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1. Simulation Files for Organic Contaminants and Atmospheric Nitrogen at the Graphene–Water Interface

   https://doi.org/10.5281/zenodo.6050816  

   Thakkar, Ravindra ; Sandun, Gajaweera ; Comer, Jeffrey ( January 2022 , Zenodo)

   This data set provides files needed to run the simulations described in the manuscript entitled "Organic contaminants and atmospheric nitrogen at the graphene–water interface: A simulation study" using the molecular dynamics software NAMD and LAMMPS. The output of the simulations, as well as scripts used to analyze this output, are also included. The files are organized into directories corresponding to the figures of the main text and supplementary information. They include molecular model structure files (NAMD psf), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD or LAMMPS configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and some trajectories in dcd format (downsampled). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts. A modified LAMMPS C++ source file is also included.

   This material is based upon work supported by the National Science Foundation under Grant No. DMR-1945589. A majority of the computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CHE-1726332, CNS-1006860, EPS-1006860, and EPS-0919443. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) and Stampede2 at the Texas Advanced Computing Center through allocation BIO200030, which is supported by National Science Foundation grant number ACI-1548562. 

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2. COAWST model simulations of warm and cool nearshore rip-current plumes

   https://doi.org/10.5281/zenodo.4272080  

   Moulton, Melissa ( January 2020 , Zenodo)

   This archive contains COAWST model input, grids and initial conditions, and output used to produce the results in a submitted manuscript. The files are:

   model_input.zip: input files for simulations presented in this paper
     ocean_rip_current.in: ROMS ocean model input file
     swan_rip_current.in: SWAN wave model input file (example with Hs=1m)
     coupling_rip_current.in: model coupling file
     rip_current.h: model header file
     
   model_grids_forcing.zip: bathymetry and initial condition files
        hbeach_grid_isbathy_2m.nc: ROMS bathymetry input file
        hbeach_grid_isbathy_2m.bot: SWAN bathymetry input file
        hbeach_grid_isbathy_2m.grd: SWAN grid input file
        hbeach_init_isbathy_14_18_17.nc: Initial temperature, cool surf zone dT=-1C case
        hbeach_init_isbathy_14_18_19.nc: Initial temperature, warm surf zone dT=+1C case
        hbeach_init_isbathy_14_18_16.nc: Initial temperature, cool surf zone dT=-2C case
        hbeach_init_isbathy_14_18_20.nc: Initial temperature, warm surf zone dT=+2C case
        hbeach_init_isbathy_14_18_17p5.nc: Initial temperature, cool surf zone dT=-0.5C case
        hbeach_init_isbathy_14_18_18p5.nc: Initial temperature, warm surf zone dT=+0.5C case

   model_output files: model output used to produce the figures
        netcdf files, zipped
        variables included:
             x_rho (cross-shore coordinate, m)
             y_rho (alongshore coordinate, m)
             z_rho (vertical coordinate, m)
             ocean_time (time since initialization, s, output every 5 mins)
             h (bathymetry, m)
             temp (temperature, Celsius)
             dye_02 (surfzone-released dye)
             Hwave (wave height, m)
             Dissip_break (wave dissipation W/m2) 
             ubar (cross-shore depth-average velocity, m/s, interpolated to rho-points)
        Case_141817.nc: cool surf zone dT=-1C Hs=1m
        Case_141819.nc: warm surf zone dT=+1C Hs=1m
        Case_141816.nc: cool surf zone dT=-2C Hs=1m
        Case_141820.nc: warm surf zone dT=-2C Hs=1m
        Case_141817p5.nc: cool surf zone dT=-0.5C Hs=1m
        Case_141818p5.nc: warm surf zone dT=+0.5C Hs=1m
        Case_141817_Hp5.nc: cool surf zone dT=-1C Hs=0.5m
        Case_141819_Hp5.nc: warm surf zone dT=+1C Hs=0.5m
        Case_141817_Hp75.nc: cool surf zone dT=-1C Hs=0.75m
        Case_141819_Hp75.nc: warm surf zone dT=+1C Hs=0.75m

   COAWST is an open source code and can be download at https://coawstmodel-trac.sourcerepo.com/coawstmodel_COAWST/. Descriptions of the input and output files can be found in the manual distributed with the model code and in the glossary at the end of the ocean.in file.

   Corresponding author: Melissa Moulton, mmoulton@uw.edu

    

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3. Experimentation Framework for Wireless Communication Systems under Jamming Scenarios Dataset

   https://doi.org/10.5281/zenodo.4749668  

   Jacovic, Marko ( January 2021 , Zenodo)

   Data files were used in support of the research paper titled "“Experimentation Framework for Wireless
   Communication Systems under Jamming Scenarios" which has been submitted to the IET Cyber-Physical Systems: Theory & Applications journal. 

   Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar
   Contact: krd26@drexel.edu

   ---------------------------------------------------------------------------------------------

   Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. 

   --------------------------------

   logs:    - data logs collected from devices under test
       - 'defenseInfrastucture' contains console output from a WARP 802.11 reference design network. Filename structure follows '*x*dB_*y*.txt' in which *x* is the reactive jamming power level and *y* is the jaming duration in samples (100k samples = 1 ms). 'noJammer.txt' does not include the jammer and is a base-line case. 'outMedian.txt' contains the median statistics for log files collected prior to the inclusion of the calculation in the processing script. 
       - 'uavCommunication' contains MGEN logs at each receiver for cases using omni-directional and RALA antennas with a 10 dB constant jammer and without the jammer. Omni-directional folder contains multiple repeated experiments to provide reliable results during each calculation window. RALA directories use s*N* folders in which *N* represents each antenna state. 
       - 'vehicularTechnologies' contains MGEN logs at the car receiver for different scenarios. 'rxNj_5rep.drc' does not consider jammers present, 'rx33J_5rep.drc' introduces the periodic jammer, in 'rx33jSched_5rep.drc' the device under test uses time scheduling around the periodic jammer, in 'rx33JSchedRandom_5rep.drc' the same modified time schedule is used with a random jammer. 

   --------------------------------

   parsers:    - scripts used to collect or process the log files used in the study
           - 'defenseInfrastructure' contains the 'xputFiveNodes.py' script which is used to control and log the throughput of a 5-node WARP 802.11 reference design network. Log files are manually inspected to generate results (end of log file provides a summary). 
           - 'uavCommunication' contains a 'readMe.txt' file which describes the parsing of the MGEN logs using TRPR. TRPR must be installed to run the scripts and directory locations must be updated. 
           - 'vehicularTechnologies' contains the 'mgenParser.py' script and supporting 'bfb.json' configuration file which also require TRPR to be installed and directories to be updated. 

   --------------------------------

   rayTracingEmulation:    - 'wirelessInsiteImages': images of model used in Wireless Insite
               - 'channelSummary.pdf': summary of channel statistics from ray-tracing study
               - 'rawScenario': scenario files resulting from code base directly from ray-tracing output based on configuration defined by '*WI.json' file 
               - 'processedScenario': pre-processed scenario file to be used by DYSE channel emulator based on configuration defined by '*DYSE.json' file, applies fixed attenuation measured externally by spectrum analyzer and additional transmit power per node if desired
               - DYSE scenario file format: time stamp (milli seconds), receiver ID, transmitter ID, main path gain (dB), main path phase (radians), main path delay (micro seconds), Doppler shift (Hz), multipath 1 gain (dB), multipath 1 phase (radians), multipath 1 delay relative to main path delay (micro seconds), multipath 2 gain (dB), multipath 2 phase (radians), multipath 2 delay relative to main path delay (micro seconds)
               - 'nodeMapping.txt': mapping of Wireless Insite transceivers to DYSE channel emulator physical connections required
               - 'uavCommunication' directory additionally includes 'antennaPattern' which contains the RALA pattern data for the omni-directional mode ('omni.csv') and directional state ('90.csv')

   --------------------------------

   results:    - contains performance results used in paper based on parsing of aforementioned log files
    

    

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4. A biodiversity dataset graph: DataONE

   https://doi.org/10.5281/zenodo.1486279  

   Poelen, Jorrit H. ( January 2018 , Zenodo)

   The intended use of this archive is to facilitate meta-analysis of the Data Observation Network for Earth (DataONE, [1]). 

   DataONE is a distributed infrastructure that provides information about earth observation data. This dataset was derived from the DataONE network using Preston [2] between 17 October 2018 and 6 November 2018, resolving 335,213 urls at an average retrieval rate of about 5 seconds per url, or 720 files per hour, resulting in a data gzip compressed tar archive of 837.3 MB .  

   The archive associates 325,757 unique metadata urls [3] to 202,063 unique ecological metadata files [4]. Also, the DataONE search index was captured to establish provenance of how the dataset descriptors were found and acquired. During the creation of the snapshot (or crawl), 15,389 urls [5], or 4.7% of urls, did not successfully resolve. 

   To facilitate discovery, the record of the Preston snapshot crawl is included in the preston-ls-* files . There files are derived from the rdf/nquad file with hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f . This file can also be found in the data.tar.gz at data/8c/67/e0/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f/data . For more information about concepts and format, please see [2]. 

   To extract all EML files from the included Preston archive, first extract the hashes assocated with EML files using:

   cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\t' '\n' | grep "hash://" | sort | uniq > eml-hashes.txt

   extract data.tar.gz using:

   ~/preston-archive$ tar xzf data.tar.gz 

   then use Preston to extract each hash using something like:

   ~/preston-archive$ preston get hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa
   <eml:eml xmlns:eml="eml://ecoinformatics.org/eml-2.1.1" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:stmml="http://www.xml-cml.org/schema/stmml_1.1" packageId="doi:10.18739/A24P9Q" system="https://arcticdata.io" scope="system" xsi:schemaLocation="eml://ecoinformatics.org/eml-2.1.1 ~/development/eml/eml.xsd">
     <dataset>
       <alternateIdentifier>urn:x-wmo:md:org.aoncadis.www::d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>
       <alternateIdentifier>d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>
       <title>Airglow Image Data 2011 4 of 5</title>
   ...

   Alternatively, without using Preston, you can extract the data using the naming convention:

   data/[x]/[y]/[z]/[hash]/data

   where x is the first 2 characters of the hash, y the second 2 characters, z the third 2 characters, and hash the full sha256 content hash of the EML file.

   For example, the hash hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa can be found in the file: data/00/00/2d/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa/data . For more information, see [2].

   The intended use of this archive is to facilitate meta-analysis of the DataONE dataset network. 

   [1] DataONE, https://www.dataone.org
   [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . DataONE was crawled via Preston with "preston update -u https://dataone.org".
   [3] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\t' '\n' | grep -v "hash://" | sort | uniq | wc -l
   [4] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\t' '\n' | grep "hash://" | sort | uniq | wc -l
   [5] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep  "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\t' '\n' | grep -v "hash://" | sort | uniq | wc -l

   This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.

    

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5. Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

   https://doi.org/10.5281/zenodo.8057232  

   wang, zhuonan ; Kumar, Jitendra ; R., Samantha Weintraub-Leff ; Todd-Brown, Katherine ; Mishra, Umakant ; Sihi, Debjani ( January 2023 , Zenodo)

   Data Description:

   To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.

   Files in collection (32):

   Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts

   gNATSGO TIF files:

   ├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage]
   ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]
   ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]
   ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]
   ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]
   ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]
   ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]
   ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]
   ├── depthWT_30arc_us.tif                                                        [depth to water table]
   ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]
   ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]
   ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]
   ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]
   ├── pondingFre_30arc_us.tif                                                     [ponding frequency]
   ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]
   ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]
   ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]
   ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]
   ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]
   └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]

   SOC TIF files:

   ├──30cm SOC mean.tif                             [30 cm depth soil SOC]
   ├──100cm SOC mean.tif                           [100 cm depth soil SOC]
   ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]
   └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]

   site observations csv files:

   ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

   ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

   
   Data format:

   Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.

   Geospatial projection: 

   GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]]

    

    

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