Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,916
datasets available to search
ShareScore release 0.9.0
Dataset results
3,916 results for “reconstruction”
Supporting data for 'Simultaneous maximum a posteriori longitudinal PET image reconstruction'
<p>This dataset contains the data used to produce the paper: <em>'Simultaneous maximum </em>a posteriori l<em>ongitudinal PET image reconstruction' </em>by Ellis and Reader, Physics in Medicine and Biology (2017). DOI: http://dx.doi.org/10.1088/1361-6560/aa7b49. Please see the article for a full description of methodology used to obtain this data. </p> <p>The dataset comprises a number of MATLAB data files (.mat), MATLAB scripts (.m), and plain text files (.txt), corresponding to each figure in the article. Running the .m script in MATLAB for each figure will reproduce that figure approximately as it appears in the article. Furthermore, the .txt files describe the contents of the .mat data files in order to allow independent exploration of the data. Note that the function plotSparseMarker is required to be able to run fig5.m.</p> <p>This work was funded by the King’s College London & Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging (grant number EP/L015226/1) and supported by the EPSRC grant number EP/M020142/1. This data has been made available in accordance with the EPSRC's policy framework on research data.</p>
GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present
<p>Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional investigation of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940-2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent datasets such as the land-ocean mass budget, large-scale water balance in 341 large river basins, and streamflow measurements at 10,168 gauges. We find that the proposed approach performs overall as well as or is more reliable than previous TWS datasets. Moreover, our reconstructions successfully reproduce the impact of climate variability, such as strong El Niño events. GTWS-MLrec dataset consists of three reconstructions based on JPL, CSR and GSFC mascons, three detrended and de-seasonalized reconstructions, and six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a broad range of applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management.</p><p>Please cite the reference: <strong>Yin J, Slater L, Khouakhi A, et al. GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present. Earth System Science Data. 2023.</strong></p><p>For any inquiry about the dataset, welcome to contact Dr. Jiabo Yin (jboyn@whu.edu.cn).</p>
Suplementary data, results and scripts: "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer"
<p>This repository contains supplementary data, models and scripts associated with "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer".</p><p>Folders content:</p><p>'data_results_matlabscripts': data, result files and scripts (original python scripts and adapted MATLAB scripts)</p><p>'supplementary_figures': supplementary figures</p><p>'supplementary_tables': supplementary tables</p>
3D reconstruction of a horse swimming
<p>3D reconstruction of a horse swimming and visualisation of the joint angles during two cycles of swimming.</p>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
Acoustical measurements of a rabab reconstructed after a pictorial source from the 13th century (Cantigas de Santa María)
<p>Instrument: rabab<strong> </strong><br>Pictorial source: <i>Cantigas de Santa Maria</i>, <i>E</i>-Codex (<i>Códice de los músicos</i>), ca. 1284, fol. 118r, <i>Cantiga</i> 110, Madrid, San Lorenzo de El Escorial, Real Biblioteca del Monasterio del Escorial, Ms. b-I-2 <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2021 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 468 mm <br>Max. Body width: 104 mm <br>Body depth: approx. 80 mm</p><p>Vibrating string lengths: <br>a-string: 403 mm <br>d-string: 401 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: maple <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: maple <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the two open rosettes on the fingerboard and then the upper one was sealed with a piece of wood (see photos of the setup).</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder cantigas_rosette_o_offen: <br>Files: cantigas_roo_1 to 6 (Description: upper rosette open) <br>File: cantigas_roo_do (Description: upper rosette open / damper moved between 2 rosettes) </p><p>Folder cantigas_rosette_o_zu: <br>Files: cantigas_roz_1 to 6 (Description: upper rosette closed with wooden sheet)</p><p>________________________</p><p>How to read VIA-Files: <br>Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad] 4th col: Real part [as Factor not dB!] 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. </p><p>Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>
Acoustical measurements of a rabab reconstructed after a pictorial source from the 14th century
<p>Instrument: rabab<strong> </strong><br>Pictorial source: Francesc Comes, <i>Madonna and Child with angel musicians</i>, around 1394, Gold and tempera on wood, Pollença (Mallorca), Museu de Pollença <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2022 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 579 mm <br>Max. Body width: 112 mm <br>Body depth: approx. 95 mm </p><p>Vibrating string lengths: <br>d-string: 500 mm <br>G-string: 497 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: serviceberry <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: boxwood <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the upper rosette and the two holes in the body closed, then with both rosettes and the body holes open, and finally only with the body holes closed.</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder comes_rosette_o_loecher_zu <br>Files: comes_rolz_1 to 6 (Description: upper rosette closed with wooden sheet, body-holes closed)</p><p>Folder comes_rosette_oO_loecher_offen <br>Files: comes_roo_lo_1 to 6 (Description: rosette open , body-holes open)</p><p>Folder comes_rosette_oO_loecher_zu <br>Files: comes_roo_lz_1 to 6 (Description: rosette open , body-holes closed) </p><p>________________________</p><p>How to read VIA-Files:</p><p>Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad], 4th col: Real part [as Factor not dB!], 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed!</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>
Acoustical measurements of a rabab reconstructed after a pictorial source from the 16th century
<p><strong>Acoustical measurements of a rabab reconstructed after a pictorial source from the 16th century</strong></p><p>Instrument: rabab<strong> </strong><br>Pictorial source: Jorge Affonso (attr.), <i>The Adoration of the Shepherds</i>, 1515, oil on wood, Lissabon, Museu Nacional de Arte Antiga <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2022 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 510 mm <br>Max. Body width: 110 mm <br>Body depth: approx. 93 mm</p><p>Vibrating string lengths: <br>d'-string: 351 mm <br>a-string: 350 mm <br>e-string: 348 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: cerry <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: boxwood <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the rosette and the two holes in the body open, then with the body holes closed.</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder modell_affonso <br>Files: affonso_1 to 6 (Description: holes open)</p><p>Folder modell_affonso_loecher_zu <br>Files: affonso_hc_1 to 6 (Description: both holes closed)</p><p>________________________</p><p>How to read VIA-Files: Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad], 4th col: Real part [as Factor not dB!], 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed!</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p> Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>
Data for: Machine-learning-accelerated simulations enable heuristic-free surface reconstruction
<p>This is the dataset for the publication "Machine-learning-accelerated simulations to enable automatic surface reconstruction", by X. Du, J.K. Damewood, J.R. Lunger, R. Millan, B. Yildiz, L. Li, and R. Gómez-Bombarelli. The repository contains the density-functional theory (DFT) data used to train the neural network force fields (NFF), selected results from our GaN(0001), Si(111), and SrTiO3(001) Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) runs, and Jupyter notebooks used for analysis and plots. To run the .ipynb's, you will need to install <a href="https://github.com/learningmatter-mit/surface-sampling">surface-sampling</a> (tested up to commit 02820d339eed6291b6af6ccb809f154ad6244110 on master) and <a href="https://github.com/learningmatter-mit/NeuralForceField">NeuralForceField</a> (tested up to commit 72d1f32f43f202c1a466116beeed15845a6456e7 on master) from the <a href="https://github.com/learningmatter-mit">Rafael Gómez-Bombarelli Group @ MIT</a>.</p>
Supporting Data: ontophylo: Reconstructing the evolutionary dynamics of phenomes using new ontology-informed phylogenetic methods
<p>This dataset contains all scripts and data for reproducing the analyses of the paper. The README files contain additional information.</p>
Impedance Reconstruction of Non-transmural Cardiac Fibrosis
<div>In this dataset we can find geometrical setups that served as an input to carry simulations with openCARP and EIDORS. The setup consists of a Lasso with point electrodes placed on patch of tissue and embedded in a box of blood. The tissue simulates a myocardium that can be either fully healthy, fully scarred, or healthy with a 8-mm width line of scar tissue.</div> <div>In addition, the voltage and local impedance maps obtained as results are included.</div> <div>Two kinds of simulations were performed: reconstruction the forward injection of a 5µA current at 14.6kHz with EIDORS, and the electrical wave propagation with openCARP.</div> <div>For the impedance reconstruction, a four electrode circuit was always used, meaning that two electrodes were part of the injecting pattern and a potential difference was measured between another two. The stimulating pair were sequentially changed among all pairs of neighbouring electrodes, whereas the rest of them contributed to the measurement.</div> <div>The voltage maps were computed taking the recovered EGMs from the electrode positions.</div> <div> </div> <h2>Data structure</h2> <div> <ul> <li>inputGeometries: 24 vtk files representing the input setup for simulations. The file names are of the form imageXX_append_cleanToGrid_LOCATION, where XX is a number among 06, 11, 52, and 55, and LOCATION refers to the position in z-axis (transmural, endo-, midmyo-, epicardio).</li> </ul> </div> <div> <ul> <li>results: <ul> <li>BidomainSimulations: the extracellular potentials of each electrical propagation simulation using the Courtemanche model are saved in each of the 16 folders. All the corresponding outputs from a pseudobidomain openCARP simulation are located in each folder. <ul> <li>2023-04-16_image06_endo_00</li> <li>2023-04-16_image06_epi_00</li> <li>2023-04-16_image06_mid_00</li> <li>2023-04-16_image06_transmural_00</li> <li>2023-04-16_image11_endo_00</li> <li>2023-04-16_image11_epi_00</li> <li>2023-04-16_image11_mid_00</li> <li>2023-04-16_image11_transmural_00</li> <li>2023-04-16_image52_endo_00</li> <li>2023-04-16_image52_epi_00</li> <li>2023-04-16_image52_mid_00</li> <li>2023-04-16_image52_transmural_00</li> <li>2023-04-16_image55_endo_00</li> <li>2023-04-16_image55_epi_00</li> <li>2023-04-16_image55_mid_00</li> <li>2023-04-16_image55_transmural_00</li> </ul> </li> <li>ImpedanceSimulations: mat and vtk files corresponding to either local electrical impedance reconstruction simulation. The file names are of the form imageXX_append_cleanToGrid_LOCATION, where XX is a number among 06, 11, 52, and 55, and LOCATION refers to the position in z-axis (transmural, endo-, midmyo-, epicardio). </li> </ul> </li> </ul> </div>
Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)
<h4><strong>Publication</strong></h4> <p>Please cite this publication if you use the dataset:</p> <p>Batool, M., Sarrazin, F. J. and Kumar, R. Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019), submitted to Earth System Science Data.</p> <p>Please also refer to the above publication for methodological details.</p> <h4><strong>License</strong></h4> <p>The "Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)" is freely available under an Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO).</p> <p><strong>Data description (v1):</strong></p> <p>This dataset consists of annual long-term reconstruction of total P surplus (both agricultural and non-agricultural soils) across Europe at a 5 arcmin spatial resolution for the period 1850 to 2019. The dataset consists of 48 P surplus estimates that account for the uncertainties resulting from methodological choices and coefficients in major components of the P surplus. This dataset offers the flexibility of aggregating the P surplus at any spatial scale of relevance to support water and land management strategies. Notably, our P surplus dataset has been developed consistently with our N surplus dataset (Batool et al. 2022), enabling joint analysis of N and P budgets across Europe, thereby facilitating holistic nutrient management studies.<br> </p> <p>1. Gridded P surplus data (NetCDF format): 48 files, each of them containing 170 years (1850-2019) of gridded data P surplus</p> <p>2. Aggregated P surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the NUTS ID's.</p> <p>3. Aggregated P surplus at European river basins (CSV format): 1 file, each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the river basin ID's.</p> <p>Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins)</p> <p>Time period: 1850-2019</p> <h4><strong>Data description (v2):</strong></h4> <p>We have updated the dataset (v1) and creared v2, which includes improvements and additional components for a more comprehensive phosphorus surplus analysis across Europe. The updates are as follows:</p> <ul> <li><strong>Refined P input estimates from mineral fertilizers (1850–1960):</strong> We have revised our methodology for historical P inputs from mineral fertilizers. In the revised estimates, instead of relying on nitrogen (N) fertilizer trends as a proxy for changes in P fertilizer, we have now incorporated a global dataset that traces the historical sources of phosphorus fertilizers from phosphate rock (1800–2000). This dataset provides a more reliable temporal trend for P fertilizer use. </li> <li><strong>Exclusion of chemical weathering inputs to urban areas:</strong> This adjustment better reflects phosphorus dynamics in urban regions.</li> <li><strong>Expanded data components:</strong> In addition to P surplus, the dataset now includes detailed estimates of P inputs (e.g., mineral fertilizers, manure) and P outputs , offering a more granular view of phosphorus flows.</li> </ul> <ol> <li>Gridded datasets <ol> <li>Total P surplus data (NetCDF format): 48 files, each of them containing 170 years (1850-2019) of gridded P surplus data</li> <li>Total P inputs data (NetCDF format): 1 file, containing 170 years (1850-2019) of gridded P inputs data</li> <li>Total P output data (NetCDF format): 1 file, containing 170 years (1850-2019) of gridded P outputs data</li> <li>P fertilizer (NetCDF format): 2 files, each of them containing 170 years (1850-2019) of gridded P inputs from mineral fertilizer data</li> <li>P animal manure (NetCDF format): 6 files, each of them containing 170 years (1850-2019) of gridded P inputs from animal manure data</li> </ol> </li> <li> <p>Aggregated P surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the NUTS ID's.</p> </li> <li> <p>Aggregated P surplus at European river basins (CSV format): 1 file, each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the river basin ID's.</p> </li> </ol> <p>Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins)</p> <p>Time period: 1850-2019</p> <h4><strong>Acknowledgments and underlying datasets</strong></h4> <p>Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A) and the Development Bank of Saxony, Research Project Funding on Resilient Zero-Pollution Wastewater Systems in Climate Change – Case Study Saxony (Project No. 100669418). We are also thankful to UFZ for providing computing power and technical support to the EVE supercomputing facility. We would like to thank people from various organizations and projects for kindly providing us with the data that were used in this study, which includes among others: FAO, Eurostat, HYDE, and IFA.</p> <h4><strong>Contact</strong></h4> <p>Further queries regarding these datasets can be directed to Masooma Batool (masooma.batool@ufz.de) and Rohini Kumar (rohini.kumar@ufz.de).</p>
Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens
<p>This repo contains Seurat objects, differential expression analysis results, and pathway gene lists for the manuscript "Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens"<br>List of files:</p> <p>1. Seurat_object_IFNB_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-beta pathway<br>2. Seurat_object_IFNG_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-gamma pathway<br>3. Seurat_object_TNFA_Perturb_seq.rds: Seurat object of the Perturb-seq data for TNF-alpha pathway<br>4. Seurat_object_TGFB1_Perturb_seq.rds: Seurat object of the Perturb-seq data for TGF-beta1 pathway<br>5. Seurat_object_INS_Perturb_seq.rds: Seurat object of the Perturb-seq data for insulin pathway<br>6. Pathway_genelist.rds: The pathway gene lists from MultiCCA analysis<br>7. Pathway_Exclusive_genelist.rds: The pathway exclusive gene lists generated from Pathway_genelist.rds<br>8. HClust_Pathway_celltype_specific_genelist.rds: The cell-line specific pathway gene lists from hierarchical clustering analysis independently done on each cell line<br>9. DE_results_all_pathway.zip: The DE test results for all the regulators, cell lines, and pathways (from Mixscale weighted DE test.)<br>10. Bulk_RNAseq_Seurat_object_IFNG_and_TGFB_stim.rds: Seurat object for the bulk RNA-seq data for interferon-gamma and TGF-beta stimulation experiments<br>11. Parse_Guide_Capture_Protocol.pdf: The guide RNA capture protocol developed for Parse Evercode Whole Transcriptome kit</p>
CLDF Dataset derived from Bender et al. 2003 "Proto-Micronesian Reconstructions"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Byron W. Bender, Ward H. Goodenough, Frederick H. Jackson, Jeffrey C. Marck, Kenneth L. Rehg, Ho-min Sohn, Stephen Trussel, and Judith W. Wang. 2003. Proto-Micronesian Reconstructions—1. Oceanic Linguistics Vol. 42(1), 1-110, DOI: 10.2307/3623449</p> <p>Byron W. Bender, Ward H. Goodenough, Frederick H. Jackson, Jeffrey C. Marck, Kenneth L. Rehg, Ho-min Sohn, Stephen Trussel, and Judith W. Wang. 2003. Proto-Micronesian Reconstructions—2. Oceanic Linguistics Vol. 42(2), 271-358, DOI: 10.2307/3623243</p> </blockquote>
Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format
<p><strong>Synopsis</strong></p> <p>Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with <a href="https://zenodo.org/records/14930299">jpata/particleflow:v2.3.0</a>. Derived from the EDM4HEP ROOT files in <a href="https://zenodo.org/record/8260741">https://zenodo.org/record/8260741</a>.</p> <ul> <li>clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV</li> <li>clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>clic-tfds.ipynb: an example notebook on how to load the files</li> </ul> <p><strong>Contents</strong></p> <p>Each .zip file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a>, <a href="https://github.com/google/array_record">array_record</a> format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events.</p> <p><strong>Dataset semantics (to be updated)</strong></p> <p>Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available:</p> <ul> <li>X: the reconstruction input features, i.e. tracks and clusters</li> <li>ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> <p>The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.</p>
LGM-Lateglacial 3D ice surface reconstructions of the Dora Baltea glacier system (western Italian Alps)
<p>3D ice surface configurations of six LGM-Lateglacial ice stages of the Dora Baltea glacier system (western Italian Alps).</p> <p>Ice-configurations were obtained by combining existing and new chronological constraints from glacial and postglacial landforms/deposits from the Dora Baltea catchment into 2D and 3D ice surface reconstructions, similar to the approach of the GlaRe ArcGIS toolbox (Pellitero et al., 2016).</p> <p>Mean position of the study area: 45.7412/7.3978 (°N/°E, WGS84)</p>
Coupling charge and topological reconstructions at polar oxide interfaces
<p>Dataset corresponding to the publication 'Coupling charge and topological reconstructions at polar oxide interfaces' (<a href="https://arxiv.org/abs/2107.03359">arXiv:2107.03359</a>) (Phys. Rev. Lett. <strong>127</strong>, 127202) </p>
OSSE dataset for assessing the sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain
<p>The data stored in this repository are part of the manuscript entitled "The sensitivity of pCO<sub>2</sub> reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach" submitted in consideration for publication for in European Geosciences Union: Biogeosciences.</p> <p> </p> <p>The netcdf file includes oceanographic (physical + biogeochemical) data, namely the partial pressure of carbon dioxide (pCO<sub>2</sub>) data at the surface ocean from the high-resolution (±10km) forced NEMO-PISCES coupled ocean model (BIOPERIANT12) and from the semi-idealized observing system simulation experiments (OSSEs) we performed.</p>
Reconstruction of full-length LINE-1 progenitors from ancestral genomes (Supplementary Data)
<p><strong>Web Supplementary Files</strong></p> <ul> <li>Web Supplementary File 1 - FASTA files containing full-length reconstruction input sequences<strong>: full_length_reconstruction_input_sequence_fastas.zip</strong></li> <li>Web Supplementary File 2 - FASTA files containing Muscle alignments of the full-length reconstruction input sequences.<strong> full_length_reconstruction_input_sequence_alns.zip</strong></li> <li>Web Supplementary File 3 - FASTA file of full-length reconstructed sequences:<strong> full_length_reconstructions.fa</strong></li> <li>Web Supplementary File 4 - Table of full-length reconstruction statistics: <strong>full_length_reconstruction_stats.csv</strong></li> <li>Web Supplementary File 5 - FASTA files containing ORF reconstruction input sequences:<strong> orf_fastas.zip</strong></li> <li>Web Supplementary File 6 - FASTA files containing Macse alignments of the ORF reconstruction input sequences:<strong> ORF_reconstruction_input_sequence_alns.zip</strong></li> <li>Web Supplementary File 7 - Table of ORF reconstruction statistics: <strong>ORF_reconstructions.fa</strong></li> <li>Web Supplementary File 8 - Table of ORF reconstruction statistics: <strong>ORF_reconstruction_stats.csv</strong></li> <li>Web Supplementary File 9 - Table of Composite Sequences: <strong>bestfl_selection_fixed_CS_seqs.csv</strong></li> <li>Web Supplementary File 10 - Database of gold standards: <strong>L1_goldstandards.csv</strong></li> </ul> <p><strong>Data Underlying Figures</strong></p> <ul> <li>RepeatMasker scans of hg38 and ancestral genomes:<strong> </strong><strong>anc_gen_RM_out_files.zip</strong></li> <li><strong>Figure 4</strong> <ul> <li>4A <ul> <li>Source alignment of 54 composite sequences: <strong>220121_dropped12+L1ME3A_muscle.nt.afa</strong></li> <li>Tree produced using the alignment and FastTree: <strong>220121_dropped12+L1ME3A.tree</strong></li> </ul> </li> <li>4B <ul> <li>Source alignment of 67 Dfam L1 subfamily 3’ end models: <strong>200123_dfam_3ends.fa.muscle.aln</strong></li> <li>Tree produced using the alignment: <strong>200123_dfam_3ends.fa.muscle.aln.tree</strong></li> </ul> </li> </ul> </li> <li><strong>Figure 5</strong> <ul> <li>KZFP-TE enrichment p-values (from Barazandeh <em>et al</em> 2018):<strong> TE_KZFP_enrichment_pvals.xlsx</strong></li> <li>KZFP-TE top 500 peak overlap (from Barazandeh <em>et al</em> 2018): <strong>top500_peak_overlap.xlsx</strong></li> </ul> </li> <li><strong>Figure 6</strong> <ul> <li>RepeatMasker .out file for the Composite Sequence custom library queried against hg38: <strong>CS_RM_hg38.fa.out.gz</strong></li> </ul> </li> <li><strong>Figure S2</strong> <ul> <li>RepeatMasker scan .out file of hg38 (CG corrected Kimura Divergence values are in last column): <strong>hg38+KimDiv_RM.out</strong></li> <li>RepeatMasker scan .out file of the Progressive Cactus eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): <strong>Progressive_Cactus_Euth+KimDiv_RM.out</strong></li> <li>RepeatMasker scan .out file of the Ancestors 1.1 eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): <strong>Ancestors_Euth+KimDiv_RM.out</strong></li> </ul> </li> <li><strong>Figure S5</strong> <ul> <li>RepeatMasker scan .out files for Progressive Cactus simian and primate reconstructed ancestral genomes: <strong>progCactus_RM_outfiles.zip</strong></li> <li>S5A <ul> <li>FASTA files containing Cactus genome-derived reconstructed sequences equivalent to the L1MA2, L1MA4, and L1MD1-3 best full-length sequences: <strong>progCactus_reconstruction_bestFL_equivalents.zip</strong></li> </ul> </li> <li>S5B <ul> <li>FASTA files containing Muscle alignments of Cactus genome-derived full-length reconstruction input sequences: <strong>progCactus_reconstruction_input_sequence_alns.zip</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S6</strong> <ul> <li>S6A <ul> <li>Results of Conserved Domain scans of Cactus genome-derived full-length reconstructed sequences: <strong>CD_search_results_short_nms.txt</strong></li> </ul> </li> <li>S6B-D <ul> <li>Character posterior probabilities of “best” full-length reconstructed sequences: <strong>best_fl_post_probs.zip</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S7</strong> <ul> <li>S7B-C <ul> <li>Results of Conserved Domain scans of translated initial full-length reconstructed sequences: <strong>initial_recons_all_3frametrans_CD-search.txt</strong></li> <li>Results of Conserved Domain scans of translated reconstructed ORFs: <strong>recons_ORF1-2_all_3frametrans_CD-search.csv</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S15</strong> <ul> <li>S15A <ul> <li>Source alignment of 67 composite sequences: <strong>bestfl_selection_fixed_CS_seqs_muscle.nt.afa</strong></li> <li>Tree produced using the alignment: <strong>bestfl_selection_fixed_CS_seqs_muscle.nt.afa.tree</strong></li> </ul> </li> <li>S15B-E <ul> <li>Source Muscle alignments for phylogenetic trees of reconstructed sequence components: <ul> <li>ORF2: <strong>ORF2_keep54_muscle.nt.afa</strong></li> <li>5’ UTR: <strong>5utr_keep54_muscle.nt.afa</strong></li> <li>ORF1: <strong>ORF1_keep54_muscle.nt.afa</strong></li> <li>3’ UTR: <strong>3utr_keep54_muscle.nt.afa</strong></li> </ul> </li> <li>Trees produced using above alignments: <ul> <li>ORF2: <strong>ORF2_keep54_muscle.nt.afa.tree</strong></li> <li>5’ UTR: <strong>5utr_keep54_muscle.nt.afa.tree</strong></li> <li>ORF1: <strong>ORF1_keep54_muscle.nt.afa.tree</strong></li> <li>3’ UTR: <strong>3utr_keep54_muscle.nt.afa.tree</strong></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Figure S17</strong> <ul> <li>Unfiltered BLAST results of Composite Sequences queried against hg38: <strong>CS_hg38_blastn.csv.zip</strong></li> <li>BED file of L1 instances annotated using BLAST pipeline: <strong>BLAST_L1_hits.bed</strong></li> </ul> </li> </ul>
Holocene temperature reconstruction using paleoclimate data assimilation
<p>A reconstruction of Holocene temperature made using paleoclimate data assimilation. Spatial and mean quantities are presented, as well as information about the experimental design and proxies. The code used to make this reconstruction is available at https://github.com/Holocene-Reconstruction/Holocene-code.</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.