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3,655 results for “Structural data”
Crystal structure of olive flounder [Paralichthys olivaceus] interferon gamma at 2.3 Angstrom resolution - diffraction data
<p>Diffraction images, Bessy II (Berlin), MX 14.1, 12.5.2017, PDB ID 6F1E</p>
Data file with manuscript titled 'A Structurally Validated Sequence Alignment of 497 Human Protein Kinase Domains'
<p>The files used in different analysis reported in the manuscript titled - 'A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains' are shared at two locations. Following is a brief description of these files.</p> <p>Location - https://github.com/DunbrackLab/Kinases<br> 1. HMM profile files - HMM files for each of the nine groups computed separately labeled as Groupname.hmm, like AGC.hmm<br> 2. HMM profile file - HMM file computed from the full alignment including all the sequences - Human-PK.hmm<br> 3. Score files - HMM scores of each kinase sequence against all the groupwise HMMs both for iteration1 (HMM-iter1-scores-tables.txt) and iteration2 (HMM-iter1-scores-tables.txt)<br> 4. Jalview session file - Kinase alignment with sequences colored by secondary structure information from PDB file if the structure is known; or predicted secondary structure if the experimental structure is not known. The file could be opened in Jalview - kinases-PDB-SSPred.jvp</p> <p>Location - https://zenodo.org/record/3445533<br> 1. The file contains list of residue pairs aligned in pairwise structural alignments of 272 human protein kinases which were used as a benchmark in the study. The alignments were created by FATCAT and optimized by SE program.</p>
Simulated and experimental data distributed to the CASP13 participants in protein structure prediction assisted with sparse NMR data
<p>All simulated and experimental data distributed to the CASP participants in protein structure prediction assisted with sparse NMR data in CASP13.</p> <p>Also available at http://predictioncenter.org/casp13/results.cgi?view=targets&model=first&tr_type=others&sub_type=N&groups_id=</p> <p> </p>
Selected data from "Small-scale universality in the spectral structure of transitional pipe flows"
<p>Data from Figure 4 and Figure 6 of "Small-scale universality in the spectral structure of transitional pipe flows", Science Advances (2019). Included are spatial spectra normalized with the Kolmogorov scale and viscosity. The collapse of the normalized spectra is indicative of the small-scale universality predicted by Kolmogorov. The data are taken from turbulent and transitional pipe flows at different Re and different positions from the wall from both experiments and simulations (experiments are at the centerline only).</p>
Data belonging to "Effect of molecular structure on the infrared signaturesof astronomically relevant PAHs"
<p>The data provided here form the basis of the publication titled:</p> <p>"Effect of molecular structure on the infrared signaturesof astronomically relevant PAHs"</p> <p>Which is published in Astronomy and Astrophysics</p> <p>The paper can be downloaded from:</p> <p>https://www.aanda.org/articles/aa/pdf/2019/01/aa34130-18.pdf</p> <p>The data contain raw spectra, both experimental and computational, mass spectrometric data and Cartesian coordinates of the optimized stuctures molecules studied in this work.</p> <p> </p>
cldf-datasets/normansinitic: Structural and lexical data for the paper by Norman (2013) on Chinese dialect classification
<p>Original source of the data:</p> <blockquote> <p>Norman, J. (2003): Chinese dialects. Phonology. In: Thurgood, G. & LaPolla, R.: The Sino-Tibetan Languages. Routledge: London and New York. 72-83.</p> </blockquote>
Data from: Birds influence vegetation coverage and structure on sandy biogeomorphic islands in the Dutch Wadden Sea.
<p><span>Small uninhabited islands form important roosting and breeding habitats for many coastal birds. Here, we assess the role of external nutrient input by coastal birds on the vegetation structure and coverage on sandy biogeomorphic islands in the Dutch Wadden Sea, where island-forming processes depend on vegetation-sedimentation feedbacks. We Used a combination of bird observations and plant stable isotope (<em>δ</em><sup>15</sup>N) analyses, to demonstrate that (i) breeding birds transport large quantities of nutrients via their faecal outputs to these islands annually and that (ii) this external nitrogen source influences vegetation development on these sandy, nutrient-limited, islands. We further discuss how this avian nutrient pump could impact island development and habitat suitability for coastal birds and discuss future directions for research. For the conservation of both threatened coastal birds and sandy back-barrier islands and the design of appropriate management strategies, we argue that three-way interactions between birds, vegetation and sandy island morphodynamics need to be further elucidated. </span></p> <p> </p> <p><span>Methods</span></p> <p><span>Data stored in this repository are bird counts from all three islands [Breeding_total.csv] and the relation between total faecal excretion rates (estimated using bird counts) and average vegetation coverage (estimated using satellite imagery) [Vegetation_bird.csv]. Additionally, we gathered data in the field including vegetation charactersitics [field.data.xls], isotopic data of the vegetation [new_isotopes.csv] and soil charactersitics [Soil_prop.csv]. Detailed description of these methods and the results can found in the accompanying publication (Reijers <em>et al. STOTEN</em>).</span></p>
Publications using EOL structured data: 2019
found primarily via Google Scholar, searching by mentions in the methods sections. Citing EOL is not required when using EOL-hosted records; only the primary source must be cited. Thus, these lists may not be exhaustive.<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
Publications using EOL structured data: 2018
found primarily via Google Scholar, searching by mentions in the methods sections. Citing EOL is not required when using EOL-hosted records; only the primary source must be cited. Thus, these lists may not be exhaustive.<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
Publications using EOL structured data: 2020
found primarily via Google Scholar, searching by mentions in the methods sections. Citing EOL is not required when using EOL-hosted records; only the primary source must be cited. Thus, these lists may not be exhaustive.<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
Publications using EOL structured data: 2015-2017
found primarily via Google Scholar, searching by mentions in the methods sections. Citing EOL is not required when using EOL-hosted records; only the primary source must be cited. Thus, these lists may not be exhaustive.<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
Data supporting the publication "Optimized Sandwich and Topological Structures for Enhanced Haptic Transparency"
<p>This dataset includes experimental modal responses of sandwich and topological structures, which are designed to enhance haptic transparency. </p>
Fire Effects Information System: FEIS Invasiveness (structured data) (750) in DwCA
The Fire Effects Information System (FEIS) provides up-to-date information about fire effects on plants, lichens, and animals. It was developed at the United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory in Missoula, Montana. The FEIS database contains literature reviews, taken from current English-language literature of about 900 plant species, 7 lichen species, about 100 wildlife species, 17 Research Project Summaries, and 16 Kuchler plant communities of North America. The emphasis of each review and summary is fire and how it affects species. Background information on taxonomy, distribution, basic biology, and ecology of each species is also included. Reviews are thoroughly documented, and each contains a complete bibliography. Managers from several land management agencies (United States Department of Agriculture, Forest Service, and United States Department of Interior, Bureau of Indian Affairs, Bureau of Land Management, Fish and Wildlife Service, and National Park Service) choose the species included in the database. Those agencies funded the original work and continue to support maintenance and updating of the database. <p></p>https://www.feis-crs.org/feis/<p></p>The Fire Effects Information System (FEIS) provides up-to-date information about fire effects on plants, lichens, and animals. It was developed at the United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory in Missoula, Montana. The FEIS database contains literature reviews, taken from current English-language literature of about 900 plant species, 7 lichen species, about 100 wildlife species, 17 Research Project Summaries, and 16 Kuchler plant communities of North America. The emphasis of each review and summary is fire and how it affects species. Background information on taxonomy, distribution, basic biology, and ecology of each species is also included. Reviews are thoroughly documented, and each contains a complete bibliography. Managers from several land management agencies (United States Department of Agriculture, Forest Service, and United States Department of Interior, Bureau of Indian Affairs, Bureau of Land Management, Fish and Wildlife Service, and National Park Service) choose the species included in the database. Those agencies funded the original work and continue to support maintenance and updating of the database. <p></p>https://www.feis-crs.org/feis/ FEIS data on EOL include invasiveness status.
USDA PLANTS structured data (old)
The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, crop information, automated tools, onward Web links, and references. This information primarily promotes land conservation in the United States and its territories, but academic, educational, and general use is encouraged. PLANTS reduces government spending by minimizing duplication and making information exchange possible across agencies and disciplines. Data published on EOL by the PLANTS database include attribute data, images and descriptive text.<p></p>
Data and Scripts for the Article "Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid"
<p>Data and scripts for the article titled "Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid".</p> <p>For further details on the contents, see the "readme.md"-file.</p> <p>Article available at <a href="https://doi.org/10.1039/D4CP02454K">10.1039/D4CP02454K</a>.</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>
Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C
<p>Supplementary example data for the work presented in "<em>Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C</em>". </p> <p><strong>Abstract: </strong></p> <p>Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.</p> <p> </p>
Data and Code For : "The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."
<p>The repository contains the principal 4D-STEM datasets and code used in the paper:</p> <p>"The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."</p> <p> </p> <p>* The measured and analyzed material is p(g3T2).</p> <p>* The code can be adjusted and used for the analysis of other conjugated polymers.</p> <p>* It should be noted that newer py4DSTEM versions with additional capabilities were released since the paper was </p> <p>submitted. </p> <p> </p> <p><strong>Contents:</strong></p> <p><strong>1. 4D-STEM_DATA_OMIECs.zip : </strong></p> <p><strong>4D-STEM data : </strong></p> <ul> <li>Dry_CL_2p1.dm4. </li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li> Calibrant_Dry_CL_2p1.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 15 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 25, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 46, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 15, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 80, E(extraction voltage): 300 kV, temprature: LN.</p> <p> </p> <p><strong>2. Notebooks.zip : </strong></p> <p><strong>Jupyter Lab Notebooks : </strong></p> <ul> <li>1A_pg3T2_dry_LN_CL2p1.ipynb. </li> </ul> <p>Analysis of dry film using CL 2.1.</p> <p>Goes with datasets: Dry_CL_2p1.dm4 and Calibrant_Dry_CL_2p1.dm4. </p> <ul> <li>1A_pg3T2_dry_LN_CL2p7.ipynb</li> </ul> <p>Analysis of dry film using CL 2.7.</p> <p>Goes with datasets: Dry_CL_2p7.dm4 and Calibrant_Dry_CL_2p7.dm4. </p> <ul> <li>1B_pg3T2_water_LN_CL2p7.ipynb</li> </ul> <p>Analysis of hydrated in water film using CL 2.7.</p> <p>Goes with datasets: Hydrated_Water_CL_2p7.dm4 and Calibrant_Hydrated_Water_CL_2p7.dm4. </p> <ul> <li>1C_pg3T2_NaCl_LN_CL2p7. </li> </ul> <p>Analysis of hydrated in 0.1 M NaCl(aq) film using CL 2.7.</p> <p>Goes with datasets: Hydrated_NaCl_CL_2p7.dm4 and Calibrant_Hydrated_NaCl_CL_2p7.dm4.</p> <ul> <li>aux_func.py: </li> </ul> <p>Contains auxilary functions and required for running the other notebooks.</p>
Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules
<p>The content of this repository accompanies the publication "Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules" and contains the complete dataset produced for the fentanyl CSP. </p> <p>Three types of datasets are present : Generation, ML-Reranker and GRACE. <br>Generation refers to all the molecular crystal structures that GRACE has generated using the tailor made force field. It's the starting pool of the reranking exercise, and it contains all the structures that will be selected by the reranking processes.<br>ML-Reranker refers to the data generated by the algorithm proposed in our manuscript. The configurations and energies are obtained by selecting from structures from the generation pool and relaxing their coordinates.<br>GRACE dataset contains the configurations which a user obtains at the end of a standard GRACE reranking procedure. Since GRACE follows differet convergence and minimization critera, the structures obtained in this dataset can differ (non-substantially) from the equivalents found in the ML-Reranker. </p> <p>*.data : contains the indices, energy of the crystal structure (kcal/mol) and, in case of the ml-reranker dataset, the indices mapping the obtained landscape to their generating pool.</p> <p>*.xyz : contains ASE formatted, extended-xyz list of structures corresponding to each exercise.</p> <p>Authors:</p> <p>Andrea Anelli, Hanno Dietrich, Philipp Ectors, Frank Stowasser, Tristan Bereau, Marcus Neumann, Joost van den Ende</p>
Data table 5 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from pull-down experiment using cerebellar cortex lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 2_cortex isogenic mouse IgG control dynabeads.xlsx" </div> <div> </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.