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677 results for “Inversion”
RDF2Vec DBpedia inverse object frequency embeddings
<p>This dataset contains the vectors from computing rdf2vec embeddings from a inverse object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse predicate frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse predicate frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse page rank split embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse page rank split weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse Page Rank frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse Page Rank frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
Magma propagation at Piton de la Fournaise from joint inversion of InSAR and GNSS - Supporting Data
<p>Processed data used in the paper Smittarello et al., JGR 2019</p> <p>Magma propagation at Piton de la Fournaise from joint inversion of InSAR and GNSS</p> <p>e.g. GNSS, seismic and InSAR data</p>
Data for: Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH Cardiovascular Magnetic Resonance
<p>Magnetic Resonance Imaging measurement data used in our paper about model-based myocardial T1 mapping with sparsity constraints. The data was obtained using a single-short inversion-recovery radial FLASH sequence and is provided in a file format used by the BART toolbox (<a href="http://doi.org/10.5281/zenodo.592960">DOI: 10.5281/zenodo.592960</a>).</p>
Applicability of the inverse dispersion method to measure emissions from animal housings - data set & R scripts
<h2>Data availability</h2> <p>Provided are:<br>- raw data of the instruments<br>- R Scripts to reproduce the findings in the publication<br>- R outputs</p> <h2>Scripts</h2> <p>In total, there are 10 scripts provided, of which most of them are needed to reproduce the data in the publication.</p> <p>Below, a brief explanation of the content of the different scripts.</p> <ul> <li>01_Datatreatment_01_Weatherstation.r ## This script reads in the weather station data and makes it ready for further use.</li> <li>01_Datatreatment_02_Sonics.r ## This script reads in the 3D ultrasonic data and makes it ready for further use.</li> <li>01_Datatreatment_03_GasFinder.r ## This script reads in the GasFinder data and makes it ready for further use.</li> <li>01_Datatreatment_04_MFC_Pressuresensor.r ## This script reads in the mass flow controller (MFC) and pressure sensor data and makes it ready for further use.</li> <li>02_Calculation_01_bLS.r ## This script is made to run the bLSmodelR and tailored to the number cruncher of the University of Applied Sciences BFH. The code should also work on your computer but you have to adopt the number of cores.</li> <li>02_Calculation_02_Concentration.r ## This script treats the unprocessed concentration data. It removes false concentrations, applies an intercalibration, and makes the data ready for further use.</li> <li>02_Calculation_03_Emissions.r ## This script calculates emissions and makes it ready for further use.</li> <li>02_Calculation_04_contourXYZ_Plume.r ## This script calculates the plume contours in the XY and XZ plane. This script is not necessary to reproduce the findings of the publication.</li> <li>03_Apply_filter.r ## This script applies the quality filtering and makes the data ready for further use.</li> <li>04_Plots_Tables.r ## With this script one can recreate all the plots and values in the tables of the publication, the supplement, and the initial submission.</li> </ul> <p>Note, for the geometry, there is no script provided. The coordinates of the different sensors and the source are solely provided as R output.</p> <h3>Naming of instruments</h3> <p>The instruments in the publication have different names than in the scripts. In some scripts the final names are also provided but throughout the evaluation the original device names are used. Only in the script 04_Plots_Tables.r are the final names introduced. Below is an overview of what original name corresponds to the final name of the devices:</p> <h4><strong>GasFinder instruments called 'OP' in the publication</strong></h4> <ul> <li>OP-UW = GF26</li> <li>OP-2.0h = GF17</li> <li>OP-5.3h = GF18</li> <li>OP-6.8h = GF16</li> <li>OP-12h = GF25</li> </ul> <p><strong>3D ultrasonic anemometer instruments called 'UA' in the publication</strong></p> <ul> <li>UA-UW = SonicC</li> <li>UA-2.0h = SonicA</li> <li>UA-5.3h = Sonic2</li> <li>UA-6.8h = SonicB</li> </ul> <p><strong>Source</strong><br>In some of the scripts, the source might be called 'Schopf' which is a local term for 'shed'.</p> <h2>Note</h2> <p>This code was written by Marcel Bühler (minor code chunks were originally written by Christoph Häni) and is intended to reproduce the findings of the linked publication. Please feel free to use and modify it (e.g., use it to run different dispersion models), but attribution is appreciated.</p> <h2>Disclaimer</h2> <p>I do not guarantee that everything works. It might be that not all variables were changed to English for better understanding correctly. Unfortunately, it is not possible to provide all the catalogs of the bLS run, as the total size is several 100s of GB. In case you run the bLS model on your own, the result will have a minimal difference, as no bLS run produces the same result twice. This should, however, not alter the findings.</p> <h2>Contact</h2> <p>In case you have questions, please contact Marcel Bühler (mb@bce.au.dk). In case this does not work, Christoph Häni might also be able to help (christoph.haeni@bfh.ch).</p>
Moment tensor inversion and uncertainty analysis for 40 Uttarakhand Earthquakes (2010-2022)
<p>This repository provides detailed descriptions of the files that were used for the Moment tensor and uncertainty analysis study of earthquakes in the Uttarakhand Himalayas. These files contain the Moment Tensor (MT) estimation results and uncertainty quantification of 40 earthquakes using different networks.</p> <p> </p> <p><strong>Contents:</strong></p> <p>1. waveform_fits.docx - Waveform fits for MT estimation</p> <p>2. confidence_plots.docx - The confidence parameters associated with each MT</p> <p>3. depth_vs_misfit_plot.docx - The confidence in the MT solution for each depth against the misfit values</p> <p>4. weight_files.zip - Weight files for 40 events read by the MTUQ package</p> <p>5. CMT_solution_files.zip - Centroid Moment Tensor (CMT) solutions for 40 events</p>
Periphyton and environmental data from nutrient-treatment inversion experiment
Open the record for dataset details and reuse information.
Data for "Intragenic DNA inversions expand bacterial coding capacity"
<p>Data for the publication "Intragenic DNA inversions expand bacterial coding capacity"</p> <p>Contigs assembled from long-read metagenomes</p>
A Deep Learning Approach for TEM Data Denoising, Inversion and Uncertainty Analysis with Monte Carlo Dropout
<p>This dataset includes the code and data for training the inversion network used in the study. The provided files cover data loading, preprocessing, and network training for transient electromagnetic (TEM) data inversion. For details on the included files and instructions on usage, please refer to the README.txt file.</p>
Saguaro recruitment data obtained by inverse-growth modelling
<p>Each year, an individual mature large saguaro cactus produces about one million seeds in attractive juicy fruits that lure seed predators and seed dispersers in a three-month feast. From the million seeds produced, however, only a few will persist into mature saguaros. A century of research on saguaro population dynamics has led to the conclusion that saguaro recruitment is an episodic event that depends on the convergence of suitable conditions for survival during the critical early stages. Because most data have been collected in Arizona, particularly in the surroundings of Tucson, most research has relied on a limited amount of environmental variation. In this study, we upscaled this knowledge on saguaro recruitment to a regional scale with a new method that used the inverse-growth modeling of 1,487 saguaros belonging to 13 populations in a latitudinal gradient ranging from arid desert to tropical thornscrub forest in Sonora, Mexico. Using generalized linear and additive mixed models, we created two 110-year-long saguaro recruitment curves: one driven only by previous size, and the second driven by size, drought, and soil structure. We found evidence that saguaro recruitment is indeed episodic with periodicities of 20–30 years possibly related to strong El Niño Southern Oscillation events. Our results suggest that saguaros rely on multidecadal periodic pulses of good beneficial years to incorporate new individuals into their populations. Inverse-growth modelling can be used in a wide variety of plant species to study their recruitment dynamics.</p>
GHG data from inverse models and UNFCCC national inventories v0.1
<p><strong>GHG (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O) data from inverse models and UNFCCC national inventories</strong></p> <p>This dataset contains 5 datasets, including GHG data from inverse models and UNFCCC national inventories in the top emitter countries:</p> <p>- <strong>CO2_inversion_1990-2019</strong>: annual CO<sub>2</sub> flux from from 6 inversion models in three sectors:</p> <ul> <li>'land flux (all land)' -> land flux from all land </li> <li>'land flux (managed land)' -> land flux from managed land</li> <li>'land flux (managed land + lateral adjustment)' -> land flux from managed land by adjusting the lateral flux</li> </ul> <p>- <strong>CH4_inversion_2000-2017</strong>: CH<sub>4</sub> flux from from 10 in-situ inversion (2000-2017) and 11 satellite inversion (2010-2017) models from four sectors:</p> <ul> <li>'anthropogenic (method x)' -> anthropogenic emissions from managed land. x could be 1, 2, 3.1 and 3.2, representing different methods to calculate the emissions in this sector:</li> <li>'fossil' -> emissions from the fossil sector</li> <li>'agriculture & waste' -> emissions from the agriculture and waste sector combined</li> <li>'biomass burning' -> emissions from biomass burning</li> </ul> <p>- <strong>N2O_inversion_1997-2016</strong>: anthropogenic N<sub>2</sub>O emissions from from 3 models.</p> <p>- <strong>Inventory_1990-2019</strong>: inventory data collecting from UNFCCC national inventories. The classification of sectors is corresponding with the inversion data files for each gas specie.</p> <p>- <strong>Inventory_1990-2019_IPCC</strong>: inventory data collecting from UNFCCC national inventories in IPCC category.</p> <ul> </ul>
HDF files for the time-dependent inversion of the 2016 moderate earthquakes along Chaman fault
<p>We use GAMMA software to process Sentinel-1 SLC data and generate InSAR data. After converting the data into UTM coordinates, we use LiCSBAS to perform the time-series analysis, and the resulting HDF files are imported to MATLAB to perform the time-dependent inversion. MATLAB source codes are available.</p> <p>Original Sentinel-1 SLC data are available from https://scihub.copernicus.eu/dhus.</p>
Inverse modelling of carbonyl sulfide: implementation, evaluation and implications for the global budget
<p>The dataset is the boundary conditions of TM5-4DVAR inversions for COS tracer in years 2016-2019. The dataset is companion with publication: </p> <p>Ma, J., Kooijmans, L. M. J., Cho, A., Montzka, S. A., Glatthor, N., Worden, J. R., Kuai, L., Atlas, E. L., and Krol, M. C.: Inverse modelling of carbonyl sulfide: implementation, evaluation and implications for the global budget, Atmos. Chem. Phys., 21, 3507–3529, https://doi.org/10.5194/acp-21-3507-2021, 2021.</p>
Database for Towards ice thickness inversion: an evaluation of global DEMs in the glacierized Tibetan Plateau
<p>x, latitude of ICESat-2 point</p> <p>y, longitute of ICESat-2 point</p> <p>icesat-2, elevation from icesat-2</p> <p>sigma-icesat-2, error of elevation from icesat-2</p> <p>date-icesat2, acquiring date of icesat-2 point</p> <p>dhdx, along track slope of icesat-2 data</p> <p>dhdx_sigma, error of along track slope of icesat-2 data</p> <p>elevation range min, the minimum elevation of glaciers where icesat-2 data is located</p> <p>elevation range med, the median elevation of glaciers where icesat-2 data is located</p> <p>elevation range max, the maximum elevation of glaciers where icesat-2 data is located</p> <p>dh, glacier surface elevation change from Shean et al. (2020)</p> <p>aw3d30, elevation from aw3d30 in EGM96 geoid</p> <p>srtmgl1, elevation from srtmgl1 in EGM96 geoid</p> <p>tandem, elevation from TanDEM-X in WGS84 ellipsoid</p> <p>srtmv41, elevation from srtmv41 in EGM96 geoid</p> <p>nasadem, elevation from NASADEM in WGS84 ellipsoid</p> <p>merit, elevation from merit in EGM96 geoid</p> <p>aw3d30slp, slope from aw3d30</p> <p>srtmgl1slp, slope from srtmgl1</p> <p>tandemslp, slope from tandem</p> <p>srtmv41slp, slope from srtmv41</p> <p>nasademslp, slope from nasadem</p> <p>meritslp, slope from merit</p> <p>aw3d30asp, aspect from aw3d30</p> <p>srtmgl1asp, aspect from srtmgl1</p> <p>tandemasp, aspect from tandem</p> <p>srtmv41asp, aspect from srtmv41</p> <p>nasademasp, aspect from nasadem</p> <p>meritasp, aspect from merit</p> <p>The elevation should be <strong>converted</strong> using geoidheight function in MATLAB</p> <p> </p> <p> </p> <p> </p>
Simulated CO2 time series data based on Jena CO2 inversion and TM3 transport model, and MIROC-ACTM
<p>Each file contains simulated CO2 time series at each surface station. The model, simulation type, and station are specified in the file name. These simulations are driven by either varying winds alone (e.g., Jena_W) or varying winds and fluxes (e.g., Jena_WF). The only MIROC-ACTM run is named ACTM_W_MLO.</p>
Temperature inversion frequency across China from 2014 to 2020
<p>This dataset presents the temperature inversion frequency calculated using radiosonde profiles sampled across China from 2014 to 2020. The frequency was defined as the ratio of the number of days with inversion to the total number of sampling days in each individual month.</p>
An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media
<p>This data includes the training and testing dataset for DNN design and the observation data of synthetic example for validation. </p> <p>The code of TNNA-AUS inversion method.</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file. </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.