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408
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Dataset results
408 results for “data repositories”
Data Repository for "Single-particle characterization of polycyclic aromatic hydrocarbons in background air in Northern Europe", Atmos. Chem. Phys.
<p>Data Repository for <br> Passig et al., "Single-particle characterization of polycyclic aromatic hydrocarbons<br> in background air in Northern Europe", Atmospheric Chemistry and Physics, 2021/22</p> <p>Details in Readme.txt</p> <p> </p>
Model data repository of "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins"
<p>This dataset contains the data used in Wu et al. (2022): "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins".</p>
Cyclic unrest in a geothermal field as a harbinger of the Fagradalsfjall eruption, Iceland - INSAR data repository
<p>This repository is providing the InSAR data generated from the Copernicus Sentinel-1A and 1B satellites and as published in the paper</p> <p>Flóvenz et al. (2022) Cyclic unrest in a geothermal field as a harbinger of the Fagradalsfjall eruption, Iceland. Nature Geosciences, NGS-2021-06-01178</p> <p>We analyse deformation and seismicity for one year prior to the March 2021 Fagradalsfjall eruption in Iceland. We generate a high-resolution catalogue of 39,500 earthquakes using optical cable recordings and develop a poroelastic model to describe three pre-eruptional uplift and subsidence cycles at the Svartsengi geothermal field, 8 km west of the eruption site. We find the observed deformation is best explained by cyclic intrusions into a permeable aquifer by a fluid injected at 4 km depth below the geothermal field, with a total volume of 0.11±0.05 km3 and a density of 850±350 kg/m3.</p> <p>The geodetic data relevant for the publication and provided here include:</p> <p>1. Displacement from ascending geometry</p> <p>2. Displacement from descending geometry</p> <p>3. Vertical displacement component</p> <p>4. Horizontal displacement component</p> <p>These data are available for the following bands and dates:</p> <p>band date<br> 58 07-01-2020<br> 57 13-01-2020<br> 56 19-01-2020<br> 55 25-01-2020<br> 54 31-01-2020<br> 53 06-02-2020<br> 52 12-02-2020<br> 51 18-02-2020<br> 50 24-02-2020<br> 49 01-03-2020<br> 48 07-03-2020<br> 47 13-03-2020<br> 46 19-03-2020<br> 45 25-03-2020<br> 44 31-03-2020<br> 43 06-04-2020<br> 42 12-04-2020<br> 41 18-04-2020<br> 40 24-04-2020<br> 39 30-04-2020<br> 38 06-05-2020<br> 37 12-05-2020<br> 36 18-05-2020<br> 35 24-05-2020<br> 34 30-05-2020<br> 33 05-06-2020<br> 32 11-06-2020<br> 31 17-06-2020<br> 30 23-06-2020<br> 29 29-06-2020<br> 28 05-07-2020<br> 27 11-07-2020<br> 26 17-07-2020<br> 25 23-07-2020<br> 24 29-07-2020<br> 23 04-08-2020<br> 22 10-08-2020<br> 21 16-08-2020<br> 20 22-08-2020<br> 19 28-08-2020<br> 18 03-09-2020<br> 17 09-09-2020<br> 16 15-09-2020<br> 15 21-09-2020<br> 14 27-09-2020<br> 13 03-10-2020<br> 12 09-10-2020<br> 11 15-10-2020<br> 10 21-10-2020<br> 9 27-10-2020<br> 8 02-11-2020<br> 7 08-11-2020<br> 6 14-11-2020<br> 5 20-11-2020<br> 4 26-11-2020<br> 3 02-12-2020<br> 2 08-12-2020<br> 1 14-12-2020</p> <p> </p>
Tracking magma spine extrusion from space: Implications for conduit and topography complexity at Shiveluch volcano, Kamchatka - Photogrammetric data repository
<p>This is a dataset relevant for a paper on lava spine extrusion at Shieveluch volcano, Kamchatka. Data was used to show that the spine elongates along a previously identified fracture line and bends to a preferred northerly direction. By repeated morphology analysis and feature tracking, we constrain a spine diameter of ~300 m, extruding at a velocity of 1.7 m/day and discharge rate of 0.3-0.7 m³/s. Results are relevant for understanding the growth and collapse hazards of spines and provide unique insights into the hidden magma-conduit architecture.</p> <p>The data consists of three parts. First, we provide the filtered and corrected three dimensional point clouds generated from Pleiades tristereo data. These 3D point clouds were co-aligned and now allow analysing subtle changes. Point clouds are provided in .las format. Second, we provide the filtered and corrected digital elevation models generated from the point cloud data, these DEMs are provided in geotiff format. The name of the files indicates the dates of their acquisition. Third and lastly, we provide an orthomap stack used to estimate displacements by tracking offsets.</p> <p> </p>
Data repository - Land use change and carbon emissions of a transformation to timber cities
<p>Data and model source code for the publication:</p> <p>Land use change and carbon emissions of a transformation to timber cities<br> (Nature Communications, 2022)<br> DOI: 10.1038/s41467-022-32244-w</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> May 2022</p> <p>See README.txt for further details.</p>
Data and code repository for Science Advances submission: Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy
<p>Data and codes related to the findings reported in the manuscript, "Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy", are deposited. Please refer to the notes located within each folder for further descriptions.</p>
Analysis of scholarly repositories' availability. Data and notebooks.
<p>These datasets and companion Jupyter notebooks supplement the publication "Knock knock! Who's there?'' A study on scholarly repositories' availability" accepted at TPDL 2022, Padova, Italy.</p>
Data repository for "3D coseismic surface displacements from historical aerial photographs of the 1987 Edgecumbe earthquake, New Zealand"
<p>This data repository includes supplementary files used in the accompanying manuscript: </p> <p>Delano, J. E, Howell, A., Stahl, T. A., Clark, K. (<em>submitted 2022</em>). 3D coseismic surface displacements from historical aerial photographs of the 1987 Edgecumbe earthquake, New Zealand. Journal of Geophysical Research: Solid Earth.</p> <p>Contents:</p> <ol> <li>Supplementary Text S1, containing additional methods and discussion</li> <li>Supplementary Figures S1-S9</li> <li>Supplementary Tables S1-S6 </li> <li>Raster files (TIFF) of SfM results and differenced DSM</li> <li>Raster files of orthophoto mosaics (pre- and post-earthquake)</li> <li>Shapefiles containing fault trace mapping and displacement locations</li> </ol> <p>See README for individual file descriptions.</p>
Data format figures-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>The original data set included noisy, missing and inconsistent data. Data<br> preprocessing improved the quality of the data and facilitated e±cient data<br> mining tasks.<br> Before the experiment, we prepared data suitable to next operation as<br> following steps:<br> ² Delete or replace missing values;<br> ² Delete redundant properties (columns);<br> ² Data Transformation;<br> ² Data Discretization;<br> ² Export data to a required .ar® or .csv format ¯le [11].<br> The original and modi¯ed formats of data set are shown in Figure 1 and<br> Figure 2.<br> Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Data to accompany the outlier-waveform-detection Github repository (internal globus pallidus, GPi)
<p>This repository contains data based on neuronal recordings from two monkeys (G and I, in the pre- and post-MPTP states) that serve as input to the code provided at <a href="https://github.com/turner-lab-pitt/outlier-waveform-detection">https://github.com/turner-lab-pitt/outlier-waveform-detection</a>. Text files located within that Github repository provide detailed instructions on how these data may be used with that code. As described in those text files, extra data are provided for Monkey G, in the pre-MPTP state.</p> <p>The data-description.txt file provides detailed information regarding the contents of each zipped tar archive. Briefly, the most important components of the files are the "snips" (individual spike waveforms) from the two monkeys and MPTP states, as extracted for each of a series of single sorted units from the internal globus pallidus (GPi). The additional G-Pre data provides examples of the high-pass filtered voltage signals from which these snips were extracted. All data are stored in the Matlab .mat format.</p> <p>All zipped files can be decompressed with 7-zip: <a href="https://www.7-zip.org/" target="_blank" rel="noopener">https://www.7-zip.org/</a></p> <p>These data and the associated Github code were used for analyses reported in an in-preparation manuscript (Kase et al., "Movement-related activity in the internal globus pallidus of the parkinsonian macaque"), and also with a preprint that is currently under review:</p> <div> <div>Detecting rhythmic spiking through the power spectra of point process model residuals</div> </div> <div>Karin M. Cox, Daisuke Kase, Taieb Znati, Robert S. Turner</div> <div>bioRxiv 2023.09.08.556120; doi: <a href="https://doi.org/10.1101/2023.09.08.556120" target="_blank" rel="noopener">https://doi.org/10.1101/2023.09.08.556120</a></div> <div> </div> <p>This research was funded in part by Aligning Science Across Parkinson's [ASAP-020519] through the Michael J. Fox Foundation for Parkinson's Research (MJFF). For the purpose of open access, the authors have applied a Creative Commons Attribution 4.0 International (CC BY) public copyright license to this dataset. </p>
FIGURE 119 in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
FIGURE 119. Tropidolaemus subannulatus (juvenile male) (Sorsogon Prov., Luzon Id.) (KU uncat.; field no. RMB 22673). Photo © RMB.
FIGURE 109 in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
FIGURE 109. Trimeresurus cf. flavomaculatus (hunting frogs) (Camiguin Norte Prov., Babuyan Ids.) (individual not collected). Photo © RMB.
FIGURE 66 in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
FIGURE 66. Rhabdophis auriculatus auriculatus (Agusan del Norte Prov., Mindanao Id.) (KU 334441). Photo © RMB.
FIGURE 11 in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
FIGURE 11. Acrochordus granulatus (blotched) (locality unknown, Philippine Ids.) (KU 327188). Photo © CDS.
FIGURE 8 in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
FIGURE 8. Malayopython reticulatus (Sorsogon Prov., Luzon Id.) (KU uncat.; field no. RMB 23519). Photo © JBF/RMB.
FIGURE 39 in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
FIGURE 39. Coelognathus erythrurus psephenourus (Sorsogon Prov., Luzon Id.) (KU uncat.; field no. RMB 23101). Photo © RMB.
FIGURE 53 in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
FIGURE 53. Oligodon maculatus (juvenile) (Zamboanga City Prov., Mindanao Id.) (KU 315172]). Photo © RMB.
Data & code repository for the article "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". </p> <p>In detail the following data sources have been included:</p> <ul> <li>the relevant code and supporting data (code_to_upload.zip and supporting_data.zip);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results.zip);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results.zip);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures.zip)</li> </ul> </li> </ul>
MAPS 37A–D in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
MAPS 37A–D. Geographic range maps for Philippine records of (A) Tropidonophis cf. negrosensis; (B) Tropidonophis dendrophiops; (C) Tropidonophis negrosensis; (D) Xenopeltis unicolor.
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.