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10,553
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Dataset results
10,553 results for “measurements”
FT18 Kraus (1) 4-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT18 Kraus (1) 4-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT25 Savary Jeune (2) 13-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT25 Savary Jeune (2)13-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.</p>
FT40 Anonymous (11) 4-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT 40 Anonymous (11) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT13 Delusse 7-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT13 Delusse 7-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT36 Gautrot aîne 16-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT36 Gautrot aîne 16-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video.</p>
FT46 Anonymous (12) 5-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT46 Anonymous (12) 5-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT27 Savary Jeune (12) 16-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT27 Savary Jeune (12) 16-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT39 Scherer (5) 4-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT39 Scherer (5) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT41 Hirsbrunner (1) 12-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT41 Hirsbrunner (1) 12-key tenoroon containing detailed measurements, photos, and an endoscopic video. </p>
FT37 Marzoli TMB 15-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT37 Marzoli TMB 15-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT29 Scherer (2) 4-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT29 Scherer (2) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT30 Scherer (4) 5-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT30 Scherer 5-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT28 Scherer (1) 4-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT28 Scherer (1) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p>
DIY Particle Detector: reference measurement data
<p>This dataset is released along the data analysis source code of <a href="https://github.com/ozel/DIY_particle_detector">github.com/ozel/DIY_particle_detector</a> and <a href="https://doi.org/10.5281/zenodo.3361755">10.5281/zenodo.3361755</a>.</p> <p>Since the binary files are large, they are not part of the github archive (but stored via github's LFS feature for large files).</p> <p>The files are in python's pickle format, created using python version 3.6.5, pandas module version 0.24.1 and numpy module version 1.14.3. </p> <p>The measurements are taken with the diode-based detector design detailed in the repository above besides the following two files, which are recorded with iPadPix (<a href="https://doi.org/10.1088/1748-0221/11/11/C11032">https://doi.org/10.1088/1748-0221/11/11/C11032</a>) for comparison:<br> 3hoursRadonBalloon_2019-02-10_14-43-21___2321___2-56.pkl<br> KCL_block_bare_2019-02-11_20-54-41___1083___1-03.pkl</p> <p> </p> <p> </p> <p> </p>
Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients (DATASET)
<p>see README.txt for descriptions of files and formats<br> </p>
FT24 Savary Pére 5-key tenoroon: measurements, photos, endoscopic video
<p>Data set of FT24 Savary Père 5-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
Laser triangulation measurement for AFP monitoring
<p>This data set was acquired in the context of EU project ZAero. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 721362. Project duration: 2016/10/01 - 2019/09/30. This data set contains a HDF5 file with data used for evaluation in a SAMPE 2019 conference paper [1].</p> <p>The main data in this package is contained in a HDF5 file: zaeroFTD1.h5. There are two entries in this file:<br> /raw: contains the raw laser range data as acuqired during AFP lay-up<br> /seg: contains the manually defined segmentation that corresponds to the laser range data</p> <p>The data was acquired for preliminary test runs for AFP monitoring in the ZAero project. It contains different regions that correspond to the following labels (as defined in /seg):<br> 1 ... gap<br> 2 ... regular tow<br> 3 ... overlap<br> 4 ... fuzzball</p> <p>This data is mainly intended for testing of algorithms that perform defect detection on laser range images of AFP data.</p> <p>For more information about the HDF5 format, please visit the HDF5 Group website:<br> https://www.hdfgroup.org/solutions/hdf5/</p> <p>An example for loading and visualizing the data in Python comes with this data set:<br> readDataExample.py</p> <p>=====================================<br> REFERENCES<br> =====================================</p> <p>[1]<br> @inproceedings{Zambal2019_SAMPE}<br> author = {Sebastian Zambal and Christoph Heindl and Christian Eitzinger},<br> title = {Machine Learning for CFRP Quality Control},<br> booktitle = {Conference of the Society for the Advancement of Material and Process Engineering (SAMPE), Nantes, France},<br> year = {2019}<br> }</p> <p>[2]<br> @inproceedings{Zambal2019_QCAV,<br> author = {Sebastian Zambal and Christoph Heindl and Christian Eitzinger and Josef Scharinger},<br> title = {End-to-End Defect Detection in Automated Fiber Placement Based on Artifcially Generated Data},<br> booktitle = {Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111721G},<br> doi = {DOI: 10.1117/12.2521739},<br> year = {2019}<br> }</p> <p> </p>
Measured BSDF and data-driven model of a laser cut panel (LCP001)
<p>Model generated from the measured Bidirectional Scattering Distribution Function (BSDF) of a LCP. The RADIANCE tool-chain pabopto2bsdf, bsdf2ttree was employed. Initial tensor resolution set to 128x128 incident, and equal number of outgoing, scattered directions (corresponding to approx. 1.4 degree). Subsequent adaptive data reduction by approx. 98%. Photometric BSDF.</p> <p>The measured data is included as Differential Scattering Function (DSF): DSF = BSDF x cos(theta_s), where theta_s is the off-normal angle of the outgoing, scattered direction of light (the first column in the measured data). The data was measured on a scanning gonio-photometer (pab advanced technologies pgII) at the optical laboratory of CC Building Envelopes. A halogen lamp was employed, focud on the detector plane for maximum resolution, with a hot-mirror installed in the illuminator to block near infrared emission. The Si photocell of the detector was equipped with a weighing filter to match photometric response v(lambda). The profiles through the unobstructed beam are given for the phi=0°,180°, and phi=90°,270° planes.</p> <p>The angular coordinates theta=90, phi=0 correspond to the intended up direction when installed vertically, e.g. in a window.</p> <p>When publishing any work making use of this data-set, please reference either this data-set, including its Digital Object Identifier (DOI:<a href="https://doi.org/10.5281/zenodo.3375294">10.5281/zenodo.3375294</a>), or (preferred) by this article which describes sample, model and its exemplary application:</p> <p>Lars Oliver Grobe. Photon mapping in image-based visual comfort assessments with BSDF models of high resolution. Journal of Building Performance Simulation. DOI:10.1080/19401493.2019.1653994</p>
Energy measurements for analyzing medical-implant-battery lifetime
<p>The dataset comprises of energy-consumption measurements pertaining to a modern ultra-low-power MCU (EFM32TG11). These numbers were used to evaluate the impact of using certain cryptographic primitives on the battery lifetime of implantable medical devices.</p> <p>Further details can be found in the relevant publication:</p> <p>Muhammad Ali Siddiqi and Christos Strydis. 2019. IMD security vs. energy: are we tilting at windmills?: POSTER. In <em>Proceedings of the 16th ACM International Conference on Computing Frontiers</em> (CF '19). ACM, New York, NY, USA, 283-285. DOI: https://doi.org/10.1145/3310273.3323421</p>
Snow Albedo Measurements in Mountainous Regions Using a Dual-sensor Unmanned Aerial Vehicle (UAV)
<p>We used a commercially available UAV (drone) to measure the albedo of the Earth in snowy, mountainous environments. These data represent four initial flights conducted during the spring of 2019 in SW Montana, USA. These UAV-based measurements of albedo allow us to measure a larger and more varied area than do measurements from a stationary tower. </p>
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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)
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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.