This is a literature review paper that did not generate any new data. This dataset is not publicly accessible because: This work did not produce new data. It can be accessed through the following means: Data mentioned in this literature review can be accessed by accessing the original sources of information, as cited within the review. Format: This paper is a literature review (i.e., no new data generated). Sources of information are appropriately cited. This dataset is associated with the following publication: Triantafyllidou, S., J. Burkhardt, J. Tully, K. Cahalan, M. DeSantis, D. Lytle, and M. Schock. Variability and sampling of lead (Pb) in drinking water: Assessing potential human exposure depends on the sampling protocol. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 146: 106259, (2021).

Processed data and codes for this study.

This dataset includes daily two-band Enhanced Vegetation Index (EVI2) at 30-m resolution over a Landsat scene (path 26 and row 31) in central Iowa. Fourteen years of daily EVI2 from 2001 to 2015 (except 2012) were generated through fusing and interpolating Landsat-MODIS data.Landsat surface reflectances were order and used in this study. Mostly clear Landsat images from each year were chosen to pair with MODIS images acquired from the same day to generate daily Landsat-MODIS surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Partially clear Landsat images were also used in generating the smoothed and gap-filled daily VI time-series. All available Landsat data including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) were used in this study.The MODIS data products were downloaded and processed. These include the daily surface reflectance at both 250m (MOD09GQ) and 500m (MOD09GA) resolution, the MODIS Bidirectional Reflectance Distribution Function (BRDF) parameters at 500m resolution, and the MODIS land cover types at 500m resolution (MCD12Q1). They were used to generated daily nadir BRDF-adjusted reflectance (NBAR) at 250m resolution for fusing with Landsat.The Landsat-MODIS data fusion results for 2001-2014 were generated from a previous study (Gao et al, 2017; doi: 10.1016/j.rse.2016.11.004). Data fusion results for 2015 were generated using Landsat 8 OLI images from day 194, 226, 258 and 338 in this study. Cloud masks were extracted from Landsat and MODIS QA layers and were used to exclude cloud, cloud shadow and snow pixels. Since Landsat 5 TM operational imaging ended in November 2011 and Landsat 8 OLI has not been launched until February 2013, Landsat 7 ETM+ Scan Line Corrector (SLC)-off images are the only available Landsat data. For this reason, 2012 was not included.Due to the cloud contamination in the Landsat and MODIS images, the fused Landsat-MODIS results still have invalid values or gaps. To fill these gaps, a modified Savitzky-Golay (SG) filter approach was built and applied to smooth and gap-fill EVI2. The SG filter is a moving fitting approach. Each point is smoothed using the value computed from the polynomial function fit to the observations within the moving window. The program removes spike points if the fitting errors are larger than the predefined threshold (default 3 standard deviation). The modified SG filter allows us to retain small variations but also fill large gaps in an unevenly distributed time-series EVI2.Daily EVI2 files are saved in one tar file per year. Each tar file contains a binary image file and a text header file that can be displayed in the ENVI software. The binary image file has the dimension of 7201 lines by 8061 samples by 365 days and is saved in BIP (band interleaved by pixel) format. EVI2 data are saved in 4-byte float number. The text header file contains necessary information including projection and geolocation. Daily EVI2 file is named as "flexfit_evi2.026031.yyyy.bin", where "026031" refers to the Landsat path and row, and yyyy represents year and ranges from 2001-2015.Resources in this dataset:Resource Title: Daily EVI2 Data Packages .File Name: Web Page, url: https://app.globus.org/file-manager?origin_id=904c2108-90cf-11e8-9672-0a6d4e044368&origin_path=/LTS/ADCdatastorage/NAL/published/node22870/These Daily EVI2 data packages are grouped by year. Each package includes a plain binary file that saves daily EVI2, and a ENVI header file (in text) that contains metadata and geolocation information. Contents are as follows: dailyVI.026031.2000.tar.gz dailyVI.026031.2001.tar.gz dailyVI.026031.2002.tar.gz dailyVI.026031.2003.tar.gz dailyVI.026031.2004.tar.gz dailyVI.026031.2005.tar.gz dailyVI.026031.2006.tar.gz dailyVI.026031.2007.tar.gz dailyVI.026031.2008.tar.gz dailyVI.026031.2009.tar.gz dailyVI.026031.2010.tar.gz dailyVI.026031.2011.tar.gz dailyVI.026031.2013.tar.gz dailyVI.026031.2014.tar.gz dailyVI.026031.2015.tar.gzSCINet users: The .tar.gz files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node22870/See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

This repository contains all data used in publication "Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects". Latitude and longitude information has been replaced with x,y,z coordinates to protect location information of data collected on private land. More coding information/data analysis can be found in the corresponding GitHub repository: https://github.com/JaleesaHoule/wind_environment_characterization.

Number/Letter System:

_1 ---> Sensor A _2 ---> Sensor B _3 ---> Sensor C _4 ---> Sensor D _5 ---> Sensor E _6 ---> Sensor F _7 ---> Sensor G _8 ---> Sensor H _9 ---> Sensor I

If a sensor was vertically orientated, there will be an additional underscore to denote this: _verticallyorientated.

Column Names/Meanings:

X_ , Y_, Z_ => masked x,y, and z coordinates for each sensor U_, V_, W_ => u,v, and w wind vectors for each sensor S2_ => 2D Wind Speed. Each underscored number corresponds to a sensor A-I. (i.e, S2_1 is sensor A, S2_2 is sensor B, etc.). The number system is preserved to the same corresponding letter throughout the datasets. Note that some dfs are missing sensors due to sensor errors or orientation (i.e, the vertically oriented sensor data is not included in these data sets) D_ => Directional data in degrees. Same number/letter system (see below). time => in epoch time

Note that some sensors may be missing in a dataset due to recording malfunctions or encounters with local wildlife.

Inter-annual variability measures the variation in water supply between years.

The files provided here are the supporting data and code files for the analyses presented in "Variability in Consumption and End Uses of Water for Residential Users in Logan and Providence, Utah, USA", an article submitted to the JWRPM (https://ascelibrary.org/journal/jwrmd5). The journal paper assessed how differences water consumption are reflected in terms of timing and distribution of end uses across residential properties. The article provides insights into the variability of indoor and outdoor residential water use at the household level from the analysis of four to 23 weeks of 4-second resolution water use data at 31 single family residential properties. The data was collected in the cities of Logan and Providence, Utah, USA between 2019 and 2021. The 4-second resolution data is publicly available on: http://www.hydroshare.org/resource/0b72cddfc51c45b188e0e6cd8927227e. Standardized monthly values for single family residents in both cities were used int he article and are publicly available on: http://www.hydroshare.org/resource/16c2d60eb6c34d6b95e5d4dbbb4653ef. The code and data included in this resource allows replication of the analyses presented in the journal paper, and the raw data included allow for extension of the analyses conducted.

Dashboard - Coping with Climate Variability - Book by Colleen Vogel published 1 time between 2017 and 2017

The repository contains the data and code to reproduce the study "Immigrant birds use payoff biased social learning in spatially variable environments". Please consult the readme file for file descriptions and locations, and descriptions of variable names. We simulated immigration events between captive experimental populations of great tits (Parus major) to test whether spatial variability in environmental cues or payoffs affected the degree to which immigrant birds used social information. We analyzed birds' preferences before and after immigration, and used Bayesian learning models to understand the mechanisms behind change (or lack-thereof) in preferences. Behavioral data was collected using automated puzzle boxes in an experiment using captive wild-caught great tits (Parus major). The experiment took place over 2 periods: Jan-March 2021, and Jan-March 2022. All work was conducted by under a nature conservation permit and animal ethics permit from the Regierungsprasidium Freiburg, no.35-9185.81/G-20/100.

Variability in epilimnion depth estimations in lakes Analysis codes and output codes available For input codes please contact author harriet.wilso@dkit.ie or available from Lough Feeagh and Lake Erken data providers:

Lough Feeagh: http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.2817 Lake Erken : available on request

Analytical data in support of the manuscript "Wintertime Variability of Surface Currents on West Southern Taiwan Strait", which is submitted to Journal of Geophysical Research: Oceans. One could use the data to generate any figure in the manuscript.

Abstract Eight reference stations in south-eastern South America are analysed to derive the auto-covariance and auto-correlations of maximum and minimum temperature. Through the analysis are observed oscillations of periods between 18 and 25 years in the series studied. These periodicities show changes over time, especially in the 1950-1970 period. The exception is Río Gallegos where a periodicity of 18 years is observed throughout the entire record. Persistence is defined as the first correlation coefficient and is a function of the number of negative terms in the auto-covariance. However for some years it is mainly driven by the magnitude of the positive terms given by extreme warm or cold outbreaks. Between 1950 and 1970 it was observed an increased variability in the analysed properties. These changes suggest a variation in the frequency of different circulation patterns having a direct impact on the regional thermal structure. Likewise, this kind of variations can have serious socio-economic impacts as these directly affect the frequency and duration of extreme events. This is relevant in the design of consistency and quality control methods to detect outliers or systematic errors.

This data holds the historical and spliced data required to run the convolutional neural network document in Sweeney et al., 2023 submitted to GRL. Data is stored in two folders (historical and spliced), and individual models are put into each category and separated by ensemble number, time period of trend, variable type (surface air temp or sea level pressure), latitude and longitude.

The water supply seasonal variability is a normalized indicator of the variation in water supply between months of the year. Estimations are given for the year 2014. Seasonal variability is calculated as the standard deviation of monthly total blue water divided by the mean of total blue water calculated using the monthly mean. The indicator was created by the World Resources Institute (WRI) and ranges from 0-5, where 0 is lowest and 5 is highest. Values represent the "All-sector" indicator, and have been rounded to the nearest tenth by AQUASTAT.For more information, see WRI original analysis here: wri.org/publication/aqueduct-country-river-basin-rankings.Visit the FAO Aquastat website: http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en

The Spitzer Deep, Wide-Field Survey (SDWFS) is a four-epoch infrared survey of 10 square degrees in the Boötes field of the NOAO Deep Wide-Field Survey using the IRAC instrument on the Spitzer Space Telescope. SDWFS, a Spitzer Cycle 4 Legacy project, occupies a unique position in the area-depth survey space defined by other Spitzer surveys. The four epochs that make up SDWFS permit - for the first time - the selection of infrared-variable and high proper motion objects over a wide field on timescales of years. Because of its large survey volume, SDWFS is sensitive to galaxies out to z ~ 3 with relatively little impact from cosmic variance for all but the richest systems. The SDWFS data sets will thus be especially useful for characterizing galaxy evolution beyond z ~ 1.5.The SDWFS Variability Catalog presents variability information for all SDWFS sources with a 5-sigma detection at 3.6 microns. For more details, see Kozlowski et al. (2010).

Field data used to support numerical simulations of variably-saturated flow focused on variability in soil-water retention properties for the U.S. Geological Survey Bay Area Landslide Type (BALT) Site #1 in the East Bay region of California, USA

ssh_front_climatology_1993Southern Ocean front locations obtained from AVISO gridded SSH using the WHOSE method.ssh_front_locations_1993.ncssh_front_locations_1994Southern Ocean front locations obtained from AVISO gridded SSH using the WHOSE method.SSH front locations 1995Southern Ocean front locations obtained from AVISO gridded SSH using the WHOSE method.ssh_front_locations_1995.ncSSH front locations 1996Southern Ocean front locations obtained from AVISO gridded SSH using the WHOSE method.ssh_front_locations_1996.ncSSH front locations 1997Southern Ocean front locations obtained from AVISO gridded SSH using the WHOSE method.ssh_front_locations_1997.ncSSH front locations 1998Southern Ocean front locations obtained from AVISO gridded SSH using the WHOSE method.ssh_front_locations_1998.ncSSH front locations 1999Southern Ocean front locations obtained from AVISO gridded SSH using the WHOSE method.ssh_front_locations_1999.ncSSH front locations 2000Southern Ocean front locations obtained fro...

Older adults following recovery from ischemic stroke have a higher incidence of orthostatic hypotension, syncope, and fall risk, which may be related to impaired autonomic responses limiting the ability to maintain cerebral blood flow. Thus, we investigated cerebrovascular and cardiovascular regulation in 23 adults ≥55 years of age, 10 diagnosed with ischemic stroke, and 13 age-matched healthy controls when sitting at rest and upon standing to compare differences of autonomic variables at ∼7 months (218 ± 41 days) poststroke.

The datasets consist of ROMS model data generated at INCOIS, India and can be used for the analysis of Bay of Bengal upper ocean characteristics.

Measuring adaptive capacity as a key component of vulnerability assessments has become one of the mostchallenging topics in the climate change adaptation context. Numerous approaches, methodologies and con-ceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assess-ments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless,they encompass various implications regarding scale specificity and the robustness issues embedded in thechoice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity indexdeveloped for Italy's regional and sub-regional administrative levels, as a part of the National Climate ChangeAdaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and tenindicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As aninnovative feature of our analysis, the sub-regional variability of the index feeds back into the regional levelassessment. The results show that composite indices estimated at higher administrative or statistical levels ne-glect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies.By considering the intra-regional variability, different patterns of adaptive capacity can be observed at regionallevel as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators thatreflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with dif-ferent compensability are preferable. Our results show that within-region variability can be better demonstratedby bottom-up aggregation methods.