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Supplemental data for unpublished PNAS manuscript 201816339.
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nanopore_03262018.fastq.gzDescription: DNA sample of individual NS12911 was sequenced by Oxford Nanopore (ONT) MinION using flowcell FLO-MIN106D. The library was constructed using ONT 1D Genomic DNA by ligation protocol. Basecalling was performed using ONT¡¯s Albacore 2.0.2 and the reads are provided in the fastq format.CallSetsDescription: Raw SV call outputs from the seven pipelines for the four datasets.Simulation_Chr20Description: Files for simulating nanopore reads from chromosome 20 using NanoSim.
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This VCF file contains 162,365 SNVs identified across 160 individuals by whole exome sequencing that were used in the study. Allele counts (AC), total allele number (AN), and allele frequencies (AF) for either Low Aneuploidy Rate group (LRG) or High Aneuploidy Rateg group (HRG) were specified in the INFO tags.
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C++ code ported from NetLogo code. Colon Crypt Model 110514 G.nlogo written in the application NetLogo. The NetLogo code and model are available at https://doi.org/doi:10.7282/T3KH0QKV. The colon crypt model is described in the publication: Bravo R, Axelrod D. A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments. Theoret Biol Med Model. 2013;10:66-89, http://www.tbiomed.com/content/10/1/66
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Supplemental tables 1 and 2.
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This project builds on our conceptual model to generate salt marsh vulnerability maps (i.e., MarshFutures maps) that assess seaward edge erosion, platform “elevation capital”, and landward migration and which predict the fate for selected Marshes of Interest (MOIs). Seven MOIs were studied: 2 in Delaware Bay; 3 in Great Bay; and, 2 in Barnegat Bay/Little Egg Harbor. This concept of elevation capital relates accretion rates and the tidal zone that dominant plants require for optimal growth to estimate the long-term prognosis of a vegetated marsh under a regime of rising sea levels. If marsh accretion rates are higher than rate of sea level rise, then elevation capital is increasing. Conversely, if marsh accretion rates are below the rate of sea level rise, then marsh elevation capital is decreasing. If any parts of MOIs are deemed to not be keeping pace with sea level rise, we have estimated years until drowning based on the remaining elevation capital. To aid in place-based decision-making the above assessments are combined to generate salt marsh vulnerability maps that highlight those geographic areas most susceptible to conversion over the coming decades. The combined modeling and mapping was undertaken at two different scales of analysis: site level (1-2 ha with a grain size of 1 m grid cells) and landscape level (across entire state of New Jersey with a grain size of 10 m grid cells). The results of the two scales were compared to determine: 1) the comparative accuracy of the landscape scale approach for planning purposes; and, 2) the value of the more refined site-level modeling approach. The data for this project was compiled by the Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA), Rutgers University, the Partnership for the Delaware Estuary, the Barnegat Bay Partnership, and Jacques Cousteau National Estuarine Research Reserve, with funding from the National Oceanic and Atmospheric Association.NOTE: The README is available at http://dx.doi.org/doi:10.7282/T38W3H37
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Three processed, alignment BAM files; three indexing BAI files, one file containing the raw RNA-seq data for all three samples, and an Excel file with the instructions on what was done and how to use the files.
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This is the archive of the Newark Celebration 350 website featuring events and programs from the commemoration of Newark’s 350th Anniversary year.
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  • Dataset
(a) A summary of the sites included in the Common Era database, (b) a summary of the tide gauges incorporated into the metaanalysis, (c) hyperparameters of the different priors for the empirical hierarchical model, (d) prior estimates of GSL rates and amplitude of variability under different priors, (e) posterior estimates of GSL rates and amplitude of variability under different priors, (f) GSL rates under different data subsets, (g) RSL rates at different sites, (h) semiempirical estimates of the probability that the observed GSL rise exceeds counterfactual projections, (i) semiempirical GSL projections, and (j) the distribution of semiempirical model parameters
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This is the archive of Newark Celebration 350's Facebook page commemorating the yearlong celebration of Newark’s rich 350-year history by celebrating the talents of its citizenry and its remarkable accomplishments.
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  • Dataset