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1,961 results for “Sensing”
FIGURES 10–22. Odontocheila nitidicollis Dejean. 10–12 in 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)
FIGURES 10–22. Odontocheila nitidicollis Dejean. 10–12—head: 10—♂, LT (MNHN); 11—♂, Paraguay, Estancia Terrado (CCJM); 12—♀, Venezuela, Canaima NP (CPVP); 13–18—labrum: 13——♂, LT (MNHN); 14—♂, Paraguay, Estancia Terrado (CCJM); 15—♂, Bolivia, Concepcion (CCJM); 16—♂, ex Dejean-Chaudoir, PLT (MNHN); 17—♀, Brazil, Barra do Carças (CMHP); 18—♀, Venezuela, Canaima NP (CPVP); 19—maxillary palpus, Paraguay, Estancia Terrado (CCJM); 20– 21—antennal scape: 20—♂, Paraguay, Estancia Terrado (CCJM); 21—♂, Bolivia, Concepcion (CCJM); 22—thoracic wing, ♀, ibid., CCJM. Bars = 1 mm.
FIGURES 37–48. Odontocheila microptera nom. nov. 37–40 in 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)
FIGURES 37–48. Odontocheila microptera nom. nov. 37–40—head: 37—♂, HT (SDEI); 38—♂, Uruguay, Tacuarembo (CCJM); 39—♂, ibid.; 40—♂, ditto, ventral view; 41—right mandible, ♀, ibid.; 42—maxillary palpus, ♀, ibid. (CCJM); 43— head part, ♀, Bahia (MFNB); 44–48—labrum: 44—♀, Bahia (MFNB); 45—♀, Uruguay, Tacuarembo (CCJM); 46—♀, Argentina, Estancia San Noria (BMNH); 47—♂, HT (SDEI); 48—♂, Uruguay, Tacuarembo (CCJM). Bars = 1 mm.
Supporting data of A versatile mass-sensing platform with tunable nonlinear self-excited microcantilevers
<p><span>Supporting data for publication "</span>A versatile mass-sensing platform with tunable nonlinear self-excited microcantilevers"</p>
Remotely sensed vegetation phenology drives large fires spreading in northwestern Europe
<p><span>In recent years, an increase in the frequency of large fires has been reported in NW Europe, a region where a deeper understanding of the conditions conducive to dangerous fire behavior is needed. This study builds on recent efforts to characterize rate of spread (ROS) variation in the region and delves into vegetation and climatic drivers. For 58 large fires in this region, we analyzed phenology (using the temporal variation of satellite-measured vegetation indices) and weather, (using as the Canadian Fire Weather Index System). Results suggest that short-term vegetation greenness play an important role in predicting ROS: High greenness correlated non-linearly with low ROS, and fires spreading in the growing season described a drastic reduction in spread. Fire weather did not prove to be a good indicator of fast spread. Contrary to expectations, high danger related to fire weather ratings were associated with low ROS. This indicates the importance of temporal variability of vegetation greenness, the capability of remote sensing in capturing times when an ignition could generate fast-spreading fires, and the need for a fire weather danger rating system tailored to regional conditions.</span></p> <p> </p> <p><span>This is the repository for the clusterd fire events and its isochrones, and ROS vectors.</span></p>
Remotely sensed temperature metrics data for Ningaloo Coast, Western Australia
<p>Here we made available four datasets of remotely sensed temperature metrics (daily SST, weekly SST, SSTA frequency, TSA DHW) from different NOAA and IMOS satellite products for the Ningaloo Coast (Western Australia) bounding box with coordinates [-23.5654,-21.66538], [113.4847,114.318], which can be used to assess thermal history and trends across coral reef areas within this World Heritage Area. </p> <ul> <li><a target="_blank" rel="noopener noreferrer">ningaloo_reef_crw_sst_1985-2022.nc:</a> Daily SST (1985-01-01 - 2022-12-29), NOAA Coral Reef Watch (CRW) Version 3.1 global 5 km daily nighttime SST (aka CoralTemp) </li> <li>ningaloo_reef_cortadv6_filled_sst_1985_2022.nc: Weekly SST (1985-12-31 - 2022-12-20), NOAA Coral Temperature Anomaly Database (CoRTAD) Version 6 global 4.6 km from weekly averaged daytime and nighttime SST;</li> <li>ningaloo_reef_cortadv6_ssta_freq_1985_2022.nc: Frequency of SSTA (1985-12-31 - 2022-12-20), NOAA Coral Temperature Anomaly Database (CoRTAD) Version 6 global 4.6 km from weekly averaged daytime and nighttime SST;</li> <li>ningaloo_reef_cortadv6_tsa_dhw_1985_2022.nc: TSA DHW (1985-12-31 - 2022-12-20), NOAA Coral Temperature Anomaly Database (CoRTAD) Version 6 global 4.6 km from weekly averaged daytime and nighttime SST;</li> <li>ningaloo_reef_imos_hinawari-8_L3C_sst_1_hour_2016-2022.nc: Hourly SST (2016-01-01 - 2022-12-13) , IMOS Hinawari-8 L3C 1 km 1 hour SST from 2016 to 2022</li> </ul> <p> </p> <p> </p>
NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)
<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>
Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images
<p>Dataset for Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images from https://github.com/WindVChen/Push-and-Pull-Network?tab=readme-ov-file</p>
Estimating carbon stock in unmanaged forests using field data and remote sensing
<p><span>The data used for the study "<strong>Estimating carbon stock in unmanaged forests using field data and remote sensing</strong>" by Thomas Leditznig et al. is published here.</span></p> <p><span><strong>Abbstract:</strong> </span><span>Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area <em>Dürrenstein-Lassingtal</em> in Austria. The entry-level consumer drone (DJI Mavic Mini) and free of charge Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) Machine Learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO<sub>2</sub> and applied to the ML-generated classification. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living Above-Ground Biomass (AGB) was estimated with an accuracy of -1.0% (± 5.9%). In conclusion, the proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.</span></p>
Land Subsidence in Pekalongan City Central Java from Remote Sensing Perspective
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Sense of "Love for Indonesian Products "Due to" Fear of Missing Out": A Study on the Role of Patriotism The Influence of Patriotism on Shoe Product Choice Ventela vs Converse on UPN "Veteran" Yogyakarta Students
<p>This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.</p>
Hyperspectral Remote-sensing reflectance and Chlorophyll-a concentration from Lakes in North Germany
<p><span>Hyperspectral Remote-sensing reflectance data collected by the </span><span>Leibniz Institute of Freshwater Ecology and Inland Fisheries </span><span>(</span><span>IGB</span><span>) within the</span><span> <span>Connectivity and synchronisation of lake ecosystems in space and time (CONNECT</span></span><span> project</span><span>)</span><span>. </span></p> <p><span>This data set is limited to records for </span><span>one field sampling campaign in 26.07.2019</span><span>. </span></p> <p><span>Field data collection and processing:</span></p> <p><span>Ogashawara, I.; Kiel, C.; Jechow, A.; Kohnert, K.;<span> </span>Ruhtz, T.; Grossart, H.-P.; Hölker, F.; Nejstgaard, J.C.; Berger, S.A.; Wollrab, S. The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany. Remote Sensing, v. 13, 1542, 2021. doi:10.3390/rs13081542 </span></p> <p><span>Data specification</span></p> <p><span>Station name – name of the station</span></p> <p><span>lat, lon - geographical latitude/longitude coordinates in decimal degrees</span></p> <p><span>chl-a</span><span> - </span><span>chlorophyll-a concentration in μg/L</span><span> </span></p> <p><span>Rrs</span><span> 380… Rrs 779 </span><span>- Remote-sensing reflectance (Rrs, units 1/sr). The sequential numbering (</span><span>380 - 779</span><span>) denote</span><span>s the</span><span> wavelength in nm. </span></p> <p> </p>
Remote Sensing DInSAR Method for Land Subsidence Detection in Semarang, Central Java, Indonesia
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Dephasing-tolerant quantum sensing of transverse magnetic fields with spin qudits. Open data set
<p>Data supporting Figs. 1, 2, 3, 4 of the related manuscript.</p>
Data used in the article "Remote sensing of a levitated superconductor with a flux-tunable microwave cavity"
<p>Data from the manuscript, a Python-based script to create PDF figures, and the resulting PDF files for convenience are provided.</p>
Real-time oceanic PWV sensing using BeiDou-3 PPP-B2b and low-cost GNSS devices
<div>This dataset consists of two parts, GNSSDATA and RESULT, which were utilized in the manuscript: "Real-time oceanic PWV sensing using BeiDou-3 PPP-B2b and low-cost GNSS devices."</div> <div><strong>GNSSDATA:</strong> This dataset contains shipborne BeiDou/GNSS data spanning four days, from 00:00 on September 21 to 00:00 on September 25, 2023. The data was collected using a low-cost GNSS receiver, the Septentrio mosaic-X5, and is stored in Septentrio Binary Format (SBF). The raw BeiDou/GNSS observations, PPP-B2b corrections (orbits/clocks/code bias), and broadcast ephemeris can be decoded from the SBF format using Septentrio RxTools.</div> <div><strong>RESULT:</strong> This dataset includes the PPP-B2b-derived zenith tropospheric delay (ZTD) solutions obtained from the above GNSS data, based on the five schemes designed in the manuscript.</div>
SpectralGPT: The first remote sensing foundation model customized for spectral data
<p>SpectralGPT is the first purpose-built foundation model designed explicitly for spectral RS data. It considers unique characteristics of spectral data, i.e., spatial-spectral coupling and spectral sequentiality, in the MAE framework with a simple yet effective 3D GPT network.</p> <p>We will gradually release the trained models (SpectralGPT, SpectralGPT+), the new benchmark dataset (SegMunich) for the downstream task of semantic segmentation, original code, and implementation instructions.</p>
Supplementary figures for the article 'Variability of remote sensing spectral indices in boreal lake basins'
<p><strong>Explanation of contents and notations:</strong></p> <p>AdivB Index correlations: correlations and p-values of the water quality parameters with the index family A/B, whole data and classification by basins and parameter values</p> <p>AmB Index correlations: correlations and p-values of the water quality parameters with the index family A-B, whole data and classification by basins and parameter values</p> <p>Band correlations: correlations and p-values of the water quality parameters with the band reflectances, whole data and classification by basins and parameter values</p> <p>Misc: Hiidenvesi map (basins and boat route), band wavelengths</p> <p>Mutual correlation of bands: correlations of bands with other bands, whole data and classification by basins</p> <p>Mutual correlation of parameters: correlations of water quality parameters with other water quality parameters, whole data and classification by basins</p> <p>Parameter profiles: parameter profiles of whole data, basins and parameter value classes (here e.g. Hist_30… means histogram with 30 bins)</p> <p>Scatter figures: scatter figures of various parameters vs indices</p> <p>Spectral reflectance signatures: spectral reflectance signatures of whole data, basins and parameter value classes (here e.g. Hist_30… means histogram with 30 bins)</p>
FIGURES 25–36 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-21. Pentacomia paranigrimarginata sp. nov. and P. nigrimarginata Huber with revised key to Pentacomia species (Coleoptera: Cicindelidae)
FIGURES 25–36. Illustrative figures for the key to Pentacomia species. 25–28—incomplete whitish elytral maculation with isolated subhumeral macula and lateromedian macula either separated from discal macula, or partly to entirely merging with it: 25—P. chrysamma, ♂, Ecuador, Macas, LT (BMNH); 26–27—P. chrysammoides, Bolivia, Rio Chimoré, PT (ASUT); 28—P. degandei, ♂, Paraguay, Cerro Cora (CCJM). 29–30—complete elytral maculation with humeral lunule, cranked lateromediandiscal band and anteapical-apical lunule almost or entirely reaching sutural spine: 29—P. egregia, ♀, Bolivia, Rio Beni (DBCN); 30—P. vallicola, Bolivia, La Paz, PT (DBCN). 31—with anteapical-apical lunule dilated along suture upwards (P. cupriventris, ♂ ex Chaudoir, MNHN). 32—with humeral lunule shortly cranked mesad (P sericina, ♂, "Brasilia", SDEI). 33–34—with cat- optric patches: 33—humeral lunule only partly obvious from above, discal catoptric path wide (P. speculifera, ♀, Bolivia, San Javier DBCN); 34—humeral lunule clearly obvious from above, discal catoptric path narrow (P. davidpearsoni, Bolivia, Camiri, HT, UASC). 35–36—antennal scape with apical and discal setae: 35—P. chrysammoides, Bolivia, Sapecho, PT (DBCN); 36—P. davidpearsoni, Bolivia, Rio Grande PT (DBCN). No scale.
FIGURES 11–24 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-21. Pentacomia paranigrimarginata sp. nov. and P. nigrimarginata Huber with revised key to Pentacomia species (Coleoptera: Cicindelidae)
FIGURES 11–24. Two species of Pentacomia. 11–12—P. paranigrimarginata sp. nov., Bolivia, Rio Piray, antennomeres 7th– 11th: 11—♂, HT (DBCN, later in UASC); 12—♀, AT (DBCN). 13–24—P. nigrimarginata Huber, Bolivia, Villa Tunari. 13–14— antennomeres 7th–11th: 13—♂, PT (CCJM); 14—♀, PT (CCJM); 15–16—habitus: 15—♂, PT (DBCN); 16—♂ (CCJM); 17– 18—labrum: 17—♂, PT (DBCN); 18—♀, PT (DBCN); 19—head, ♂, PT (DBCN); 20—aedeagus, ♂, PT (DBCN); 21—ditto, internal sac; 22—pronotum, ♂, PT (DBCN); 23–24—elytron: 23—♂, PT (DBCN); 24—♀, PT (DBCN). Bars = 1 mm.
FIGURES 1–10 in Taxonomic and nomenclatorial revision within the Neotropical genera of the subtribe Odontocheilina W. Horn in a new sense-21. Pentacomia paranigrimarginata sp. nov. and P. nigrimarginata Huber with revised key to Pentacomia species (Coleoptera: Cicindelidae)
FIGURES 1–10. Pentacomia paranigrimarginata sp. nov., Bolivia, Rio Piray. 1—habitus, ♂, HT (DBCN, later in UASC); 2– 3—labrum: 2—♂, HT; 3—♀, AT (DBCN); 4–5—head: 4—♂, HT; 5—♀, AT; 6—♂, apical half of aedeagus, HT; 7–8—elytron: 7—♂, HT; 8—♀, AT; 9–10—pronotum: 9—♂, HT; 10—♀, AT. Bars = 1 mm.
ScienceDex guides
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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.