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1,961 results for “Sensing”
Fig. 4 in The sixth sense in mammalian forerunners: Variability of the parietal foramen and the evolution of the pineal eye in South African Permo-Triassic eutheriodont therapsids
Fig. 4. Skulls of Dicynodontia illustrating the great variability of the shape and size of parietal foramen in Therapsida. A. Large and circular parietal foramen, with a pineal boss in Rachiocephalus, RC 95, Derdedrif, Adendorp, South Africa, Cistecephalus–Tropidostoma AZ, 260–255 Ma. B. Slit-like parietal foramen in Dinanomodon, RC09, Stylkrans, South Africa, Cistecephalus AZ, 250–255 Ma. C. Absent foramen in Cistecephalus, BPI/1/506, Towerwater, Murraysburg, South Africa, Cistecephalus AZ, 250–255 Ma.
Fig. 5 in The sixth sense in mammalian forerunners: Variability of the parietal foramen and the evolution of the pineal eye in South African Permo-Triassic eutheriodont therapsids
Fig. 5. CT-sections through the sagittal crest in Cynodontia (A–D) and Therocephalia (E–H) illustrating the variability of the parietal foramina. A. Cynosaurus suppostus Schmidt, 1927, BPI/1/1563 (voxel size: 0.0291 mm); Ringsfontein, Murraysburg, South Africa, Daptocephalus AZ, 255–251 Ma. B. Cynosaurus suppostus Schmidt, 1927, BPI/1/3926 (voxel size: 0.0708 mm); Tweefontein, Nieu Bethesda, South Africa, Daptocephalus AZ, 255–251 Ma. C, D. Cynognathia. C. Diademodon tetragonus Seeley, 1895, BPI/1/3776a (voxel size: 0.0801 mm); Cragievar, Burgersdorp, South Africa, Cynognathus AZ, 245–237 Ma. D. Trirachodon berryi Seeley, 1895, AM461 (voxel size: 0.0668 mm); Burgersdorp, South Africa, Cynognathus AZ, 245–237 Ma. E, F. Whaitsiidae, Theriognathus microps Owen, 1876. E. BPI/1/512 (voxel size: 0.0801 mm); Suurplaas, Graaf-Reinet, South Africa, Daptocephalus AZ, 255–251 Ma. F. BPI/1/100 (voxel size: 0.0756 mm); Vlakteplaas, Graaf-Reinet, South Africa, Daptocephalus AZ, 255–251 Ma. G, H. Baurioidea. G. Tetracynodon darti Sigogneau 1963, NMQR3756 (voxel size: 0.0445 mm); Fairydale, South Africa, Lystrosaurus AZ, 251–245 Ma. H. Bauria cynops Broom 1909, BPI/1/3770 (voxel size: 0.0728 mm); Cragievar, Burgersdorp, South Africa, Cynognathus AZ, 245–237 Ma.
Fig. 2 in The sixth sense in mammalian forerunners: Variability of the parietal foramen and the evolution of the pineal eye in South African Permo-Triassic eutheriodont therapsids
Fig. 2. Evolution of the frequency of the presence of the parietal foramen ( Fq), and average (Av) and median (Med) size of the parietal foramen across the phylogeny of Therapsida. Number of specimens examined (n) is indicated for each group. Asterisks indicate the branches of the tree where a relaxation of constraints resulting from a functionless third eye is hypothesized (zone of variability). See material and methods section for the more details about the phylogenetic tree.
Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries
<p><strong>Abstract</strong> (our paper)</p> <p>WordNet is one of the largest handcrafted concept dictionaries visualizing word connections through semantic relationships. It is widely used as a word sense inventory in natural language processing tasks. However, WordNet's fine-grained senses have been criticized for limiting its usability. In this paper, we semantically match sense definitions from Cambridge dictionaries and WordNet and develop new coarse-grained sense inventories. We verify the effectiveness of our inventories by comparing their semantic coherences with that of Coarse Sense Inventory. The advantages of the proposed inventories include their low dependency on large-scale resources, better aggregation of closely related senses, CEFR-level assignments, and ease of expansion and improvement. Our inventories are publicly available for free use.</p> <p><strong>Publication</strong></p> <p>These datasets are part of our research results. If you make use of our datasets, please cite:</p> <ul> <li>Masato Kikuchi, Masatsugu Ono, Toshioki Soga, Tetsu Tanabe, Tadachika Ozono. Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries. In <em>Proceedings of the 11th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA 2024)</em>. 6 pages, 2024.</li> </ul>
A dataset on "Coating of self-sensing AFM cantilevers with boron-doped nanocrystalline diamond films at low temperatures"
<p>The data set to paper: </p> <p>Coating of self-sensing AFM cantilevers with boron-doped nanocrystalline diamond at low temperatures</p> <p>Štěpán Potocký1*, Jaroslav Kuliče1k, Egor Ukraintsev1, Ondřej Novotný2, Alexander Kromka3, and Bohuslav Rezek1</p> <p>1 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 16627 Prague, Czech Republic<br>2 NenoVision s.r.o., Purkyňova 649, 61200 Brno, Czech Republic <br>3 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic<br>*corresponding author: potocky@fel.cvut.cz</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 10. 2023 - 31. 3. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Figure 1: pdf<br>Figure 2: pdf<br>Figure 3: pdf, csv<br>Figure 4: pdf, csv, gwy<br>Figure 5: pdf, gwy<br>Figure S1: pdf<br>Figure S2: pdf</p> <p>The comma separated values file (csv) always contain the description of the columns in the first row. Gwy correspond to free Gwyddion SPM data analysis software (gwyddion.net). In case of composed image the name of the file corresponds to the corresponding figure.</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI:10.1002/pssa.202400553.</p>
DVB-T Spectrum Sensing for Federated Learning
<p>These files contain measurement results achieved in the following scenario:<br>Rohde&Schwarz SMBV100A signal generator transmits OFDM signal (center frequency 2000.0 MHz) with different amplifier gain.<br>A single PC with GNU Radio and connected USRP receives a signal with a central frequency of 2000 MHz (filtered 8 MHz bandwidth). <br><br>Received is set in six different positions (location of sensors presented on the figure "position.png") - creating 6 files - and collects data to detect the presence of a transmitted signal. Data that can be found in the files is stored in CSV format to be easily analyzed in ML models. </p> <p>Data collected during this experiment contains:<br>- column with average received power in the analyzed channel (in mW)<br>- column with autocorrelation value<br>- column with decision metric (calculated as maximum eigenvalue divided by minimum eigenvalue - look below)<br>- 8 columns with eigenvalues of the Pearson correlation matrix (8 subsequent vectors of 32768 samples)<br>- column with information about signal transmission (label; 0 - noise, 1 - signal transmitted)</p>
Dataset for the manuscript "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data"
<p>The dataset contains cryoseismological data recorded in July 2020 on the Rhonegletscher, Switzerland, collected using both Distributed Acoustic Sensing and seismometers.<br>This dataset provides the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.</p> <p> </p> <p>Abstract: </p> <p>One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing (DAS), we can observe seismicity over multiple kilometers. DAS systems turn common fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiberoptic cable that covered the entire glacier, from its accumulation to its ablation zone, recording seismicity for one month. The highly active and dynamic cryospheric environ ment, in combination with poor coupling, resulted in DAS data characterized by a low Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to ef fectively denoise this dataset.<br>We use a self-supervised J -invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g., stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.</p>
Linked collectors and determiners for: Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense- 22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae).
Natural history specimen data linked to collectors and determiners held within, "Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense- 22. Genus Cheilonycha Lacordaire, 1842 (Coleoptera: Cicindelidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/14dfc815-148a-4ed7-a4c4-34af7019a1eb">https://bionomia.net/dataset/14dfc815-148a-4ed7-a4c4-34af7019a1eb</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/14dfc815-148a-4ed7-a4c4-34af7019a1eb">https://gbif.org/dataset/14dfc815-148a-4ed7-a4c4-34af7019a1eb</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense — 18. Six Mexican and Central American species related to Odontocheila mexicana Laporte de Castelnau and O. ignita Chaudoir, with a description of O. potosiana sp. nov..
Natural history specimen data linked to collectors and determiners held within, "Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense — 18. Six Mexican and Central American species related to Odontocheila mexicana Laporte de Castelnau and O. ignita Chaudoir, with a description of O. potosiana sp. nov.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c2de422a-9725-4ee7-855e-3b8f9faf234a">https://bionomia.net/dataset/c2de422a-9725-4ee7-855e-3b8f9faf234a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c2de422a-9725-4ee7-855e-3b8f9faf234a">https://gbif.org/dataset/c2de422a-9725-4ee7-855e-3b8f9faf234a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense- 19. Odontocheila microptera nom. nov., a new replacement name for O. euryoides W. Horn, 1922, and lectotype designation of O. nitidicollis (Dejean, 1825) (Coleoptera: Cicindelidae).
Natural history specimen data linked to collectors and determiners held within, "Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense- 19. Odontocheila microptera nom. nov., a new replacement name for O. euryoides W. Horn, 1922, and lectotype designation of O. nitidicollis (Dejean, 1825) (Coleoptera: Cicindelidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/32cfba27-8d59-4094-baa1-cd729e17eff9">https://bionomia.net/dataset/32cfba27-8d59-4094-baa1-cd729e17eff9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/32cfba27-8d59-4094-baa1-cd729e17eff9">https://gbif.org/dataset/32cfba27-8d59-4094-baa1-cd729e17eff9</a>. Formatted as a Frictionless Data package.
Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis
<p>We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available.</p> <p>Please cite: </p> <p>https://pubmed.ncbi.nlm.nih.gov/24521231/ </p> <p>https://pubs.acs.org/doi/10.1021/ci400709d</p>
Dataset used in "Ocean floor imaging with Distributed Acoustic Sensing and water phases reverberations" by Spica et al. in Geophysical Research Letters
<p>earthquake #1<br> earthquake #2</p>
Dataset of "Knowledge-based Sense Disambiguation of Multiword Expressions in Requirements Documents"
<p>This is the dataset used in the paper "Knowledge-based Sense Disambiguation of Multiword Expressions in Requirements Documents" at AIRE'21</p> <p> </p> <p>In this paper, we explore the use of a multiword expression detection in combination with a knowledge-based word sense disambiguation to disambiguate expressions in requirements documents.</p> <p>The dataset comprises a gold standard for multiword expression detection and sense disambiguation for Wikipedia and WordNet 3.1.</p> <p>It covers 18 projects: CM1, EBT and GANTT as well as the 15 projects of the NFR dataset.</p> <p> </p> <p><strong>File format</strong></p> <p>We use a tab-separated version of the DiMSUM file format and extended it with sense information.</p> <p>The nine original DiMSUM tab-separated columns:</p> <p>1. token offset</p> <p>2. word</p> <p>3. lowercase lemma</p> <p>4. POS</p> <p>5. MWE tag</p> <p>6. offset of parent token (i.e. previous token in the same MWE), if applicable</p> <p>7. strength level encoded in the tag, if applicable. Currently not used</p> <p>8. supersense label, Currently not used</p> <p>9. sentence ID</p> <p> </p> <p>and the two further columns for sense information:</p> <p>10. Wikipedia article name</p> <p>11. WordNet 3.1 synset</p> <p> </p> <p>The last two columns might end with .1 or .0 indicating that the sense is a fully applicable or partial sense of a multiword expression.</p> <p><strong>Attribution (of datasets used)</strong></p> <p>The NFR Dataset can be attributed to Jane Cleland-Huang.<br> Jane Cleland-Huang, Sepideh Mazrouee, Huang Liguo, & Dan Port. (2007). nfr [Data set]. Zenodo. Available: <a href="http://doi.org/10.5281/zenodo.268542">http://doi.org/10.5281/zenodo.268542</a><br> </p> <p>The CM1, EBT and GANTT datasets were retrieved from the Center of Excellence for Software & Systems Traceability (CoEST) <a href="https://doi.org/10.5281/zenodo.3309669">http://coest.org/</a></p>
Dataset for "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets"
<p>This dataset is associated with a research article entitled "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets".</p>
Dataset - Impact of 3D radiative transfer on airborne NO2 imaging remote sensing over cities with buildings
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2021) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.md </strong></em>text file.</p> <p>The dataset contains:</p> <p>- libRadtran output (radiances and AMFs)</p> <p>- Synthetic SCDs</p>
A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App
<p>Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).</p>
Dataset related to "Atomic Force Microscope with an Adjustable Probe Direction and Integrated Sensing and Actuation"
<p>These original measurement data relate to the publication: J. Schaude, T. Hausotte: Atomic Force Microscope with an Adjustable Probe Direction and Integrated Sensing and Actuation, Nanomanufacturing and Metrology 5, pp. 519-148, <a href="https://doi.org/10.1007/s41871-022-00143-9">https://doi.org/10.1007/s41871-022-00143-9</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p> <p>All data are in ASCII-format. Each file contains six columns, where column one to three are the <em>x</em>, <em>y</em>, and <em>z</em>-coordinates of the positioning system, column four is the demodulated signal of the AFM (<em>R</em><sub>AFM</sub>) and columns five and six are the raw signals of the <em>z</em>-interferometer (<em>Q</em><sub>A</sub> and <em>Q</em><sub>B</sub>).</p> <p><strong>Content of the folders</strong></p> <p>10_Calibrations: Repeated calibration of the AFM against the <em>z</em>-interferometer of the NMM-1.</p> <p>20_Standstill-Measurements: Three standstill measurements for 90 s each.</p> <p>30_Long-Term-Precision: Standstill measurements for 15 s every 10 min for in sum 18 hours.</p> <p>40_Scans: Repeated closed-loop scans on a calibration grating.</p> <p>50_SINCOS: Repeated movement of the stage in z-direction for 2 µm with the cantilever being in free air just before the sample.</p> <p><strong>Acknowledgement</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) –TRR 285 -Project-ID 418701707, subproject C05</p>
Contractes públics adjudicats sense procediments de publicitat dins la Comunidad de Madrid
<p>Dataset sobre els contractes públics adjudicats sense procediments de publicitat dins la Comunidad de Madrid. </p>
Ground-Based Remote Sensing Observations at Marquette, Michigan
<p>This dataset contains observations used in "Multi-year analysis of rain-snow levels at Marquette, Michigan," Shates, Pettersen, L'Ecuyer, and Kulie, submitted to Journal of Geophysical Research - Atmospheres, in revision.</p> <p> </p> <p>The dataset includes daily files of Micro Rain Radar2 (MRR) and Precipitation Imaging Package (PIP) observations. The MRR and PIP are both hosted at the National Weather Service office in Marquette, MI (Pettersen, Kulie, et al., 2020; Kulie et al., 2021). The MRR is a 24 GHz vertically profiling radar and observations have been post-processed using Maahn and Kollias (2012). Key variables include radar reflectivity, Doppler velocity and spectral width. The PIP is a custom video disdrometer that records shadows of hydrometeors to obtain key microphysical variables that include particle size distributions and vertical velocity distributions (Pettersen, Bliven, et al., 2020). Additional processing provides precipitation rates in liquid water equivalent and the effective density (Pettersen et al., 2021). </p> <p>The files are separated into daily MRR files, daily PIP Particle Size Distribution (PSD) files, daily PIP Vertical Velocity Distribution (VVD) files, and daily PIP precipitation rate (rain and non-rain) and effective density (edensity) files. The PSD and VVD files contain one-minute resolution particle counts and fall speeds, respectively, for particle diameter bins.</p>
SNAPPING PSI surface motion measurements over selected sites presented in MDPI Remote Sensing paper "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping"
<p>SNAPPING PSI surface motion measurements over selected sites as presented in the paper with the title "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping" by Michael Foumelis, Jose Manuel Delgado Blasco, Fabrice Brito, Fabrizio Pacini, Elena Papageorgiou, Panteha Pishehvar and Philippe Bally on Remote Sensing Open Access Journal.</p> <p>Whenever using this dataset, please cite its original paper (<a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a>) and include the reference to this dataset (<a href="https://doi.org/10.5281/zenodo.7369653">https://doi.org/10.5281/zenodo.7369653</a>).</p> <p>This dataset includes average Line-of-Sight velocities for the following sites and dates:</p> <table> <tbody> <tr> <td><strong>Site name</strong></td> <td><strong>Country</strong></td> <td><strong>Period</strong></td> <td><strong>Relative orbit</strong></td> <td><strong>Orbit direction</strong></td> </tr> <tr> <td>Cap-Haïtien</td> <td>Haiti</td> <td>Jan-2017 / Dec-2019</td> <td>106</td> <td>ascending</td> </tr> <tr> <td>Gran Renaissance Ethiopian Dam</td> <td>Ethiopia</td> <td>Jan-2019 / Jun-2021</td> <td>50</td> <td>descending</td> </tr> <tr> <td>La Palma Volcano</td> <td>Spain</td> <td>Jun-2019 / Dec-2021</td> <td>169</td> <td>descending</td> </tr> <tr> <td>Santorini Volcano</td> <td>Greece</td> <td>Apr-2015 / May-2021</td> <td>29</td> <td>ascending</td> </tr> <tr> <td>San Francisco</td> <td>USA</td> <td>Jan-2016 / Dec-2020</td> <td>115</td> <td>descending</td> </tr> <tr> <td>Thessaloniki International Airport (SKG)</td> <td>Greece</td> <td>Apr-2015 / Dec-2020</td> <td>102</td> <td>ascending</td> </tr> </tbody> </table>
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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)
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.