This dataset includes model configurations, scripts and outputs to process and recreate the outputs from Ladwig et al. (2021): Long-term Change in Metabolism Phenology across North-Temperate Lakes. The provided scripts will process the input data from various sources, as well as recreate the figures from the manuscript. Further, all output data from the metabolism models of Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout are included.
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Modeling dataset: Long-term Change in Metabolism Phenology across North-Temperate Lakes, Wisconsin, USA 1979-2019
This dataset includes model configurations, scripts and outputs to process and recreate the outputs from Ladwig et al. (2021): Long-term Change in Metabolism Phenology across North-Temperate Lakes. The provided scripts will process the input data from various sources, as well as recreate the figures from the manuscript. Further, all output data from the metabolism models of Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout are included.
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- Award ID(s):
- 1934633
- NSF-PAR ID:
- 10385716
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Scripts, model configurations and outputs to process the data and recreate the figures from Ladwig, R., Rock, L.A, Dugan, H.A. (-): Impact of salinization on lake stratification and spring mixing. This repository includes the setup and output from the lake model ensemble (GLM, GOTM, Simstrat) ran on the lakes Mendota and Monona. Scripts to run the models are located under /numerical and the scripts to process the results for the discussion of the paper are in the top main repository. The scripts to derive the theoretical solution are located under /analytical. Buoy monitoring data are located under /fieldmonitoring. The final figures are located under /figs_HD.
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Abstract The Rocky Mountain Biological Laboratory (RMBL; Colorado, USA) is the site for many research projects spanning decades, taxa, and research fields from ecology to evolutionary biology to hydrology and beyond. Climate is the focus of much of this work and provides important context for the rest. There are five major sources of data on climate in the RMBL vicinity, each with unique variables, formats, and temporal coverage. These data sources include (1) RMBL resident billy barr, (2) the National Oceanic and Atmospheric Administration (NOAA), (3) the United States Geological Survey (USGS), (4) the United States Department of Agriculture (USDA), and (5) Oregon State University's PRISM Climate Group. Both the NOAA and the USGS have automated meteorological stations in Crested Butte, CO, ~10 km from the RMBL, while the USDA has an automated meteorological station on Snodgrass Mountain, ~2.5 km from the RMBL. Each of these data sets has unique spatial and temporal coverage and formats. Despite the wealth of work on climate‐related questions using data from the RMBL, previous researchers have each had to access and format their own climate records, make decisions about handling missing data, and recreate data summaries. Here we provide a single curated climate data set of daily observations covering the years 1975–2022 that blends information from all five sources and includes annotated scripts documenting decisions for handling data. These synthesized climate data will facilitate future research, reduce duplication of effort, and increase our ability to compare results across studies. The data set includes information on precipitation (water and snow), snowmelt date, temperature, wind speed, soil moisture and temperature, and stream flows, all publicly available from a combination of sources. In addition to the formatted raw data, we provide several new variables that are commonly used in ecological analyses, including growing degree days, growing season length, a cold severity index, hard frost days, an index of El Niño‐Southern Oscillation, and aridity (standardized precipitation evapotranspiration index). These new variables are calculated from the daily weather records. As appropriate, data are also presented as minima, maxima, means, residuals, and cumulative measures for various time scales including days, months, seasons, and years. The RMBL is a global research hub. Scientists on site at the RMBL come from many countries and produce about 50 peer‐reviewed publications each year. Researchers from around the world also routinely use data from the RMBL for synthetic work, and educators around the United States use data from the RMBL for teaching modules. This curated and combined data set will be useful to a wide audience. Along with the synthesized combined data set we include the raw data and the R code for cleaning the raw data and creating the monthly and yearly data sets, which facilitate adding additional years or data using the same standardized protocols. No copyright or proprietary restrictions are associated with using this data set; please cite this data paper when the data are used in publications or scientific events.
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Data files were used in support of the research paper titled “Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning" which has been submitted to the IET Communications journal.
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All 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/:'
- 'ModeSelect.py': class used to defined the antenna state selection algorithm
- 'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna
- 'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning
- 'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only
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Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar
Contact: krd26@drexel.edu---------------------------------------------------------------------------------------------
Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.
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classifier_training: files used for training classifiers that are integrated into SDR platform
- 'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)
- 'classTrain.py' is the main parser for training the classifiers
- 'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'
post_processing_classifier: contains logs of online classifier outputs and processing script
- 'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario
- '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'
post_processing_mgen: contains MGEN receiver logs and parser
- 'configs' contains JSON files to be used with parser for each experiment
- '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'
ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.
- Directory contains detailed 'readme.txt' for understanding.
- 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.
- S. 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.
results: contains results obtained from study
- 'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'
- 'mitigation_performance' contains figures generated by 'post_processing_mgen.'
- 'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'
tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study
- '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.
- 'data_summary.txt'this file contains the summaries from all the CSV files for convenience.
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Abstract Premise The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre‐defined, higher‐order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually. Methods and Results The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s). Conclusions GO annotations are a widely used “universal language” for describing gene functions and products. GOgetter is a fast and easy‐to‐implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.more » « less
