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   https://doi.org/10.5281/zenodo.7570133  

   Rohwer, Robin R.; Ladwig, Robert; Dugan, Hilary A.; Hanson, Paul C.; Walsh, Jake R.; Vander Zanden, M. Jake (January 2023, Zenodo)

   This repository includes the setup and output from the analysis ran on Lake Mendota to explore the trophic cascade caused by invasion of spiny water flea in 2010. Scripts to run the model are located under /src, and the processed results for the discussion of the paper are located under /data_processed.</p> 

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2. Modeling dataset: Long-term Change in Metabolism Phenology across North-Temperate Lakes, Wisconsin, USA 1979-2019

   https://doi.org/10.6073/pasta/af991c26bace5af8d4b3bb66d7b18af7  

   Ladwig, Robert; Appling, Alison P.; Delany, Austin; Dugan, Hilary A.; Gao, Qiantong; Lottig, Noah; Stachelek, Joseph; Hanson, Paul C. (January 2022, Environmental Data Initiative)

   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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3. Modeling dataset: Long-term Change in Metabolism Phenology across North-Temperate Lakes, Wisconsin, USA 1979-2019

   https://doi.org/10.6073/pasta/bf44814c101e7e085b2663556a9188ca  

   Ladwig, Robert; Appling, Alison P.; Delany, Austin; Dugan, Hilary A.; Gao, Qiantong; Lottig, Noah; Stachelek, Joseph; Hanson, Paul C. (January 2022, Environmental Data Initiative)

   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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4. C ² Metadata: Automating the Capture of Data Transformations from Statistical Scripts in Data Documentation

   https://doi.org/10.1145/3299869.3320241  

   Song, Jie; Alter, George; Jagadish, H. V. (June 2019, Proceedings of the 2019 International Conference on Management of Data)

   null (Ed.)

   Datasets are often derived by manipulating raw data with statistical software packages. The derivation of a dataset must be recorded in terms of both the raw input and the manipulations applied to it. Statistics packages typically provide limited help in documenting provenance for the resulting derived data. At best, the operations performed by the statistical package are described in a script. Disparate representations make these scripts hard to understand for users. To address these challenges, we created Continuous Capture of Metadata (C2Metadata), a system to capture data transformations in scripts for statistical packages and represent it as metadata in a standard format that is easy to understand. We do so by devising a Structured Data Transformation Algebra (SDTA), which uses a small set of algebraic operators to express a large fraction of data manipulation performed in practice. We then implement SDTA, inspired by relational algebra, in a data transformation specification language we call SDTL. In this demonstration, we showcase C2Metadata’s capture of data transformations from a pool of sample transformation scripts in at least two languages: SPSS®and Stata®(SAS®and R are under development), for social science data in a large academic repository. We will allow the audience to explore C2Metadata using a web-based interface, visualize the intermediate steps and trace the provenance and changes of data at different levels for better understanding of the process. 

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5. GRITCLEAN Code and Files -- Part 1

   https://doi.org/10.5281/zenodo.10547476  

   Girgaonkar, Raghav (January 2024, Zenodo)

   This dataset contains a compressed folder of the data and MATLAB scripts used produce relevant figures and candidates for GRITCLEAN: A glitch veto scheme for Gravitational wave data as presented in https://arxiv.org/abs/2401.15237  The codes in this dataset include: A PSO-based matched filtering search pipeline which can be run on either the positive or the negative chirp time space. A standalone MATLAB script called GRITCLEAN.m which can run the GRITCLEAN hierarchical vetoes on a set of positive and negative chirp time space estimated parameters.  A plotting script to generate relevant figures. The files in this dataset include: GVSsegPSDtrainidxs.mat, a binary MATLAB file containing training indices for all segments from which the Power Spectral Densities (PSDs) are estimated, this is done via the scripts provided, namely, getsegPSD.m and createPSD.m. A sample HDF5 file used (H-H1_GWOSC_O3a_4KHZ_R1-1243394048-4096.hdf5) JSON files containing information about the data segments and the strain data files from which they originate from.  Text files containing the parameters estimated by the PSO-based pipeline across the positive and negative chirp time space runs.  Detailed instructions on dependencies, downloading the dataset and running the codes are given in a README.txt file included with this dataset. The user is recommended to go through this file first. The scripts enclosed have dependencies on JSONLAB , the Parallel Computing Toolbox and Signal Processing Toolbox for MATLAB, along with additional scripts provided in GitHub repositories  Accelerated-Network-Analysis  and SDMBIGDAT19 . Instructions on installing these dependencies are provided in README.txt. All codes have been developed and tested on MATLAB R2022 and R2023. 

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