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88 results for “buoys”
North Temperate Lakes LTER: High Frequency Water Temperature Data - Lake Mendota Buoy 2006 - current
The instrumented buoy on Lake Mendota is equipped with a thermistor chain that measures water temperature. In 2006, the thermistors were placed every half-meter from the surface through 7m, and every meter from 7m to 15m. Since 2007, the thermistors were placed every half-meter from the surface through 2m, and every meter from 2m to 20m. The sensor at the water surface is as close to the surface as feasible. A list of sensors used since the first deployment in 2006 is provided as a downloadable CSV file. Hourly and daily water temperature averages are computed from high resolution (1 minute) data.The buoy is removed during the winter when the lake is ice-covered (typically Dec-Apr). Meteorological and limnological data collected at the same buoy site can be found in an ancillary dataset. Sampling Frequency: one minute. Number of sites: 1. Location lat/long: 43.0995, -89.4045 Notes: The thermistor string failed in June 2014, so there is not data between June 17, 2014 and the start of the 2016 season. The thermistor string failed again in August 2023. It was replaced by Hobo temp loggers at 0, 5,10, 15, 20 meters depth for the remainder of 2023.
North Temperate Lakes LTER: High Frequency Meteorological and Metabolism Data - Trout Bog Buoy 2003 - present
The instrumented buoy on Trout Bog is equipped with a dissolved oxygen sensor, a thermistor chain, light sensor, and in the past, meteorological sensors that provide fundamental information on lake thermal structure and lake metabolism. A surface buoy was used from 2003 - 2014, which included a met station (air temp and winds). Since then, only a subsurface buoy as been used (no met station), and includes frequent over-winter deployments under the ice. The thermistor chain data is included in a separate dataset (knb-lter-ntl.70). Hourly and daily averages are provided for the met data, while hourly averages are provided for the dissolved oxygen and light data. To accommodate the under-ice deployments, sensor depths vary somewhat year-to-year. Light data is collected with HOBO pendant light and temperature sensors. The make of the dissolved oxygen sensor has changed over the years: Greenspan DO (2003-2005), D-Opto (2006-2014), and PME miniDOT (2015-present).
North Temperate Lakes LTER: High Frequency Water Temperature Data - Trout Bog Buoy 2003 - current
The instrumented buoy on Trout Bog is equipped with a thermistor chain that measures water temperature from depths ranging from the surface to 7m placed every 0.5-1m throughout the water column. From the initial deployment in 2003 through 2014, a surface buoy was used in the open water season. Since 2015, a subsurface buoy has been used, including under-ice deployments during the winter. Recorded depths can vary slightly depending on specifics of the year's deployment hardware. The sampling frequency:varies for instantaneous samples. Prior to 2011, the sample frequency was every 10 minutes, with some short periods of 2 minutes in 2003. Beginning in 2011, the sample frequency has been one per minute. Hourly and daily averages are also provided. Number of sites: 1.
Measured and modelled significant wave height time series at the Bothnian Sea Wave buoy in the Baltic Sea
<p>Significant wave height data at the location of FMI's wave buoy in the Bothnian Sea, Baltic Sea (61 degrees 8' N, 20 degrees 14' E). Contains 2011-2019 wave buoy observations, 1965-2005 SWAN modelled data (Björkqvist et al. 2018), and 1979-2013 WAM modelled data (Tuomi et al. 2019).</p>
Salinity, Turbidity, Wind from the E1 buoy at the LTER site Delta del Po and Costa Romagnola (2012-2021)
<p>The present database comprises observations spanning from 2012 to 2021, focusing on abiotic parameters collected from the E1 meteo-oceanographic buoy in the Northern Adriatic Sea (north of Rimini city on a bottom depth of 10.5 m), Italy. Specifically, it encompasses measurements taken atmospheric parameters above the water surface and measurements at a defined nominal depth (https://vocab.nerc.ac.uk/collection/P01/current/ADEPZZ01/) of salinity (URI: https://vocab.nerc.ac.uk/collection/OD1/current/SAL/) in PSU (Practical Salinity Units), turbidity (URI: http://vocab.nerc.ac.uk/collection/P25/current/TURB/) in NTU (Nephelometric Turbidity Units; http://vocab.nerc.ac.uk/collection/P06/current/USTU/), wind speed (URI: http://vocab.nerc.ac.uk/collection/P25/current/WINDS/) in m/s (meters per second; http://vocab.nerc.ac.uk/collection/P06/current/PMPS/), and wind from direction (URI: http://vocab.nerc.ac.uk/standard_name/wind_from_direction/) in degrees (angular degrees, 0 represents the true north; http://vocab.nerc.ac.uk/collection/P06/current/UAAA/). The buoy is located at 44,14° N; 12,57° E (WGS-84 coordinate system) and is managed by the Institute of Marine Science of the National Research Council (ISMAR-CNR) in Bologna. The dataset relies on a Comma Separated Values (CSV) file and it is composed by 82391 records, offering an invaluable insight into the dynamic characteristics of the marine environment in this area over nearly a decade. The E1 buoy is part of the site “Delta del Po and Costa Romagnola”, which belongs to the Long Term Ecological Research national and international networks (LTER-Italy, LTER-Europe and ILTER) and eLTER-RI. </p>
Aerial Water Buoys Dataset
<p><strong>Aerial Water Buoys Dataset:</strong></p> <p>Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technologies have made possible advanced UAV-based search and rescue operations with transformative impact on the outcome of critical life-saving missions. This dataset aims into helping the challenging task of multi-castaway tracking and following using a single UAV. Due to the difficulty and data protection of capturing footage of people in the sea, we have captured a dataset of buoys in order to conduct experiments on multi-castaway tracking and following. A paper on multi-castaway tracking and following technical details and experiments will be published soon using this dataset.</p> <p>The dataset consists of top-view images of buoys from various altitudes on the coasts of Larnaca and Protaras in Cyprus. Images were captured at different altitudes in order to challenge object detectors to be able to detect smaller objects in case a UAV needs to track multiple targets, which leads to flying at a higher altitude. There is only one class annotated on all images which is labeled as 'buoy'. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (buoys):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Buoys</td> </tr> <tr> <td>Training</td> <td>10814</td> <td>14811</td> </tr> <tr> <td>Validation</td> <td>1350</td> <td>1865</td> </tr> <tr> <td>Testing</td> <td>1352</td> <td>1827</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong></p> <blockquote> <p>Antreas Anastasiou, Rafael Makrigiorgis, & Panayiotis Kolios. (2022). Aerial Water Buoys Dataset (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7288444</p> </blockquote>
Buoy and Other Sensors Data at the South East Coast of Iceland (Höfn area) on Nov 20 - 24, 2020
<p>This dataset contains the information about sea state and environmental parameters in the area of tidal inlet near Höfn city, South East coast of Iceland. Buoy data is given with original half-hour time spacing. Wind and tidal sensors' data are given with the original 10 min time spacing. All of the given parameters did not undergo any re-interpolation, filtering or any other processing.</p> <p>structure of the "Buoy_data_20201120T010100_to_20201124T125900.csv" file</p> <pre><code>Comma-separated values'(.csv) table (217 rows 3 columns) with the the following fields: 1st column: time in the format dd-Mmm-yyyy HH:MM:SS; 2nd column: Significant wave height (Hs) in meters (m); 3rd column Peak wave period in seconds (s).</code></pre> <p>structure of the "Sea_level_tide_wind_data_20201120T010100_to_20201124T125900.csv" file (originally data was obtained from the <a href="http://vedur.mogt.is/harbor/index.php?action=ReadingsDrillDown&harborid=4&stationid=1011">web page</a> of Weather information system MogT ehf. Hornafjörður, Iceland - the corresponding table fields' names are doubled in Icelandic for convenience) </p> <pre><code>Comma-separated values'(.csv) table (649 rows 5 columns) with the the following fields: 1st column: time in the format dd-mm-yyyy HH:MM:SS; 2nd column: observed sea level (Sjávarhæð) in meters (m); 3rd column astronomical tide (Flóðatafla) in meters (m); 4th column wind speed (Vindur) in meters per second (m/s); 5th column wind direction (Vindátt) in degrees (deg).</code></pre> <p>The corresponding data chunks are published after the kind allowance of the data owners: Vignir Júlíusson and Greipur Sigurðsson.</p> <p> </p>
High Frequency Meteorological, Drift-Corrected Dissolved Oxygen, and Thermistor Temperature Data - Lake Sunapee Buoy, NH, USA, 2007 – 2013
The Lake Sunapee Protective Association (Sunapee, New Hampshire, USA) has been operating an instrumented buoy on Lake Sunapee (maximum depth 33.7 meters) beginning on 27 August 2007. The environmental sensors on the buoy from 2007 - 2013 provided information on weather conditions, lake thermal structure, and oxygen dynamics, and their data can be used to calculate physical and biological variables such as buoyancy frequency, thermocline depth, thermal stability, and lake metabolism. The sensors were programmed to collect environmental data every 10 minutes. The buoy collected meteorological data 1.7 meters above the lake surface, including wind speed and direction (Vaisala WXT52 anemometer), air temperature and humidity (Vaisala HMP50), and photosynthetically active radiation (PAR Li-Cor). The water temperature sensors (TempLine thermistors from Apprise Technology in 2007-2010; NexSens T-node sensors 2010-2013) were situated at 0.5-2 meter intervals from 0-14 meters deep with the bottom sensor approximately 1 meter from the sediments. The dissolved oxygen (Zebra-Tech d-opto) sensor was deployed at approximately 1 meter below the surface and recorded oxygen concentration (mg/L), oxygen saturation (%), and temperature at the sensor (oxygen saturation is not included in this dataset). The buoy was anchored at approximately 15 meter deep water near the Loon Island lighthouse in the northern half of the lake, near the deepest part of the lake (43.390 N, -72.057 W). During the winter of 2007 - 2008, the buoy froze into the ice and continually recorded data with the uppermost thermistor below the bottom of the ice. In winter of 2008 - 2009, the buoy was damaged by ice and data were not collected until re-deployment on 29 July 2009. In subsequent years, the buoy was deployed from April or May to October or November at the Loon Island location and limited data were obtained during the winter months at the Sunapee Harbor (43.386 N, -72.081 W). In 2013, the buoy was taken offl
Lake snow removal experiment buoy, light, and chlorophyll data, 2019-2021
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents chlorophyll, light, and high frequency buoy data collected from this project. Related datasets are: https://doi.org/10.6073/pasta/962fa57959ff9828eb6f1cbda79b82c0 https://doi.org/10.6073/pasta/f6e271634a04819e25bc7c913cd67155 https://doi.org/10.6073/pasta/9a26e819522152e878d802df76cf90d7
Buoy-based detection of low-energy cosmic-ray neutrons (Seelhausener See, July 15 to Dec 02, 2014)
<p>Contains two resources used in Schrön & Rasche et al. (2024):</p> <ol> <li><strong>Raw</strong> measurement data files from the buoy detector. Column names are provided in the header of the files. For detailed information about column names and descriptions, see the readme.</li> <li><strong>Processed</strong> measurement data of the buoy detector. Data has been stored as CSV files, the column names are described in `Buoy.csv.readme`. Additional PDF files show the corresponding plots. Two versions of data are provided: <ol> <li><strong>Buoy-1h</strong> contains data aggregated to 1 hour, and</li> <li><strong>Buoy-1h-mavg25</strong> contains the same data but the neutrons underwent a moving average filter with a window size of 25 (1 day).</li> </ol> </li> </ol> <p>Processing has been performed using Corny v0.8.2 (<a title="Corny" href="https://git.ufz.de/CRNS/cornish_pasdy">git.ufz.de/CRNS/cornish_pasdy</a>) with the configuration file <code>Buoy-1h.cfg</code>.</p>
Recordings from: Evaluation of a coastal acoustic buoy for cetacean detections, bearing accuracy, and exclusion zone monitoring
<p>1.<span> </span>There is strong socio-political support for offshore wind development in US territorial waters, and construction is planned off several east coast states. Some of the planned development sites coincide with important habitat for critically endangered North Atlantic right whales. Both exclusion zones and passive acoustic monitoring are important tools for managing interactions between marine mammals and human activities. Understanding where animals are with respect to exclusion zones is important to avoid costly construction delays while minimizing the potential for negative impacts. Impact piling from construction of hundreds of offshore wind turbines likely requires exclusion zones as large as 10 km.</p> <p>2.<span> </span>We have developed a three-hydrophone passive acoustic monitoring system that provides bearing information along with marine mammal detections to allow for informed management decisions in real-time. Multiple units form a monitoring system designed to determine whether marine mammal calls originate from inside or outside of an exclusion zone. In October 2021 we undertook a full system validation, with a focus on evaluating the detection range and bearing accuracy of the system with respect to right whale upcalls. Five units were deployed in Mid-Atlantic waters and we played more than >3,500 simulated right whale upcalls at known locations to characterize the detection function and bearing accuracy of each unit. The modeled results of the detection function error were then used to compare the effectiveness of a bearing-based system to a single sensor that can only detect a signal but not ascertain directivity.</p> <p>3.<span> </span>Field trials indicated maximum detection ranges from 4–7.3 km depending on source and ambient noise levels. Simulations showed that incorporating bearing detections provides a substantial improvement in false alarm rates (6 to 12 times depending on number of units, placement, and signal to noise conditions) for a small increase in the risk of missed detections inside of an exclusion zone (1–3%). </p> <p>4.<span> </span>We show that the system can be used for monitoring exclusion zones and clearly highlight the value of including bearing estimation into exclusion zone monitoring plans while noting that placement and configuration of units should reflect anticipated ambient noise conditions.</p>
Bailong Buoy Data
<p><span>Observations from the buoy were provided by the Center for Ocean and Climate Research, First Institute of Oceanography, Ministry of Natural Resources, China. The data can be accessed from the website at </span><a href="http://www.bailongbuoy.org/#/fbgzt"><u><span><span>http://www.bailongbuoy.org/#/fbgzt</span></span></u></a><span>. Please note that the website is currently only available in Chinese. Data Extracted for the Period December 25th, 2018 to January 10th, 2019.</span></p>
Fig. 9 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs
Fig. 9 — Generated voltage due to the change of buoy acceleration in terms of wave flume shutter frequency
Fig. 6 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs
Fig. 6 — The measured heave (m), pitch (degree), and roll (degree) while varying the wave period from 1.22 to 2.13 s
Fig. 5 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs
Fig. 5 — The variations in the wave height (m) with respect to the variations in wave period during different shutter frequencies (60 Hz, 55 Hz, 50 Hz, 45 Hz, 40 Hz & 35 Hz)
Fig. 4 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs
Fig. 4 — The PEH setup in the wave flume environment: (a) Wave generator hardware setup along with the Lab view software, (b) PEH buoy in the wave flume before the experiment, and (c) Data logger and microcontroller module of PEH system
Fig. 7 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs
Fig. 7 — Measured acceleration (m/s2) in X, Y & Z-axis for the varying wave period from 1.22 to 2.13 s
Fig. 10 in Microenergy harvester for remote ocean buoys using piezoelectric sensors coupled with superballs
Fig. 10 — Generated voltage and power in terms of the wave conditions with the changing shutter frequency from 60 Hz to 35 Hz
SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report
<p>The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.</p>
Measurements of nearshore waves through coherent arrays of free-drifting wave buoys
<p>Surface gravity wave breaking occurs along coastlines in complex spatial and temporal patterns that significantly impact erosion, scalar transport, and flooding. Numerical models are used to predict these processes, but many models lack sufficient evaluation with observations during storm events. To fill the need for more nearshore wave measurements during extreme conditions, we deployed coherent arrays of small-scale, free-drifting wave buoys named microSWIFTs. The result is a large dataset covering a range of conditions. The microSWIFT is a small wave buoy with a GPS module, and Inertial Measurement Unit (IMU) used to directly measure the buoy's global position, horizontal velocities, rotation rates, accelerations, and heading. We use an Attitude and Heading Reference System (AHRS), 9 degrees-of-freedom Kalman filter to rotate the measured accelerations from the reference frame of the buoy to the Earth reference frame. We then use the corrected accelerations to compute the vertical velocity and sea surface elevation. The measurements were collected over a 27-day field experiment in October 2021 at the US Army Corps of Engineers Field Research Facility in Duck, NC. The microSWIFTs were deployed as a series of coherent arrays. They all sampled simultaneously with a common time reference, leading to a robust spatial and temporal dataset during each deployment. We evaluate wave spectral energy density estimates from individual microSWIFTs by comparing them with a nearby acoustic waves and currents (AWAC) sensor. We also compare significant wave height estimates from the coherent arrays with the nearby AWAC estimates. A zero crossing algorithm is applied to each buoy time series of sea surface elevation to extract realizations of measured surface gravity waves, yielding 116,307 wave realizations throughout the experiment. These measurements spanned offshore significant wave heights ranging from 0.5 meters to 3 meters and peak wave periods ranging from 5 to 15 seconds over the entire experiment. </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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.