Hybrid Soil Microbiome Modeling - Combining process-based models with machine learning to predict microbial dynamics and organic matter turnover in soil systems [* Cross-Reference *]

Version 1.0

P. Collart; Jürgen Gall; Andrea Schnepf; Alberto Vinicius Sousa Rocha; Malte Herold; Kate M. Buckeridge; Holger Pagel, 2026, "Hybrid Soil Microbiome Modeling - Combining process-based models with machine learning to predict microbial dynamics and organic matter turnover in soil systems [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.GD2ZZY, LIST Open Repository, V1

Learn about Data Citation Standards.

Contact Owner

Dataset Metrics

0 Downloads

Description	Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate climate change and environmental threats. Integrating microbial dynamics in process-based soil models is critical for predicting how soil carbon flows and stocks change in ecosystems with time. Functional traits can be inferred from amplicon sequencing data and metagenome assembled genomes to leverage model parameterization. However, informing models using omics-based datasets is challenging due to their large dimensional nature and the nonlinear relationship between genomes and the actual function microbes express. We present a hybrid modeling framework that combines machine learning to analyze metagenomic and DNA sequencing data with a simple microbial explicit process-based model. This hybrid model is conditioned using a convolutional network trained with data from the LUCAS 2018 database (Land Use and Coverage Area frame Survey), which includes soil metagenomes, 16S sequencing data in combination with soil carbon, microbial biomass and soil respiration measurements. Using trait inference from genomes, the model can learn several biokinetic parameters such as growth rates, dormancy rates, affinities to organic matter, growth yields or decay rates. We present the concept of the hybrid soil modelling framework and discuss what data is informative for these models and how to best link machine learning with process-based models. (2025-03-14) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/egusphere-egu25-10523 for the original and latest version of the publication* (2026-07-01)
Subject	Computer and Information Science; Mathematical Sciences; Physics; Social Sciences
Keyword	Process (computing), Microbiome, Organic matter, Soil organic matter, Computer science, Environmental science, Biochemical engineering, Soil science, Soil water, Ecology, Engineering, Biology, Bioinformatics
License/Data Use Agreement	CC BY 4.0


There are no files in this dataset.

Citation Metadata

Persistent Identifier	perma:LIST.GD2ZZY
Publication Date	2026-07-06
Title	Hybrid Soil Microbiome Modeling - Combining process-based models with machine learning to predict microbial dynamics and organic matter turnover in soil systems [* Cross-Reference *]
Other Identifier	https://doi.org/10.5194/egusphere-egu25-10523 OpenAlex ID: https://openalex.org/W4408434114
Author	P. Collart (University of Bonn, Sphere Institute) Jürgen Gall (University of Bonn, Lamarr Institute for Machine Learning and Artificial Intelligence) Andrea Schnepf (University of Bonn, Sphere Institute) - ORCID: https://orcid.org/0000-0003-2203-4466 Alberto Vinicius Sousa Rocha (Luxembourg Institute of Science and Technology, Cooperative Educational Service Agencies) - ORCID: https://orcid.org/0000-0001-8952-9565 Malte Herold (Luxembourg Institute of Science and Technology, Cooperative Educational Service Agencies) - ORCID: https://orcid.org/0000-0003-2627-0159 Kate M. Buckeridge (Luxembourg Institute of Science and Technology, Cooperative Educational Service Agencies) - ORCID: https://orcid.org/0000-0002-3267-4216 Holger Pagel (University of Bonn, Sphere Institute) - ORCID: https://orcid.org/0000-0003-2424-351X
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate climate change and environmental threats. Integrating microbial dynamics in process-based soil models is critical for predicting how soil carbon flows and stocks change in ecosystems with time. Functional traits can be inferred from amplicon sequencing data and metagenome assembled genomes to leverage model parameterization. However, informing models using omics-based datasets is challenging due to their large dimensional nature and the nonlinear relationship between genomes and the actual function microbes express. We present a hybrid modeling framework that combines machine learning to analyze metagenomic and DNA sequencing data with a simple microbial explicit process-based model. This hybrid model is conditioned using a convolutional network trained with data from the LUCAS 2018 database (Land Use and Coverage Area frame Survey), which includes soil metagenomes, 16S sequencing data in combination with soil carbon, microbial biomass and soil respiration measurements. Using trait inference from genomes, the model can learn several biokinetic parameters such as growth rates, dormancy rates, affinities to organic matter, growth yields or decay rates. We present the concept of the hybrid soil modelling framework and discuss what data is informative for these models and how to best link machine learning with process-based models. (2025-03-14) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/egusphere-egu25-10523 for the original and latest version of the publication* (2026-07-01)
Subject	Computer and Information Science; Mathematical Sciences; Physics; Social Sciences
Keyword	Process (computing) Microbiome Organic matter Soil organic matter Computer science Environmental science Biochemical engineering Soil science Soil water Ecology Engineering Biology Bioinformatics
Topic Classification	Image Processing and 3D Reconstruction Scientific Computing and Data Management
Deposit Date	2025-03-14
Data Type	Preprint

Dataset Terms

License/Data Use Agreement

Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

Creative Commons Attribution 4.0 International License. CC BY 4.0

Dataset Version	Summary	Contributors	Published on
No records found.

Edit File

This file has already been deleted (or replaced) in the current version. It may not be edited.

Restrict Access

Restricting limits access to published files. People who want to use the restricted files can request access by default. If you disable request access, you must add information about access to the Terms of Access field.

Learn about restricting files and dataset access in the User Guide.

Request Access

Enable access request

You must enable request access or add terms of access to restrict file access.

Terms of Access for Restricted Files

Save Changes

Edit Embargo

The selected file or files have already been published. Contact an administrator to change the embargo date or reason of the file or files.

Edit Retention Period

The selected file or files have already been published. Contact an administrator to change the retention period date or reason of the file or files.

Delete Files

The file will be deleted after you click on the Delete button.

Files will not be removed from previously published versions of the dataset.

Select File(s)

Please select one or more files.

Share Dataset

Share this dataset on your favorite social media networks.

Continue

Dataset Citations

Citations for this dataset are retrieved from Crossref via DataCite using Make Data Count standards. For more information about dataset metrics, please refer to the User Guide.

Sorry, no citations were found.

Inaccessible Files Selected

The selected file(s) may not be downloaded because you have not been granted access or the file(s) have a retention period that has expired or the files can only be transferred via Globus.

You may request access to any restricted file(s) by clicking the Request Access button.

Ineligible Files Selected

The selected file(s) may not be transferred because you have not been granted access or the file(s) have a retention period that has expired or the files are not Globus accessible.

You may request access to any restricted file(s) by clicking the Request Access button.

Download Options

The files selected are too large to download as a ZIP.

You can select individual files that are below the 100.0 MB download limit from the files table, or use the Data Access API for programmatic access to the files.

Select File(s)

Please select a file or files to be downloaded.

Inaccessible Files Selected

The selected file(s) may not be downloaded because you have not been granted access or the file(s) have a retention period that has expired.

Click Continue to download the files you have access to download.

Ineligible Files Selected

Some file(s) cannot be transferred. (They are restricted, embargoed, with an expired retention period, or not Globus accessible.)

Click Continue to transfer the elligible files.

Delete Dataset

Are you sure you want to delete this dataset and all of its files? You cannot undelete this dataset.

Delete Draft Version

Are you sure you want to delete this draft version? Files will be reverted to the most recently published version. You cannot undelete this draft.

Unpublished Dataset Preview URL

Preview URL can only be used with unpublished versions of datasets.

Unpublished Dataset Preview URL

Are you sure you want to disable the Preview URL? If you have shared the Preview URL with others they will no longer be able to use it to access your unpublished dataset.

Delete Files

The file(s) will be deleted after you click on the Delete button.

Files will not be removed from previously published versions of the dataset.

Compute

This dataset contains restricted files you may not compute on because you have not been granted access.

Deaccession Dataset

Are you sure you want to deaccession? This is permanent and the selected version(s) will no longer be viewable by the public.

Deaccession Dataset

Are you sure you want to deaccession this dataset? This is permanent an it will no longer be viewable by the public.

Version Differences Details

Please select two versions to view the differences.

Version Differences Details

Version:
Last Updated:

Select File(s)

Please select a file or files for access request.

Select File(s)

Embargoed files cannot be accessed. Please select an unembargoed file or files for your access request.

Edit Tags

Select existing file tags or create new tags to describe your files. Each file can have more than one tag.

Request Access

You need to Log In to request access.

Dataset Terms

Please confirm and/or complete the information needed below in order to request access to files in this dataset.

This dataset is made available under the following terms. Please confirm and/or complete the information needed below in order to continue.

License/Data Use Agreement

Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

Creative Commons Attribution 4.0 International License. CC BY 4.0

Preview Guestbook

Upon downloading files the guestbook asks for the following information.

Guestbook Name

Collected Data

Account Information

Package File Download

Use the Download URL in a Wget command or a download manager to download this package file. Download via web browser is not recommended. User Guide - Downloading a Dataverse Package via URL

Download URL

https://lore.list.lu/api/access/datafile/

Compute Batch

Clear Batch

Dataset	Persistent Identifier	Change Compute Batch

Compute Batch

Submit for Review

You will not be able to make changes to this dataset while it is in review.

Publish Dataset

Are you sure you want to republish this dataset?

Select if this is a minor or major version update.

Minor Release (1.1)

Major Release (2.0)

Publish Dataset

This dataset cannot be published until LIST Open Repository is published by its administrator.

Return to Author

Return this dataset to contributor for modification. The reason for return entered below will be sent by email to the author.

Curation Status History

Status	Date	Assigner
No records found.

Add/Edit a Version Note

Styled Citation