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1,961 results for “Sensing”
Forest Fire Dataset for Peninsular Malaysia (2001-2023) Extracted from Multiple-Source Remote Sensing Data using Google Earth Engine
<ul> <li>Dataset: Forest Fire data</li> <li>Time Period: 2001 to 2023</li> <li>Location: Peninsular Malaysia</li> <li>Historical Fire Source: MCD64A1 and FIRMS Hotspots</li> <li>Fire Factors Extracted: Global Remote Sensing Data from GEE</li> </ul> <p>The framework extraction process can be reffered from the following publication:</p> <ul> <li>Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis</li> <li>Journal: <em>Forests</em> <strong>2024</strong>, <em>15</em>(6), 923;</li> <li><a href="https://doi.org/10.3390/f15060923">https://doi.org/10.3390/f15060923</a></li> <li>The variables name such as AET (actual evapotranspiration) can be found from the article.</li> </ul> <p>Access the framework code from: </p> <ul> <li><a href="https://github.com/chewyeejian/GEE_FrameworkForestFireDataset">https://github.com/chewyeejian/GEE_FrameworkForestFireDataset</a></li> </ul> <p>The time sequence in the variable indicate whether it's a monthly data / yearly accumulated data / seasonal data, example:</p> <ul> <li>200101_aet (Year 2001, Month 01, value for aet (actual evapotranspiration)</li> <li>2001_aet_DJF (Average of December, January, February)</li> <li>2001_aet_MAM (Seasonal Average of March, April, May)</li> <li>2001_aet_JJA (Seasonal Average of June, July, August)</li> <li>2001_aet_SON (Seasonal Average of September, October, November)</li> <li>2001_aet_annual (Annual average of 2001)</li> </ul>
Dataset for " Direct or Indirect Sonication in Ecofriendly MoS2 Dispersion for NO2 and NH3 Gas-Sensing Applications
<p>This zip file contains the opj data of the published paper entitled: Direct or Indirect Sonication in Ecofriendly MoS2 Dispersion for NO2 and NH3 Gas-Sensing Applications published in ACS Omega 2024, 9, 23, 2597-25308</p> <p>DOI: <a title="DOI URL" href="https://doi.org/10.1021/acsomega.4c03166">10.1021/acsomega.4c03166</a></p> <p> </p>
New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities
<p>The dataset is the vegetation type map for India as per <a href="https://www.sciencedirect.com/science/article/pii/S0303243415000574?via%3Dihub">Roy et 2015 "<span>New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities". </span></a></p> <p><span>The dataset consistes of two files- (1) a raster GIS file at 60m spatial resolution (EPSG 32643 WGS 84/ UTM Zone 34) in which each pixel value means a vegetation class as defined and mapped in Roy et al., 2015 and (2) a csv file which contains information matching the pixel value with the vegetation type. <br><br>For all additional information, please refer to the pper reviewed publication. </span></p>
Where is the heat threat in a city? Different perspectives on people-oriented and remote sensing methods: the case of Prague
<p>This is supplementary data for a paper called ‘Where is the heat threat in a city? Different perspectives of people-oriented and remote sensing methods, the case of Prague’ to be submitted to the journal Heliyon. Dataset contains three folders:</p> <ol> <li><strong>LST</strong> <br>– Layer Landsat_ecostress_data = vector polygon layer containing fishnet which includes values from 15 sattelite images from 3 different sources - ECOSTRESS, Landsat 8 and Landsat 9<br>Attribute percent_av contains mean percentile from all 15 images</li> <li><strong>Participatory mapping</strong><br>Raw data from participatory mapping campaign held on August 2022 in Prague-Holešovice</li> <li><strong>Thermal walk</strong><br>Layer Data_app = Raw data from thermal walk held on August 3rd 2022 (Declared time in the attribute table is UTC)<br>Layer DataApp_corr = Corrected coordinates from thermal walk (For these corrections, precisely prepared routes were used. Data were post-processed using an algorithm for a minimization of the distance between the measured and expected point location. The measured point was moved using its normal on-route position and his distance was compared with the previous point. If the distance was larger than 115% of expected, the point was moved closer, and when the distance was smaller than 85% of expected, the point was moved forward.)</li> </ol> <p> </p> <p><em>Acknowledgements: This work was supported by the Faculty of Science, Palacký University Olomouc internal grant IGA_PrF_2024_022 – Novel approaches to studying the human thermal environment in urban areas. This work was also supported by Strategy AV21 project ‘City as Lab of changes’, financed by the Czech Academy of Sciences.</em></p>
PVLAB: Remotely Sensed Surface Currents from Field Experiments (2013, 2018, 2021, 2022)
<p>This archive contains 2-min mean remotely sensed surface currents generated using optical imagery and PIV (Dooley et al., 2024) from field experiments (2013, 2018, 2021, 2022) occurring at the U.S. Army Corps of Engineers Field Research Facility, in Duck, NC. The README.txt file contains information regarding variables found in the MATLAB (PVLAB_PIV_SurfaceFlows.mat) file, including estimate locations, units, and timestamps.</p> <p>Estimates were generated at times with appropriate conditions for remote sensing (e.g., sufficient foam tracer) that were within 1-day of measured bathymetry at the field site. Additional data for the field site (obtained by the USACE Field Research Facility or by NOAA) including the measured bathymetry and wave conditions can be found at: https://chlthredds.erdc.dren.mil/thredds/catalog/frf/catalog.html</p> <p>Remote sensing estimates are not perfect and errors in filtering out bad data are possible. Please reach out to Ciara Dooley at cdooley@whoi.edu, Steve Elgar at selgar@mac.com, and Britt Raubenheimer at braubenheimer@whoi.edu if you have any questions. Additionally, the optical imagery (large dataset, many TBs) used to generate flow estimates can be accessed by contacting the authors.</p> <p> </p>
Source data - Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output
<p>Research data supporting the findings of "<em>Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output</em>" by Lennart Grabenhorst, Martina Pfeiffer, Thea Schinkel, Mirjam Kümmerlin, Gereon A. Brüggenthies, Jasmin B. Maglic, Florian Selbach, Alexander T. Murr, Philip Tinnefeld and Viktorija Glembockyte. For questions concerning this data, please reach out to Philip Tinnefeld or Viktorija Glembockyte.</p>
European funded projects related to integrated robotic sensing
<p>This repository contains the metadata of 1371 projects in tabular form (combinedProjectData.xlsx). The data was extracted from the European Union’s COmmunity Research and Development Information Service (CORDIS) repository.</p> <p>The dataset was used to support a review of the latest advancements in integrated robotic sensing. CORDIS was interrogated using a Boolean search, combining multiple chosen search terms using precise logical relationships, such as AND and OR. This search approach was used to obtain precise and relevant search results by specifying the relationships among the search terms, saving time and effort while minimising the likelihood of encountering irrelevant or unrelated material. The following Boolean search string was used: “(‘robot’ OR ‘robotic’ OR ‘robotically’ OR ‘roboti?ed’) AND (‘non-destructive’ OR ‘inspection’ OR ‘evaluation’ OR ‘NDT’ OR ‘NDE’ OR ‘sensing’ OR ‘sensor’)”. This resulted in searching projects whose title and short description (teaser) contained at least one of the words in the first set of brackets and at least one in the second set. Note that the “?” in ‘roboti?ed’ allowed looking for the presence of both the British English word “robotised” and the respective American English version “robotized”.</p> <p>Additionally, the search results were filtered according to the funding schemes. For the sake of reviewing the recent landscape, only projects funded through the HORIZON 2020 and HORIZON EUROPE schemes were considered. The described filtered search returned 1371 projects. The resulting metadata was extracted from the CORDIS repository for each of the found projects: the project start date, the end date, the total cost, the total EU contribution, the fields of science related to the project, the coordinating institution, and the participating institutions. The fields of science of each project are given as a list of strings detailing the fields of science related to the project. Each string shows a variable-depth hierarchy from the broadest classification to specific fields (e.g., “engineering and technology/materials engineering/composites”), following the hierarchical framework adopted by the European Commission. Finally, whereas each project has one and only one coordinator, it can have none, one or multiple participants. For the coordinator and each participant (if present), the following information was extracted: country of the coordinating/participating institution, amount of EU contribution received, amount of other funds available to the institution and the project outcome in terms of peer-reviewed journal papers, conference contributions and filed patents.</p> <p>Thus, the project metadata extracted from CORDIS was thoroughly analysed. The "fieldsOfScience_SunburstPlot.xlsx" file contains a sunburst chart that offers a lucid overview of the diverse scientific disciplines of the selected projects. The analysis of the fields of science strings has revealed a hierarchical depth going up to the seventh classification level, showing great permeance of robotic NDT and robotic sensing into numerous and specific fields.</p>
Data from: Structural basis for activation and allosteric modulation of full-length calcium-sensing receptor
<p>Calcium-sensing receptor (CaSR) is a class C G protein-coupled receptor (GPCR) that plays an important role in calcium homeostasis and parathyroid hormone secretion. Here, we present multiple cryo-electron microscopy structures of full-length CaSR in distinct ligand-bound states. Ligands (Ca<sup>2+</sup> and l-tryptophan) bind to the extracellular domain of CaSR and induce large-scale conformational changes, leading to the closure of two heptahelical transmembrane domains (7TMDs) for activation. The positive modulator (evocalcet) and the negative allosteric modulator (NPS-2143) occupy the similar binding pocket in 7TMD. The binding of NPS-2143 causes a considerable rearrangement of two 7TMDs, forming an inactivated TM6/TM6 interface. Moreover, a total of 305 disease-causing missense mutations of CaSR have been mapped to the structure in the active state, creating hotspot maps of five clinical endocrine disorders. Our results provide a structural framework for understanding the activation, allosteric modulation mechanism, and disease therapy for class C GPCRs.</p>
Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity
<p>Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH's flawed window-based spatial analysis and underutilized temporal component. In particular, In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversityplant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.</p>
Accompanying data for the paper "Making Sense of Wildlife Habitat Use on Active Oil Sands Mines: Quasi-experiments, Occupancy Models, Trends Assessments, and Upland Habitat Reclamation"
<p>This data set contains both the raw species detection records and the derived occupancy model data used to assess usage patterns for the nine species of wildlife. Data have been anonymized by using non-identifying company and lease names. These attributes are not required to reproduce the results in this paper and was done per contractual requirements between LGL Limited and its clients.</p> <p>Data is currently being reviewed by the client and will be shared publicly once final approval has been received.</p>
Fiber-optic seismic sensing of vadose zone soil moisture dynamics data sets
<p>CC_daily.h5: Daily cross-correlation functions for common-offset DAS channels.</p> <p>RCC_dv_v_full.csv: Summary of all the measured dv/v from the ballistic surface waves in daily cross-correlation functions.</p>
Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis
<div>This dataset contains key analysis and plotting scripts, data, and sample images.</div> <div> </div> <div>Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis</div> <div> </div> <div>Magnetic Resonance in Medicine Journal | DOI: 10.1002/mrm.30206</div> <div> </div> <div>§ Swetha Pala(1), § Antti Paajanen(1), Aapo Ristaniemi(1), Ervin Nippolainen(1), Isaac O. Afara(1), Olli Nykänen (1), Mikko J. Nissi (1*)</div> <div> </div> <div>1Department of Technical Physics, University of Eastern Finland</div> <div>§Shared authorship </div> <div> </div> <div> </div> <div> </div> <div>*Corresponding author</div> <div>Mikko J. Nissi</div> <div>Department of Technical Physics</div> <div>University of Eastern Finland, Kuopio Finland</div> <div>POB 1627</div> <div>70211 Kuopio</div> <div>mikko.nissi@uef.fi</div> <div>+358-50-5955517</div> <div> </div> <div> </div> <div>Keywords: Quantitative MRI, T1ρ relaxation, T1ρ dispersion, Compressed-sensing, radial acquisition.</div> <div> </div> <div>Included folders and files are:</div> <div>- Article_figures: all figures published in the manuscript (.eps format)</div> <div>- Data: MRI data files from 27 human cadaver samples with subfolders and files:</div> <div>- Human samples data: raw data files, along with generic analysis ROIs, zone divison inside specific samples folder, and within the parameter related data folder there are smaple specific analysis ROIs, computed profiles per spin lock amplitude. </div> <div>- CS reconstructed data files: </div> <div>- DataTables_used_for_analysis: Contains data tables per AF and reference data used for data analysis </div> <div>- Scripts: Matlab functions used for data processing and T1ρ computation, aedes plugins, and data analysis with subfolders and files:</div> <div> - Aedes_plugins: Plugins for aedes (http://aedes.uef.fi) for calculation of profiles from ROI. </div> <div> - Data analysis: Key scripts used for analysis and plotting.</div> <div> - Common functions: Some common functions that are required by the scripts/plugins. </div> <div> </div> <div>- README.txt: this file describing the contents of the dataset.</div> <div> </div> <div> </div> <div>See more info in separate readme files included in sub-folders.</div> <div> </div> <div> </div> <div>(Swetha Pala, 02 July 2024)</div> <p> </p>
Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city
<p>This dataset contains the processed data and analysis code used in the article:</p> <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. <em>Quarterly Journal of the Royal Meteorological Society</em>, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div><em>LST_data</em> contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> <p><em>UM100_output</em> contains model output from initial and final runs for the four study days</p> <p>The python script <em>plot.py </em>can be used to generate the figures shown in this article. </p>
Atlantic bluefin tuna (Thunnus thynnus) presence and pseudoabsence locations with 13 remotely sensed environmental variables
<p>Atlantic bluefin tuna (<em>Thunnus thynnus</em>; ABFT) are a highly important fisheries species of economic and conservation concern. Understanding their distributions, particularly under climate change is imperative for effective management. Here we assemble a dataset of 4,216 true presence locations for ABFT tagged with pop-up satellite archival tags off the west coast of Ireland between 2016-2021 and 392,009 pseudoabsence locations simulated by a series of correlated random walk modelling. The dataset also contains remotely sensed data at each location for 13 environmental variables including: bathymetry (m), rugosity (m), absolute dynamic topography (m) and it's spatial standard deviation, log surface chlorophyll-a (Log mg m-3), mixed layer depth (m), mean primary productivity over the top 200m of the water column (mg m-3), mean oxygen over the top 200m of the water column (mmol m-3), sea level height anomaly (m) and it's standard deviation, sea surface temperature (°C) and it's spatial standard deviation, and log eddy kinetic energy (Log m<sup>2 </sup>s<sup>-2</sup>).</p>
Figure 1 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 1. The Black Sea coast of the Krasnodar Krai and the Republic of Abkhazia.
Fig. 1 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 1. Key characteristics of the five haemoprotozoan assemblages.
raw drone sensing data - 2
<p>These are the raw data collected with Dji mavic 3M. Each folder (for example DJI_202405251139_003_leccino) contains data related to a single drone flight. Each folder contains RGB photos, related multispectral images (Red, Red-Edge, Green, Nir) and flight data mission files (.mrk). Gis information is embedded into photo metadata and mrk file. Part 2.</p>
raw drone sensing data - 1
<p>These are the raw data collected with Dji mavic 3M. Each folder (for example DJI_202405251139_003_leccino) contains data related to a single drone flight. Each folder contains RGB photos, related multispectral images (Red, Red-Edge, Green, Nir) and flight data mission files (.mrk). Gis information is embedded into photo metadata and mrk file. Part 1.</p>
Dataset used in Kittel et al., 2017 (https://doi.org/10.5194/hess-2017-549), including model files and processed remote sensing observations (CryoSat-2 radar altimetry and GRACE total water storage)
<p>Dataset used in</p> <p>Kittel, C. M. M., Nielsen, K., Tøttrup, C., Bauer-Gottwein, P., 2017.Informing a hydrological model of the Ogooué with multi-mission remote sensing data. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-549</p> <p>The dataset contains</p> <p>Model files:</p> <ul> <li>River delineation of the Ogooué river based on the SRTM 3 arc-second DEM </li> <li>Climate input data for the Ogooué model subbasins (TRMM and FEWS-RFE precipitation and ECMWF temperature)</li> <li>Parameter files</li> </ul> <p>Processed remote sensing data:</p> <ul> <li>CryoSat-2 satellite altimetry data over the Ogooué River from July 2010 to February 2015</li> <li><strong> </strong>Water mask derived from Sentinel-1 SAR, used to filter CryoSat-2 data</li> <li>GRACE TWS time series for the Ogooué basin</li> </ul> <p>The data is provided in a .zip file with a README.txt file providing additional information and details on the data, including where to obtain similar/original datasets.</p> <p>(c) Author(s) and Technical University of Denmark (DTU) 2018</p>
Making Sense Barcelona Noise Pilot Datasets
<p><strong>Raw Datasets From the Making Sense Barcelona Noise Pilot captured by the community using the Smart Citizen Kit 1.5</strong></p> <p> </p> <p>Data archived from the Smart Citizen Plaform:</p> <p><a href="https://smartcitizen.me/kits/4294">https://smartcitizen.me/kits/4294</a> <a href="https://smartcitizen.me/kits/4293">https://smartcitizen.me/kits/4293</a> <a href="https://smartcitizen.me/kits/4282">https://smartcitizen.me/kits/4282</a> <a href="https://smartcitizen.me/kits/4283">https://smartcitizen.me/kits/4283</a> <a href="https://smartcitizen.me/kits/4271">https://smartcitizen.me/kits/4271</a> <a href="https://smartcitizen.me/kits/4307">https://smartcitizen.me/kits/4307</a> <a href="https://smartcitizen.me/kits/4309">https://smartcitizen.me/kits/4309</a> <a href="https://smartcitizen.me/kits/4308">https://smartcitizen.me/kits/4308</a> <a href="https://smartcitizen.me/kits/4301">https://smartcitizen.me/kits/4301</a> <a href="https://smartcitizen.me/kits/4302">https://smartcitizen.me/kits/4302</a> <a href="https://smartcitizen.me/kits/4262">https://smartcitizen.me/kits/4262</a> <a href="https://smartcitizen.me/kits/4265">https://smartcitizen.me/kits/4265</a> <a href="https://smartcitizen.me/kits/4281">https://smartcitizen.me/kits/4281</a> <a href="https://smartcitizen.me/kits/4280">https://smartcitizen.me/kits/4280</a> <a href="https://smartcitizen.me/kits/4267">https://smartcitizen.me/kits/4267</a> <a href="https://smartcitizen.me/kits/4300">https://smartcitizen.me/kits/4300</a> <a href="https://smartcitizen.me/kits/4299">https://smartcitizen.me/kits/4299</a> <a href="https://smartcitizen.me/kits/4296">https://smartcitizen.me/kits/4296</a> <a href="https://smartcitizen.me/kits/4295">https://smartcitizen.me/kits/4295</a> <a href="https://smartcitizen.me/kits/4297">https://smartcitizen.me/kits/4297</a> <a href="https://smartcitizen.me/kits/4298">https://smartcitizen.me/kits/4298</a> <a href="https://smartcitizen.me/kits/4468">https://smartcitizen.me/kits/4468</a> <a href="https://smartcitizen.me/kits/4470">https://smartcitizen.me/kits/4470</a></p> <p>You can also explore the data using the Smart Citizen API:</p> <p><a href="https://developer.smartcitizen.me/">https://developer.smartcitizen.me/</a></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.