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.
3,377
datasets available to search
ShareScore release 0.9.0
Dataset results
3,377 results for “Scanning”
Scanned images of monocultures and mixtures of six grassland plant species roots, and of simulated fine roots
<p>Soil core samples were taken from a multi-species grassland experiment with field plots of monocultures and mixtures of six grassland plant species: <em>Lolium perenne</em> L. (PRG),<em> Phleum pratense</em> L. (TIM), <em>Trifolium pratense</em> L. (RC), <em>Trifolium repens</em> L. (WC), <em>Cichorium intybus </em>L. (CHIC), and <em>Plantago lanceolata </em>L.. The multi-species plots had a two species mixture with <em>Trifolium repens </em>L. and<em> Lolium perenne</em> L. (PRGWC), and a 6 species mixture with all species mentioned above. The cores were separated into soil depths of 0-10 cm, 10-15 cm and 15-20 cm and the roots separated from the soil.</p> <p>A ground-truth image set was created to simulate fine roots using fishing line. The fishing line used was a clear copolymer monofilament (Greys<sup>TM</sup> Greylon Tippet Material 3 lb), measured using a scanning electron microscope (Hitachi SU8200) to be 0.14 mm in diameter. The fishing line was used in its clear colour or coloured black using a permanent marker to simulate unstained and stained fine roots respectively. The fishing line was cut into lengths of 30 cm or 5 cm. </p> <p>Roots and fishing line were scanned using an Epson Perfection V800 flatbed scanner at 600 dpi. </p> <p>The Roots ZIP file contains a folder for the scanned root images and the Line zip file contains a folder with the scanned fishing line. The excel spreadsheet describes the naming convention for the images.</p> <p>Further details about the root sampling and image acquisition can be found in the publication that analyses these images: <a href="https://doi.org/10.1002/ppj2.20034">https://doi.org/10.1002/ppj2.20034</a></p>
CaptuRING and scanned wood core samples
<p>Wood samples in digitized cores with CaptuRING (CR_ in files) and scanned with Epson 750VPRO (scan_) for sample digitization comparison.</p> <p>Species:</p> <ol> <li><em>Fagus sylvatica </em>(FS)</li> <li><em>Quercus faginea </em>(QF)</li> <li><em>Quercus ilex </em>(QI)</li> <li><em>Quercus pyrenaica </em>(QY)</li> <li><em>Juniperus cedrus </em>(JC)</li> <li><em>Juniperus thurifera </em>(JT)</li> <li><em>Pinus nigra </em>(PN)</li> <li><em>Pinus pinaster </em>(PP) </li> </ol> <p>Graphical scale was fitted with empirical values for CR samples, and theoretical resolution value in scanning software for scanned ones.</p> <p>SupplementaryMaterials2.csv file includes summary table from image comparison data.</p>
Dataset for "A robust tip-less positioning device for near-field investigations: Press and Roll Scan (PROscan)"
<p>The dataset contains the data relevant for the publication "A robust tip-less positioning device for near-field investigations: Press and Roll Scan (PROscan)". The data has been acquired using optical measurement techniques as described in detail in the publication https://arxiv.org/abs/2203.05527. The data is structured according to Figures presented in the publication.</p> <p>The authors acknowledge financial support by the Max Planck Society and by the QuantERA project RouTe through the Federal Ministry of Education and Research (BMBF) (13N14839). This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska Curie Grant Agreement No. 101025918.</p>
Historic building's interior (iPhone LiDAR scan)
<p>This model shows a LiDAR-scan of selected room interiors of a historic building located in Lower Austria (AUT) recorded during building-archaeological measures by the archaeological company <strong><a href="https://www.ardig.at/">ARDIG</a></strong>, triggered by recent remodeling work in the course of house renovation. Scanning was done in two rounds using 3dScannerApp on an iPhone 13 Pro. Each round took approx. < 5 min of capturing and another < 5 min of on-board processing time. After the first room was fully captured in 3D and with textures in the first round, the scan was (nearly) seamlessly extended by another room in a second round using the corresponding app function. Therefore, <strong>within less than approx. 20 minutes, both rooms could be fully documented in 3D by a scaled and textured 3D model</strong>. Considering the extreme flexibility and fast acquisition and processing time, the method holds tremendous future potential for archaeological work, even if still several minor errors occur in the final product.</p>
DS.RFSAT.OBJECT-3D-SCANS
<p>This dataset contains the DS.RFSAT.OBJECT-3D-SCANS, as referred to in the <em>ARCH Data Management Plan</em> deliverable (D1.3).</p> <p>It contains SOLELY the 3D models produced by RFSAT Limited from aerial drone images captured by Hamburg City.</p> <p>Models processing was done using PIX4D v4 engine linked to from RFSAT autonomous scanning software (SWSCAN).</p> <p>Comparative models have been produced also with <a href="https://www.agisoft.com/">Agisoft Metashape</a> engine.</p> <p><strong>NOTE</strong>: this is a preliminary version with updated to be published shortly.</p>
UAV Laser Scanning surveys of the lake terminating glacier Fjallsjokull in SE Iceland, captured in July, 2021.
<p>This dataset consists of 5 separate laser scanning surveys performed between the 8th and 15th July, 2021. Two surveys were conducted in the morning and afternoon of the 8th and the 9th, and then only the morning of the 15th. The point clouds have been cleaned to remove erroneous points. The point clouds were processed using the methods and code available at <a href="https://github.com/christomsett/Direct_Georeferencing">Direct_Georeferencing</a>. All point clouds are georeferenced in the projected WGS 1984 UTM 28N system, and provided in the widely used compressed 'laz' format. An accuracy assessment of the data showed that all surveys were consistent to within 0.1 m of each other, apart from the second flight (afternoon) on the 8th July. Any users of this data should be aware of its limitations in a challenging cryospheric environment. </p>
Slide scans of diatom preparations from river Menne
<p>This archive contains the slide scans used for the deep learning experiments published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint. The diatom material was sampled at six different locations from the river Menne (Germany), cleaned/oxidized and mounted in Naphrax. Please refer to the manuscript for details.</p> <p>Each scan represents area of ca. 5 × 3.5 mm² scanned by an VS200 slide scanning microscope (Olympus Europa SE & Co. KG, Hamburg, Germany). 61 focal planes were imaged in bright-field mode at a distance of 0.28 µm each using a UPLXAPO60XO 60x/1.42 oil immersion objective. Focus stacking and stitching were performed with the built-in functions of the VS200 ASW software (v3.2.1). Subsequently the RGB color data was reduced to 8bit monochrome grayscale images. This process resulted in the six virtual slide images, each roughly 55,000 × 39,000 pixel.</p> <p>These images depict benthic diatoms Menne river comprising more than 110 different taxa (estimated by identifying a subset of specimens). The sampling of this stream was authorized by the Kreis Paderborn authorities through the 4032-20-450 permit.</p>
Scans dataset for evaluation of AR-based assembly pipeline of half-timber dry-stone structures
<p><strong>Dataset description</strong></p> <p><em>The dataset is realized by the EPFL Laboratories of IBOIS, and EESD on the occasion of joint collaboration with the common goal of exploring augmented reality (AR)-based fabrication for half-timber dry-stone construction. It is open sourced and free to be used for anyone's own research.</em></p> <p>The current dataset has been employed to evaluate the designed digital fabrication pipeline. It consists of raw point clouds and reconstructed models of two digitized prototypes of one-layered dry-stone structures with a 1.7 m length, ~0.6 height, and 0.7 m width, each with a different composition of building units. The first wall presents 40 mineral by-products uniquely from by-products of quarry processes involving sawing and water-cutting. In contrast, the second wall features 30 mineral scraps issued from all mixed quarry's operations of transformations.</p> <p>The collection contains both the reconstructed models of the as-built artifacts and the corresponding recorded model of the AR-guided assembly processes for the two walls. The recorded model contains meshes of the placed stones accordingly to the developed geometric planner. By comparing the misalignment between each stone of both models (e.g. Root Mean Square Error distances between two set of points) is possible to obtain metrics about the performance of the proposed AR-assembly method.</p> <p> </p> <p><strong>Dataset labeling guide</strong></p> <table summary="test2"> <thead> <tr> <th scope="row">label</th> <th scope="col">content</th> </tr> </thead> <tbody> <tr> <th scope="row">01/02</th> <td>First or second wall.</td> </tr> <tr> <th scope="row">rs</th> <td>Raw data: unprocessed scans.</td> </tr> <tr> <th scope="row">ab</th> <td>As-built model: model presenting all the registered stones.</td> </tr> <tr> <th scope="row">rc</th> <td>Recorded model: collection of tracked stones during the construction. Each stone has being placed following the AR system is recorded in this sub-set.</td> </tr> <tr> <th scope="row">ev</th> <td>Evaluation set: documentation and working files for the evaluation of the developed AR assembly system.</td> </tr> <tr> <th scope="row">colored_stone</th> <td>Evaluation set outcome: a graphical representation of the misalignment between each stone of the as-built model (ab) with the recorded model (rc).</td> </tr> </tbody> </table> <p> </p> <p><strong>Equipement specs</strong></p> <p>All the raw scans present in the dataset have been obtained from a FARO Freestyle 2 equipped with a Mobile PC for live point cloud processing.</p> <pre><code>@manual{farofreestyle2, title = {FARO Freestyle 2 and Mobile PC}, year = 2022, month = feb, note = {User Manual}, organization = {FARO Technologies Inc.}, url = {https://downloads.faro.com/index.php/s/sqcRBipgSy9GaEq?dir=undefined&openfile=138985} } </code></pre> <p> </p> <p><strong>For extra info</strong></p> <p>The open-sourced code for the developed AR assembly:</p> <pre><a href="https://doi.org/10.5281/zenodo.7181087">https://doi.org/10.5281/zenodo.7181087</a></pre> <p>The complete dataset of digitized mineral scraps:</p> <pre><a href="https://doi.org/10.5281/zenodo.7189478">https://doi.org/10.5281/zenodo.7189478</a></pre> <p> </p> <p><strong>Version notes</strong></p> <p>- The current dataset is published as linked documentation to a future publication, yet not reviewed.</p> <p><strong>Change log</strong></p> <p>- Typos</p> <p>- Correct order of authors</p>
Nitric oxide (NO) data set (60--160 km) from SCIAMACHY nominal limb scans
<p><strong>Overview</strong><br> Contains the nitric oxide (NO) number densities (in cm<sup>-3</sup>) from 60 km to 160 km retrieved from SCIAMACHY nominal (~0--90 km) limb scans.</p> <p>SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The nominal limb mode was carried out daily (apart from outages and a few days dedicated to other measurement modes) from 08/2002 until the end of the mission. The limb scans were performed from ground to about 90 km tangent altitude, and the retrieval was performed on a 2.5° x 2 km latitude--altitude grid from 90°S--90°N and from 60 km--160 km. This data set comprises all SCIAMACHY nominal NO measurements sorted by date and year, each day comprised about 15 orbits. See the accompanying README for the dimension and variable descriptions.</p> <p>The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2017. It is adapted from the MLT NO retrieval described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at<br> https://earth.esa.int/web/guest/data-access/browse-data-products.<br> The spectra were calibrated with ESA's `SciaL1C` command line tool available for download at<br> https://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073.</p> <p>The SCIAMACHY MLT NO data were previously compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties. This nominal data set here was not yet validated with other measurements but compares well to the SCIAMACHY MLT NO measurements below 90 km.</p> <p><strong>Acknowledgements</strong><br> The development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space. ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his research team comprising his colleagues in Bremen and international scientific collaborators led the scientific support and development of SCIAMACHY and the scientific exploitation of its data products.</p> <p>The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various international institutions: University of Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL), University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground segment. Support with respect to mission planning and operations is given by the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.</p>
Three-dimensional Reconstructions and Quantitative Indicators for colloidal particles in Dry and Liquid Conditions in Scanning Transmission Electron Microscope (STEM)
<p>This dataset accompanies the research presented in the paper:</p> <div>Esteban, D.A., Wang, D., Kadu, A., Olluyn, N., Iglesias, A.S., Perez, A.G., Casablanca, J.G., Nicolopoulos, S., Liz-Marzán, L.M. and Bals, S., 2023. Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies. <em>arXiv preprint arXiv:2311.05309</em>. [<a href="https://arxiv.org/pdf/2311.05309" target="_blank" rel="noopener">link</a>]</div> <p>It provides a comprehensive collection of three-dimensional reconstructions and quantitative descriptors for small colloidal particles. These gold nanoparticles are arranged in tetrahedral and other intricate geometries under both dry and liquid conditions. The dataset contains 3D reconstructions and quantitative indicators such as centroids, volumes, surface areas, solidity measures, and principal axis lengths for assemblies with 4, 5, and 6 particles. </p> <p>The dataset includes: <code>N4_dry_dart.rec</code> and <code>N4_liquid_dart.rec</code> for the 3D reconstructions of an assembly with 4 particles in dry and liquid conditions respectively; <code>N4_quant_descriptors_dry.mat</code> and <code>N4_quant_descriptors_liquid.mat</code> providing quantitative descriptors for these conditions. Similar files are provided for assemblies with 5 and 6 particles, such as <code>N5_dry_dart.rec</code>, <code>N5_liquid_dart.rec</code>, <code>N5_quant_descriptors_dry.mat</code>, <code>N5_quant_descriptors_liquid.mat</code>, and the corresponding files for N6. </p> <p>This dataset can be used to study the structural dynamics of nanoparticle assemblies and studies in colloidal chemistry, materials science, and nanotechnology. The <code>.rec</code> files can be visualized using volume rendering software (e.g. Amira or Avizo), while the <code>.mat</code> files contain structured data for analysis in MATLAB. The supporting code and scripts for this dataset are available on the GitHub repository: <a href="https://github.com/ajinkyakadu/LiquidET_NatComm2024" target="_new" rel="noreferrer">https://github.com/ajinkyakadu/LiquidET_NatComm2024</a>. </p>
IODP Expedition 379 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
3D output of idealized large-eddy simulations with varying speed and surface heating to assess Doppler lidar scan patterns
<p><span>This dataset consists of nine idealized large-eddy simulations that were designed to systematically investigate the ability of different Doppler lidar scan patterns to measure the 3-dimensional wind vector at one point or in one profile. For more information, please see the documentation.</span></p>
IODP Expedition 360 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 397 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
Alien Futures Horizon Scanning dataset
<p>The data are the result of the Alien Futures Horizon Scanning project. They were collected through an open online survey (EnglishSurveyFinal.pdf) to poll specialists and stakeholders from around the world as to their opinion on the three most important issues that may affect the future global and local management of biological invasions in the next 20 to 50 years both globally and at their respective local working level.</p> <p>The dataset also contains the categorisation of these issues into topics conducted by the Alien Futures team and presented in:</p> <p>Dehnen-Schmutz, K., Boivin, T., Essl, F., Groom, Q. J., Harrison, L., Touza, J. M., Bayliss, H. (2018): Alien Futures: what is on the horizon for biological invasions?. <em>Diversity & Distributions </em>DOI:10.1111/ddi.12755</p> <p> </p>
RAKSILA 3D. Laser scanning survey of the street fronts and green areas in Raksila, Oulu (FINLAND)
<p>The video shows the preliminary results of the laser scanner survey of Raksila district in Oulu, Finland. Raksila is an important historical trace in the development of the urban planning of the city of Oulu. The district of Raksila is mainly a well-preserved residential Neighborhood characterized by a strong typicality.The general plan consists of a regular structure and a system of street fronts on the road are ordered and in an homogeneous profile. Despite this, Raksila still has no detailed and updated guidelines capable of managing all different types of interventions allowed (renovation, restoration, repair actions, possible modifications). For this reason, a laser scanner survey and detailed documentation have been created, through which all the elements and characteristics of the place have been defined and collected in sort of atlas and inventory reports. This new documentation is going to constitute the base for the definition of new guidelines, a practical support and analysis for future interventions that can be carried out in total respect of this heritage. This topic is inserted as case study for developing the Research Project n. 746215 entitled "Preserving Wooden Heritage". The project is financed by the European Commission with an Individual Marie S. Curie Fellowship assigned to PostDoctoral Researcher Sara Porzilli, who is working at the University of Oulu - Finland.</p>
Dual energy CT scan of ordinary objects
<p>Dual energy CT scan of ordinary objects: wires, pen, fruits (orange, avocado), pastery, bacon, butter, cheese.</p> <p>The purpose of these scans is to enable experimenting with CT scans using various kernels and iterative reconstructions. For instance, studying metal artifacts at different energies, material identification using dual energy index, examining the relation between reconstruction kernel sharpness, iterative reconstruction strength and noise.</p> <p>The dataset also can be used to set up mock trials, e.g. where readers have to choose the sharpest image, or the one with least disturbing metal artifacts. Similarly, it could serve debug purposes, e.g. testing the workflow, DICOM readers, etc.</p> <p>Zenodo-get ( https://doi.org/10.5281/zenodo.1261812 ) could be used to download the whole record at once.</p>
LIDAR scan, photos and image-based reconstruction of sofa corner
<p>This data set provides photographs and a continuous LIDAR scan (using the Velodyne VLP-16) of a sofa corner at Simula Research Laboratory that were created for the BUISAR project funded by the researcher council of Norway (project number 270951). A 3D reconstruction of the sofa corner was created using Meshroom (http://github.com/alicevision/meshroom) with support from the LADIO project.</p> <p>The LIDAR scan takes the form of a network trace (.pcap file). This file can be read and interpreted as a point cloud by the Point Cloud Library (PCL, https://github.com/PointCloudLibrary/pcl). Since the VLP-16 comes without motion sensors, the user of the LIDAR scan can either interpret the data as a series of frames, or attempt to align the recorded voxels in a global space. No ground truth for this is provided.</p>
Electron Bessel beam diffraction patterns, line scan of Si/SiGe multilayer
<p>Electron diffraction patterns taken with a conical illumination (electron Bessel beams) and can be used to measure strain.</p> <p>The experimental diffraction patterns, in DM3 format, are included in the file experimental_data.7z while FEM strain simulations for the same sample are in the file reference_strain_experimental.csv</p> <p>Simulated diffraction patterns are included in the file simulated_patterns.7z while the strain in the model used is in the file reference_strain_simulated_patterns.csv</p> <p> </p> <p>The two python scripts attached allow the extraction of the strain, and rely on the code published at:</p> <p>https://bitbucket.org/lutosensis/tem-thesis/</p> <p> </p>
Dataset of Horizon scanning to identify invasion risk of ornamental plants marketed in Spain
<p>Full dataset for the research entitled "Horizon scanning to identify invasion risk of ornamental plants marketed in Spain". We classified non-native species into six different lists based on their invasion status in Spain and elsewhere, their climatic suitability in Spain, and their potential environmental and socioeconomic impacts.</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.