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79 results for “High-Frequency data”
High-frequency light attenuation measurements in 35 lakes: companion data from "Coefficients in Taylor’s Law increase with the time scale of water clarity measurements in a global suite of lakes"
Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor’s Law of fluctuation scaling, which states that the variance of a population is proportional to a positive power of the mean, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. This dataset uses light attenuation observations from 10,531 days of high-frequency measurements in 35 globally distributed lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor’s Law, and that model coefficients increase in a predictable yet non-linear manner with decreasing observation frequency.
High-frequency water quality data for three urban streams in Boston, MA (USA), 2021-2022
This dataset contains high-frequency water quality data for three urban stream locations in the great Boston, Massachusetts metropolitan area. Multiparameter sondes with sensors to measure temperature, pH, specific conductivity, optical dissolved oxygen (DO), turbidity, colored dissolved organic matter (CDOM), and optical brighteners (OB) were deployed from 23 November 2021 to 20 December 2022. Data were collected at 15-minute intervals.
High-frequency water temperature and dissolved oxygen data and derived stability and metabolism metrics for nine lakes in northeastern North America for months before and after Tropical Cyclone Irene, Fall 2011
This dataset is used in the analysis published in the following manuscript: Klug, J.L., D.C. Richardson, H.A. Ewing, B.R.Hargreaves, N. R. Samal, D. Vachon, D.C. Pierson, A. E. Lindsey, D. O'Donnell, S.W. Effler, and K.C. Weathers. 2012. Ecosystem effects of a tropical cyclone on a network of lakes in northeastern North America. Environmental Science and Technology 46(21): 11693–11701. We include Quality Assurance Quality Controlled (QAQC) high-frequency dissolved oxygen, wind speed, and water temperature data from nine lakes and reservoirs in northeastern North America which were near the track of Tropical Cyclone Irene in August 2011. These data were collected using a set of in situ, automated monitoring systems associated with the Global Lake Ecological Observatory Network (GLEON) that record data at high frequency (10 min to 6 h). These sensor data were the basis for the derived measures of Schmidt stability, net ecosystem production, respiration, and gross primary production included in the dataset. We also include daily rainfall data collected at on-site or nearby weather stations. All data cover the period from 01 August through 15 October 2011.
High-frequency temperature data from four near-shore sites, Lake Sunapee, NH, USA, 2006-2018
Onset temperature loggers were located at four near-shore sites in Lake Sunapee, New Hampshire, USA. The temperature sensors were co-located at sites where monitoring for Gloeotrichia echinulata, a large colonial cyanobacterium, occurred (see EDI data package edi.497). Temperature loggers were deployed during the summer months (approximately June through September) in 2006-2018 at Herrick Cove South (site = HerrickCoveSouth), 2007-2018 South of the Fells (site = SouthOfTheFells), Newbury (site = Newbury), North Sunapee Harbor (site = NorthSunapeeHarbor).
High-Frequency Water Temperature Data at Lake Lillinonah, Connecticut, USA, 2018-current
An instrumented buoy maintained by Friends of the Lake and Fairfield University has been deployed on Lake Lillinonah (Connecticut, USA) during the summer months to collect high-frequency water quality data. The buoy is deployed early each summer and remains in the lake until late October. The instrumented buoy is equipped with an RBR temperature sensor chain, collected data every meter from a depth of 0.6 meters to 14.5 meters and every two meters to a depth of 18.5. meters. Data was collected every fifteen minutes. Included in this data package are data sets containing 15-minute data as well as daily averages. Prior to the deployment of the RBR temperature sensor, the instrumented buoy was equipped with a Nexsens T-node thermistor chain (2011-2015) with sensors at different depths. These data are available in the data package EDI557. Additional high-frequency water quality data for Lake Lillinonah including dissolved oxygen, specific conductance and pigment fluorescence are available in packages EDI559 and EDI560.
High-frequency water quality data for water temperature, dissolved oxygen, specific conductivity, pH, phycocyanin fluorescence and chlorophyll a fluorescence at Lake Lillinonah, Connecticut, USA, 2011-2017.
From 2011 to 2017, an instrumented buoy maintained by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University was deployed on Lake Lillinonah (Connecticut, USA) during the summer months to collect high-frequency water quality data. The buoy was deployed early each summer and remained in the lake until late October. The instrumented buoy was equipped with a YSI 6600 sonde at a depth of 1 meter. The YSI 6600 collected water temperature, dissolved oxygen, specific conductivity, pH, chlorophyll a fluorescence and phycocyanin fluorescence data. Additionally, the instrumented buoy is equipped with an In-Situ RDO sensor which collects water temperature and dissolved oxygen readings at a depth of 15 meters. Included in this data package are datasets containing 15-minute data as well as daily averages from all sensors. Following the summer of 2017, the YSI 6600 sonde was replaced with a YSI EXO2 sonde which collected data on the same variables. Data from the YSIEXO2 sonde, as well as data from the In-Situ RDO sensor during the same period, are available in the EDI data package EDI560. Additionally, the instrumented buoy at Lake Lillinonah is equipped with a water temperature thermistor chain . These data can be found in data packages EDI557 (2011-2015) and EDI558 (2018-current).
High-frequency water quality data for water temperature, dissolved oxygen, specific conductivity, pH, phycocyanin fluorescence and chlorophyll a fluorescence at Lake Lillinonah, Connecticut, USA, 2018-current.
Since 2011, an instrumented buoy maintained by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University has been deployed on Lake Lillinonah (Connecticut, USA) during the summer months to collect high-frequency water quality data. The buoy is deployed early each summer and remains in the lake until late October. Since 2018, the instrumented buoy has been equipped with a YSI EXO2 sonde at a depth of 1 meter. The YSI EXO2 collects water temperature, dissolved oxygen, specific conductivity, pH, chlorophyll a fluorescence and phycocyanin fluorescence data. Additionally, the instrumented buoy is equipped with an In-Situ RDO sensor which collects water temperature and dissolved oxygen readings at a depth of 15 meters. Included in this data package are datasets containing 15-minute data as well as daily averages from both sensors from 2018 to present. Prior to 2018, the instrumented buoy was equipped with a YSI 6600 sonde, which collected data on the same variables (2011-2017). Data from the YSI 6600 sonde, as well as data from the In-Situ RDO sensor during the same period, are available in the EDI data package EDI559. Additionally, the instrumented buoy at Lake Lillinonah is equipped with a water temperature thermistor chain sensor. These data can be found in data packages EDI557 (2011-2015) and EDI558 (2018-current).
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from February 2016 through December 2016
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from February through December 2016. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2017 through December 2017
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2017 through December 2017. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
High-frequency sensor data collected by Stroud Water Research Center in a meadow reach of White Clay Creek from Janurary 2018 through December 2018
High-frequency sensor data from a YSI 600 OMS Optical Monitoring System (every 15 minutes) and Sontek IQ (every 10 minutes) in a meadow reach at White Clay Creek from January 2018 through December 2018. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameters in this data package are water temperature, depth, turbidity, conductivity, specific conductance, water pressure, discharge, rivers ection area, and velocity. Data are presented in four tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. IQ_stream and YSI_stream present only the data from the respective sensors. These tables are gapfilled so that there are no time gaps. Formatted as a "wide" human-readable table. The full_stream table is all of the data, raw and cleaned, from both sensors. It is organized as a long, tidy table and is optimal for machine readability. All of the parameters and table are further explained in the metadata.
Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.
This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).
Data to reproduce the results presented in Sehgal et al. 2022. Water Resources Research, https://doi.org/10.1029/2021WR030624 ("Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer")
<p>This repository consists data to reproduce results as presented in: "Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer", Water Resorces Research. Kindly refer to the readme.text file to navigate through the dataset.</p> <p> </p> <p> </p>
Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")
<p>This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (<span>h</span><span>t</span><span>t</span><span>p</span><span>s</span><span>:</span><span>/</span><span>/</span><span>d</span><span>o</span><span>i</span><span>.</span><span>o</span><span>r</span><span>g</span><span>/</span><span>1</span><span>0</span><span>.</span><span>1</span><span>0</span><span>1</span><span>6</span><span>/</span><span>j</span><span>.</span><span>j</span><span>h</span><span>y</span><span>d</span><span>r</span><span>o</span><span>l</span><span>.</span><span>2</span><span>0</span><span>2</span><span>4</span><span>.</span><span>1</span><span>3</span><span>1</span><span>9</span><span>3</span><span>0).</span> Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.</p>
High-frequency sensor data collected by Stroud Water Research Center in a recovery reach of White Clay Creek site ID WCC017 from April through December 2018
High-frequency sensor data at 5-min intervals using a s::can®oxy::lizer IITM and Solinst level logger, an Apogee SQ-212 sensor, and an s::can® field spectrophotometer in a recovery reach at White Clay Creek from April through December 2018. These data were collected as part of a study focused on baseflow dynamics of DOC and nitrate in White Clay Creek, Stroud Water Research Center. The parameters in this data package are water temperature, dissolved oxygen, depth, Photosynthetically Active Radiation (PAR), dissolved oxygen concentration, dissolved organic carbon concentration (DOC) and nitrate concentration. Data are presented in two tables. All the parameters and table are further explained in the metadata.
High-frequency sensor turbidity data collected by Stroud Water Research Center in a meadow reach of White Clay Creek site ID WCC017 from January 2014 through May 2021
High-frequency sensor data from a Campbell Scientific OSB3 (every 5-minutes) in a recovery reach at White Clay Creek from January 2014 through May 2021. Funded by NSF and DEB as part of the LTREB grant to study the recovery of stream ecosystem structure and function during reforestation, Stroud Water Research Center. The parameter in this data package is turbidity. Data are presented in two tables which likely have significant overlap. The raw data table presents the data exactly as it was downloaded from the Aquarius Database. It is formatted as a "wide" human-readable table. Turbidity at White Clay Creek Site WCC017 since 2014 OSB3 presents only the data from the respective sensors. These tables are gap filled for intervals greater than 10 minutes and less than 24 hours, formatted as a "wide" human-readable table. All of the parameters and table are further explained in the metadata.
Data for: Performance of a high-frequency (180 kHz) acoustic array for tracking juvenile Pacific salmon in the coastal ocean
<p><b>Background</b></p> <p>Acoustic telemetry is now a key research tool used to quantify juvenile salmon survival, but transmitter size has limited past studies to larger smolts (>130 mm fork length). New, smaller, higher-frequency transmitters ("tags") allow studies on a larger fraction of the smolt size spectrum (>95 mm); however, detection range and study duration are also reduced, introducing new challenges. The potential cost implications are not trivial. With these new transmitters in mind, we designed, deployed, and tested the performance of a dual-frequency receiver array design in the Discovery Islands region of British Columbia, Canada. We double-tagged 50 juvenile steelhead (<i>Oncorhynchus mykiss</i>) with large 69 kHz tags (VEMCO model V9-1H) and small 180 kHz tags (model V4-1H). The more powerful 69 kHz tags were used to determine fish presence in order to estimate the detection efficiency (DE) of the 180 kHz tags. We then compared the standard error of the survival estimate produced from the tracking data using the two tag types which has important implications for array performance and hypothesis testing in the sea.</p> <p><b>Results</b></p> <p>Perfect detection of the 69 kHz tags allowed us to determine the DE of the 180 kHz tags. Although the 180 kHz tags began to expire during the study, the estimated DE was acceptable at 76% (SE = 9%) when we include single detections. However, confidence intervals on steelhead survival (64%) were 1.5x larger for the 180 kHz tags (47-85% vs. 51-77% for 69 kHz) because of the reduced DE.</p> <p><b>Conclusions</b></p> <p>The array design performed well; however, single detections of the 180 kHz tags indicates that under slightly different circumstances the DE could have been compromised, emphasizing the need to carefully consider the interaction of animal migration characteristics, study design, and tag programming when designing telemetry arrays. To increase DE and improve the precision of 180 kHz-based survival estimates presented here requires either an increase in receiver density, an increase in tag sample size (and modified transmitter programming), or both. The optimal solution depends on transmitter costs, array infrastructure costs, annual maintenance costs, and array use (i.e., contributors). Importantly, the use of smaller tags reduces potential tag burden effects and allows early marine migration studies to be extended to Pacific salmon populations that have been previously impossible to study.</p>
High-frequency measurements of aeolian saltation flux: time series data
<p>High-frequency (25-50 Hz) coupled observations of wind speed and aeolian saltation flux (i.e, the wind-blown movement of sand) were measured at three field sites: Jericoacoara, Brazil; Rancho Guadalupe, California; and Oceano, California. The dataset provided here contains the full record of raw and processed time series of saltation flux and wind speed measured at multiple heights above the sediment surface.</p>
Lab-scale membrane bioreactor (MBR) high-frequency online data
<p>This dataset is composed of 4 periods of more or less continuous operation, but with varying length. The measured online data, with a frequency of per second, are stored chronologically in 4 compressed csv-files 'Data_MBR_Period_x'. The installation was running with some fixed and variable settings. The variable settings are stored in 'Settings_MBR'. They were only logged when changed, making this a small file. All additional information on the used sensors etc. is listed in the 'Metadata'-file, including the values of the fixed settings. Finally, 'Schematic.png' gives an overview of the MBR setup and the location of the sensors.</p>
Data and code for the paper titled "Impact of the Tibetan Plateau on Global High-frequency Temperature Variability"
<p>This is the data and code for the paper titled "<span>Impact of the Tibetan Plateau on </span><span><span>G</span><span>lobal High-frequency Temperature Variability</span></span>", which was submitted to <em>Journal of Climate</em>.</p>
Supporting data for "The high-frequency tidal response of ocean worlds: Application to Europa and Ganymede"
<p>Data files containing the static Love numbers that are used to compute the dynamic Love numbers.</p> <p> </p>
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