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1. SDR Querier: A Visual Querying Framework for Cross-National Survey Data Recycling

   https://doi.org/10.1109/TVCG.2023.3261944  

   Tu, Yamei; Li, Olga; Wang, Junpeng; Shen, Han-Wei; Powałko, Przemek; Tomescu-Dubrow, Irina; Slomczynski, Kazimierz M.; Blanas, Spyros; Jenkins, J. Craig (June 2023, IEEE Transactions on Visualization and Computer Graphics)

   Public opinion surveys constitute a widespread, powerful tool to study peoples’ attitudes and behaviors from comparative perspectives. However, even global surveys can have limited geographic and temporal coverage, which can hinder the production of comprehensive knowledge. To expand the scope of comparison, social scientists turn to ex-post harmonization of variables from datasets that cover similar topics but in different populations and/or at different times. These harmonized datasets can be analyzed as a single source and accessed through various data portals. However, the Survey Data Recycling (SDR) research project has identified three challenges faced by social scientists when using data portals: the lack of capability to explore data in-depth or query data based on customized needs, the difficulty in efficiently identifying related data for studies, and the incapability to evaluate theoretical models using sliced data. To address these issues, the SDR research project has developed the SDR Querier, which is applied to the harmonized SDR database. The SDR Querier includes a BERT-based model that allows for customized data queries through research questions or keywords (Query-by-Question), a visual design that helps users determine the availability of harmonized data for a given research question (Query-by-Condition), and the ability to reveal the underlying relational patterns among substantive and methodological variables in the database (Query-by-Relation), aiding in the rigorous evaluation or improvement of regression models. Case studies with multiple social scientists have demonstrated the usefulness and effectiveness of the SDR Querier in addressing daily challenges. 

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2. SDR2 Database

   https://doi.org/10.7910/DVN/YOCX0M  

   Slomczynski, Kazimierz M.; Tomescu-Dubrow, Irina; Wysmulek, Ilona; Powałko, Przemek; Jenkins, J. Craig; Ślarzyński, Marcin; Zieliński, Marcin W.; Skora, Zuzanna; Li, Olga; Lavryk, Denys; et al (January 2023, Harvard Dataverse)

   The SDR Database v.2.0 (SDR2) is a multi-country, multi-year database for research on political participation, social capital, and well-being. It comprises harmonized information from 23 international survey projects, covering over 4.4 million respondents from 156 countries in the period 1966 – 2017. SDR2 provides both target variables and methodological indicators that store source survey and ex-post harmonization metadata. SDR2 consists of three datasets. The MASTER file, which stores harmonized information for a total of 4,402,489 respondents. The auxiliary PLUG-SURVEY file containing controls for source data quality and a set of technical variables needed for merging this file with the MASTER file. And the PLUG-COUNTRY file, which is a dictionary of countries and territories used in the MASTER file. An overall description of the SDR2 Database, and detailed information about its datasets are available in the SDR2 documentation. SDR2 is a product of the project Survey Data Recycling: New Analytic Framework, Integrated Database, and Tools for Cross-national Social, Behavioral and Economic Research, financed by the US National Science Foundation (PTE Federal award 1738502). We thank the Ohio State University and the Institute of Philosophy and Sociology, Polish Academy of Sciences, for organizational support. 

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3. 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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4. Heating, Ventilation, and Air Conditioning (HVAC) Data of Buildings in US

   https://doi.org/10.5281/zenodo.15662534  

   Nghiem, Truong X (June 2025, Zenodo)

   This data set contains measurements from real HVAC (heating, ventilation, and air conditioning) systems of real buildings in the US.  Each ZIP file contains CSV data files of a building for different scenarios.  Refer to the README file in each ZIP file for details. The document `data_info.pdf` provides explanations of the variables/columns in the data files. This work was supported by the U.S. National Science Foundation (NSF) under grants 2514584 and 2513096. 

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5. 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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