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1,838 results for “location”
Passive RFID indoor location dataset
<p>We use four monostatic UHF antennas that operate in the frequency range from 902 to 928 MHz with a 6 dBi gain (isotropic antenna gain) for the implementation environment preparation. The equipment we used to perform the readings was the ThingMagic Mercury 6, a high-performance UHF RFID reader, supporting up to four monostatic antennas, digital inputs and outputs, and a Wi-Fi connection. Both devices are commercially available.</p> <p>We affixed 400 labels to objects placed side by side on the shelves in an auto parts store. The distance between the antennas and tags was 115 cm, and the distance between the antenna group was 250 cm. The reader interrogates the tags every 5 seconds and organizes the input information in a data collection formed by TagID, RSSI, Read Count (RC), True_x, and True_y. This interrogation time was defined by the minimum limit at which all tags were identified at least once by the antenna. In order to carry out data collection, the objects were placed in known positions. We performed 100 readings on each target tag, measuring the RSSI and the RC of each of the four antennas, totaling 40,000 readings. </p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles (presentation video)
<p>Video of the presentation for the publication M. Kamal, A. Barua, C. Vitale, C. Laoudias and G. Ellinas, "GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles," 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 1-7, doi: 10.1109/VTC2021-Fall52928.2021.9625567.</p>
Weather data (forecast and observation) at three locations in France over 2021 for Machine Learning Training
<p>The data provided data are historical weather measurement and forecast at three location in France.</p> <p>Measurements are inside files named OBS_xxx</p> <p>Forecasts are inside files names YYY_xxx, with YYY is the name of the forecast simultion (GFS0.25, WRF12km or WRF3KM).</p> <p>In the two cases, xxx is the name of the site (Site 1, Site2 or Site3).</p> <p><br> <strong>Description of OBS_xxx files:</strong><br> - One line per measurement with hourly resolution<br> - columns are: Date(TU),Temperature2m_degC,WindSpeed10m_m/s,WindDirection10m_m/s<br> Date = date of measurement in TU and format DD/MM/YYYY HH:MM<br> Temperature2m_degC = air temperature at 2m height in °Celsius<br> WindSpeed10m_m/s = wind speed at 10m height in m/s<br> WindDirection10m_deg = wind direction at 10m height in deg. (0 or 360 = wind from north to south, 45°=wind from east to east, ....)<br> If measurement is not available for a specific hour for one parameter, the value "-999" is used.</p> <p>The observation data go:<br> from 16/04/2021 00H <br> to 31/01/2022 23H</p> <p><br> <strong>Description of YYY_xxx files:</strong><br> - One line per forecast with hourly resolution<br> - columns are: First date run (TU),forecast hour,Temperature2m_degC,WindSpeed10m_m/s,WindDirection10m_m/s<br> First date run (TU) = date of start of the forecast in TU and format DD/MM/YYYY HH:MM. HH could be 00 and 12 according to the cycle of forecast start.<br> forecast hour = forecast hour from the start of the forecast date. 00 = forecast for "first date run". 01 = forecast for "First date run" + 1 hour. .... 95 = forecast for "First date run" + 95 hours.<br> For GFS0.25, forecast hour go from 00 to 95<br> For WRF12km, forecast hour go from 00 to 95<br> For WRF3m, forecast hour go from 00 to 95<br> Temperature2m_degC = air temperature at 2m height in °Celsius<br> WindSpeed10m_m/s = wind speed at 10m height in m/s<br> WindDirection10m_deg = wind direction at 10m height in deg. (0 or 360 = wind from north to south, 45°=wind from east to east, ....)<br> If measurement is not available for a specific hour for one parameter, the value "-999" is used.</p> <p>The forecast data go:<br> from 13/04/2021 00H + 72H = first forecast for the 16/04/2021 00H<br> to 31/01/2022 12H + 11H = last forecast for the 31/01/2022 23H</p>
Location-based augmented reality (LBAR) spatial data test
<p>This repository gathers video data (screen capture) collected on a field test conducted on the 11th of May 2022, at the HEIG-VD in Yverdon-les-Bains, Switzerland.<br> <br> The goal of the test was to submit LBAR interfaces to different sources of spatial data. The 5 conditions compared were:<br> <br> 1) ARCore interface (visual odometry) fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 2) ARCore interface (visual odometry) fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 3) A-Frame + LBAR.js interface fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 4) A-Frame + LBAR.js interface fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 5) A-Frame + LBAR.js interface fed with position and orientation data provided by an external Inertial Navigation Station Xsens MTi-680g (IMU + GNSS/RTK).</p>
Great tits (Parus major) flexibly learn that herbivore-induced plant volatiles indicate prey location – an experimental evidence with two tree species
<p>1. When searching for food, great tits (Parus major) can use herbivore-induced plant volatiles (HIPVs) as an indicator of arthropod presence. Their ability to detect HIPVs was shown to be learned, and not innate, yet the flexibility and generalization of learning remains unclear. 2. We studied if, and if so how, naïve and trained great tits (Parus major) discriminate between herbivore-induced and non-induced saplings of Scotch elm (Ulmus glabra) and cattley guava (Psidium cattleyanum). We chemically analysed the used plants and showed that their HIPVs differed significantly and overlapped only in a few compounds. 3. Birds trained to discriminate between herbivore-induced and non-induced saplings preferred the herbivore-induced saplings of the plant species they were trained to. Naïve birds did not show any preferences. Our results indicate that the attraction of great tits to herbivore-induced plants is not innate, rather it is a skill that can be acquired through learning, one tree species at a time. 4. We demonstrate that the ability to learn to associate HIPVs with food reward is flexible, expressed to both tested plant species, even if the plant species has not coevolved with the bird species (i.e. guava). Our results imply that the birds are not capable of generalising HIPVs among tree species but suggest that they either learn to detect individual compounds or associate whole bouquets with food rewards.</p>
The effect of co-location on human communication networks
<p>Representative dataset for "The effect of co-location on human communication networks." The files are serialized python objects pickled using python 3.8. dist_dict_* contains data on the pairwise distance between researchers, while undir_semiactive_* contains networks representing daily email counts between researchers.</p>
Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type
<p>Contains all raw data measured in the Schwingbach Earth Observatory (SEO) from Spring, Summer and Autumn 2020. Data was measured with an on-site LGR laser from the GHG emissions, and with 100cm³ soil cores for the soil characteristics. Details can be found in the corresponding manuscript "Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type"</p>
Co-located Multi-device Audio Experiences Dataset
<p>This dataset contains the survey responses obtained from the survey on co-located multi-device audio experiences, in the form of a .csv file.</p>
Labeled data and models for COVID-19 vaccine related tweets with stance, location, and topics
<p>The dataset contains Tweet IDs along with the location and tweet timestamp. The tweets are labeled based on motivating/demotivating status, stance towards the COVID-19 vaccine, and topic in the tweet text. To comply with Twitter guidelines, we removed the tweet texts and author information. You can use Hydrator API to hydrate the tweets.</p> <p>The repository also contains the machine-learning models for topic modeling, de/motivation classifier, and stance detection from the tweets.</p>
Home and hub: pet trade and traditional medicine impact reptile populations in source locations and destinations
<p>The pet trade and Traditional Chinese Medicine (TCM) consumption are major drivers of global biodiversity loss. Tokay geckos (<em>Gekko gecko</em>) are among the most traded reptile species worldwide. In Hong Kong, pet and TCM markets sell tokay geckos while wild populations also persist. To clarify connections between trade sources and destinations, we compared genetics and stable isotopes of wild tokays in local and nonlocal populations to dried individuals from TCM markets across Hong Kong. We found that TCM tokays are likely not of local origin. Most wild tokays were related to individuals in South China, indicating a probable natural origin. However, two populations contained individuals more similar to distant populations, indicating pet trade origins. Our results highlight the complexity of wildlife trade impacts within trade hubs. Such trade dynamics complicate local legal regulation when endangered species are protected, but the same species might also be non-native and possibly damaging to the environment.</p>
3D models: the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași)
<p>This dataset is part of a larger project on the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași), supervised by the ArchaeoSciences Division of the Research Institute of the University of Bucharest (ICUB) and Kiel University (Germany), in partnership with HOGENT, University of Applied Sciences and Arts (Belgium), Museum of Bucharest, Museum of the Lower Danube Călărași, Museum of Gumelnița Civilization Oltenița, and "Vasile Pârvan" Institute of Archaeology (Romania), under the "Sultana School of Archaeology" initiative.</p> <p>Spatial data play a crucial role in archaeological research, and orthophotos, digital elevation models, and 3D models are frequently used for the mapping, documentation, and monitoring of archaeological sites. Thanks to the availability of compact and low-cost uncrewed airborne vehicles, the use of UAV-based photogrammetry is well matured in this field over the last two decades. More recently, compact airborne systems are also available that allow the recording of thermal data, multispectral data, and airborne laser scanning. For this project, various platforms and sensors are applied at the Chalcolithic archaeological sites in the Mostiștea Basin and Danube Valley (Southern Romania). By analyzing the performance of the systems and the resulting data, insight is given into the selection of the appropriate system for the right application. This analysis requires thorough knowledge of data acquisition and data processing as well. As both laser scanning and photogrammetry typically result in very large amounts of data, a special focus is also required on the storage and publication of the data. Hence, the objective of this project is to provide a full overview of various aspects of 3D data acquisition for UAV-based mapping. Based on the conclusions drawn in our related publications, it is stated that photogrammetry and laser scanning can result in data with similar geometrical properties when acquisition parameters are appropriately set. On the one hand, however, the used ALS-based system outperforms the photogrammetric platforms in terms of operational time and the area covered. On the other hand, conventional photogrammetry provides flexibility that might be required for very low-altitude flights, or emergency mapping. Furthermore, as the used ALS sensor only provides a geometrical representation of the topography, photogrammetric sensors are still required to obtain true color- or false color composites of the surface. Lastly, the variety of data, like pre- and post-rendered raster data, 3D models, and point clouds, requires the implementation of multiple methods for the online publication of data. Various client-side and server-side solutions are presented to make the data available for other researchers.</p>
New Zealand OISST data from 14 coastal locations
<p>The following MATLAB .mat file ( sponge_data_oisst_v2.mat) contains daily records of sea surface temperature spanning 01 Jan 1992 to 31 May 2022 from 14 locations around the New Zealand coastline, extracted from the OISST data set (https://doi.org/10.25921/RE9P-PT57), as analysed in Bell et al. (2022).</p> <p>Also included are MATLAB .m files to (1) extract daily time series of SST from a local copy of the global OISST netcdf files (bell_etal_extract_OISST.m) and (2) undertake the analysis of marine heatwaves in the sponge_data_oisst_v2.mat subset of these data (bell_etal_analyze_OISST.m), as performed in Bell et al. (2022).</p> <p>We acknowledge the NOAA OI SST V2 High Resolution Dataset provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov">https://psl.noaa.gov</a></p>
Text-fig. 1. Location of the study site. a: the location of Lühe Town, Yunnan, SW China; b: fossil bearing section, white arrow indicates the fossil collection stratum; c: geological map of fossil site. in Fraxinus L. (Oleaceae) Fruits From The Early Oligocene Of Southwest China And Their Biogeographic Implications
Text-fig. 1. Location of the study site. a: the location of Lühe Town, Yunnan, SW China; b: fossil bearing section, white arrow indicates the fossil collection stratum; c: geological map of fossil site.
Text-fig. 1. Relief map of Africa to show the location of the Cheringoma Plateau at the southern extremity of the African Rift System. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 1. Relief map of Africa to show the location of the Cheringoma Plateau at the southern extremity of the African Rift System.
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth.
Text-fig. 1. a: Po Plain and foothills of the Northern Apennine in Northern Italy (inset) with the location of Oriolo (black star) and other Early and Middle Pleistocene plant localities, Enza and Stirone. Red lines indicate the frontal thrust arcs (modified from Martinetto et al. 2015). b: The "La Salita" section, Oriolo and chronology of the two "Sabbie gialle" cycles based on large mammals and palaeomagnetic correlation (modified from Toniato et al. 2017; IMMS 2020* [Italian Mediterranean Marine Stages] updated from Cohen and Gibbars 2020; GTS 2021* [Global Time Scale] updated from Head et al. 2021). c: Quarry "La Salita", Oriolo, in 1987. Main unconformities (U) separating the two "Sabbie gialle" cycles and terrestrial deposits on top are shown. Leaf symbols indicate the positions of some of the layers rich in fossil leaves (photo by G. B. Vai, modified). d: Surroundings of Faenza with the location of Oriolo and adjacent coeval sites yielding plant macrofossils. in The Late Early Pleistocene Flora Of Oriolo, Faenza (Italy): Assembly Of The Modern Forest Biome
Text-fig. 1. a: Po Plain and foothills of the Northern Apennine in Northern Italy (inset) with the location of Oriolo (black star) and other Early and Middle Pleistocene plant localities, Enza and Stirone. Red lines indicate the frontal thrust arcs (modified from Martinetto et al. 2015). b: The "La Salita" section, Oriolo and chronology of the two "Sabbie gialle" cycles based on large mammals and palaeomagnetic correlation (modified from Toniato et al. 2017; IMMS 2020* [Italian Mediterranean Marine Stages] updated from Cohen and Gibbars 2020; GTS 2021* [Global Time Scale] updated from Head et al. 2021). c: Quarry "La Salita", Oriolo, in 1987. Main unconformities (U) separating the two "Sabbie gialle" cycles and terrestrial deposits on top are shown. Leaf symbols indicate the positions of some of the layers rich in fossil leaves (photo by G. B. Vai, modified). d: Surroundings of Faenza with the location of Oriolo and adjacent coeval sites yielding plant macrofossils.
Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015). in First Water Lily, A Leaf Of Nymphaea Sp., From The Miocene Clarkia Flora, Northern Idaho, Usa: Occurrence, Taphonomic Observations, Floristic Implications
Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015).
Text-fig. 1. Geographical location of the studied post-evaporitic sections (Piedmont Basin): Govone (1), Sioneri (2), Ciabòt Cagna (3) and Pollenzo (4). in Late Messinian Flora From The Post-Evaporitic Deposits Of The Piedmont Basin (Northwest Italy)
Text-fig. 1. Geographical location of the studied post-evaporitic sections (Piedmont Basin): Govone (1), Sioneri (2), Ciabòt Cagna (3) and Pollenzo (4).
Text-fig. 1. Context and location of the Govone outcrop. a: Location of the Piedmont Basin at the northern margin of the Mediterranean Basin and distribution of Messinian evaporites. b: Simplified geological map of the Piedmont Basin showing the location of the Govone outcrop close to the town of Alba. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 1. Context and location of the Govone outcrop. a: Location of the Piedmont Basin at the northern margin of the Mediterranean Basin and distribution of Messinian evaporites. b: Simplified geological map of the Piedmont Basin showing the location of the Govone outcrop close to the town of Alba.
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.