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.
39
datasets available to search
ShareScore release 0.9.0
Dataset results
39 results for “SfM”
Malthi2015_SfM_RawData
<p>Malthi provides a nearly unique example of a fully excavated Middle Hellenic (MH) settlement. The excavated remains include a series of houses, storage facilities, entrance ways, and possible public architecture, enclosed by a settlement wall. Malthi is perhaps the first MH site at which a major restructuring of the settlement architecture was observed, as proposed by Valmin. The PIs research focuses on the socio-cultural motivations for the decision to make substantial revisions to the urban layout, as well as impacts on the life of the settlement and the surrounding area. PIs Rebecca Worsham, Prof. Donald Haggis, and Prof. Michael Lindblom collaborated with SPARC researchers to produce an accurate and analysis-ready plan of the exposed standing remains and a digital elevation model (DEM) of the settlement at Malthi, using a combination of scanning and structure from motion techniques. </p> <p>This work was embedded in a larger project, and aimed to reconsider the architecture of the site including prior identifications of room types (by Valmin), and to attempt to identify coherent buildings. This improved survey, mapping and interpretation exercise was intended to support a rethinking about the settlement’s organization, and re-organization, as a whole. The production of a local DEM of the site was intended to aid in a consideration of access routes and forms a starting point for establishing the role of Malthi in the larger Soulima Valley network, which may function as a major corridor.</p> <p>This upload contains the Structure from Motion (SfM) raw data created during the Malthi project in 2015. See the Index file for a list of files and folders.</p>
Application of Close-Range SfM Photogrammetry on three bases of Etruscan Bronze Candelabra from Spina Necropolises
<p>Application of Close-Range SfM Photogrammetry on three bases of Etruscan Bronze Candelabra from Spina Necropolises. </p> <p>This dataset contains the photogrammetry made 3D models of three Etruscan Bronze Candelabra found at Spina, dated to the 4th cent. BC, kept in the Archaeological Museum of Ferrara.</p> <p>The models were made through Agisoft Metashape from previously taken RAW photos, at the National Archaeological Museum of Ferrara, for master thesis purposes. Some minor issues of the meshes were solved using Blender before texturing the models.</p> <p>The folders are divided according to the tombs of provenance of the candelabra (T. 545, T. 1122, T. Unknown) and every folder contain:</p> <ul> <li>The OBJ</li> <li>The texture</li> <li>An additional folder with the reports generated from Agisoft Metashape</li> </ul>
Rambla Honda (Alicante, Spain) 3DPC TLS and SfM
<p>3D point clouds of a old limestones quarry in Rambla Honda, Alicante (Spain). It was scanned using a Leica C10 Scanstation and a RPAS to apply the SfM technique. </p> <p>The datasets were used for this work:</p> <p>https://www.sciencedirect.com/science/article/pii/S001379522200103X#!</p> <p>There are two files:</p> <ol> <li>210205 Rambla Honda 2-8 Georreferenciado.las --> TLS file</li> <li>210205 Rambla Honda SfM.las --> SfM file</li> </ol>
A principal components (PCs) dataset of the leaf and canopy levels used for SIF retrieval in SFM-PCA approach
<p><span>A principal components (PCs) dataset (640–850 nm) generated using a principal component analysis approach to reconstruct the shape of the reflectance spectrum for the leaf and canopy levels. For the leaf level, a novel SIF-free leaf spectra dataset (n = 849, species = 95) collected at three sites in Beijing, China during June and August 2023, was used. For the canopy level, a total of 19,380 SIF-free spectra generated using a SCOPE model based on the measured leaf reflectance and transmittance was employed.</span></p>
Dataset: Sprouts Farmers Market, Inc. (SFM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
WASHTREET. Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.
<p><strong>WASHTREET</strong><strong> - </strong><strong>Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.</strong></p> <p>This dataset contains raw data and surface elevations results from the application of the Structure from Motion (SfM) photogrammetric technique in a 36 m<sup>2</sup> full-scale urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). This work is part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The accurately measurement of the surface elevations is needed for a proper representation of surface flow, which is key in the detachment and transport of solids in the model surface. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>)</p> <p>A detailed description of experimental procedure and data collected can be consulted in ‘<em>1_ExperimentalProcedure.pdf’</em>. Raw images taken as input for the SfM software are included in ‘<em>2_RawImages.zip’</em>. Then, the point cloud resulted is provided in ‘<em>3_SFM_RawPointCloud.ply</em>’. This point cloud was processed and the final elevation map with a resolution of 5 mm is included in ‘<em>4_SfM_ElevationMap(m).xyz</em>’.</p> <p>Further details of the physical model and hydraulic and sediment transport experiments can be consulted in the dataset <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET - Hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques are provided in the dataset <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13.<a href="http://doi.org/10.1038/s41597-020-0384-z"> https://doi.org/10.1038/s41597-020-0384-z</a></li> </ul>
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </p>
SfM-MVS derived orthomosaics of the Otemma glacier forefield (2020)
<p><strong>Orthomosaics (2020) of the Otemma glacier forefield derived from SfM-MVS photogrammetry</strong></p> <p>This dataset includes the orthomosaics of the Otemma glacier forefield generated through SfM-MVS photogrammetry. This dataset is based upon the images collected during summer 2020. Details on the image acquisition and image processing can be found in Roncoroni et al. (2022) at this URL: https://doi.org/10.1080/01431161.2022.2079963</p> <p>Details:</p> <ul> <li>Format: .tif </li> <li>Name format: mmddyyyy<em>x</em>m_Orthomosaic (where mm is the month, dd the day, yyyy the year, and xm is AM or PM)</li> <li>Coordinate system: CH1903+ LV95 (EPSG:2056)</li> <li>Spatial resolution: 0.05 m</li> </ul> <p> </p>
Buddha - Draft Meshing from SfM
This model is the result of draft meshing directly from Structure-from-Motion output. It has been cleaned up in Meshlab (bounding box cut-out) and retextured in Meshroom. **Details** - Meshroom 2019.1.0 - Input: 220 images - Camera: Sony A7S II Source: Objaverse 1.0 / Sketchfab
Nämforsen Brådön E2-3. Ådalsliden 193 SfM-Detalj
Nämforsen, Brådön, Hallström_E2-3, Ådals-Liden 193, Ångermanland. SfM-detalj. For more information see www.namforsen.com and www.shfa.se Source: Objaverse 1.0 / Sketchfab
Tombstone 2 - SfM To Sketchfab Export
Tombstone of Thomas Robert Egleston (1826-1896) from Historic Oakland Cemetery, Atlanta GA Source: Objaverse 1.0 / Sketchfab
Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs
<p>Digital oblique photos of an approximately 2.0 km alongshore reach of seaward-facing coastal bluff faces on the Strait of Juan de Fuca, Washington State, were collected at least quarterly between 2016 and 2022. In total over 3900 photos were collected over 38 separate surveys. The photos were collected to support digital surface reconstructions using Structure-from-Motion (SfM) photogrammetry, and specifically to assess the quality of surfaces generated with photos collected using relatively simple techniques that could be accessible to community science programs. A secondary goal was to use the photos, and the digital surfaces generated with them, to evaluate patterns and rates of erosion on the bluff face. </p>
SFM Büste Gotthelf
<p><strong>3D Modell der Büste von Jeremias Gotthelf im Daheimpark Zug (Schweiz)</strong></p> <p>Büste von Jeremias Gotthelf auf einer Stele (1907)</p> <p>Material: Muschelkalk Masse: ca. 66 x 39 x 32 cm</p> <p>Standort: Daheimpark, Zug (Schweiz) Künstler: Johann Michael Bossard (1874-1950)</p> <p>Die Büste ist mit drei weiteren Heldenfiguren der Schweiz gruppiert.</p> <p>Weitere Informationen im Dokument "Bericht_Gotthelf".</p> <p> </p> <p>Inhalt:</p> <p><em><strong>Bericht_Gotthelf.pdf </strong></em>Bericht mit Vorgehensweise und Koordinaten der Targets</p> <p><em><strong>Fotos_Gotthelf.rar </strong></em>Alle Fotos der Statue (JPG-Format)</p> <p><em><strong>Gotthelf_Modell.rar </strong></em>3D-Modell der Statue mit Struktur (OBJ/MTL/JPG-Datei)</p> <p><em><strong>Mesh_Gotthelf.obj </strong></em>Mesh ohne Struktur (OBJ-Datei)</p> <p><em><strong>Report_Gotthelf.pdf </strong></em>Automatisch generierter Bericht von Agisoft Metashape</p>
141128 3DPC Talud Embalse el Atazar SfM HQ
<p>3DPC of a rocky slope in the El Atazar Dam, Madrid (Spain) in 28th, November 2014</p> <p>Generated via SfM and the photos were captured using a Nikon Coolprix</p>
41VV0218 LV2 BISONMANDIBLES SFM FINAL
This SfM model is part of a before-and-after glimpse into archaeology and the processs of excavation. This mass of bone is a grouping of bison antiquis (extinct bison) bones and mandibles (lower jaw). The large boulder in the model might have been used by humans to break open the bison longbones for marrow extraction. The SfM model following this one shows the excavation starting from the sediment deposit above, down to this level. Source: Objaverse 1.0 / Sketchfab
SFM Statue
Structure from motion, digital imaging module, University of York, masonry, context needed Source: Objaverse 1.0 / Sketchfab
Coastal bluff point clouds derived from SfM near Elwha River mouth, Washington from 2016-04-18 to 2020-05-08
<p>Point Clouds of an approximately 2.0 km alongshore reach of seaward-facing coastal bluff faces on the Strait of Juan de Fuca, Washington State, were derived using structure-from-motion (SfM) photogrammetry from digital photos collected at least quarterly between 2016 and 2022. The point clouds were derived to assess spatial and temporal patterns of erosion on the bluff face and deposition at the base of the bluff. Photos from Miller, et al. (2022) were aligned using a modified USGS published workflow (Over, et al., 2022) with Agisoft Metashape Professional 1.8.5. Photos were aligned within a single chunk in a 4D approach described by Wernette, et al. (2022), and the sparse point cloud was filtered by reconstruction uncertainty (Ru) and projection accuracy (Pa). Dense point clouds were generated independently for each survey date by disabling all cameras except for a single photo date and then generating the dense cloud. This was repeated for each of the 30 photo survey dates, resulting in 30 dense point clouds (one point cloud per photo survey date).</p>
Dataset of images SfM - FRM
<p>This collection of images was employed to examine the impact of various configurations in the 3D modeling process for short-distance environments. Through this analysis, settings were established to achieve submillimeter accuracy in the RMSE values of the analyzed points. The configurations evaluated included camera calibration, overlap percentage, different scale bar arrangements, and the use of both vertical and oblique images.</p>
Improving UAV-SfM photogrammetry for modeling high-relief terrain image collection strategies and ground control quantity
<p>This is data for the paper "Improving UAV-SfM photogrammetry for modeling high-relief terrain image collection strategies and ground control quantity". (<strong>DOI: </strong><a href="https://doi.org/10.1002/esp.5665">https://doi.org/10.1002/esp.5665</a>)</p> <p>This data is openly available, provided the original work is properly cited. The unzip Password can be found on the original paper in ESPL.</p>
Nämforsen, Brådön B4, Ådals-Liden 193. SFM
Nämforsen, Brådön, Hallström_B4, Ådals-Liden 193, Ångermanland. SFM. For more information see www.namforsen.com and www.shfa.se Source: Objaverse 1.0 / Sketchfab
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.