Industrial Ecology Freiburg

Research group at the Faculty of Environment and Natural Resources

Welcome to the Industrial Ecology Data Commons

The Industrial Ecology Data Commons (IEDC) is an entirely new type of database that contains more than 300 datasets for industrial ecology and socio-metabolic research, including stocks, flows, process inventories, process yield factors, material composition and lifetimes of products, and many more. The goals of the IEDC are to provide easy access to socio-metabolic, industrial ecology, and circular economy data to researchers and consultants and to provide researchers with an infrastructure that facilitates systematic data formatting and labelling. Data are extracted from a variety of sources, formatted into the IEDC data model, matched to the IEDC classifications, and uploaded. Launched in 2018, the IEDC is continuously improved and expanded. The IEDC is part of a larger open science effort of the industrial ecology group in Freiburg.

2025 is the year when we move the industrial ecology data commons prototype, launched in 2018, into a functional and helpful data archiving and retrieval tool for the entire industrial ecology community! Read about the 2025 IEDC critical mass sprint here.

IEDC scope
The IEDC is a platform for a broad spectrum of socio-metabolic, built environment, industrial systems, and material cycle data. The IEDC is built on a general data model for socioeconomic metabolism, which covers table-based data of up to 12 index dimensions. Objects and processes in a given system are quantified at different layers (items, mass, energy, monetary, …), either at scale or per unit. The IEDC data model covers more than 35 specific data types in 8 data categories. Data from a broad array of sources, including more than 250 journal papers, were formatted into the data model and then uploaded to the IEDC.
Scheme of IEDC database

Currently, data collection focusses on the following data types:

  • Material composition (3_MC), specific energy consumption (3_EI), and lifetime (3_LT) of products, buildings, vehicles, and infrastructure, as well as process yield coefficients (4_PY) of the related manufacturing and recycling processes. These data types are of interest to a broad group of researchers.
  • Sankey diagrams and other MFA stock and flow data (1_F, 2_S, 2_IUS). This effort strengthens the MFA/SEM community via systematic formatting and easy access to published data.
  • Inequality-related data (Lorenz curves for flows, stocks, and normalized: 1_LCF, 2_LCS, 3_LC). This effort supports the research on socio-metabolic inequality of the Freiburg team.
IEDC database structure and available infrastructure

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The scheme below shows the structure of the IEDC database. The data table is at the core of the IEDC. Here, each single number is recorded on a separate row, with the required aspects (identifies) to locate the individual data point in the system definition (which material/commodity, process, time, etc.). The data points are grouped into datasets, corresponding to their common source. E.g., the product lifetime data extracted from a certain publication or the steel production statistics from a certain year form their respective datasets. The data points that belong to the same dataset share a common dataset ID, for which an entry in the IEDC dataset table (the catalogue of datasets) exists. Each dataset description contains of a unique ID, a unique tuple (dataset_name, dataset_version), a description, the aspects required to locate the data in a system definition, and the metadata. For a better overview, datasets can be grouped into data groups and further into projects. The IEDC Excel data templates have two sheets: The Cover sheet corresponds to the dataset description, and the Data sheet corresponds to the data points for the data table.

A number of lookup tables for data types, layers, provenance, licenses, and units enables the systematic recording of these features. A number of constraints apply here, e.g., only units and data types that are defined in the lookup tables can be uploaded.

To place the data into their respective system definition, the IEDC departs from general system dimensions (space/region, time, material, product/commodity, process, scenario, …). To describe each data point in these dimensions, different labels (‘Brazil’, ‘2024’, …) are used, and these labels are grouped into classifications. The IEDC provides general classifications such as the ISO 3166 country codes or the HS commodity classification, IEDC-specific classification for materials or processes, and custom classifications for specific datasets. Each dataset has specific aspects that describe how exactly the data points relate to the system dimensions. E.g., for a flow, the ‘process’ system dimension is used for two aspects: ‘process of origin’ and ‘process of destination’. Both these aspects can then use the same general classification for the ‘process’ dimension.



The scheme below shows the data workflow and the available infrastructure of the IEDC web application. Links to the different features are provided further down, in the sections “sourcing data” and “finding data”.

We create regular backup copies of this database and archive them on Zenodo and with the International Society for Industrial Ecology. The Sankey diagram feature is still under development.

IEDC Resources
Sourcing data: IEDC data templates, validation, and uploading

The IEDC welcomes data submissions from the community! Users can validate their own data against the IEDC data model and classifications. A workflow description for formatting data in IEDC templates, validating them against the IEDC data model and classifications, and submitting them for upload is described in a video tutorial and (coming soon!) in the IEDC handbook. This workflow will soon include the use of large language models to match a given list of labels for products, materials, etc. to those already defined in the different IEDC classifications.

Finding data: IEDC search, filter, and download options

Next to browsing the data catalogue by keyword below, we offer several special search and filter pages, which you can access via the buttons below. See the video tutorial (coming soon) and the IEDC handbook (coming soon) for detailed instructions and descriptions.

Overview of the different IEDC search options
I am looking for...
… all datasets that match a given keyword
… all datasets of a certain data type
… all datasets from a certain project
… a dataset with a given name or id
Data catalogue search by keyword
(see bottom of this page)
… all datasets that contain a certain product/material/region
e.g., what data are there on copper / Brazil?
Find all datasets with a given label
… all datasets of a given type that contain a certain product/material/region
e.g., are there any lifetime data for bicycles?
Search for datasets by main aspects
… all datasets of a given type that contain any combination of labels
(material, product, process) for up to three different aspects
Search for datasets by all aspects
… data for a specific label (material, region, product, process, …) in a given dataset Filter a dataset for a given label
… data from a specific publication (DOI) or from a certain author Search for datasets by DOI or author
IEDC Excel data template
Cover sheet | Data sheet

Download button on the different search pages

On Search for datasets by main aspect, you can select the main datatypes together with their main aspects: flow by material, lifetime by commodity, or yield coefficient by process. Then, all material/commodity/process labels for which data are present are shown for you to select one, for which all available datasets are then listed.

On Search for datasets by all aspects, you first select a data type (product lifetime, material stock, …) and then up to three aspects (product, material, region, time) and the corresponding labels (‘steel’, ‘Brazil’, …) to see if there are any data available.

On Find all datasets with a given label, you first choose from the IEDC main classifications (material, product, process, region, …) and then a label from the selected classification. The search returns a list of all datasets in the IEDC that contain data for the chosen label. Datasets can be inspected further by the filtering function:

On Filter a dataset for a given label, you specify the name or ID of a given dataset and can then search for a specific label (‘bicycle’, ‘copper’, …) in that dataset.

All datasets for which data are present can be downloaded by entering their name below.



How to cite datasets downloaded from this database: Always refer to the source publication by using the citation information given in the suggested_citation property of each dataset! Before using the data in your own work, double-check the correctness of each dataset downloaded by comparing it to the original publication and report any mistakes you encounter!

Browse the data catalogue and download datasets:

  • Searching for data? Use keywords!
  • Searching for a specific data type? Use the data type key, such as '1_F' or '3_MC', cf. the list of all data types!
  • Searching for data from a specific publication? Use main author name or DOI! If data source does not have a DOI, use keywords from report title and publishing institution!
  • Searching for a specific dataset from the catalogue? Use the dataset name!

Catalogue of Data Sets

Acknowledgement and contact info

The development of the IEDC is supported by the EU-Horizon framework via the (CIRCOMOD) project and by the (Faculty of Environment and Natural Resources) of the University of Freiburg. We acknowledge the work of many colleagues who contributed data to the IEDC (see dataset descriptions), especially the Freiburg team as part of the CIRCOMOD input database compilation and those who contributed data as part of the 2025 IEDC (Critical Mass Sprint). We are interested in your feedback and bug reports and welcome any feedback and suggestions via in4mation@indecol.uni-freiburg.de!