The eastern coastal basins of Brazil are a series of small and isolated rivers that drain directly into the Atlantic Ocean. During the Pleistocene, sea-level retreat caused by glaciations exposed the continental shelf, resulting in enlarged paleodrainages that connected rivers that are isolated today. Using Geographic Information System (GIS), we infer the distribution of these paleodrainages, and their properties for the east Brazilian coast. Here, we publicly make available the shapefiles that demonstrate the paleodrainage structure along the Brazilian coast during the largest sea-level retreats in the Pleistocene, the riverine vectors during the same period and the coastal line for a drop of -125m in the sea.
Percent Weight Change Data:
The model was run continuously on a daily time step for seasonal intervals (Spring: March thru May; Summer: June thru August; Fall: September thru November) as well as contiguously from Spring to Fall to assess total growth over the likely growing season (March thru November). CSV files represent the simulated weight change (%) of Bighead and Silver Carp for the respective time periods associated with the file name. Initial fish mass for each seasonal interval and growing season was 4350 g for Silver Carp and 5480 g for Bighead Carp. Maximum and mean total weight change (%) was determined for three depth ranges (near surface depths [NS]: 0 – 10 m; deep chlorophyll layer depths [DCL]: 10 - 50 m; and whole water column [WC]). Coordinates are in decimal degrees.
File naming convention: speciesSeasonWtChange (e.g. bigheadFallWtChange = % weight change of Bighead Carp from September through November)
Monthly Habitat Quality Data:
Rdata files contain matrices of Bighead or Silver carp growth rate potential as represented as a mass-proportional growth rate (gram of carp/gram of carp/day [g/g/d]) for the 15th day of each month. Habitats with growth rate potential >= 0 g/g/d were deemed suitable.
Rows: Row numbers refer to the spatial node with 20 equally-spaced vertical layers.
Columns: Columns 1-20 refer to the growth rate potential value for each vertical layer of each node. Vertical layers are evenly spaced based on the total depth of the water column for each node. Depth for each node can be found in the grid attributes data file. Columns 21 ("meanG") and 22 ("Gmax") represent the average and maximum growth rate potential, respectively, of the fish across the whole water column for the corresponding node.
File naming convention: species_MonthNumber (e.g. silver_06 = Silver carp growth rate potential in June)
Spatial coordinates for each node can be found in the grid attributes data files.,
Grid attributes data:
This Rdata file provides the spatial reference data and other grid attributes. Coordinates are provided in UTM (x & y) and latitude and longitude (decimal degrees). Depth (meters) for each node is listed in this file.
, GRP Model code:
Details bioenergetics equations, foraging equation, functions for running the model on a monthly time-step and daily time step, and functions for basic analyses. Model is coded in R., and
The simulated input data (prey and temperature) used to run our model is not included in this data set. Instead we provide the model code, grid attributes, and outputs of the model.
The readRDS() function (R Base Package v.3.5.1) is required to read in .Rdata files in R.
This dataset contains all data used to generate the figures in The Cryosphere manuscript “Measuring Snow Specific Surface Area with 1.30 and 1.55 micro-meter Bidirectional Reflectance Factors,” by Adam Schneider, Mark Flanner, and Roger De Roo. These data support the theory, calibration, and application of the Near-Infrared Emitting and Reflectance Monitoring Dome (NERD), an instrument engineered to rapidly retrieve surface snow specific surface area in the field. Note that this deposit includes a microCT scan database for natural snowfall samples collected in New Hampshire during 2015-2017, comprised of raw tiff files as well as reconstructions, binarized reconstructions, and some 3D model reconstructions. and Running python scripts generally require that the following packages are installed: NumPy, SciPy, Matplotlib, Pandas, and ipdb (for debugging).
The data were collected as part of the Stewardship Gap project, an 18-month study to investigate how research data and creative outputs supported by public or non-profit funding in the United States are being stewarded. These data were collected as part of a literature search of sources about research data stewardship and relate most directly to work describing “What We Know About the Stewardship Gap.” In this work, we categorized “gaps” in stewardship identified in the literature, how the gaps were related to one another, and efforts to measure and develop metrics for the gaps.
Investigating minimum human reaction times is often confounded by the motivation, training, and state of arousal of the subjects. We used the reaction times of athletes competing in the shorter sprint events in the Athletics competitions in recent Olympics (2004-2016) to determine minimum human reaction times because there's little question as to their motivation, training, or state of arousal.
The reaction times of sprinters however are only available on the IAAF web page for each individual heat, in each event, at each Olympic. Therefore we compiled all these data into two separate excel sheets which can be used for further analyses.
In this work , we study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs.
We consider multi-stage interactions between a network administrator and cybercriminals, formulated as a security game.
We propose parameterized heuristic strategies for the attacker and defender and provide detailed analysis of their time complexity.
Our heuristics exploit the topological structure of attack graphs and employ sampling methods to overcome the computational complexity in predicting opponent actions.
Due to the complexity of the game, we employ a simulation-based approach and perform empirical game analysis over an enumerated set of heuristic strategies.
Finally, we conduct experiments in various game settings to evaluate the performance of our heuristics in defending networks, in a manner that is robust to uncertainty about the security environment.
Raw SNP genotypes are provided in STRUCTURE format, with a maximum of one SNP reported per ddRAD locus. The files "caryco_SNP.str" and "caryov_SNP.str" are genotypes for Carya cordiformis and Carya ovata, respectively. The first column of each file is the individual name, the second column is the population (see original publication for information on population locations), and the remaining columns are genotypes of individual SNPs. Rows represent individuals, with the diploid genotypes contained on two lines per individual. Missing data are entered as "0" (zero). The first row is a header with a unique identifier for each SNP. and Occurrence records for each species are provided in the file "occs_carya.csv" and contain the latitude and longitude of each record.