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1,118 results for “Time series”
Data from: Soil chemical variation along a four-decade time-series of reclaimed water amendments in northern Idaho forests
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Predator-prey time series: Monthly densities of least Killifish and Eastern Mosquitofish over five years
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Bat-aggregated time series workflow
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Organic carbon, temperature, oxygen, and inflow volume time series for Falling Creek Reservoir, Vinton, Virginia: Summer 2014
Weekly and subweekly monitoring data collected from June to October 2014 were used to study organic carbon dynamics in Falling Creek Reservoir, a drinking water reservoir owned and managed by the Western Virginia Water Authority and located in Vinton, Virginia, USA. The dataset consists of: 1) daily inflow volumes and the temperature of water entering the reservoir, measured at a gauged weir on Falling Creek Reservoir’s primary inflow tributary; 2) weekly to subweekly depth profiles of temperature and oxygen collected at the deepest site of the reservoir adjacent to the dam; and 3) approximately weekly grab samples of dissolved organic carbon and dissolved carbon dioxide collected from subsurface (0.1 m depth) and just above the sediments (9.0 m depth). This dataset also includes approximately weekly grab samples of organic carbon and dissolved carbon dioxide in the inflow tributary measured at the gauged weir, dissolved oxygen concentrations in the inflow tributary measured at the gauged weir, and organic carbon deposition rates measured at 8 m depth at the deepest site of the reservoir approximately every 4 weeks with sedimentation traps.
Multidecadal Time Series of Measured Chlorophyll-a in Lakes and Estuarine-Coastal Ecosystems, 1966-2024
The photosynthetic pigment chlorophyll-a is a commonly measured index of phytoplankton biomass and water quality across all aquatic ecosystem types. Some monitoring and research programs have sustained chlorophyll-a measurements for decades at monthly or higher frequency. Each of these time series is an invaluable record of phytoplankton variability at a particular location. The patterns of that variability have been essential for identifying the underlying processes of phytoplankton change at time scales of days, months, seasons, years and decades. Multidecadal series are rare and valuable because they provide empirical records of phytoplankton changes over the recent decades of unprecedented global change. These records also provide an empirical basis for comparing patterns and rates of change across geographic regions and ecosystem types. This data package contains multidecadal time series of measured chlorophyll-a concentration in three ecosystem types: 134 freshwater lakes (including a small number of reservoirs) that do not freeze; 78 high latitude lakes that do freeze; and 176 coastal ecosystems defined as water bodies where freshwater and seawater mix, including estuaries, coastal bays and lagoons, tidal rivers, and the Baltic Sea. Although there are other published compilations of chlorophyll-a time series, this package was compiled specifically to report observations made at monthly or higher frequency and sustained over multiple decades. The mean time series duration in this package is 33 years, and the mean number of sampling dates per site was 503 (range 186 to 2381). Thus, this data package provides an empirical basis for analyses to measure and compare decadal-scale patterns and rates of phytoplankton biomass variability between inland lakes and water bodies at the land-ocean interface. All chl-a measurements reported here were accessed from published repositories, except these four sites. We acknowledge and thank the following data providers for perm
Temperature and Relative Humidity Time Series across 60 forest plots at Sagehen Creek Field Station, 2016-2019
Database contains data downloaded from HOBO loggers placed at 60 sites within the Sagehen Experimental Forest. These plots are a subset of 500+ forest monitoring plots established in 2004 and 2005 for the purpose of testing strategically-placed land area treatments (SPLATS) that impede forest fire progression (Vaillant 2008, UC Berkeley Doctoral Dissertation). HOBO loggers sampled dates between fall 2016 and spring 2019. Some sites have intermittent data due to deactivation for logging activities and some interference from being buried in snow or from wild animals. Please see comments in the plot information file (Logger_Plot_Data.csv) detailing all plot metadata. This monitoring is ongoing through the Sagehen Forest Monitoring Project. Contact the Tahoe National Forest for forest treatment dates, additional information and GIS data.
High-frequency time series of stage height, stream discharge, and water quality (specific conductivity, dissolved oxygen, pH, temperature, turbidity) for Stroubles Creek in Blacksburg, Virginia, USA 2013-2018
A pressure transducer (CS450, Campbell Scientific Inc., Logan, UT, USA) and a multi-parameter water quality sonde (YSI Series 6:6920 v2, YSI Inc., Yellow Springs, OH, USA) were deployed by the Virginia Tech Biological Systems Engineering (BSE) Stream Research, Education, and Management (StREAM) Lab (vtstreamlab.weebly.com/) from January 2013 to December 2018 to assess stage and water quality, and calculate discharge in Stroubles Creek, Blacksburg, VA (4118326.215 N, 549236.02 W). Stroubles Creek is a 19 km tributary of the New River in Blacksburg, Virginia, USA. Stroubles Creek begins immediately below the Virginia Tech campus and flows north into the Kanawha River in the Valley and Ridge physiographic province. Stroubles Creek was a historic water supply for the town of Blacksburg and remains a highly visible waterway in the town, as it traverses both the downtown area and much of the Virginia Tech campus. In 2002, Stroubles Creek was placed on Virginia’s 303(d) list of impaired water bodies with a benthic impairment by the Virginia Department of Environmental Quality (VADEQ). A total maximum daily load (TMDL) plan was subsequently developed in 2003 for the stream in collaboration with Virginia Tech’s BSE department, the Virginia Department of Environmental Quality, and the Virginia Department of Conservation and Recreation. During 2008-2010, a 2.1 km reach of Stroubles Creek, mostly upstream of the StREAM Lab, was restored by means of cattle exclusion, natural revegetation, bank reshaping, and creating an inset floodplain. The water quality dataset provided here is associated with the upper Stroubles Creek watershed which drains approximately 14.5 km2. Currently, data from the StREAM Lab are used by 8-10 faculty and their graduate students in 7 departments across 5 colleges at Virginia Tech for research and at least 16 classes for field labs and/or assignments.
Dissolved silica time series for Beaverdam Reservoir, Carvins Cove Reservoir, Claytor Lake, Falling Creek Reservoir, Gatewood Reservoir, Smith Mountain Lake, and Spring Hollow Reservoir in southwestern Virginia, USA during 2014
Water column dissolved silica (SiO2) was analyzed during 2014 in seven freshwater reservoirs in southwestern Virginia (VA), USA. These reservoirs are: Beaverdam Reservoir (Vinton, VA), Carvins Cove Reservoir (Roanoke, VA), Claytor Lake (Pulaski, VA), Falling Creek Reservoir (Vinton, VA), Gatewood Reservoir (Pulaski, VA), Smith Mountain Lake (Bedford, VA), and Spring Hollow Reservoir (Salem, VA). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia; Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia; and Smith Mountain Lake is jointly treated by Bedford Regional Water Authority and Western Virginia Water Authority as a drinking water source for Franklin County, Virginia. Claytor Lake is utilized for hydroelectric power generation by Appalachian Power Company. The dataset consists of depth profiles of dissolved silica samples generally measured at the deepest site of each reservoir adjacent to the dam and an inflow stream into Falling Creek Reservoir. The water column samples were collected approximately fortnightly from April-June, weekly from June-July and sporadically from July-October at Beaverdam Reservoir; weekly from April-July and fortnightly from July-November at Carvins Cove Reservoir; sporadically from April-August at Claytor Lake; weekly from April-November at Falling Creek Reservoir; fortnightly from April-October at Gatewood Reservoir; fortnightly from May-November at Spring Hollow Reservoir; and fortnightly from May-October at Smith Mountain Lake.
Time series of iron (II) and sulfate concentrations for Beaverdam and Falling Creek Reservoirs in southwestern Virginia, USA during 2016
Water chemistry data were collected approximately weekly from May through October 2016 to study the biogeochemical cycling in Falling Creek Reservoir (FCR) and Beaverdam Reservoir (BVR). FCR and BVR are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, USA. The dataset consists of depth profiles of sulfate (SO4) and dissolved Fe(II) samples measured at the deepest site of each reservoir adjacent to the dam. The dissolved Fe(II) samples were speciated from total dissolved Fe using the ferrozine method adapted from Viollier et al. (2000).
Time series of environmental parameters and organic matter analyses for dissolved and particulate organic matter in the Neuse River Estuary, North Carolina, USA 2015-2016
Environmental parameters and organic matter analyses (concentration, absorbance, fluorescence) for dissolved and particulate organic matter for the Neuse River Estuary, North Carolina, USA from 20 July 2015-28 July 2016. Samples were collected bi-weekly from July 2015-October 2015 and March 2016-July 2016 and monthly from November 2015-February 2016. The dataset consists of environmental parameters measured (water temperature, salinity, percent dissolved oxygen, turbidity, chlorophyll-a) and calculated (flushing time) as well as organic matter analyses for dissolved and particulate organic matter (concentration, absorbance, fluorescence) from surface (0.2 m below surface) and bottom (0.5 m above bottom) at 11 stations from the furthest extent of saltwater intrusion (Station 0) to the mouth of the estuary (Station 180). Data were collected as part of the Neuse River Estuary Modeling and Monitoring Program (ModMon; http://paerllab.web.unc.edu/projects/modmon/) at the University of North Carolina - Chapel Hill, Institute of Marine Science.
McMurdo Dry Valleys Lake Vanda thermocline temperature time series
This data set includes the deployment of anchored thermistors Lake Vanda, Wright Valley, as part of an Antarctica New Zealand project. Thermistors were positioned at the sediment-water interface and 10 cm above the lake bottom within the thermocline at 24 m depth. This dataset is funded through the New Zealand Foundation for Research, Science and Technology grant # CO1X0306 and the NASA grant NNX13AI60G, with USAP event G-063. This project is not funded through the MCM LTER program. MCM LTER hosts this synergistic project.
MCR LTER: Coral Reef: Seawater pH, Temperature and Depth Time Series from Bottom-mounted Sensors on the Fringing Reef, January-March 2012
Bottom-mounted instrumentation (SeaFET, Seabird thermisters, Hobo water level data loggers) sampled for 8 weeks on the fringing reef of Moorea Island, French Polynesia at site LTER Fringe 1. Sampling began in January 2012. The instruments were secured to a cement piling at 3.3 meters depth and 0.7 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. Adjacent to the SeaFET were two thermistors and two HOBO® water level data loggers, synchronized with the SeaFET to simultaneously record temperature and depth.
Taiwan Coral Reef: Seawater pH, Temperature and Depth Time Series from Bottom-mounted Sensors on the Fringing Reef in Nanwan Bay, May-July 2012
Bottom-mounted instrumentation (SeaFET, Seabird conductivity/temperature sensor, Hobo water level data loggers) sampled for 7 weeks on the Hobihu fringing reef in Nanwan Bay, Taiwan. Sampling began in May 2012. The instruments were secured to anchored fencing stakes at 4 meters depth and 0.6 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. Adjacent to the SeaFET were a Seabird sensor and two HOBO® water level data loggers, synchronized with the SeaFET to simultaneously record conductivity, temperature and depth.
SBC LTER: Time series of kelp biomass in the canopy from Landsat 5, 1984 -2011
This data package has been deprecated because the models/methods have been changed. Please see the new data package using updated models: Kelp canopy biomass from Landsat 5, 7, and 8. These data are a time series of canopy biomass of the giant kelp, Macrocystis pyrifera, derived from LANDSAT 5 Thematic Mapper satellite imagery. The kelp canopy is composed of the portions of fronds floating on the surface of the water. Biomass data (wet weight, kg) are given for individual 30 x 30 meter pixels in the coastal areas extending from near Pt. Reyes, California, to Punta Abreojos, Baja California Sur, Mexico, including the Northern and Southern Channel Islands. Data were derived from Landsat 5 TM images. Observations are made on a 16 day reqeat cycle but the temporal coverage is irregular because of cloud cover and instrument failure. Estimates of kelp canopy biomass are derived from the relationship between satellite surface reflectance and empirical measurements of kelp canopy biomass in long-term SBC LTER study plots obtained using SCUBA. Data are organized into yearly files, and zipped to compress. In its entirety this dataset is large, more than 3 GB and 80 million lines. To receive an archive of all data, or for assistance with subsets (e.g., range of dates or a latitude/longitude) contact sbclter@msi.ucsb.edu.
A time series of images of egret and cormorant colonies on Chimney Pole Marsh, VA 2010-2011
This dataset contains images used to observe double-crested cormorant and great egret nests on Chimney Pole Marsh, VA, a low-lying barrier marsh on the Atlantic coast of the Delmarva Peninsula. The camera was in operation June 15, 2010 to July 7, 2010, Aug, 3, 4 and 31, 2010, and May 10 through Aug. 11, 2011. Images were captured roughly once every 10 seconds during the day, resulting in a total of 287,564 images.
Time series for log_10(DLL/10), for the years 1995-2019 and omega = 1mHz
<p>This is an accompanying set of files for the article entitled "Electromagnetic Radial Diffusion in the Earth's Radiation Belts as Determined by the Solar Wind Immediate Time History and a Toy Model for the Electromagnetic Fields", by Lejosne, 2020</p> <p>The format is described in the file headers.<br> Please refer to the article for definitions and discussion of the method<br> </p>
Multivariate time series for testing -- RacketSports dataset
<p>The original data was retrieved from http://www.timeseriesclassification.com/description.php?Dataset=RacketSports</p> <p>Original data description:<br> The data was created by university students plyaing badminton or squash whilst wearing a smart watch (Sony Smart watch 35). The watch relayed the x-y-z coordinates for<br> both the gyroscope and accelerometer to an android phone (One Plus 56). The phone<br> wrote these values to an Attribute-Relation File Format (arff) file using an app developed<br> by a UEA computer science masters student. The problem is to identify which sport and which stroke the players are making. The data was collected at a rate of 10 HZ over 3 seconds whilst the player played<br> either a forehand/backhand in squash or a clear/smash in badminton.<br> The data was collected as part of an undergraduate project by Phillip Perks in 2017/18.</p> <p>Pre-processing<br> Data processing was done as described in: https://github.com/NLeSC/mcfly-tutorial/blob/master/utils/tutorial_racketsports.py<br> The original data was split into train and test set. Here the data was loaded and further divided into train, test, validation sets.<br> To keep it simple we here simply divided the original test part into test and validation.<br> The resulting data was stored as numpy .npy files.</p> <p>The zip file contains three sets of time series data (X_train, X_test, X_valid) and the respective labels (y_train, y_test, y_valid).</p> <p>Reference:<br> http://www.timeseriesclassification.com/description.php?Dataset=RacketSports<br> (The data was collected as part of an undergraduate project by Phillip Perks in 2017/18.)</p>
Machine-learning-long-term-wind-power-time-series
<p>In this research work we assess how time-series generated by machine learning models (MLM) compare to Renewables.ninja in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. With this archive, we want to make three machine learning derived time-series available in the Feather format for everyone interested.</p>
Time series of snow cover area products over the Kananaskis Country
<p>This dataset contains maps of the snow cover area over the Kananaskis Country (Canada) from 01 September 2017 to 31 August 2018. The products were derived from Sentinel-2 observations. All available Sentinel-2 level 1C products were processed to level 2A (surface reflectance and cloud mask) using the <a href="https://github.com/CNES/Start-MAJA">MAJA software</a>. Then, level 2A products were processed using the <a href="https://gitlab.orfeo-toolbox.org/remote_modules/let-it-snow/">LIS software</a> to generate the snow cover maps.</p> <p>The area is covered by four tiles: T11UPT (81 dates), T11UPS (116 dates), T11UNT (114 dates), T11UNS (87 dates). The data are provided as GeoTIFF images coded as follows:</p> <ul> <li>0: No-snow</li> <li>100: Snow</li> <li>205: Cloud including cloud shadow</li> <li>255: No data</li> </ul> <p>Read more about these products in <a href="https://labo.obs-mip.fr/multitemp/snow-cover-duration-in-the-canadian-rockies-from-sentinel-2-observations/">this blog post</a>.</p>
High-resolution wind power generation time series for Germany in the period 2000-2015
<p>High-resolution wind power generation time series for Germany in the period 2000-2015. A paper describing the applied methodology can be found in this repository as well.</p> <p>The final temporal resolution is hourly and the spatial resolution NUTS 3. The data is stored in csv and hdf5 files.</p> <p>More information on the data set, e.g. missing time stamps and versioning, can be found in readme.txt.</p>
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.