Renamed the "Unindexed dimensions" section in the Dataset and DataArray repr (added in v0.9.0) to "Dimensions without coordinates".

cmomy is a python package to calculate central moments and co-moments in a numerical stable and direct way. Behind the scenes, cmomy makes use of Numba to rapidly calculate moments. cmomy provides utilities to calculate central moments from individual samples, precomputed central moments, and precomputed raw moments. It also provides routines to perform bootstrap resampling based on raw data, or precomputed moments. cmomy has numpy array and xarray DataArray interfaces.

This repository contains the data cube (an xarray DataArray) used in Jiang et al. 2023 Revisiting ε Eridani with NEID: Identifying New Activity-Sensitive Lines in a Young K Dwarf Star (in press). The cube contains all line parameters (centroid, depth, FWHM, and integrated flux) for each line in the compiled line list over 32 NEID observations of ε Eridani spanning a six-month period from September 2021 to February 2022, as well as the measured RV and activity indices for each observation. For information on how the line parameters are measured, see the paper.

1. Dataset Description

This dataset provides simulated data on plastic and substance flows and stocks in buildings and infrastructure as described in the data article "Plastics in the German Building and Infrastructure Sector: A High-Resolution Dataset on Historical Flows, Stocks, and Legacy Substance Contamination". Besides simulated data, the repository contains input data and model files used to produce the simulated data.

Files Included

Data & Data Visualization: The dataset contains input data and simulated data for the six main plastic applications in buildings and infrastructure in Germany in the period from 1950 to 2023, which are profiles, flooring, pipes, insulation material, cable insulations, and films. For each application the data are provided in a sub-directory (1_ ... 6_) following the structure described below.

Input Data:
The input data are stored in an xlsx-file with three sheets: flows, parameters, and data quality assessment. The data sources for all input data are detailed in the Supplementary Material of the linked Data in Brief article.

Simulated Data:
Simulated data are stored in a sub-folder, which contains:

• Data visualization:
  • flows_and_stocks_by_product_type.png: Illustration of consumed products, in-use-stocks, and end-of-life flows, aggregated by product type (median values).
  • flows_and_stocks_by_polymer.png: Illustration of consumed products, in-use-stocks, and end-of-life flows, aggregated by polymer (median values).
  • flows_and_stocks_with_uncertainty.png: Illustration of consumed products, in-use-stocks, and end-of-life flows, aggregated by product (median values and 68% confidence interval).
  • contaminants_in_F3-4.png: Illustration of simulated legacy contaminant concentrations in consumed products (median values and 68% confidence interval).
  • contaminants_in_F4-5.png: Illustration of simulated legacy contaminant concentrations in end-of-life-flows (median values and 68% confidence interval).
• Data:
  • simulated_data_[product].xlsx – Time series of flow and stock values, aggregated by product, type, polymer, and substance. Each data point includes:
    • Mean
    • Standard deviation
    • Median
    • 2.5%-quantile
    • 16%-quantile
    • 84%-quantile
    • 97.5%-quantile
  • MFA_model.pkl.gz – Model structure and input parameters, including:
  • Model classification – A dictionary summarizing the model structure {model_dimension: [items per model dimension]}
  • param_df – A dataframe containing input parameter values for each Monte Carlo run
  • outputmatrix.pkl.gz – Matrix of deterministic values
  • openlooprecycling.pkl – Xarray DataArray containing flow values of flow E7.1 for open-loop recycling (only available for sub-models that generate recycled plastics for open-loop recycling)
  • full_arrays-folder (contains non-aggregated data for all Monte Carlo runs):
    • flow_[flow_ID].pkl / stock_[stock_ID].pkl – Complete simulated flow and stock data.

Note: All files in the [product]/simulated_data folder are automatically replaced with updated model results upon execution of immec_dmfa_calculate_submodels.py.

To reduce storage requirements, data are stored in gzipped pickle files (.pkl.gz), while smaller files are provided as pickle files (.pkl). To open the files, users can use Python with the following code snippet:

import gzip

# Load a gzipped pickle file
with gzip.open("filename.pkl.gz", "rb") as f:
  data = pickle.load(f)

# Load a regular pickle file
with open("filename.pkl", "rb") as f:
  data = pickle.load(f)

Please note that opening pickle files requires compatible versions of numpy and pandas, as the files may have been created using version-specific data structures. If you encounter errors, ensure your package versions match those used during file creation (pandas: 2.2.3, numpy: 2.2.4).

Simulated data are provided as Xarray datasets, a data structure designed for efficient handling, analysis, and visualization of multi-dimensional labeled data. For more details on using Xarray, please refer to the official documentation: https://docs.xarray.dev/en/stable/

Core Model Files:

• immec_dmfa_calculate_submodels.py – The primary model file, orchestrating the execution by calling functions from other files, running simulations, and storing results.
• immec_dmfa_setup.py – Sets up the material flow model, imports all input data in the required format, and stores simulated data.
• immec_dmfa_calculations.py – Implements mass balance equations and stock modeling equations to solve the model.
• immec_dmfa_visualization.py – Provides functions to visualize simulated flows, stocks, and substance concentrations.
• requirements.txt – Lists the required Python packages for running the model.

Computational Considerations:
During model execution, large arrays are generated, requiring significant memory. To enable computation on standard computers, Monte Carlo simulations are split into multiple chunks:

• The number of runs per chunk is specified for each submodel in model_aspects.xlsx.
• The number of chunks is set in immec_dmfa_calculate_submodels.py.

Dependencies
The model relies on the ODYM framework. To run the model, ODYM must be downloaded from https://github.com/IndEcol/ODYM (S. Pauliuk, N. Heeren, ODYM — An open software framework for studying dynamic material systems: Principles, implementation, and data structures, Journal of Industrial Ecology 24 (2020) 446–458. https://doi.org/10.1111/jiec.12952.)

7_Model_Structure:

• model_aspects.xlsx: Overview of model items in each dimension of each sub-model
• parameters.xlsx: Overview of model parameters
• processes.xlsx: Overview of processes
• flows.xlsx: Overview of flows (P_Start and P_End mark the process-ID of the source and target of each flow)
• stocks.xlsx: Overview of stocks

8_Additional_Data: This folder contains supplementary data used in the model, including substance concentrations, data quality assessment scores, open-loop recycling distributions, and lifetime distributions.

• concentrations.xlsx – Substance concentrations in plastic products, provided as average, minimum, and maximum values.
• pedigree.xlsx – Pedigree scores for data quality assessment, following the methodology described in: D. Laner, J. Feketitsch, H. Rechberger, J. Fellner (2016). A Novel Approach to Characterize Data Uncertainty in Material Flow Analysis and its Application to Plastics Flows in Austria. Journal of Industrial Ecology, 20, 1050–1063. https://doi.org/10.1111/jiec.12326.
• open_loop_recycling.xlsx – Distribution of open-loop recycled plastics into other plastic applications in buildings and infrastructure.
• Lifetime_Distributions
  • hibernation.xlsx – Assumed retention time of products in hibernating stocks.
  • lifetime_dict.pkl – Dictionary containing Weibull functions, used to determine the best fits for LifetimeInputs.xlsx.
  • LifetimeInputs.xlsx – Input data for identifying lifetime functions.
  • LifetimeParameters.xlsx – Derived lifetime parameters, used in dynamic stock modeling.
  • Lifetimes.ipynb – Jupyter Notebook containing code for identifying suitable lifetime distribution parameters

2. Methodology

The dataset was generated using a dynamic material flow analysis (dMFA) model. For a complete methodology description, refer to the Data in Brief article (add DOI).

3. How to Cite This Dataset

If you use this dataset, please cite: Schmidt, S., Verni, X.-F., Gibon, T., Laner, D. (2025). Dataset for: Plastics in the German Building and Infrastructure Sector: A High-Resolution Dataset on Historical Flows, Stocks, and Legacy Substance Contamination, Zenodo. DOI: 10.5281/zenodo.15049210

4. License & Access

This dataset is licensed under CC BY-NC 4.0, permitting use, modification, and distribution for non-commercial purposes, provided that proper attribution is given.

5. Contact Information

For questions or further details, please contact:
Sarah Schmidt
Center for Resource Management and Solid Waste Engineering
University of Kassel
Email: sarah.schmidt@uni-kassel.de