Find research datasets worth reusing

The Icy dataset - a multi-modalities dataset for icy surface reconstruction

Three-dimensional (3D) reconstruction serves as a cornerstone in various robotic applications, playing critical roles in scene understanding and navigation. Traditionally, LiDAR has been instrumental in generating precise point clouds of the environment, providing essential data for these applications. However, the efficacy of LiDAR sensors is significantly hindered in challenging conditions, such as the presence of water or icy surfaces. The complex interplay between laser beams and icy or non-ideal surfaces can result in signal degradation, distortion, or even complete signal loss, adversely affecting the accuracy and reliability of the 3D reconstruction process. The reflective and refractive properties of ice, along with its variable surface conditions, present challenges that traditional LiDAR sensors struggle to address. This paper proposes a diverse dataset to facilitate a multimodal approach for detecting and reconstructing icy surfaces using various sensors. A preliminary study on our dataset demonstrates that, in addition to the geometrical surface obtained by registering consecutive scans from LiDAR, regions with ice can be identified by leveraging visual data to enhance understanding of the surface texture. The integration of distinct data sources can thus improve the robustness of reconstruction algorithms in diverse scenarios.

opencc-by-4.0Dec 2021View details →

Nitrate δ15N values and surface mass balance reconstructions from East Antarctica

Geographic information, surface mass balance (SMB) data, and sub-photic zone (>0.3 m) nitrate concentration and nitrogen isotopic composition (δ15NNO3) for 135 sites across East Antarctica. This database was used to examine and define the relationship between δ15NNO3 and SMB in Antarctica as part of the SCADI (Snow Core Accumulation from Delta-15N Isotopes) and EAIIST (East Antarctic International Ice Sheet Traverse) projects. Of these 135 sites, 92 are newly reported here while the other site data were previously published and are cited accordingly. Snow bearing nitrate was sampled from snow pits and firn/ice cores at different dates depending on the original scientific campaign, but predominately between 2010 and 2020, with the earliest sampling occurring in 2004. Nitrate was later extracted from the snow, concentrated, and analyzed for δ15NNO3. Surface mass balance data comes from a combination of previous ground-based observations (e.g., stakes, ice core data) and the output from Modèle Atmosphérique Régional version 3.6.4 with European Centre for Medium-Range Weather Forecasts “Interim” re-analysis data (ERA-interim) data, adjusted for observed model SMB biases. Elevation data were extracted from the Reference Elevation Model of Antarctica (REMA, <a href="https://doi.org/10.5194/tc-13-665-2019">https://doi.org/10.5194/tc-13-665-2019</a>). Also contains nitrate concentration and isotopic (δ15NNO3) data, ice density, and surface mass balance estimates from the ABN1314-103 ice core. This 103 m long core was drilled beginning on 07 January 2014 as one of three ice cores at Aurora Basin North, Antarctica (-71.17, 111.37, 2679 m.a.s.l), in the 2013-2014 field season. The age-depth model for ABN1314-103 was matched through ion profiles from an annually-resolved model (ALC01112018) originally developed for one of the other ABN cores through seasonal ion and water isotope cycles and constrained by volcanic horizons. Each 1 m segment of the core was weighed and measured for ice density calculations, and then sampled for nitrate at 0.33 m resolution. Nitrate concentrations were taken on melted ice aliquots with ion chromatography, while isotopic analysis was achieved through bacterial denitrification and MAT 253 mass spectrometry after concentrating with anionic resin. Using the density data and the age-depth model’s dates for the top and bottom of each 1 m core segment, we reconstructed a history of surface mass balance changes as recorded in ABN1314-103. Additionally, we also estimated the effect of upstream topographic changes on the ice core’s surface mass balance record through a ground penetrating radar transect that extended 11.5 km against the direction of glacial ice flow. The modern SMB changes along this upstream transect were linked to ABN1314-103 core depths by through the local horizontal ice flow rate (16.2 m a-1) and the core’s age-depth model, and included here for comparative analysis.

opencc-by-4.0Aug 2024View details →

Float+SOCAT sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed

Here we provide sampling masks used in the study "The importance of adding unbiased Argo observations to the ocean carbon observing system" (Heimdal & McKinley, 2024, Scientific Reports). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 2 different sampling masks used in the experiments presented in Heimdal & McKinley (2024). These masks represent two different float sampling schemes (+SOCAT) including 500 floats, corresponding to historical Argo float observations (https://fleetmonitoring.euro-argo.eu/dashboardpatterns) and potential optimized float sampling (following Chamberlain et al., 2023, <a href="https://doi.org/10.1175/JTECH-D-22-0093.1" target="_blank" rel="noopener">https://doi.org/10.1175/JTECH-D-22-0093.1</a>).

opencc-by-4.0Jul 2024View details →

Data for Exploring Inhomogeneous Surfaces: Ti-rich SrTiO3(110) Reconstructions via Active Learning

The archive "supplementary_data.tar.gz" contains structures, training data, training scripts, models and an evolution script obtained and reported in the study: "Exploring Inhomogeneous Surfaces: Ti-rich SrTiO3(110) Reconstructions via Active Learning". See README for more information on the archive content.

Surfaces/regoliths used in the training and testing of the deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains the surfaces/regoliths in terms of elemental surface composition used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".

Inputs and outputs for exospheric simulations used in the deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains the inputs and outputs of the exospheric simulations performed for v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".

Inputs and outputs for the training and testing of a deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains inputs (datasets) and outputs (trainings and tests) used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".

opencc-zeroApr 2020View details →

dryad36/100

Data from: High-speed surface reconstruction of a flying bird using structured light

Animal flight requires aerodynamic power, which is challenging to determine accurately in vivo. Existing methods rely on approximate calculations based on wake flow field measurements, inverse dynamics approaches, or invasive muscle physiological recordings. In contrast, the external mechanical work required for terrestrial locomotion can be determined more directly by using a force platform as an ergometer. Based on an extension of the recent invention of the aerodynamic force platform, we now present a more direct method to determine the in vivo aerodynamic power by taking the dot product of the aerodynamic force vector on the wing with the representative wing velocity vector based on kinematics and morphology. We demonstrate this new method by studying a slowly flying dove, but it can be applied more generally across flying and swimming animals as well as animals that locomote over water surfaces. Finally, our mathematical framework also works for power analyses based on flow field measurements.

opencc-by-4.0Sep 2021View details →

Nicosia, Bedestan. Plan of the complex prepared in 1980-81 combined with resistivity tomography at a depth of 1.25m below the current surface with hypothetical reconstruction of the early Byzantine church.

Nicosia, Bedestan. Plan of the complex prepared in 1980-81 combined with resistivity tomography at a depth of 1.25m below the current surface with hypothetical reconstruction of the early Byzantine church.

opencc-by-4.0Sep 2021View details →

Nicosia, Bedestan. Plan of the complex prepared in 1980-81 combined with resistivity tomography at a depth of 1.0m below the current surface with hypothetical reconstruction of the middle Byzantine church

Nicosia, Bedestan. Plan of the complex prepared in 1980-81 combined with resistivity tomography at a depth of 1.0m below the current surface with hypothetical reconstruction of the middle Byzantine church. Drawing by M. Wills, M. Cozzolino and Vicki Herring.

opencc-by-4.0Dec 2022View details →

Reconstructed Aneto glacier surfaces from historic aerial image photogrammetry (1981) and remote sensing techniques (2020, 2021, 2022)

The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time.

opencc-by-4.0Apr 2023View details →

Spatially and temporally continuous reconstruction of Antarctic Amundsen Sea sector ice sheet surface velocities: 1996-2018

Spatially and temporally continuous reconstruction of ice sheet surface velocities for the Amundson Sea Sector of the Antarctica. The reconstruction is derived from the synthesis of annual published InSAR (R14: Rignot et al. 2014) and optical (G18: Gardner et al., 2018 & Gardner et al., 2022) surface velocities. Data are posted on a uniform 240 m by 240 m grid in Antarctic Polar Stereographic (EPSG:3031) coordinates. The temporal posting is every 2.4 months or 1/5 of a year. R14 and G18 annual velocity data have large errors and data gaps in both space and time that make the data challenging to work with. For this reason, a Spatially and temporally continuous reconstruction was made. These are the preprocessing steps that were applied to create the reconstruction: <ol> <li>R14 component velocities [vx/vy] are mapped to the same 240-m grid as G18 for the Amundson Sea sector.</li> <li>Velocities falling outside of mapped ice extents (see Paolo et al., 2022) are set to no data values.</li> <li>A reference velocity is defined as the 1996 velocity field or the earliest valid measurement thereafter. The average of both velocities is taken if multiple observations exist for the first year of data.</li> <li>For areas moving faster than 200 m/yr., the percentage anomalies are calculated for all years relative the reference velocity. This was done for both G18 and R14 velocities separately.</li> <li>Annual velocity anomalies are then filter with a 5-km windowed moving median.</li> <li>G18 and R14 filtered anomalies are merge by taking the mean of each year. Years with less than 30% coverage for fast moving ice (>= 200 m/yr.) were discarded.</li> <li>If missing annual values were within 25 km of a valid datapoint they are filled using natural neighbor interpolation, otherwise anomalies were set to zero.</li> <li>Outside of fast-moving areas, annual anomalies are tapered to zero using a 10-km cosine taper.</li> <li>Merged and filled annual anomalies are then smoothed one last time using a 5-km windowed moving mean.</li> <li>To create a continuous record of velocity, annual anomalies are interpolated in time to every 1/5 of a year for every 240 m pixel using a spline interpolant and multiplied by the reference velocity.</li> </ol> All x and y component velocities [vx/vy] and velocity magnitudes [v] are stored as individual geotiff files and are contained in the .zip included file. A visualization of the velocity magnitudes is included as an animated gif.     References: Gardner, A., M. Fahnestock, and T. Scambos. (2022). MEaSUREs ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/6II6VW8LLWJ7. Date Accessed 04-07-2019. Gardner, A. S., Moholdt, G., Scambos, T., Fahnstock, M., Ligtenberg, S., van den Broeke, M., & Nilsson, J. (2018). Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. The Cryosphere, 12(2), 521–547. https://doi.org/10.5194/tc-12-521-2018 Paolo, F., Gardner, A., Greene, C., Nilsson, J., Schodlok, M., Schlegel, N., & Fricker, H. (2022). Widespread slowdown in thinning rates of West Antarctic Ice Shelves. EGUsphere, 2022, 1–45. https://doi.org/10.5194/egusphere-2022-1128 Rignot, E., J. Mouginot, and B. Scheuchl. (2014). MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0545.001. Date Accessed 04-07-2019.

opencc-by-4.0May 2023View details →

Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach

Daily gridded lake surface temperature (LST) data (1979-2020) for each Great Lake - Superior (GLS), Michigan (GLM), Huron (GLH), Erie (GLE) and Ontario (GLO) - derived from LSTM detailed in Kayastha et al. (2023) paper: "Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach". Each matfile contains longitude (lon), latitude (lat), as well as the LST for each grid point. The files also contain the variable 'art1' (Area of Node-Base Control volume) required to calculate lake-wide average LST. The depth at each location (dep) is also provided.

opencc-by-4.0Aug 2023View details →

Analytical method for reconstructing the stress on a spherical particle from its surface deformation

Supplemental data for the following manuscript: Lea Johanna Krüger, Michael te Vrugt, Stephan Bröker, Bernhard Wallmeyer, Timo Betz, Raphael Wittkowski, "Analytical method for reconstructing the stress on a spherical particle from its surface deformation".Bead1_ExperimentalData.tif and Bead2_ExperimentalData.tif contain the measured bead shapes as TIF files. Bead1_ExperimentalData.txt and Bead2_ExperimentalData.txt contain the measured bead shapes as point clouds. Bead1_SphericalHarmonicsExpansionCoefficients.txt and Bead2_SphericalHarmonicsExpansionCoefficients.txt contain expansions of the bead shapes into spherical harmonics.

dryad36/100

CT reconstructions and 3D surface models of preserved impressions found on a Rijksmuseum terracotta sculpture

This dataset contains 3D micro Computed Tomography (CT) reconstructions (*.tiff files) and 3D surface models (Wavefront OBJ files) of preserved impressions found on the terracotta sculpture "Study for a Hovering Putto", dated between 1735 and 1750, and attributed to Laurent Delvaux (Gent, 17 January 1696 – Nivelles, 24 February 1778, Rijksmuseum, BK-NM-9352). The preserved impressions are fingermarks and toolmarks, which we analyse in the manuscript "Artist profiling using micro-CT scanning of a Rijksmuseum terracotta sculpture" by Sero et al. (<a href="https://doi.org/10.1126/sciadv.adg6073">DOI: 10.1126/sciadv.adg6073</a>). Folder names correspond to the names assigned to each impression in the manuscript. Each folder contains a stack of *.tiff files and a "Segmentation.obj", which is the 3D model obtained from Otsu's segmentation method in Slicer3D. The stack of *tiff files and the 3D models can be visualized together in Slicer3D.

opencc-zeroAug 2023View details →

dryad36/100

CT reconstructions and 3D surface models of preserved impressions found on a Rijksmuseum terracotta sculpture

Open the record for dataset details and reuse information.

publicOct 2023View details →

FIGURE 8 in Oziella sibirica (Acari: Eriophyoidea: Phytoptidae), a new eriophyoid mite species described using confocal microscopy, COI barcoding and 3 D surface reconstruction

FIGURE 8. CLSM image of the internal genitalia of Oziella sibirica sp. nov. A. Spermatheca; B. Pre-spermathecal swelling (distal segment of spermathecal tube); C. Proximal segment of spermathecal tube; D. Longitudinal bridge; E. Transverse apodeme; F. Laterodistal fold of transversal apodeme.

FIGURE 6 in Oziella sibirica (Acari: Eriophyoidea: Phytoptidae), a new eriophyoid mite species described using confocal microscopy, COI barcoding and 3 D surface reconstruction

FIGURE 6. Oziella sibirica sp. nov., nymph. A. Dorsal view of the mite; B. Ventral view; C. Prodorsal shield; D. Coxigenital area; E. Right leg I (arrow indicates a spine); F. Right leg II; G. Typical 4/3-rayed empodium; H. Typical 4/4-rayed empodium; I. Abnormal empodium. Scale bar: A & B = 140; C & D = 50; E & F = 45; G, H & I = 12.

FIGURE 1. 3D in Oziella sibirica (Acari: Eriophyoidea: Phytoptidae), a new eriophyoid mite species described using confocal microscopy, COI barcoding and 3 D surface reconstruction

FIGURE 1. 3D model of Oziella sibirica sp. nov. prodorsal shield (the same female as Fig. 2C). A. Gray scale prodorsal shield, B. Colourised prodorsal shield (notifications of lines follows that of Amrine et al. 1994 & Amrine et al. 2003; admedian lines colourised in red, additional line between admedian and submedian-2 line colourised in black). Scale bar A & B = 30 mkm. Note: setae ve and c1 are short on images A & B because only proximal parts of the setae of eriophyoid mites can be observed on CLSM images using blue laser, 405 nm (Chetverikov 2012b).

FIGURE 2 in Oziella sibirica (Acari: Eriophyoidea: Phytoptidae), a new eriophyoid mite species described using confocal microscopy, COI barcoding and 3 D surface reconstruction

FIGURE 2. Variation of the prodorsal shield design among four females of Oziella sibirica sp. nov. (black & white inverted CLSM images). Admedian lines colourised in red (A & D), additional lines in green (B) and red (C; the same female as in Fig. 2A & 2B). Scale bar = 30 mkm.