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1. A biodiversity dataset graph: DataONE

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

   Poelen, Jorrit H. (January 2018, Zenodo)

   {"Abstract":["The intended use of this archive is to facilitate meta-analysis of the Data Observation Network for Earth (DataONE, [1]). <\/p>\n\nDataONE 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 .  <\/p>\n\nThe 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. <\/p>\n\nTo 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]. <\/p>\n\nTo extract all EML files from the included Preston archive, first extract the hashes assocated with EML files using:<\/p>\n\ncat 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<\/p>\n\nextract data.tar.gz using:<\/p>\n\n~/preston-archive$$ tar xzf data.tar.gz <\/p>\n\nthen use Preston to extract each hash using something like:<\/p>\n\n~/preston-archive$$ preston get hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa\n<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">\n  <dataset>\n    <alternateIdentifier>urn:x-wmo:md:org.aoncadis.www::d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>\n    <alternateIdentifier>d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>\n    <title>Airglow Image Data 2011 4 of 5</title>\n...<\/p>\n\nAlternatively, without using Preston, you can extract the data using the naming convention:<\/p>\n\ndata/[x]/[y]/[z]/[hash]/data<\/p>\n\nwhere 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.<\/p>\n\nFor example, the hash hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa can be found in the file: data/00/00/2d/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa/data . For more information, see [2].<\/p>\n\nThe intended use of this archive is to facilitate meta-analysis of the DataONE dataset network. <\/p>\n\n[1] DataONE, https://www.dataone.org\n[2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . DataONE was crawled via Preston with "preston update -u https://dataone.org".\n[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\n[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\n[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<\/p>\n\nThis work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.<\/p>"]} 

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2. Data for Printability Prediction in Projection Two-Photon Lithography via Machine Learning Based Surrogate Modeling of Photopolymerization

   https://doi.org/10.17632/8z29gzf6rd.1  

   Saha, Sourabh (January 2023, Mendeley)

   {"Abstract":["Data description \nThis dataset presents the raw and augmented data that were used to train the machine learning (ML) models for classification of printing outcome in projection two-photon lithography (P-TPL). P-TPL is an additive manufacturing technique for the fabrication of cm-scale complex 3D structures with features smaller than 200 nm. The P-TPL process is further described in this article: \u201cSaha, S. K., Wang, D., Nguyen, V. H., Chang, Y., Oakdale, J. S., and Chen, S.-C., 2019, "Scalable submicrometer additive manufacturing," Science, 366(6461), pp. 105-109.\u201d This specific dataset refers to the case wherein a set of five line features were projected and the printing outcome was classified into three classes: \u2018no printing\u2019, \u2018printing\u2019, \u2018overprinting\u2019. \n \nEach datapoint comprises a set of ten inputs (i.e., attributes) and one output (i.e., target) corresponding to these inputs. The inputs are: optical power (P), polymerization rate constant at the beginning of polymer conversion (kp-0), radical quenching rate constant (kq), termination rate constant at the beginning of polymer conversion (kt-0), number of optical pulses, (N), kp exponential function shape parameter (A), kt exponential function shape parameter (B), quantum yield of photoinitiator (QY), initial photoinitiator concentration (PIo), and the threshold degree of conversion (DOCth). The output variable is \u2018Class\u2019 which can take these three values: -1 for the class \u2018no printing\u2019, 0 for the class \u2018printing\u2019, and 1 for the class \u2018overprinting\u2019. \n\nThe raw data (i.e., the non-augmented data) refers to the data generated from finite element simulations of P-TPL. The augmented data was obtained from the raw data by (1) changing the DOCth and re-processing a solved finite element model or (2) by applying physics-based prior process knowledge. For example, it is known that if a given set of parameters failed to print, then decreasing the parameters that are positively correlated with printing (e.g. kp-0, power), while keeping the other parameters constant would also lead to no printing. Here, positive correlation means that individually increasing the input parameter will lead to an increase in the amount of printing. Similarly, increasing the parameters that are negatively correlated with printing (e.g. kq, kt-0), while keeping the other parameters constant would also lead to no printing. The converse is true for those datapoints that resulted in overprinting. \n\nThe 'Raw.csv' file contains the datapoints generated from finite element simulations, the 'Augmented.csv' file contains the datapoints generated via augmentation, and the 'Combined.csv' file contains the datapoints from both files. The ML models were trained on the combined dataset that included both raw and augmented data."]} 

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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)

   {"Abstract":["Data files were used in support of the research paper titled "\u201cExperimentation Framework for Wireless\nCommunication Systems under Jamming Scenarios" which has been submitted to the IET Cyber-Physical Systems: Theory & Applications journal. <\/p>\n\nAuthors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar\nContact: krd26@drexel.edu<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nTop-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. <\/p>\n\n--------------------------------<\/p>\n\nlogs:    - data logs collected from devices under test\n    - '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. \n    - '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. \n    - '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. <\/p>\n\n--------------------------------<\/p>\n\nparsers:    - scripts used to collect or process the log files used in the study\n        - '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). \n        - '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. \n        - 'vehicularTechnologies' contains the 'mgenParser.py' script and supporting 'bfb.json' configuration file which also require TRPR to be installed and directories to be updated. <\/p>\n\n--------------------------------<\/p>\n\nrayTracingEmulation:    - 'wirelessInsiteImages': images of model used in Wireless Insite\n            - 'channelSummary.pdf': summary of channel statistics from ray-tracing study\n            - 'rawScenario': scenario files resulting from code base directly from ray-tracing output based on configuration defined by '*WI.json' file \n            - '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\n            - 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)\n            - 'nodeMapping.txt': mapping of Wireless Insite transceivers to DYSE channel emulator physical connections required\n            - 'uavCommunication' directory additionally includes 'antennaPattern' which contains the RALA pattern data for the omni-directional mode ('omni.csv') and directional state ('90.csv')<\/p>\n\n--------------------------------<\/p>\n\nresults:    - contains performance results used in paper based on parsing of aforementioned log files\n <\/p>"]} 

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4. Enhancing understanding of the hydrological cycle via pairing of process‐oriented and isotope ratio tracers

   https://doi.org/10.5061/dryad.8w9ghx3mr  

   Fiorella, Richard; Siler, Nicholas; Nusbaumer, Jesse; Noone, David (January 2021, Dryad)

   {"Abstract":["This dataset contains monthly average output files from the iCAM6\n simulations used in the manuscript "Enhancing understanding of the\n hydrological cycle via pairing of process-oriented and isotope ratio\n tracers," in review at the Journal of Advances in Modeling Earth\n Systems. A file corresponding to each of the tagged and isotopic variables\n used in this manuscript is included. Files are at 0.9° latitude x 1.25°\n longitude, and are in NetCDF format. Data from two simulations are\n included: 1) a simulation where the atmospheric model was\n "nudged" to ERA5 wind and surface pressure fields, by adding an\n additional tendency (see section 3.1 of associated manuscript), and 2) a\n simulation where the atmospheric state was allowed to freely evolve, using\n only boundary conditions imposed at the surface and top of atmosphere.\n Specific information about each of the variables provided is located in\n the "usage notes" section below. Associated article abstract:\n The hydrologic cycle couples the Earth's energy and carbon budgets\n through evaporation, moisture transport, and precipitation. Despite a\n wealth of observations and models, fundamental limitations remain in our\n capacity to deduce even the most basic properties of the hydrological\n cycle, including the spatial pattern of the residence time (RT) of water\n in the atmosphere and the mean distance traveled from evaporation sources\n to precipitation sinks. Meanwhile, geochemical tracers such as stable\n water isotope ratios provide a tool to probe hydrological processes, yet\n their interpretation remains equivocal despite several decades of use. As\n a result, there is a need for new mechanistic tools that link variations\n in water isotope ratios to underlying hydrological processes. Here we\n present a new suite of \u201cprocess-oriented tags,\u201d which we use to explicitly\n trace hydrological processes within the isotopically enabled Community\n Atmosphere Model, version 6 (iCAM6). Using these tags, we test the\n hypotheses that precipitation isotope ratios respond to parcel rainout,\n variations in atmospheric RT, and preserve information regarding\n meteorological conditions during evaporation. We present results for a\n historical simulation from 1980 to 2004, forced with winds from the ERA5\n reanalysis. We find strong evidence that precipitation isotope ratios\n record information about atmospheric rainout and meteorological conditions\n during evaporation, but little evidence that precipitation isotope ratios\n vary with water vapor RT. These new tracer methods will enable more robust\n linkages between observations of isotope ratios in the modern hydrologic\n cycle or proxies of past terrestrial environments and the environmental\n processes underlying these observations.  "],"Methods":["Details about the simulation setup can be found in section 3 of the\n associated open-source manuscript, "Enhancing understanding of the\n hydrological cycle via pairing of process\u2010oriented and isotope ratio\n tracers." In brief, we conducted two simulations of the atmosphere\n from 1980-2004 using the isotope-enabled version of the Community\n Atmosphere Model 6 (iCAM6) at 0.9x1.25° horizontal resolution, and with 30\n vertical hybrid layers spanning from the surface to ~3 hPa. In the first\n simulation, wind and surface pressure fields were "nudged"\n toward the ERA5 reanalysis dataset by adding a nudging tendency,\n preventing the model from diverging from observed/reanalysis wind fields.\n In the second simulation, no additional nudging tendency was included, and\n the model was allowed to evolve 'freely' with only boundary\n conditions provided at the top (e.g., incoming solar radiation) and bottom\n (e.g., observed sea surface temperatures) of the model. In addition to the\n isotopic variables, our simulation included a suite of\n 'process-oriented tracers,' which we describe in section 2 of\n the manuscript. These variables are meant to track a property of water\n associated with evaporation, condensation, or atmospheric transport."],"Other":["Metadata are provided about each of the files below; moreover, since the\n attached files are NetCDF data - this information is also provided with\n the data files. NetCDF metadata can be accessed using standard tools\n (e.g., ncdump). Each file has 4 variables: the tagged quantity, and the\n associated coordinate variables (time, latitude, longitude). The latter\n three are identical across all files, only the tagged quantity changes.\n Twelve files are provided for the nudged simulation, and an additional\n three are provided for the free simulations: Nudged simulation files\n iCAM6_nudged_1980-2004_mon_RHevap: Mass-weighted mean evaporation source\n property: RH (%) with respect to surface temperature.\n iCAM6_nudged_1980-2004_mon_Tevap: Mass-weighted mean evaporation source\n property: surface temperature in Kelvin\n iCAM6_nudged_1980-2004_mon_Tcond: Mass-weighted mean condensation\n property: temperature (K) iCAM6_nudged_1980-2004_mon_columnQ: Total\n (vertically integrated) precipitable water (kg/m2).  Not a tagged\n quantity, but necessary to calculate depletion times in section 4.3 (e.g.,\n Fig. 11 and 12). iCAM6_nudged_1980-2004_mon_d18O: Precipitation d18O (\u2030\n VSMOW) iCAM6_nudged_1980-2004_mon_d18Oevap_0: Mass-weighted mean\n evaporation source property - d18O of the evaporative flux (e.g., the\n 'initial' isotope ratio prior to condensation), (\u2030 VSMOW)\n iCAM6_nudged_1980-2004_mon_dxs: Precipitation deuterium excess (\u2030 VSMOW) -\n note that precipitation d2H can be calculated from this file and the\n precipitation d18O as d2H = d-excess - 8*d18O.\n iCAM6_nudged_1980-2004_mon_dexevap_0: Mass-weighted mean evaporation\n source property - deuterium excess of the evaporative flux\n iCAM6_nudged_1980-2004_mon_lnf: Integrated property - ln(f) calculated\n from the constant-fractionation d18O tracer (see section 3.2).\n iCAM6_nudged_1980-2004_mon_precip: Total precipitation rate in m/s. Note\n there is an error in the metadata in this file - it is total\n precipitation, not just convective precipitation.\n iCAM6_nudged_1980-2004_mon_residencetime: Mean atmospheric water residence\n time (in days). iCAM6_nudged_1980-2004_mon_transportdistance: Mean\n atmospheric water transport distance (in km). Free simulation files\n iCAM6_free_1980-2004_mon_d18O: Precipitation d18O (\u2030 VSMOW)\n iCAM6_free_1980-2004_mon_dxs: Precipitation deuterium excess (\u2030 VSMOW) -\n note that precipitation d2H can be calculated from this file and the\n precipitation d18O as d2H = d-excess - 8*d18O.\n iCAM6_free_1980-2004_mon_precip: Total precipitation rate in m/s. Note\n there is an error in the metadata in this file - it is total\n precipitation, not just convective precipitation."]} 

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5. Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning Dataset

   https://doi.org/10.5281/zenodo.6304194  

   Jacovic, Marko; Rivas, Xaime Rey (January 2022, Zenodo)

   {"Abstract":["Data files were used in support of the research paper titled \u201cMitigating RF Jamming Attacks at the Physical Layer with Machine Learning<\/em>" which has been submitted to the IET Communications journal.<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nAll data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'<\/p>\n\n'ModeSelect.py': class used to defined the antenna state selection algorithm<\/li>'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna<\/li>'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning<\/li>'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only<\/li><\/ul>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nAuthors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar\nContact: krd26@drexel.edu<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nTop-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nclassifier_training: files used for training classifiers that are integrated into SDR platform<\/p>\n\n'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)<\/li>'classTrain.py' is the main parser for training the classifiers<\/li>'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'<\/li><\/ul>\n\npost_processing_classifier: contains logs of online classifier outputs and processing script<\/p>\n\n'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario<\/li>'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'<\/li><\/ul>\n\npost_processing_mgen: contains MGEN receiver logs and parser<\/p>\n\n'configs' contains JSON files to be used with parser for each experiment<\/li>'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'<\/li><\/ul>\n\nray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.<\/p>\n\nDirectory contains detailed 'readme.txt' for understanding.<\/li>Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently. \n\tS. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.<\/li><\/ul>\n\t<\/li><\/ul>\n\nresults: contains results obtained from study<\/p>\n\n'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'<\/li>'mitigation_performance' contains figures generated by 'post_processing_mgen.'<\/li>'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'<\/li><\/ul>\n\ntuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study<\/p>\n\n'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.<\/li>'data_summary.txt'this file contains the summaries from all the CSV files for convenience.<\/li><\/ul>"]} 

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