BIOCLIM+ climatology 1981-2010 (Brun et al. 2022: Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data, 14(12), 5573–5603. https://doi.org/10.5194/essd-14-5573-2022; Karger et al. 2017: Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4(1), 170122. https://doi.org/10.1038/sdata.2017.122). </p> <p>We note that the enormous size of the full set of manipulated point clouds (original + thinned + individual flightlines: ~400 GB) and the derived raster products (~200 GB) by far exceeds limits on data storage in Zenodo. However, all analyses can be recreated from scratch from the openly available data and the R code in this repository. In addition, we include derived products for the nine study sites that allow to replicate results in the main text without any point cloud processing (CHMs and other rasters across thinned point clouds + summary statistics). </p> <p>The different data layers are:</p> <p><strong>01_rscripts.zip:</strong></p> <ul> <li>contains a sample script to test the processing pipeline (<em>test.processing.R</em>) as well as a collection of helper functions (<em>ALS_processing_helperfunctions_v40.R</em>); the script can be run directly after unzipping the folder, but an installation of LAStools (https://rapidlasso.de) is necessary (path_lastools = "PATH/TO/LASTOOLS/BIN"); we note that the script was developed on Windows PCs, its application with the recent Linux distribution of LAStools has not yet been tested</li> <li>contains the full set of scripts necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data (<em>create.CHMs.R)</em> as well as the overall robustness analysis (<em>analyze.CHMs.R</em>); to replicate the processing of the raw point clouds step by step, .laz files should be downloaded from the TERN repository (cf. citation above) and placed in a "data" folder, with subfolders for each site and with the same naming conventions as in this repository (e.g., "/data/Alice Mulga")</li> </ul> <p><strong>02_reference.zip</strong></p> <ul> <li>contains reference digital surface models (DSMs), canopy height models (CHMs) and digital terrain models (DTMs) for all nine TERN sites, based on the original ALS point clouds</li> <li>note that these reference layers are produced with the "CHMhighest" algorithm, which provides an easily interpretable canopy description as long as pulse densities are high (>= 20 shots per squaremetre)</li> </ul> <p><strong>03_climate.zip</strong></p> <ul> <li>contains site coordinates</li> <li>contains the climate layers from the CHELSA climatology (cf. citation above, only used to evaluate climatic ranges of sites)</li> </ul> <p><strong>04_robson_additional.zip</strong></p> <ul> <li>contains biomass estimates for Robson Creek</li> <li>contains shapefiles for large trees at Robson Creek (only used for visualization purposes)</li> </ul> <p><strong>05_downsampling_pulse_[Site name].zip</strong></p> <ul> <li>[Site name] is a stand-in for the nine TERN sites (e.g., "Alice Mulga.zip", "Credo.zip", etc.)</li> <li>contains the data necessary to reproduce results in the main text of the study, i.e. DSMs, CHMs, and DTMs for all nine TERN sites, and at different pulse density levels (from 16 down to 0.5 laser shots per squaremetre)</li> <li>also contains calculated summary statistics for each site</li> </ul> <p>All zip files should be extracted into the same folder, except for 05_downsampling_pulse_[Site name].zip which should all be moved to a subfolder called "downsampling_pulse".</p>
Unleashing the power of data through organization: Structure and connections for meaning, learning, and discovery
<p><span>Knowledge organization is needed everywhere. Its importance is marked by its pervasiveness. This paper will show many areas, tasks, and functions where proper use of Knowledge Organization, construed as broadly as the term implies, provides support for learning and understanding, for sensemaking and meaning making, for inference, and for discovery by people and computer programs and thereby will make the world a better place. The paper focuses not on metadata but rather on structuring and representing the actual data or knowledge itself and argues for more communication between the largely separated KO, Ontology, Data Modeling, and Semantic Web communities to address the many problems that need better solutions. In particular, the paper discusses the application of knowledge organization in Knowledge bases for question answering and cognitive systems; Knowledge bases for information extraction from text or multimedia; Linked data; Big data and data analytics; Electronic health records as one example; Influence diagrams (causal maps), dynamic system models, process diagrams, concept maps, and other node-link diagrams; Information systems in organizations; Knowledge organization for understanding and learning; and Knowledge transfer between domains. The paper argues for moving beyond triples to a more powerful representation using entities and multi-way relationships but not attributes.</span></p>
Massive Compression for High Data Rate Macromolecular Crystallography (HDRMX): Impact on Diffraction Data and Subsequent Structural Analysis: Subset with data from 2 deposited PDBs.
<p>Diffraction data from a lysozyme crystal. Data collected at 7.5 keV at the AMX beamline, NSLS-II. 360 degrees were collected, with 0.2 deg per frame. This data set contains 2 folders; 1 from uncompressed data and 1 from data compressed using lossy compression as follow: frames were summed (2x), pixels were binned (2x) and Hcompress with level 24 was applied to uncompressed data. </p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</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.