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.

Variation in traits is essential for natural selection to operate and genetic and environmental effects can contribute to this phenotypic variation. From domesticated populations, we know that families can differ in their level of within-family variance, which leads to the intriguing situation that within-family variance can be heritable. For offspring traits, such as birth weight, this implies that within-family variance in traits can vary among families and can thus be shaped by natural selection. Empirical evidence for this in wild populations is however lacking. We investigated whether within-family variance in fledging weight is heritable in a wild great tit (Parus major) population and whether these differences are associated with fitness. We found significant evidence for genetic variance in within-family variance. The genetic coefficient of variation (GCV) was 0.18 and 0.25, when considering fledging weight a parental or offspring trait, respectively. We found a significant quadratic relationship between within-family variance and fitness: families with low or high within-family variance had lower fitness than families with intermediate within-family variance. Our results show that within-family variance can respond to selection and provides evidence for stabilizing selection on within-family variance.

Heart Rate Variability Analysis System Market size was valued at USD 13.53 Billion in 2023 and is projected to reach USD 37.23 Billion by 2031, growing at a CAGR of 10.7% during the forecast period 2024-2031.

Global Heart Rate Variability Analysis System Market Drivers

The market drivers for the Heart Rate Variability Analysis System Market can be influenced by various factors. These may include:

Data security and privacy issues: Since the healthcare industry handles sensitive patient data, outsourcing RCM procedures may give rise to questions with data security, privacy, and third-party vendor trust, as well as regulatory compliance (such as HIPAA in the US).

Issues with Regulatory Compliance: There are many regulations in the healthcare sector. Modifications to healthcare laws, billing standards, or policies may make it difficult for outsourcing partners to maintain compliance, which could result in inefficient operations.

Global Heart Rate Variability Analysis System Market Restraints

Several factors can act as restraints or challenges for the Heart Rate Variability Analysis System Market. These may include:

Data security and privacy issues: Since the healthcare industry handles sensitive patient data, outsourcing RCM procedures may give rise to questions with data security, privacy, and third-party vendor trust, as well as regulatory compliance (such as HIPAA in the US).

Issues with Regulatory Compliance: There are many regulations in the healthcare sector. Modifications to healthcare laws, billing standards, or policies may make it difficult for outsourcing partners to maintain compliance, which could result in inefficient operations.

Processed data and codes for this study.

The current dataset contributes to assess the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Heart Rate Variability (HRV) and ElectroDermal Activity (EDA) signals were recorded over six experimental conditions (i.e., Baseline, Video Clip, Scream, No Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The raw quality of the physiological signals was enhanced by means of robust semi-automatic reconstruction algorithms. Heart Rate Variability time-domain parameters showed high accuracy in motion-free experimental conditions, while Heart Rate Variability frequency-domain parameters reported sufficient accuracy in almost every experimental condition.

Folder 01 contains both HRV and EDA parameters for every experimental condition, according to the Gold Standard measurement system and the Empatica 4 device, in two separate Excel files.

Folder 02 contains supplementary material on the assessment of the signals quality.

Folder 03 contains the Bland-Altman plot for each HRV and EDA parameter and for each condition (1 .png file per each parameter), and an excel file that resumes the Bland-Altman analyses numerical outcomes.

Variability data (JHK bands) of several star-forming regions, cleaned by Tom Rice's custom data reduction software.

This is version 2, with an updated errorbar-correction approach, to be described in an upcoming paper (Rice et al. 2022, in prep).

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.

a shallow-water acoustic variability experiment (savex15) was conducted from 14th to 28th of may 2015 in the northeastern east china sea focusing on a relatively small area (~10 by 10 km) ~100 km southwest of jeju island, republic of korea, where water depth is ~100 m. during the experiment, two vertical line arrays (vlas) equipping hydrophones were deployed in the area where 25 temperature loggers (u12-015 hobo) and 5 star-oddi temperature-depth-tilt sensors were attached to collect time-series of water temperature at nominal depths ranging from 22 to 80 m and 2 to 22 m, respectively, with a sampling time interval of 30 sec. along with the moored sensors, conductivity-temperature-depth (ctd, 24-hz sea-bird electronics 911plus) and underway ctd (uctd, 16-hz teledyne oceanscience) were used to collect ship-based vertical profiles of water temperature and salinity data. typical descending speeds of the ctd and uctd were less than ~1 and ~4 m s-1, respectively, and the uctd data were collected in three different modes – freefall, tow-yo, and static modes at ship speeds of 2–10 kt. the total number of vertical profiles collected using ctd and uctd were 26 and 1026, respectively. the uploaded data files contain variables in netcdf format that are obtained using the vlas, ctd, and uctd during the savex15, processed, quality controlled, and quality assured.

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).

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.

Thompson, A., Schel, M. A., & Steinbeis, N. (2021). NeuroImage

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).

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

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.

a new version of the isas product is available at : https://doi.org/10.17882/52367 the monthly fields of temperature ans salinity produced by the isas-13 analysis have been averaged over the period 2004-2014 to produce a monthly and annual climatology. we provide here : the annual mean temperature, salinity and pressure fields, the monthly mean temperature, salinity the monthly mean mixed layer depth (computed according to temperature: mldt, or density: mlds criteria) . the temperature and salinity variance of the data relative to the monthly mean cycle and the number of avalable data.

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.

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.

This repository contains our raw data, as well as the scripts we used to process them for our ISSTA 2024 submission, "An Empirical Study of Static Analysis-Based Variability Bug Detection."The structure of this repository is as follows:- raw_data: Our raw results from the three analyses we performed.- data: Intermediate data we used to generate figures and tables in the paper.- scripts: Scripts that are used to process data and generate other intermediate data, or figures and tables from the paper.Both data and scripts have their own READMEs explaining each item.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate with three maps that show the mean annual number of days with measurable precipitation, the mean annual number of days with measurable snowfall, and the variability of annual precipitation. A day with sufficient measurable precipitation (a precipitation day) is considered as a day on which the recorded rainfall amounts to one one-hundredth of an inch (0.0254 cm) or more, or the snowfall measured is one-tenth of an inch (0.254 cm) or more. At any one location the annual precipitation may vary considerably from one year to the next. This variability of annual precipitation is expressed in terms of the coefficient of variation. This coefficient is obtained by dividing the standard deviation of the annual precipitation by the mean annual precipitation.

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...