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This dataset contains BEPAM model code and input data to replicate the outcomes for "The Economic and Environmental Costs and Benefits of the Renewable Fuel Standard". The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output.gms. (BEPAM-Social cost model-ERL.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

#Overview These are reference packages for the TIPP3 software for abundance profiling and/or species detection from metagenomic reads (e.g., Illumina, PacBio, Nanopore, etc.). Different refpkg versions are listed. TIPP3 software: https://github.com/c5shen/TIPP3 #Changelog V1.2 (`tipp3-refpkg-1-2.zip`) >>Fixed old typos in the file mapping text. >>Added new files `taxonomy/species_to_marker.tsv` for new function `run_tipp3.py detection [...parameters]`. Please use the latest release of the TIPP3 software for this new function. V1 (`tipp3-refpkg.zip`) >>Initial release of the TIPP3 reference package. #Usage 1. unzip the file to a local directory (will get a folder named "tipp3-refpkg"). 2. use with TIPP3 software: `run_tipp3.py -r [path/to/tipp3-refpkg] [other parameters]`

File Name: WordsSelectedByInformationGain.csv Data Preparation: Xiaoru Dong, Linh Hoang Date of Preparation: 2018-12-12 Data Contributions: Jingyi Xie, Xiaoru Dong, Linh Hoang Data Source: Cochrane systematic reviews published up to January 3, 2018 by 52 different Cochrane groups in 8 Cochrane group networks. Associated Manuscript authors: Xiaoru Dong, Jingyi Xie, Linh Hoang, and Jodi Schneider. Associated Manuscript, Working title: Machine classification of inclusion criteria from Cochrane systematic reviews. Description: the file contains a list of 1655 informative words selected by applying information gain feature selection strategy. Information gain is one of the methods commonly used for feature selection, which tells us how many bits of information the presence of the word are helpful for us to predict the classes, and can be computed in a specific formula [Jurafsky D, Martin JH. Speech and language processing. London: Pearson; 2014 Dec 30].We ran Information Gain feature selection on Weka -- a machine learning tool. Notes: In order to reproduce the data in this file, please get the code of the project published on GitHub at: https://github.com/XiaoruDong/InclusionCriteria and run the code following the instruction provided.

This geodatabase serves two purposes: 1) to provide State of Illinois agencies with a fast resource for the preparation of maps and figures that require the use of shape or line files from federal agencies, the State of Illinois, or the City of Chicago, and 2) as a start for social scientists interested in exploring how geographic information systems (whether this is data visualization or geographically weighted regression) can bring new meaning to the interpretation of their data. All layer files included are relevant to the State of Illinois. Sources for this geodatabase include the U.S. Census Bureau, U.S. Geological Survey, City of Chicago, Chicago Public Schools, Chicago Transit Authority, Regional Transportation Authority, and Bureau of Transportation Statistics.

One of the newest types of multimedia involves body-connected interfaces, usually termed haptics. Haptics may use stylus-based tactile interfaces, glove-based systems, handheld controllers, balance boards, or other custom-designed body-computer interfaces. How well do these interfaces help students learn Science, Technology, Engineering, and Mathematics (STEM)? We conducted an updated review of learning STEM with haptics, applying meta-analytic techniques to 21 published articles reporting on 53 effects for factual, inferential, procedural, and transfer STEM learning. This deposit includes the data extracted from those articles and comprises the raw data used in the meta-analytic analyses.

This dataset includes two data files that provide the time series (Jul. - Sep. 2017) data of sun-induced chlorophyll fluorescence (SIF_760) collected under sunny conditions at two maize sites (one rainfed and the other irrigated) in Nebraska in 2017. Data contain 392 SIF_760 records at the rainfed site and 707 records at the irrigated site. The timestamp uses local standard time. Data are available for the sunny conditions from 8 am to 5 pm (corresponding to 9 am to 6 pm local time) throughout the study period.

Per the authors' request, the data files for this dataset are now suppressed. Please visit this new dataset for the complete and updated data files: Huang, Yijing; Fahad , Mahmood (2025): Data for Observation of a Magneto-chiral Instability in Photoexcited Tellurium. University of Illinois Urbana-Champaign.<a href="https://doi.org/10.13012/B2IDB-1409842_V1">https://doi.org/10.13012/B2IDB-1409842_V1</a> ==================== The data and code provided in this dataset can be used to generate key plots in the manuscript. It is divided into four subfolders (B parallel/perpendicular to the tellurium c axis and field/ temperature dependence), each containing the raw data (saved in .mat format), the oscillator parameters obtained through linear prediction (saved in .mat format), and the plot-generating code (.m files). The code was written using MATLAB R2024a. To run the code, go to each folder, and run the .m file in that folder, which generates two plots.