This dataset is derived from a study exploring the Effect of Example Variability on the Implicit Learning of Multiple Non-adjacent Rule. The dataset contains all behavioral data and corresponding statistical analysis codes from two experiments in the study. Please refer to the attached Readme.txt file for file description and data structure, which provides detailed information on data format, variable meanings, and code execution instructions.

Heart rate variability data.

Climate Variability and Predictability (CLIVAR)

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

The United Nations Framework Convention on Climate Change (UNFCCC) requires the annual reporting of greenhouse gas emissions. These inventories focus on emissions within a territory, and do not capture the effect of de-carbonization in developed countries that has resulted simply by the relocation of emissions-intensive production to other countries. Consumption based carbon accounting (CBCA) has been proposed as a complementary method to capture the emissions occurring globally due to final demand in a country. A number of global models have been developed in the last decade in order to operationalise CBCA. However, direct comparison of results from different models yields significant discrepancies in country-level CBCA, which causes concern for the practical use of CBCA. There is a body of existing work on model intercomparison and reliability, but this literature has largely overlooked a main use case of CBCA results: trends over time. To facilitate temporal intercomparison, we present results of all the major global models and normalise the model results by looking at changes over time of each model relative to a common base year value. We give an analysis of the variability across the models, both before and after normalisation in order to give insights into robustness (variance) at both national and regional level. The paper is accompanied by the dataset of CBCA results of each country/year with harmonised results (based on the means) and measures of dispersion, providing a useful and often requested baseline dataset for CBCA validation and analysis.

We designed novel field experimental infrastructure to resolve the relative importance and interactions among changes in precipitation mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance in a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. We collected samples of soils, biological soil crusts, leaves of the foundation plant species, and roots of the two dominant grass species each year during peak productivity (September-October). These samples enable us to address the question: How do interactions between the mean and variance of precipitation alter the biogeochemistry and microbiomes of plants and soils. This data package includes accession numbers for all samples collected from the Mean x Variance Experiment at the Sevilleta National Wildlife Refuge, Socorro, NM.

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.

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.

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.

ABSTRACT: Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments.

The dem.csv dataset contains the Digital Elevation Model of the study site, while measurements.csv are the measured variables along the 15 measuring occasions of the study.

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.

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.

This study describes a novel method to reweight a comparison group used for causal inference, so the group is similar to a treatment group on observable characteristics yet avoids highly variable weights that would limit statistical power. The proposed method generalizes the covariate-balancing propensity score (CBPS) methodology developed by Imai and Ratkovic (2014) to enable researchers to effectively prespecify the variance (or higher-order moments) of the matching weight distribution. This lets researchers choose among alternative sets of matching weights, some of which produce better balance and others of which yield higher statistical power. We demonstrate using simulations that our penalized CBPS approach can improve effect estimates over those from other established propensity score estimation approaches, producing lower mean squared error. We discuss applications where the method or extensions of it are especially likely to improve effect estimates and we provide an empirical example from the evaluation of Comprehensive Primary Care Plus, a U.S. health care model that aims to strengthen primary care across roughly 3000 practices. Programming code is available to implement the method in Stata.

Understanding temporal variability and synchrony across biological hierarchical levels (from species to metacommunities) and ecosystems, and their underlying drivers, remains a fundamental ecological question. However, most studies are performed within an ecosystem, overlooking the complexity structuring natural high-level metacommunities across ecosystems. By applying theoretical frameworks of metacommunity variability with phytoplankton datasets across a pond-stream-lake continuum, we show that (i) temporal variability decreases from species to metacommunities, while synchrony exhibits complex hierarchical patterns and varies depending on spatial scales; (ii) temporal variability and synchrony are lower in streams than ponds or the lake, and spatiotemporal community-environment relationships are stronger in the lake than the ponds or streams. These patterns are strongly related to environmental fluctuations, dispersal and species diversity. Our study advances the theoretical and empir..., , , Title: biotic variability and synchrony across hierarchical levels and freshwater networks

We have submitted our raw data (The data supporting the results of the manuscript.xlsx).

Description of files:

Data are compiled in an Excel spreadsheet with 10 tabs containing the following information:

1. Fig.3_data, data supporing for Fig.3;
2. Fig.4_data, data supporing for Fig.4;
3. Fig.5ABC_data, data supporing for Fig.5ABC;
4. Fig.5DEF_data, data supporing for Fig.5DEF;
5. Fig.5GHI_data, data supporing for Fig.5GHI;
6. Fig.5JKL_data, data supporing for Fig.5JKL;
7. Fig.5MNO_data, data supporing for Fig.5MNO;
8. Richness_data，data may be interesting for audiences
9. Cell density_data，data may be interesting for audiences
10. Species list_data，data may be interesting for audiences

Variables:

Species variability: temporal variability in abundance at species level (Note overall that abundance is cell density (cells/mL))

Community variability: temporal variability in abundance at comm...,

Data for "Quantifying variability in Lagrangian particle dispersal in ocean ensemble simulations: an information theory approach"

This repository contains the post-processed data: the connectivity statistics and the hexbin probability distributions for all the cases and ensemble members. The data.zip files contains files necessary for the post-processing, such as the hexbin grid used for the binning the particles.

The code and scripts to run the Lagrangian analysis can be found in the following repository: https://doi.org/10.5281/zenodo.17310522

Please write to Thierry Penduff to get access to the NATL025-CJMCYC3 output files.

Processed data and codes for this study.

1. Several devastating forest pathogens are suspected or known to have entered the UK through imported planting material. The nursery industry is a key business of the tree trade network. Variability in demand for trees makes it difficult for nursery owners to predict how many trees to produce in their nursery. When in any given year, the demand for trees is larger than the production, nursery owners buy trees from foreign sources to match market demand. These imports may introduce exotic diseases.
2. We have developed a model of the dynamics of plant production linked to an economic model to quantify the effect of demand variability on the risk of introducing an exotic disease.
3. We find that
4. when the cost of producing a tree in a UK nursery is considerably smaller than the cost of importing a tree (in the example presented, less than half the importing cost), the risk of introducing an exotic disease is hardly affected by an increase in demand variability.
5. when the cost of produc...

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).

Database primary productivity and metapopulation dynamicsNestorFernandez_DataArvicola.csv