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2,516 results for “Seri”
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>
plan4res - public dataset for case study 1 part MIM-1: time series used for multi-modal investment pathway modelling
<p>Public data set which is used within the plan4res project for performing case study 1 "Multi-modal European energy concept for achiving COP21" - Multi-modal Investment modelling (MIM) Part 1: Time series for the reference year 2015</p> <p>The related documentation is included in plan4res' deliverable D4.5 chapter 3.2 (see 10.5281/zenodo.3785010) </p> <p>The data set includes the following data:</p> <p>a) characteristic annual load profiles for large industrial heat demand for chemical, iron & steel, food & beverage and pulp & paper industries for the reference year 2015</p> <p>HOTMAPS__TD_OUT_D_CHEM__20200608T160653__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_FOOD__20200608T160724__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_IRON__20200608T160705__20200422T120000Z__v01.csv HOTMAPS__TD_OUT_D_PAPER__20200608T160715__20200422T120000Z__v01.csv </p> <p>b) characteristic demand profiles for road-side car passenger transport and availability of cars for charging while (home) parking for the reference year 2015 </p> <p>SIEMENS__TD_OUT_D_RoadCar__20200608T160627__20200401T120000Z__v01.csv SIEMENS__TD_CAP_CarPark__20200608T160637__20200401T120000Z__v01.csv </p> <p>c) load profiles for exogeneous demand of electricity for the reference year 2015. The exogenous demand includes all electricity consumptions not explicitly modeled within MIM modeling.</p> <p>HRE4__TD_OUT_ElectricityExo__20200608T160732__20200401T120000Z__v01.csv </p> <p>c) regionally resolved demand profiles for (individual) space heating and space cooling for the reference year 2015</p> <p>HRE4__TRD_CAP_Cool_2015__20200608T160051__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_HeatInd_2015__20200608T155849__20200401T120000Z__v01.csv</p> <p>d) regionally resolved generation profiles of electricity from photovoltaic, wind onshore, wind offshore, hydro run-of-river, and for heat generation from solar thermal for the reference year 2015</p> <p>NINJA__TRD_CAP_PV_2015__20200608T160440__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindOFF_2015__20200608T155422__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindON_2015__20200608T155251__20191104T120000Z__v01.csv HRE4__TRD_CAP_HydroRoR_2015__20200608T155550__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_SolarThermal_2015__20200608T155718__20200401T120000Z__v01.csv </p> <p>e) regionally resolved generation profile of electricity from wind offshore transformed in a way to represent potential capacity factors in future as stated by doi:10.2760/041705. Data based on reference year 2015</p> <p>SIEMENS__TRD_CAP_WindOFF_2040__20200608T155127__20200401T120000Z__v01.csv </p> <p>x) A list of geographical description of the zone hierarchy data used in MIM for the EU33 region set.:</p> <p>SIEMENS__ZoneHierarchy_MIM_EU33__20181231T120000Z___20200131T1200000Z__v001.csv </p> <p>Further info:</p> <p>Time series are based on historical data for the reference year 2015. </p> <p>Values are normalized over one reference year in a way that either the maximum = 1 (CAP) or the integral = 1 (OUT).</p> <p>All values are listed in arbitrary units. </p> <p>All country names are according to ISO 3166-1 alpha-2.</p>
Science Communication Webinars Series: Developing Public Outreach Activities
<p>Blue-Action coordinated a series of webinars on climate science communication in 2020. These were aimed at early-career researchers, but anyone who wishes to engage people with their research at any stage will find them valuable.</p> <p>This webinar was delivered on 22nd May 2020 by <a href="https://www.sciencefestival.co.uk/staff/creative">Laura Curtis-Moss</a>, part of the Blue-Action <a href="http://www.blue-action.eu/index.php?id=3503&L=788">Societal Engagement Group</a>. Laura works as a freelance education and science engagement specialist <a href="http://www.linkedin.com/in/lauramosseducation">www.linkedin.com/in/lauramosseducation</a>. </p>
InSAR Time-series of Jakobshavn and Petermann from Sentinel-1 Data
<p>Dataset 1: Sentinel-1 ascending track 90, descending track 127</p> <p>Study areas: Jakobshavn glacier in Greenland. We separate Jakobshavn into three individual areas (N, NE, and S) based on different reference locations.</p> <p>Date: Ascending: April 2016 to March 2020; Descending: July 2017 to April 2020.</p> <p>Processor: ISCE/topsStack + MintPy</p> <p>Dataset 2: Sentinel-1 ascending track 90, descending track 26</p> <p>Study areas: Petermann glacier in Greenland. </p> <p>Date: Ascending: April 2017 to April 2020; Descending: January 2017 to April 2020.</p> <p>Processor: ISCE/topsStack + MintPy</p>
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
<p><strong>Background</strong>. We evaluated the impact of a pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care.<br> <strong>Methods and findings</strong>. SMASH comprised: (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in forty-three general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having one or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% confidence interval [CI], 20.3% to 36.8%) at 24 weeks and by 40.7% (95% CI, 29.1% to 54.2%) at twelve months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%) at 24 weeks and by 23.5% (95% CI, -4.5% to 61.6%) at 12 months. After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. We did not randomise practices but enrolled them in a naturalistic fashion. All our measurements were based on routinely kept electronic health records.<br> <strong>Conclusions</strong>. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of high-risk prescribing between practices.</p>
Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series
<p>The datasets contain extreme time series obtained from the post-processed 3D wave fields simulated using HOS-Ocean, a high-order spectral model (HOSM) that solves the deterministic propagation of nonlinear wave fields in deep water (Ducrozet et al., 2016).</p> <p>Voermans. (2020). Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4028014</p> <p> </p>
Simulated time series and indicators for the Zambezi River Basin case study
<p>The archive includes:</p> <ul> <li>monthly time series over 2020-2059 time horizon of different physical variables, simulated by the Decision Analytic Framework (DAF) Strategic model of the Zambezi River Basin for the 7 interesting pathways identified in the Negotiation Simulation Lab meeting;</li> <li>a set of evaluation indicators, aggregated at different spatial and temporal scales, for the 7 interesting pathways.</li> </ul> <p>More details on physical variables and indicators included are available in the DAFNE Deliverable D7.3.</p> <p>Files are provided in the <em>csv </em>format, one per each physical variable or indicator: the first 13 rows contain metadata, while data starts at row 14 and use as many columns as the pathways considered. Metadata are the following:</p> <ul> <li><em>name</em>, time series or indicaror label</li> <li><em>author, </em>reference person or team for the specific time series</li> <li><em>created,</em> timestamp of the file creation</li> <li><em>description, </em>description of file content</li> <li><em>unit, </em>unit of measurement</li> <li><em>origin, </em>description of the simulation</li> <li><em>time_sampling,</em> whether the variable is sampled at a given point in time (instantaneous) or represents a time interval (interval)</li> <li><em>frequency, </em>time step of the time series</li> <li><em>scenario, </em>label of scenario</li> <li><em>marker_id, </em>label of the related model component</li> <li><em>id_type, </em>type of time series</li> <li><em>timestart,timeend,P1,P2,P3,P4,P5,P6,P7 : </em>header row of the data, with time information (in the matlab datenum format) at the beginning and pathways on each column</li> </ul>
Data from: The clinical, histological, and genotypic spectrum of SEPN1-related myopathy: a case series
<p><b><span>Objective: </span></b><span>To clarify the prevalence, long-term natural history and severity determinants of SEPN1-related myopathy (SEPN1-RM), we analyzed a large international case series. </span></p> <p><b>Methods: </b>Retrospective clinical, histological and genetic analysis of 132 pediatric and adult patients (2-58 years) followed-up for several decades.</p> <p><b><span>Results: </span></b><span>The clinical phenotype was marked by severe axial muscle weakness, spinal rigidity and scoliosis (86.1%, from 8.9±4 years), with relatively-preserved limb strength and previously-unreported ophthalmoparesia in severe cases. All patients developed respiratory failure (from 10.1±6 years), 81.7% requiring ventilation while ambulant. Histopathologically, 79 muscle biopsies showed large variability, partly determined by site of biopsy and age. Multi-minicores were the most common lesion (59.5%), often associated with mild dystrophic features and occasionally with eosinophilic inclusions. Identification of 65 SEPN1 mutations, including 32 novel ones and the first pathogenic CNV, unveiled exon 1 as the main mutational hotspot and revealed the first genotype-phenotype correlations, bi-allelic null mutations being significantly associated with disease severity (<i>p</i>=0.017). SEPN1-RM was more severe and progressive than previously thought, leading to loss of ambulation in 10% cases, systematic functional decline from the end of the third decade and reduced lifespan even in mild cases. The main prognosis determinants were scoliosis/respiratory management, <i>SEPN1</i> mutations and body mass abnormalities, which correlated with disease severity. Finally, we propose a set of severity criteria, provide quantitative data for outcome identification and establish a need for age stratification.</span></p> <p><b><span>Conclusion</span></b><span>: Our results inform clinical practice, improving diagnosis and management, and represent a major breakthrough for clinical trial readiness in this not-so-rare disease.</span></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
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.