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3D Reconstruction of Shoulder Muscles in Hominoid Primates: Correlating Scapular Attachment Areas with Muscle Volume
<h2><strong>How To Cite:</strong></h2> <p>If you use this data or code in your research, please cite the associated open-access <strong>manuscript, </strong>which you can find here: <a href="https://doi.org/10.1111/joa.14199">https://doi.org/10.1111/joa.14199</a><br>and this <strong>zenodo repository</strong>.</p> <h2><strong>Online Visualization:</strong></h2> <p>You can access an interactive, web-based view of the notebooks and analyses <a title="Shoulder Muscle Reconstruction Code" href="https://juliavanbeesel.github.io/ShoulderMuscleReconstructions/intro.html" target="_blank" rel="noopener">here</a>.</p> <h2><strong>Repository Description:</strong></h2> <p>This repository contains two zip files related to the analysis and visualization of 3D reconstructed muscle volumes and lengths from various hominoid specimens.</p> <ol> <li> <p><strong>MeshFiles.zip:</strong></p> <ul> <li><strong>Contents:</strong> This zip file includes all <code>.obj</code> files for 3D reconstructed muscle volumes and associated anatomical structures. Specifically, it contains: <ul> <li><strong>Muscles:</strong> Supraspinatus, Infraspinatus, Subscapularis, Teres Major, Teres Minor</li> <li><strong>Bones:</strong> Scapula and Humerus</li> <li><strong>Attachment Sites</strong></li> </ul> </li> <li><strong>Organization:</strong> The files are organized into folders by specimen. There are 9 hominoid specimens from the following species: <ul> <li><em>Hylobates lar</em></li> <li><em>Symphalangus syndactylus</em></li> <li><em>Pongo pygmaeus</em></li> <li><em>Pongo abelii</em></li> <li><em>Gorilla gorilla</em></li> <li><em>Pan troglodytes</em></li> <li><em>Homo sapiens</em></li> </ul> </li> <li><strong>Surface Scans of Muscle Geometry: </strong>The specimens <em>Pongo</em> (ID 3) and <em>Symphalangus </em>(ID 122) also contain surface scans that depict the muscle geometry of the listed muscles. These surface scans can be used for training with the iterative polygonal modelling approach. The scans are stored as <code>.obj</code>, <code>.mtl</code> and <code>.png</code> files. To view textures on these meshes, keep all three files together in the same folder.</li> <li><strong>Additional Details:</strong> Muscle reconstructions were performed for different arm positions. Each folder contains multiple humerus files, with each file representing a humerus in a specific position aligned with the corresponding muscles. The humerus file names indicate the muscles the humerus is aligned with.<br><br></li> </ul> </li> <li> <p><strong>DataAndCode.zip:</strong></p> <ul> <li><strong>Contents:</strong> <ul> <li><strong>Excel File:</strong> The original data used for analysis, presented in Table 2 of the manuscript.</li> <li><strong>Jupyter Notebook Files: </strong>These notebooks provide the analyses and figures as described in the manuscript: <ul> <li><em>Accuracy_Muscle_Length_Reconstruction:</em> Analysis of muscle length measurement comparisons, detailed in Supplementary Information Section 3: <em>Accuracy of estimating Muscle Length from 3D reconstructions</em>.</li> <li><em>Accuracy_Muscle_Volume_Reconstruction:</em> Analysis of muscle volume measurement comparisons, detailed in Results Section 3.2: <em>Accuracy of Muscle Volume and Length Reconstruction</em>.</li> <li><em>Correlation_Analysis_SIS:</em> Correlation analysis of muscle origin area to volume for the supraspinatus, infraspinatus, and subscapularis muscles, detailed in Results Section 3.3:<em> Correlation Analysis</em>.</li> <li><em>Correlation_Analysis_TT:</em> Correlation analysis of muscle origin area to volume for the teres major and minor muscles, detailed in Supplementary Information Section 1: <em>Correlation results of teres major and minor</em>.</li> </ul> </li> <li><strong>Requirements.txt:</strong> A file listing the necessary packages required to run the Jupyter notebooks.</li> </ul> </li> <li><strong>Purpose:</strong> The Python files include code for performing statistical analyses and generating figures as described in the manuscript.</li> </ul> </li> </ol> <h2><strong>Usage Instructions:</strong></h2> <ul> <li>For analyzing muscle volumes and lengths, refer to the Jupyter notebooks included in the <code>DataAndCode.zip</code>. Ensure all dependencies listed in the <code>requirements.txt</code> file are installed.</li> <li>The <code>MeshFiles.zip</code> contains the 3D models necessary for visualizing muscle and bone reconstructions, organized by specimen and arm position.</li> </ul>
Schematic 3D reconstruction hypothesis of the house of the painter Gillis van Coninxloo at the Oude Turfmarkt and adjacent houses
<p>This is a schematic, grey scale 3D reconstruction of the vanished house of the painter Gillis van Coninxloo and adjacent houses resulting from the research conducted in the framework of the <em>Virtual Interiors</em> project. The research questions that this 3D reconstruction aimed to explore relate to the identification of the exact location of the house on the Oude Turfmarkt and its internal spatial arrangement. Especially the references that are contained in Coninxloo's probate inventory to a ‘Coninxloos winckel’ and an ‘achter winckel’ on the first floor of his house were investigated with the 3D model. </p> <p>An introduction to the Coninxloo case study and to the first phase of the 3D reconstruction project of his house is briefly presented in C. Piccoli and W. Li 2021. ‘Dealing with multidimensional uncertainty: The house of the painter Gillis van Coninxloo’, https://www.virtualinteriorsproject.nl/2021/08/19/dealing-with-multidimensional-uncertainty-the-house-of-the-painter-gillis-van-coninxloo/ (last accessed November 2022). An update on archival research and new insights on this and the neighbouring houses is given in C. Piccoli 2022. ‘The house of Gillis van Coninxloo at the Oude Turfmarkt: New insights’ https://www.virtualinteriorsproject.nl/2022/11/23/the-house-of-gillis-van-coninxloo-at-the-oude-turfmarkt-new-insights/ (last accessed November 2022).</p> <p>The sources that were used to propose this reconstruction hypothesis are listed in the *.csv file.</p> <p>Note: This 3D reconstruction is a provisional version and must be considered hypothetical. Aspects that could be clarified by further research include a possible difference in ground floor’s level between the front and the back in Coninxloo’s house, which would impact the spatial arrangement of the interior and require the presence of steps to bridge the two parts.</p> <p><strong>Historical and archival research</strong>: Chiara Piccoli, Bart Reuvekamp, Frans Grijzenhout.<br> <strong>3D modelling</strong>: Chiara Piccoli<br> <strong>3D modelling software</strong>: Blender<br> <strong>Acknowledgements</strong>: Virtual Interiors project, Gabri van Tussenbroek, Weixuan Li, Judith Brouwer, Madelon Simons.</p>
3D reconstruction hypothesis of the 17th century entrance hall ('voorhuis') of Herengracht 573, Amsterdam
<p>3D reconstruction hypothesis of the 17<sup>th</sup> century entrance hall (‘voorhuis’) of Herengracht 573 in Amsterdam based on information retrieved from the probate inventory (10.5281/zenodo.7501160) and the VOC almanacs of Pieter de Graeff, and building historical research. The 3D reconstruction hypothesis and related sources are discussed in Chiara Piccoli, 'Home-making in 17th century Amsterdam: A 3D reconstruction to investigate visual cues in the entrance hall of Pieter de Graeff (1638-1707)', in G. Landeschi and E. Betts (eds.), <em>Capturing the Senses. Digital Methods for Sensory Archaeologies</em> (Cham: Springer, 2023 forthcoming).</p> <p>The 3D <em>voorhuis</em> can be interactively explored via the prototype <em>Virtual Interiors</em> webviewer (https://www.virtualinteriorsproject.nl/output/). A screencast of the interactive exploration can be viewed at <a href="https://doi.org/10.1515/opar-2020-0142">https://doi.org/10.1515/opar-2020-0142</a> or at <a href="https://dx.doi.org/10.21942/uva.14424218">https://dx.doi.org/10.21942/uva.14424218</a></p> <p>For further details about the aims and the development of the webviewer, see Hugo Huurdeman and Chiara Piccoli 2021. ‘3D Reconstructions as Research Hubs: Geospatial Interfaces for Real-Time Data Exploration of Seventeenth-Century Amsterdam Domestic Interiors’, <em>Open Archaeology</em>, vol. 7 (1), 314-336. <a href="https://doi.org/10.1515/opar-2020-0142">https://doi.org/10.1515/opar-2020-0142</a> and Hugo Huurdeman 2021. ‘Analyze & Experience: Towards a Research Environment for 3D Reconstructions’ (<a href="https://www.virtualinteriorsproject.nl/2021/08/04/towards-a-3d-research-environment/">https://www.virtualinteriorsproject.nl/2021/08/04/towards-a-3d-research-environment/</a>)</p> <p>This research was part of the NWO-funded project <em>Virtual Interiors</em> (2018-2022; https://www.virtualinteriorsproject.nl/).</p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions"
<p>DFEI dataset</p> <p><em>The full description can also be found in README.md.</em></p> <p>The dataset was used in the paper “GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions”. The project describes a full event interpretation at the LHCb experiment, situated at the Large Hadron Collider in CERN, Geneva. An “event” consists of detector responses that were converted to tracks - each track represents a particle.</p> <p>The aim of the algorithm is to make sense of the tracks and bundle together tracks coming from the same origin, as well as interpreting their decay hierarchy.</p> <p>Generated events</p> <p>The events in this dataset are based on simulation generated with <a href="https://www.pythia.org/">PYTHIA8</a> and <a href="https://evtgen.hepforge.org/">EvtGen</a>, in which the particle-collision conditions expected for the LHC Run 3 are replicated as shown in the table.</p> <table> <thead> <tr> <th>LHCb period</th> <th>Num. vis. pp collisions</th> <th>Num. tracks</th> <th>Num. b hadrons</th> <th>Num. c hadrons</th> </tr> </thead> <tbody> <tr> <td>Runs 3-4 (Upgrade I)</td> <td> ∼ 5</td> <td> ∼ 150</td> <td> ≪ 1</td> <td> ∼ 1</td> </tr> </tbody> </table> <p>Additionally, an approximate emulation of the LHCb detection and reconstruction effects is applied, as described in the paper in the appendix “Simulation”. In the generated dataset, each event is required to contain at least one b-hadron, which is subsequently allowed to decay freely through any of the standard decay modes present in PYTHIA8. On average, 40% of those events contain more than one b-hadron decay, with a maximum b-hadron decay multiplicity of five. Only charged stable particles that have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region (as defined in the paper) are included in the datasets.</p> <p>Datasets</p> <p>The datasets are divided in three categories</p> <p>Training and testing</p> <p>The file <code>Dataset_InclusiveHb_Training.root</code> contains the training dataset (40,000 events) test dataset (10,000 events) of inclusive decays.</p> <p>Evaluation</p> <p>The inclusive dataset <code>Dataset_InclusiveHb_Evaluation.root</code> contains the evaluation events (50,000).</p> <p>Exclusive decays</p> <p>In addition to this inclusive dataset, several other smaller samples (of few thousand events each) have also been generated, requiring that all the events in each sample contained a specific (exclusive) type of b-hadron decay. The specific modes have been chosen to be representative of the most common classes of decay topologies of physics interest for LHCb. These samples contain only events in which all the particles originating from each of the considered exclusive decays have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region.</p> <p>The datasets contained are:</p> <ul> <li><code>Dataset_Bd_DD.root</code></li> <li><code>Dataset_Bd_Kpi.root</code></li> <li><code>Dataset_Bd_Kstmumu.root</code></li> <li><code>Dataset_Bs_Dspi.root</code></li> <li><code>Dataset_Bs_Jpsiphi.root</code></li> <li><code>Dataset_Bu_KKpi.root</code></li> <li><code>Dataset_Lb_Lcpi.root</code></li> </ul> <p>More information on them can be found in the paper.</p> <p>Loading the data</p> <p>The dataset is saved in the binary ROOT format with a key-array mapping. It can be loaded using the <a href="https://github.com/scikit-hep/uproot5#readme">uproot</a> Python library to convert it to a pandas DataFrame or similar.</p> <p>An example snippet is given here:</p> <pre><code>import uproot # treename = "Particles" treename = "Relations" with uproot.open('/path/to/file.root') as file: df = file[treename].arrays( # we can specify only a set of branches # ['EventNumber', "FromSamePV_true"], library='pd') # 'pd' for pandas </code></pre> <p>The returned <code>file</code> behaves like a mapping that contains two different data holders. They are accessible with <code>Relations</code> or <code>Particles</code> that contain either the relations between the particles or the particles themselves.</p> <p>Regarding the <code>Relations</code>, only edges connecting two different particles are contained in the dataset. The edges are treated as not directional, so a single edge is considered for each pair of particles.</p> <p>Variables</p> <p>The relevant features used in the GNN are described in the following. A cartesian right-handed coordinate system is used, with the <em>z</em> axis pointing along the beamline, the <em>x</em> axis beinng parallel to the horizontal and the <em>y</em> axis being vertically oriented. When specified in the name of the variables, the suffix “_true” refers to ground-truth information, and the suffix “_reco” refers to the output of the emulated LHCb reconstruction.</p> <ul> <li> <p>General:</p> <ul> <li>EventNumber: unique number to identify the event that the entry belongs to.</li> </ul> </li> <li> <p>Node variables:</p> <ul> <li> <p>ParticleKey: unique number to identify each particle in a given event.</p> </li> <li> <p>Identity (ID): numerical code identifying the type of particle, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>.</p> </li> <li> <p>FromPrimaryBeautyHadron: boolean variable indicating whether the particles has been produced in a beauty hadron decay or not.</p> </li> <li> <p>Transverse momentum (<em>p</em><sub><em>T</em></sub>): component of the three-momentum transverse to the beamline, i.e. the <em>x</em> and <em>y</em> component combined.</p> </li> <li> <p>Impact parameter with respect to the associated primary vertex (IP): distance of closest approach between the particle trajectory and its associated primary vertex (proton-proton collision point), defined as the one with the smallest IP for the given particle amongst all the primary vertices in the event.</p> </li> <li> <p>Pseudorapidity (<em>η</em>): spatial coordinate describing the angle of a particle relative to the beam axis, computed as <em>η</em> = arctanh(<em>p</em><sub><em>z</em></sub>/∥<em>p⃗</em>∥).</p> </li> <li> <p>Charge (<em>q</em>): for the stable particles under consideration, the charge can take the value 1 or -1.</p> </li> <li> <p><em>O</em><sub><em>x</em></sub>, <em>O</em><sub><em>y</em></sub>, <em>O</em><sub><em>z</em></sub>: cartesian coordinates of the origin point of the particle.</p> </li> <li> <p><em>p</em><sub><em>x</em></sub>, <em>p</em><sub><em>y</em></sub>, <em>p</em><sub><em>z</em></sub>: cartesian coordinates of the three-momentum.</p> </li> <li> <p><em>P</em><em>V</em><sub><em>x</em></sub>, <em>P</em><em>V</em><sub><em>y</em></sub>, <em>P</em><em>V</em><sub><em>z</em></sub>: cartesian coordinates of the position of the associated primary vertex.</p> </li> </ul> </li> <li> <p>Edge variables:</p> <ul> <li> <p>FirstParticleKey: ParticleKey of one of the two particles connected by the edge.</p> </li> <li> <p>SecondParticleKey: ParticleKey of the other particle, verifying FirstParticleKey > SecondParticleKey.</p> </li> <li> <p>FromSamePrimaryBeautyHadron: boolean variable indicating whether the two particles originate from the same beauty hadron decay.</p> </li> <li> <p>Opening angle (<em>θ</em>): angle between the three-momentum directions of the two particles.</p> </li> <li> <p>Momentum-transverse distance (<em>d</em><sub> ⊥ <em>P⃗</em></sub>): distance between the origin point of the two particles defined on a plane which is transverse to the combined three momentum of the two particles.</p> </li> <li> <p>Distance along the beam axis (<em>Δ</em><sub><em>z</em></sub>): difference between the <em>z</em>-coordinate of the origin points of the two particles.</p> </li> <li> <p><em>F</em><em>r</em><em>o</em><em>m</em><em>S</em><em>a</em><em>m</em><em>e</em><em>P</em><em>V</em>: boolean variable indicating whether the two particles share the same associated primary vertex.</p> </li> <li> <p>Order of the “topological” Lowest Common Ancestor (<em>T</em><em>o</em><em>p</em><em>o</em><em>L</em><em>C</em><em>A</em><em>O</em><em>r</em><em>d</em><em>e</em><em>r</em>): variable that can take the values 0, 1, 2 or 3, as explained in the paper.</p> </li> <li> <p>Identity of the “topological” Lowest Common Ancestor (<em>T</em><em>o</em><em>p</em><em>o</em><em>L</em><em>C</em><em>A</em><em>I</em><em>D</em>): numerical code identifying the particle type of the ancestor, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>.</p> </li> </ul> </li> </ul>
Data for paper "Magnetohydrodynamic Equilibrium Reconstruction with Consistent Uncertainties"
<p>Data and scripts for the conference paper "Magnetohydrodynamic Equilibrium Reconstruction with Consistent Uncertainties" for the 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering.</p> <p><strong>Abstract</strong>: We report on progress towards a probabilistic framework for consistent uncertainty quantification and propagation in analysis and numerical modeling of physics in magnetically confined plasmas in the stellarator configuration. A frequent starting point in this process is the calculation of a magnetohydrodynamic equilibrium from plasma profiles. Profiles and therefore the equilibrium are typically reconstructed from experimental data. What sets equilibrium reconstruction apart from usual inverse problems is that profiles are given as functions over a magnetic flux derived from the magnetic field, rather than spatial coordinates. This makes it a fixed-point problem that is traditionally left inconsistent or solved iteratively in a least-squares sense[1–3]. The aim here is towards a straightforward and transparent process to quantify and propagate uncertainties and their correlations for function-valued fields and profiles in this setting. We propose a framework that utilizes a low dimensional prior distribution of equilibria, constructed with principal component analysis. A surrogate of the forward model[4] is trained to enable faster sampling.</p> <p><strong>Funding</strong>: The present contribution is supported by the Helmholtz Association of German Research Centers under the joint research school HIDSS-0006 'Munich School for Data Science - MUDS'. This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No 101052200 - EUROfusion). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.</p>
A harmonized Landsat Sentinel-2 (HLS) dataset for benchmarking time series reconstruction methods of vegetation indices
<p>Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed to fill the gaps present in the images, and some works also presented performance comparisons between them (Roerink et al., 2000; Moreno-Martínez et al., 2020; Siabi et al., 2022). Because of the lack of a ground truth for the reconstructed images, the performance evaluation requires the creation of datasets where artificial gaps are introduced in a reference image, such that metrics like the root mean square error (RMSE) can be computed comparing the reconstructed images with the reference one. Different approaches have been used to create the reference images and the artificial gaps, but in most cases, the artificial gaps are introduced using arbitrary patterns and/or the reference image is produced artificially and not using real satellite images (e.g. Kandasamy et al., 2013; Liu et al., 2017; Julien & Sobrino, 2018). In addition, to the best of our knowledge, few of them are openly available and directly accessible allowing for fully reproducible research.</p> <p>We provide here a benchmark dataset for time series reconstruction method based on the<strong> <a href="https://hls.gsfc.nasa.gov/">harmonized Landsat Sentinel-2 (HLS)</a> </strong>collection where the artificial gaps are introduced with a realistic spatio-temporal distribution. In particular, we selected six tiles that we considered representative for most of the main climate classes (e.g. equatorial, arid, warm temperature, boreal and polar), as depicted in the preview.</p> <p>Specifically, following the <strong><a href="https://hls.gsfc.nasa.gov/products-description/tiling-system/">relative tiling system</a></strong> shown above, we downloaded the Red, NIR and F-mask bands from both the HLSL30 and HLSS30 collections for the tiles 19FCV, 22LEH, 32QPK, 31UFS, 45WFV and 49MWM. From the Red and NIR band we derived the NDVI as:</p> <p><span class="math-tex">\(NDVI = {NIR - Red \over NIR + Red}\)</span></p> <p>only for clear-sky on lend pixels (F-mask bits 1, 3, 4 and 5 equal zero), setting as not a number the remaining pixels. The images are then aggregated on a 16 days base, averaging the available values for each pixel in each temporal range. The so obtained data, are considered from us as the reference data for the benchmarking, and stored following the file naming convention</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI.tif</em></p> <p>where <em>TILE_NAME</em> is one between the above specified ones, <em>YYYY</em> is the corresponding year (spanning from 2015 to 2022) and <em>DDD</em> is the day of the year from which the corresponding 16 days range starts. Finally, for each tile, we have a time series composed of <strong>184</strong> images (23 images for 8 years) that can be easily manipulated, for example using the <strong><a href="https://github.com/scikit-map/scikit-map/tree/master">Scikit-Map library</a></strong> in Python.</p> <p>Starting from those data, for each image we considered the mask of currently present gaps, we randomly rotated it by 90, 180 or 270 degrees and we added artificial gaps in the pixels of the rotated mask. Doing so, we believe that the spatio-temporal distribution will be still realistic, providing a solid benchmark for gap-filling methods that work on time series, on spatial pattern or combination of the both.</p> <p>The data including the artificial gaps are stored with the naming structure</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI_art_gaps.tif</em></p> <p>following the previously mentioned convention. The performance metrics, such as RMSE or normalized RMSE (NRMSE), can be computed by applying a reconstruction method on the images with artificial gaps, and then comparing the reconstructed time series with the reference one only on the artificially created gaps locations. </p> <p>This dataset was used to compare the performance of some gap-filling methods and we provide a <strong><a href="https://github.com/OpenGeoHub/EO-benchmark/blob/main/gap_filling_methods/gap_filling_comparison.ipynb">Jupyter notebook</a></strong> that shows how to access and use the data. The files are provided in GeoTIFF format and projected in the coordinate reference system WGS 84 / UTM zone 19N (EPSG:32619). </p> <p>If you succeed to produce higher accuracy or develop a new algorithm for gap filling, please contact authors or post on our GitHub repository. May the force be with you!</p> <p>References:</p> <ol> <li> <p>Julien, Y., & Sobrino, J. A. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección, (51), 19-31. <a href="https://doi.org/10.4995/raet.2018.9749">https://doi.org/10.4995/raet.2018.9749</a> </p> </li> <li> <p>Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6), 4055-4071. <a href="https://doi.org/10.5194/bg-10-4055-2013">https://doi.org/10.5194/bg-10-4055-2013</a> </p> </li> <li> <p>Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179. <a href="https://doi.org/10.1016/j.rse.2016.11.023">https://doi.org/10.1016/j.rse.2016.11.023</a> </p> </li> <li> <p>Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901.<a href="https://doi.org/10.1016/j.rse.2020.111901"> https://doi.org/10.1016/j.rse.2020.111901</a> </p> </li> <li> <p>Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. <a href="https://doi.org/10.1080/014311600209814">https://doi.org/10.1080/014311600209814</a></p> </li> <li> <p>Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2022). Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images. Computers and Electronics in Agriculture, 193, 106619.<a href="https://doi.org/10.1016/j.compag.2021.106619"> https://doi.org/10.1016/j.compag.2021.106619</a></p> </li> </ol>
Data supplement to 'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150'
<p>This is a data supplement to <strong>'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150</strong>'. It presents a global-scale Vertical Land Motion (VLM) reconstruction that resolves height changes in the period 1995-2020. It is based on the joint probabilistic analysis of an extensive network of more than 11,000 GNSS stations, tide gauges, and satellite altimetry. The approach used to derive this reconstruction is described in the paper. The dataset variables are explained in the .pdf file.</p>
Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
<p>This dataset contains the data used in the paper</p> <blockquote> <p>de Dumast, P., Sanchez, T., Lajous, H., Bach Cuadra, M. (2023). Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction. MICCAI 2023. LNCS, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_32</p> </blockquote> <p>A preprint can also be found on <a href="https://arxiv.org/abs/2211.14274">arXiv</a>. If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This paper studied the impact of the regularization parameter <span class="math-tex">\(\alpha \)</span> on the super-resolution reconstruction of fetal brain magnetic resonance (MR) images. It used simulated T2-weighted data MR images generated using FaBiAN v2.0, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation. The dataset contains the raw simulated data, the corresponding ground truths as well as corresponding super-resolution (SR) reconstructions using MIALSRTK and NiftyMIC with varying regularization parameters <span class="math-tex">\(\alpha \)</span>.</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p>
Deep Image Reconstruction
Open the record for dataset details and reuse information.
Climate reconstructions for the SMPDSv1 modern pollen data set
<p>The dataset contains estimates of three bioclimatic variables at modern pollen sites from the SMPDSv1 modern pollen data set (Harrison, 2019). The bioclimatic variables are mean temperature of the coldest month (MTCO), growing degree days above 0°C (GDD0), and an annual Moisture Index, defined as the ratio of annual precipitation to annual potential evapotranspiration (MI). Estimates of these bioclimatic variables were derived using geographically-weighted regression of gridded climate data in order to correct for elevation differences between each pollen site and the corresponding grid cell. The climatological data (mean monthly temperature, precipitation, and fractional sunshine hours) were derived from the CRU CL v2.0 gridded dataset of modern (1961-1990) surface climate at 10 arc minute resolution (~18 km) (New et al., 2002).Geographically- weighted regression (GWR) was carried out in ArcGIS (v10.3, ESRI, 2014). A fixed bandwidth kernel of 1.06 ° (~140km) was used in the GWR because this optimized model diagnostics and reduced spatial clustering of residuals relative to other bandwidths. The climate of each pollen site was then estimated based on its longitude, latitude, and elevation. MTCO was taken directly from the GWR regression. GDD0 were estimated from daily data using a mean-conserving interpolation of the monthly mean temperatures. MI was calculated for each pollen site using code modified from SPLASH v1.0 (Davis et al., 2017) based on daily values of precipitation, temperature and sunshine hours again obtained using a mean-conserving interpolation of the monthly values of each.</p>
Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll
<p>Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll.</p> <p>TargetDeformation.R: R code with for the Target Deformation procedure.<br> IGF560.ply: 3D mesh of the holotype IGF560 in ply extension.<br> IGF560_set.txt: landmark set of the holotype IGF560.<br> Dm. 5/154.3/4.A4.5.ply: 3D mesh of Dm 5/154.3/4.A4.5 in .ply extension.<br> Dm_set.txt: landmark set of the Dm 5/154.3/4.A4.5 sample.<br> IGF11023: 3D mesh of IGF11023 in.ply extension.<br> IGF11023_set.txt: landmark set on the IGF11023 sample.<br> IGF560R: 3D mesh of IGF560R in.ply extension.<br> IGF560W: 3D mesh of IGF560W in.ply extension.<br> IGF560R-s: 3D mesh of IGF560R-s in.ply extension.<br> IGF560W-s: 3D mesh of IGF560W-s in.ply extension.<br> IGF560_IGF560R_IGF560W.html: file that contain WebGL code to reproduce the 3D meshes of IGF560, IGF560R and IGF560W in a browser.<br> IGF560Rs_IGF560Ws.html: file that contain WebGL code to reproduce the 3D meshes of IGF560R-S and IGF560W-S in a browser.<br> IGF560W Mesh area variation.html: file that contain WebGL code to reproduce two 3d meshes of IGF560W using localmeshDist() and meshdist() in a browser.<br> </p>
[2019 QSM Reconstruction Challenge] Submissions Stage 1
<p>This repository contains the original, unaltered files submitted to Stage 1 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge.</p> <p>The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available <a href="https://doi.org/10.5281/zenodo.4559540">here</a>. Information about the submitted solutions and resulting analysis metrics are available <a href="https://doi.org/10.5281/zenodo.3687196">here</a>.</p> <p>The results of the challenge are fully reported in the journal article "<a href="http://doi.org/10.1002/mrm.28754">QSM Reconstruction Challenge 2.0: Design and Report of Results</a>".</p>
Data for: Physics-based Reconstruction Methods for Magnetic Resonance Imaging
<p>Magnetic Resonance Imaging measurement data used in our paper about 'Physics-based Reconstruction Methods for Magnetic Resonance Imaging' (DOI: 10.1098/rsta.2020.0196). (In version 2 the IR-FLASH data set was replaced with one which is from the same volunteer and slice as the ME-SE data set.) </p> <p>The data is acquired from healthy volunteers and stored in the format of the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p>The acquisition parameters are shown in the following table:</p> <p>flip angle[◦] TR/TE/ Delta TE[ms] bandwidth [Hz/px] matrix spokes TA[s] FOV[mm] slice[mm]</p> <p>IR-FLASH 6 4.10/2.58 630 256 × 256 1020 4 192 5<br> ME-SE 90/180 2500/9.9/9.9 390 256 × 256 25 × 16 80 192 3<br> ME-FLASH 5 10.60/1.37/1.34 960 200× 200 33 × 7 0.35a 320 5<br> PC-FLASH 10 4.46/2.96 1250 210 × 210 2 × 7 15 320 5<br> fmSSFPb 15 4.5/2.25 840 192× 192 4 × 101 × 40 137 192 1</p>
Time-resolved reconstruction of M87*
<p>The dataset contains 160 approximate posterior samples of the time-variable shadow of M87*</p> <p>Details can be found in readme.txt</p>
Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)
<p>These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing).</p> <p>Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.</p>
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the upper limb joints in planar robot-aided therapies
<p>These files contain the raw data (acquired from different users) necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the upper limb joints in planar robot-aided therapies</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Jorge A. Díez, Luis D. Lledó, Francisco J. Badesa, Nicolas Garcia-Aracil</p> <p>Conference: ICORR 2015, IEEE 14th International Conference on Rehabilitation Robotics, August 2015</p> <p><br> All the orientations are expressed regarding the origin of the robot.</p> <p>a) Robot Joints: Planar robot joints acquired during the experiment, in radians (j1-j3 columns). This robot is referenced in the paper.<br> b) Quaternion IMU shoulder: Unit quatenion acquired through a 9DoFs Inertial Measurement Unit (IMU) developed by Shimmer (qw1-qz columns).<br> c) Upper arm acceleration: Acceleration acquired from a 3-axial accelerometer developed by Shimmer (X-Z columns). It is normalized regarding the gravity (9.81m/s^2).<br> d) Quaternion Tracker onto Shoulder: unit quaternion of the tracker placed onto the shoulder acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).<br> e) Quaternion Tracker onto Upper Arm: unit quaternion of the tracker placed onto the upper arm acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).</p>
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot"
<p>This file contains the raw data necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Luis D. Lledó, Jorge A. Díez, Jose M. Catalan, Nicolas Garcia-Aracil.</p> <p>Conference: EMBC 2015, IEEE 37th International Conference in Medicine and Biology Society, August 2015.</p> <p>Raw data acquired necessary to perform thee algorithm introduced in this paper.</p> <p>a) Robot Joints: Robot joints generated to develop the simulation, in radians (j1-j7 colums). This robot is referenced in the paper.<br> b) Direct Upper Limb Joints: Upper limb joints generated to develop the simulation, in radians (q1-q7 columns). This data is used to simulate the accelerometer value.</p>
Training material for de novo transcriptome reconstruction from RNA-seq data
<p>The data provided here are part of a Galaxy tutorial that analyzes RNA-seq data from a study published by Wu et al., 2014 (DOI:10.1101/gr.164830.113). The goal of this study was to investigate "the dynamics of occupancy and the role in gene regulation of the transcription factor Tal1, a critical regulator of hematopoiesis, at multiple stages of hematopoietic differentiation." To this end, RNA-seq libraries were constructed from multiple mouse cell types including G1E - a GATA-null immortalized cell line derived from targeted disruption of GATA-1 in mouse embryonic stem cells - and megakaryocytes. This RNA-seq data was used to determine differential gene expression between G1E and megakaryocytes and later correlated with Tal1 occupancy. This dataset (GEO Accession: GSE51338) consists of biological replicate, paired-end, polyA selected RNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to chromosome 19 and a subset of interesting genomic loci identified by Wu et al.</p>
Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction.
<p>These two excel files gather genotyping data used in the following publication:</p> <p>Vidal M, Plomion C, Raffin A, Harvengt L, Bouffier L (2017) Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction. Annals of Forest Science, 74(1). DOI 10.1007/s13595-016-0596-8</p> <p>The dataset describes genotyping profiles (with 56 or 63 SNPs) for the G1 and G2 individuals sampled in this paper. For each individual, the following information is mentioned: identity, preselection option (only for G2 individuals), the generation to which the individual belongs, pedigree (only for G2 individuals), alleles for each SNP.</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.