# E-MUSE - Data Management Plan

Eva Balsa-Canto, Michèle Bou Habib, Mitja Briscik, Sébastien Déjean, Eric Dugat-Bony, Hamza Faquir, Vincent Hervé, Muhammad Ahmed Ihsan, Lokamand Koduru, Alina Kyrylenko, et al.

## ► To cite this version:

Eva Balsa-Canto, Michèle Bou Habib, Mitja Briscik, Sébastien Déjean, Eric Dugat-Bony, et al. E-MUSE - Data Management Plan. INRAE. 2025. hal-05195171

HAL Id: hal-05195171

https://hal.science/hal-05195171v1

Submitted on 1 Aug 2025

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<figure><img src="image_1.png" /></figure>
# E-MUSE - Data Management Plan

2025-07-31

## Description of the E-MUSE action - MSCA-ITN-2020

To understand a complex microbial ecosystem and identify levers to control and/or predict its evolution, biological, mathematical, statistical and machine learning tools must be developed.

There is a bottleneck in the methodologies for extracting meaningful results from high-throughput data analyses of complex biological systems, in part due to a still underdeveloped interdisciplinary interaction between biologists, mathematicians, and computer scientists. With E-MUSE, Europe is at the forefront of these methodologies, which impact many domains linked to societal and economic challenges, like novel and more controlled methods for safe and tasty food production. There can also be applications in green chemistry to predict the evolution of microbial ecosystems for bioconversion processes or in sustainable development for bio-compound production.

The E-MUSE training programme aimed at developing young researchers' skills at the interface between artificial intelligence and life sciences. The research programme of the E-MUSE network has been to develop innovative methodologies that will be applied to model structural and dynamic features of microbial communities, to identify key processes and biomarkers for specific applications.

The macroscopic properties of a fermented product emerge from the action of microbial communities, which in turn depend on the environmental conditions. To be able to predict the emerging properties, innovative modelling techniques, combining mechanistic and data-driven approaches, must be developed to connect the scales and include the dynamics of the microbial communities. The early-stage researchers (ESRs) with biology, mathematics or modelling projects have interacted to understand the structure and function of microbial communities and predict their evolution.

Data have been generated at multi-scale levels from single-cells to microbial populations and to macroscopic properties. Genome-scaled models have been constrained with the data produced. Multi-scale data have been integrated and correlated to design mathematical models and develop predictive models.
Funder

European Commission||EC

Grant

No 956126 https://cordis.europa.eu/project/id/956126

Project

Complex microbial ecosystems multiscale modelling: mechanistic and
data-driven approaches integration. https://www.itn-emuse.com/

Licence

CC-BY-4.0 https://creativecommons.org/licenses/by/4.0/

Beneficiaries

-INSTITUT NATIONAL DE RECHERCHE POUR L'AGRICULTURE,
L'ALIMENTATION ET L'ENVIRONNEMENT (INRAE),

-UNIVERSITE TOULOUSE III - PAUL SABATIER TOULOUSE III (UT3),

2

-AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES
CIENTIFICAS M.P. (CSIC),

-STICHTING VU (VUA)

-KATHOLIEKE UNIVERSITEIT LEUVEN (KUL),

-UNIVERSITÀ TA MALTA (UM),

-NIZO FOOD RESEARCH BV (NIZO),

-ALMA MATER STUDIORUM - UNIVERSITÀ DI BOLOGNA (UNIBO),

-SZEGEDI TUDOMANYEGYETEM (USZ),

-CHR. HANSEN A/S – NOVONESIS (CHRH)

Template: Horizon 2020
Table of contents

<table><tr><td>E-MUSE - Data Management Plan</td><td>1</td></tr><tr><td>Description of the E-MUSE action - MSCA-ITN-2020</td><td>1</td></tr><tr><td>Beneficiaries</td><td>2</td></tr><tr><td>WP1 Systems modelling</td><td>8</td></tr><tr><td>ESR 1 project - Francesco Moro</td><td>8</td></tr><tr><td>STICHTING VU</td><td>8</td></tr><tr><td>Datasets Description</td><td>8</td></tr><tr><td>1 Data Summary</td><td>8</td></tr><tr><td>2 Reused Data</td><td>10</td></tr><tr><td>3 FAIR Data</td><td>10</td></tr><tr><td>4 Allocation of resources</td><td>14</td></tr><tr><td>5 Data security</td><td>15</td></tr><tr><td>6 Ethical aspects</td><td>15</td></tr><tr><td>7 Other</td><td>15</td></tr><tr><td>ESR2 project - Geoffrey Roudaut</td><td>16</td></tr><tr><td>AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS</td><td>16</td></tr><tr><td>Datasets Description</td><td>16</td></tr><tr><td>1 Data Summary</td><td>16</td></tr><tr><td>2 Reused Data</td><td>17</td></tr><tr><td>3 FAIR Data</td><td>18</td></tr><tr><td>4 Allocation of resources</td><td>19</td></tr><tr><td>5 Data security</td><td>19</td></tr><tr><td>6 Ethical aspects</td><td>19</td></tr><tr><td>7 Other</td><td>20</td></tr><tr><td>ESR3 project - Hamza Faquir</td><td>21</td></tr><tr><td>AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS</td><td>21</td></tr><tr><td>Dataset Description</td><td>21</td></tr><tr><td>1 Data Summary</td><td>21</td></tr><tr><td>2 Reused Data</td><td>23</td></tr><tr><td>3 FAIR Data</td><td>23</td></tr><tr><td>4 Allocation of resources</td><td>25</td></tr><tr><td>5 Data security</td><td>26</td></tr><tr><td>6 Ethical aspects</td><td>26</td></tr><tr><td>7 Other</td><td>26</td></tr><tr><td>ESR4 project - Teresa Osset</td><td>27</td></tr><tr><td>CHR. HANSEN A/S - NOVONESIS</td><td>27</td></tr><tr><td>Dataset Description</td><td>27</td></tr></table>
<table><tbody><tr><td>1 Data Summary</td><td>27</td></tr><tr><td>2 Reused Data</td><td>28</td></tr><tr><td>3 FAIR Data</td><td>29</td></tr><tr><td>4 Allocation of resources</td><td>30</td></tr><tr><td>5 Data Security</td><td>31</td></tr><tr><td>6 Ethical aspects</td><td>31</td></tr><tr><td>7 Other</td><td>32</td></tr><tr><td>ESR5 project – Rina Mekuli</td><td>33</td></tr><tr><td>INSTITUT NATIONAL DE RECHERCHE POUR L'AGRICULTURE, L'ALIMENTATION ET L'ENVIRONNEMENT</td><td>33</td></tr><tr><td>Datasets Description</td><td>33</td></tr><tr><td>1 Data Summary</td><td>33</td></tr><tr><td>2 Reused Data</td><td>35</td></tr><tr><td>3 FAIR Data</td><td>35</td></tr><tr><td>4 Allocation of resources</td><td>38</td></tr><tr><td>5 Data Security</td><td>39</td></tr><tr><td>6 Ethical aspects</td><td>39</td></tr><tr><td>7 Other</td><td>40</td></tr><tr><td>WP2 Data-based modelling</td><td>41</td></tr><tr><td>ESR6 project – Mitja Briscik</td><td>41</td></tr><tr><td>UNIVERSITE TOULOUSE III - PAUL SABATIER TOULOUSE III – UNIVERSITE DE TOULOUSE</td><td>41</td></tr><tr><td>Datasets Description</td><td>41</td></tr><tr><td>1 Data Summary</td><td>41</td></tr><tr><td>2 Reused Data</td><td>42</td></tr><tr><td>3 FAIR Data</td><td>43</td></tr><tr><td>4 Allocation of resources</td><td>46</td></tr><tr><td>5 Data Security</td><td>47</td></tr><tr><td>6 Ethical aspects</td><td>47</td></tr><tr><td>7 Other</td><td>47</td></tr><tr><td>ESR7 project – Anis Mansouri</td><td>48</td></tr><tr><td>ALMA MATER STUDIORUM - UNIVERSITÀ DI BOLOGNA</td><td>48</td></tr><tr><td>Datasets Description</td><td>48</td></tr><tr><td>1 Data Summary</td><td>48</td></tr><tr><td>2 Reused Data</td><td>49</td></tr><tr><td>3 FAIR Data</td><td>50</td></tr><tr><td>4 Allocation of resources</td><td>52</td></tr><tr><td>5 Data Security</td><td>53</td></tr><tr><td>6 Ethical aspects</td><td>53</td></tr><tr><td>7 Other</td><td>53</td></tr></tbody></table>
<table><tbody><tr><td>ESR8 project - Gabriele Tazza</td><td>54</td></tr><tr><td>SZEGEDI TUDOMANYEGYETEM</td><td>54</td></tr><tr><td>Datasets Description</td><td>54</td></tr><tr><td>1 Data Summary</td><td>54</td></tr><tr><td>2 Reused Data</td><td>55</td></tr><tr><td>3 FAIR Data</td><td>56</td></tr><tr><td>4 Allocation of resources</td><td>58</td></tr><tr><td>5 Data Security</td><td>59</td></tr><tr><td>6 Ethical aspects</td><td>59</td></tr><tr><td>7 Other</td><td>59</td></tr><tr><td>ESR9 project - Dario Ruggeri</td><td>60</td></tr><tr><td>SZEGEDI TUDOMANYEGYETEM</td><td>60</td></tr><tr><td>Datasets Description</td><td>61</td></tr><tr><td>1 Data Summary</td><td>61</td></tr><tr><td>2 Reused Data</td><td>62</td></tr><tr><td>3 FAIR Data</td><td>63</td></tr><tr><td>4 Allocation of resources</td><td>66</td></tr><tr><td>5 Data Security</td><td>66</td></tr><tr><td>6 Ethical aspects</td><td>67</td></tr><tr><td>7 Other</td><td>67</td></tr><tr><td>ESR10 project - Mahtab Shoukat</td><td>68</td></tr><tr><td>INSTITUT NATIONAL DE RECHERCHE POUR L'AGRICULTURE, L'ALIMENTATION ET L'ENVIRONNEMENT</td><td>68</td></tr><tr><td>Datasets Description</td><td>68</td></tr><tr><td>1 Data Summary</td><td>68</td></tr><tr><td>2 Reused Data</td><td>70</td></tr><tr><td>3 FAIR Data</td><td>70</td></tr><tr><td>4 Allocation of resources</td><td>73</td></tr><tr><td>5 Data Security</td><td>74</td></tr><tr><td>6 Ethical aspects</td><td>74</td></tr><tr><td>7 Other</td><td>74</td></tr><tr><td>WP3 Predictive modelling</td><td>75</td></tr><tr><td>ESR11 project - Jian Wang</td><td>75</td></tr><tr><td>KATHOLIEKE UNIVERSITEIT LEUVEN</td><td>75</td></tr><tr><td>Datasets Description</td><td>75</td></tr><tr><td>1 Data Summary</td><td>75</td></tr><tr><td>2 Reused Data</td><td>76</td></tr><tr><td>3 FAIR Data</td><td>76</td></tr><tr><td>4 Allocation of resources</td><td>79</td></tr></tbody></table>
<table><tbody><tr><td>5 Data Security</td><td>80</td></tr><tr><td>6 Ethical aspects</td><td>80</td></tr><tr><td>7 Other</td><td>80</td></tr><tr><td><strong>ESR12 project - Muhammad Ahmed Ihsan</strong></td><td><strong>81</strong></td></tr><tr><td><strong>UNIVERSITÀ TA MALTA</strong></td><td><strong>81</strong></td></tr><tr><td>Datasets Description</td><td>81</td></tr><tr><td>1 Data Summary</td><td>81</td></tr><tr><td>2 Reused Data</td><td>82</td></tr><tr><td>3 FAIR Data</td><td>83</td></tr><tr><td>4 Allocation of resources</td><td>85</td></tr><tr><td>5 Data Security</td><td>86</td></tr><tr><td>6 Ethical aspects</td><td>86</td></tr><tr><td>7 Other</td><td>86</td></tr><tr><td><strong>ESR13 project - Alina Kyrylenko</strong></td><td><strong>87</strong></td></tr><tr><td><strong>NIZO FOOD RESEARCH BV</strong></td><td><strong>87</strong></td></tr><tr><td>Datasets Description</td><td>87</td></tr><tr><td>1 Data Summary</td><td>87</td></tr><tr><td>2 Reused Data</td><td>88</td></tr><tr><td>3 FAIR Data</td><td>89</td></tr><tr><td>4 Allocation of resources</td><td>91</td></tr><tr><td>5 Data Security</td><td>92</td></tr><tr><td>6 Ethical aspects</td><td>92</td></tr><tr><td>7 Other</td><td>92</td></tr><tr><td><strong>ESR14 project - Huabin Luo</strong></td><td><strong>93</strong></td></tr><tr><td><strong>KATHOLIEKE UNIVERSITEIT LEUVEN</strong></td><td><strong>93</strong></td></tr><tr><td>Datasets Description</td><td>93</td></tr><tr><td>1 Data Summary</td><td>93</td></tr><tr><td>2 Reused Data</td><td>94</td></tr><tr><td>3 FAIR Data</td><td>94</td></tr><tr><td>4 Allocation of resources</td><td>97</td></tr><tr><td>5 Data Security</td><td>97</td></tr><tr><td>6 Ethical aspects</td><td>98</td></tr><tr><td>7 Other</td><td>98</td></tr><tr><td><strong>ESR15 project - Michèle Bou Habib</strong></td><td><strong>99</strong></td></tr><tr><td><strong>INSTITUT NATIONAL DE RECHERCHE POUR L'AGRICULTURE, L'ALIMENTATION ET L'ENVIRONNEMENT</strong></td><td><strong>99</strong></td></tr><tr><td>Datasets Description</td><td>99</td></tr><tr><td>1 Data Summary</td><td>99</td></tr><tr><td>2 Reused Data</td><td>100</td></tr><tr><td>3 FAIR Data</td><td>100</td></tr></tbody></table>
<table><tr><td>4 Allocation of resources</td><td>103</td></tr><tr><td>5 Data Security</td><td>103</td></tr><tr><td>6 Ethical aspects</td><td>103</td></tr><tr><td>7 Other</td><td>104</td></tr></table>
# WP1 Systems modelling

The main objective of WP1 has been to define multiscale mechanistic data-based models of microbial species and microbial communities in cheese fermentation or ripening conditions and to implement model-based control to guarantee the maximum quality of the final product.

## ESR 1 project – Francesco Moro

### STICHTING VU

The project focused on the development and application of methods which leverage genome-scale metabolic models (GEMs) and constraint-based analyses (COBRA) to investigate the physiological aspects of cooperative microbial communities.

## Datasets Description

### 1 Data Summary

#### 1.1 Data Summary

##### 1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

* To obtain information

* To make informed decisions

The project aims to develop a new modelling approach that allow to model cooperative interactions (at a metabolic level) in microbial communities.

The data encompass all what is related to the method development and validation (specifically in the biological scenario of yogurt fermentation).

##### 1.1.2 What are the types of the described generated/collected data?

**Simulation:**

The data consist of:

- the description of the theoretical aspects of the method

- the code to apply the method

- simulations, in which the model is applied to toy models and compare to other methods
- simulations of yogurt fermentation

- the genome scale models (GSMMs) needed for the simulations of yogurt starter cultures

- data (from previous projects) to validate/evaluate the model's predictions

### 1.1.3 What are the formats of the described generated/collected data?

- Text files

- Numerical

- Models

- Software

The description of the theoretical aspects of the method is in LaTeX files.

The code to apply the method is composed of:

- Mathematica notebooks with toy models' simulations,

- a Mathematica package file with the functions used in the notebooks,

- a Python library (with documentation) with the implementation of the method compatible with full-size GSMMs,

9

- Python notebooks with the simulations of yogurt starter cultures.

Data from previous projects are:

- the GSMMs of *L. bulgaricus* and *S. thermophilus* in SBML format,

- data from bioreactor experiments, stored as Excel files.

### 1.1.4 What is the origin of the described data?

- Primary data

- Secondary data

### 1.1.5 What is the expected size of the described data?

MB (megabyte)

All the files mentioned are typically on the order of 10-100kB.

### 1.1.6 To whom might it be useful ('data utility')?

- Researchers

- Research communities
The data can be used by other researchers to verify the soundness of our method and analyses.

The mathematica code can be use to gauge the effect of the introduction of new type of
constraints or other modifications in our modelling framework.

The python library and its documentation will allow other researchers, in the public or private sector, to apply our method to new biological scenarios.

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

The data were provided upon request. We store copies of them alongside our other data. At the completion of the project, for the data which proved to be valuable in the method validation and which are allowed to be made publicly available, we will store them in the Zenodo repository we will set up for the project.

* To reproduce and validate findings

* To follow-up research on a specific area

#### 2.1.2 Where do the described data reside?

GitHub

SURFdrive (VU Amsterdam online storage), OverLeaf

#### 2.1.3 Which data will be re-used?

Bioreactor data from YoghurtDesign project

## 3 FAIR data

### 3.1.1 Making data findable, including provisions for metadata

#### 3.1.1.1 Will you use metadata to describe the data?

Yes

Method description comes with title, author, abstract, keywords, and date.

Code and documentation are also associated with author, brief description, date and keywords.

Also for the reused data we will provide title, author/creator, abstract, keywords and the DOI of the publication.
3.1.1.3 Will your metadata use standardised vocabularies?

No

We didn't find a proper metadata specification for our data.

3.1.1.5 Will you make the metadata available free-of-charge?

Yes

3.1.1.6 Will your metadata be harvestable?

Yes

3.1.1.7 Will you use naming conventions for your data?

Yes

3.1.1.8 Please provide more details and examples on used naming conversions

http://bigg.ucsd.edu/

for metabolites and reactions, we be consistent with the naming scheme of BiGG
database for GSMMs

3.1.1.9 Will you provide clear version numbers for your data?

Yes

All Mathematica code has a version number.

Version control for Python code is handled by GitHub.

3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

The most relevant part of the code and data is or is going to be accessible through open
access scientific publications, which have there own DOI.

At the end of the project a Zenodo (which is associated to a DOI) repository will be created to store all data from the project, and also the experimental data from third party for which we have authorization ot share (reorganized in files that makes reproducing our results more straigthforward).

3.1.1.11 Persistent identifiers

DOI

3.1.1.12 Will you provide searchable metadata for the described data?
No

Our novelty resides in the method, not in the dataset (which, when applicable, has already been properly indicized)

### 3.1.1.15 Will you use standardised formats for the described data?

Yes

### 3.1.1.16 Provide information about used standardised formats

https://sbml.org/

SBML

### 3.1.1.18 Are the file formats you will use open?

Yes

Theory explanation will be available in PDF files.

Python is an open language.

Mathematica and Wolfram Language files instead require a Mathematica license, but we will also provide PDF of the compiled notebooks.

### 3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

### 3.1.1.21 Please describe if data require proprietary tools to access the data.

Mathematica and Wolfram Language files instead require a Mathematica license to be accessed, modified and compiled.

### 3.1.1.22 Will you provide metadata describing the quality of the data?

No

Not applicable

## 3.1.2 Making data openly accessible

### 3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

### 3.1.2.2 Will the described data be openly accessible?

Yes

### 3.1.2.3 How will the data be made available?
* Project website

* University repository

* Repository of Archive

SURFdrive (VU Amsterdam university repository system)

Zenodo (long term storage)

GitHub

### 3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

Backup is also provided with regular copy in hard drives.

### 3.1.2.6 Are there any methods or tools required to access the described data?

No

Eventually all data and code will be freely accessible in the Zenodo repository

### 3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

no auxiliary data

### 3.1.3 Making data interoperable

#### 3.1.3.1 Will you use a controlled vocabulary for the described data?

Yes

SBML conventions

#### 3.1.3.2 Will you provide a mapping to more commonly used ontologies?

Mapping to Gene Ontology and KeGG

### 3.1.4 Increase data reuse

#### 3.1.4.1 When do you plan to make the described data available for reuse?

after publication

MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling. Gabriele Tazza, Francesco Moro, Dario Ruggeri, Bas Teusink, László Vidács.

bioRxiv 2025.05.14.653943; doi: https://doi.org/10.1101/2025.05.14.653943
3.1.4.4 What internationally recognised licence(s) will you use for the described data?

Academic Free License v3.0

Grant of Source Code License

3.1.4.5 Do you have documented procedures for quality assurance of the described data?

Yes

All the code is provided of test scripts to assess effectiveness and repeatability of its
functions.

All the models will be checked for adherence to SBML standards.

3.1.4.7 Describe the data quality assurance processes

* Use of tools for automatic checks

* Data conform to format specification

* Consistency verified with data models and standards

3.1.4.8 Will you provide any support for data reuse?

Yes

Support will be provided from the corresponding authors after publication

3.1.4.9 How long do you intend to support data reuse?

More than 10 years

14

# 4 Allocation of resources

4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of the institution infrastructure

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

4.1.3 Identify the people or roles that will be responsible for the management of the described data

Francesco Moro ([orcid:0000-0003-3683-187X](https://orcid.org/0000-0003-3683-187X))
Data producer, Data management

Bas Teusink (orcid: 0000-0003-3929-0423)

Data management

#### 4.1.4 How do you intend to ensure data reuse after your project finishes?

* Institutional archive

* Data Center Archive Storage

* Personal Archive

Zenodo (publicly accessible and connected to the official publication)

Github (publicly accessible and connected to the official publication)

Personal hard disk and online storage

## 5 Data security

### 5.1 Data Security

#### 5.1.1 Where do you plan to keep the described data?

- Kept on secure, managed storage for limited time

- Zenodo and GitHub (unlimited time)

15

## 6 Ethical aspects

### 6.1 Ethical aspects

#### 6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

- No

#### 6.1.2 Are the described data sensitive?

- No

#### 6.1.3 Are the described data personal?

- No

## 7 Other

### 7.1 Other

#### 7.1.1 Do you make use of other procedures for data management?

- No
ESR2 project – Geoffrey Roudaut
AGENCIA ESTATAL CONSEJO
SUPERIOR DE INVESTIGACIONES
CIENTIFICAS

Multi-scale modelling of microbial communities dynamics. The project focused on gaining novel biological insights into the behaviour of microbial communities of interest as well as developing a systematic approach to modelling microbial communities of interest in the biotechnology industry. This will be achieved by building dynamic models of said microbial metabolism of cellular population using omics data.

Datasets Description

1 Data Summary

1.1 Data Summary

1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

16

* To obtain information

• To combine with other data

The purpose of modelling microbial communities dynamics is to gain information on the behavior of said communities, i.e, how they interact with the substrates and what products are secreted depending on the experimental conditions, and to identify and characterize the inter and intra species interactions. In order to build the different models in the project, data will be collected from former project and from the literature, and from the use of the data will result the generation of simulated data along with the models equations and estimated parameters. Some of the works in this project also contain the development of an optimal experimental design.

1.1.2 What are the types of the described generated/collected data?

Simulation:

Simulation data: The data generated will be on one hand, a generalized Lotka-Volterra model coupled with n algorithm from a toolbox in Matlab enabling the making of an optimal experimental design which will ensure the identifiability of the model related to the experiments. On the other hand, there will be model equations, sets of solution for the parameters, and simulations of this very model studying the fermentation process of Lactic
Acid Bacteria of interests in the Yoghurt making process. Some additional information will be derived from genome scale metabolic models.

### 1.1.3 What are the formats of the described generated/collected data?

* Text files

* Numerical

* Models

A major part of the work is performed on Matlab, the equations are written in Excel files and the results are in .mat files although the reports for the parameter estimations can be opened as text files. To sum up:

- .csv

- .mat

- .txt

### 1.1.4 What is the origin of the described data?

Secondary data

### 1.1.5 What is the expected size of the described data?

17

MB (megabyte)

The sizes for the excel files are of the order of KB, same for the matlab files, therefore gathering some of them might amount to a few MB.

### 1.1.6 To whom might it be useful ('data utility')?

* Researchers

* Industry

These data can be used for research purposes. The predictive aspect of these models have the potential to be of interest for the industry where the species of interest are used.

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

* To reproduce and validate findings
* To follow-up research on a specific area

## 3 FAIR Data

### 3.1.1 Making data findable, including provisions for metadata

#### 3.1.1.1 Will you use metadata to describe the data?

Yes

#### 3.1.1.5 Will you make the metadata available free-of-charge?

Yes

#### 3.1.1.7 Will you use naming conventions for your data?

Yes

#### 3.1.1.8 Please provide more details and examples on used naming conversions

a. yyyymmdd_E-MUSE_ESR2_explicitfilename.extension

b. Species_ModelVersion_ddmmyy.extension

#### 3.1.1.9 Will you provide clear version numbers for your data?

Yes

Different versions of models are described in an excel file

#### 3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

DOI

#### 3.1.1.18 Are the file formats you will use open?

No

Some of the file format provided will be MATLAB files, for which is not a free software.
Most of the other shared files will be open.

#### 3.1.1.19 Please provide more details if data are not in an open format.

.mat, .xlx

#### 3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

Open Office can be used to open excel files

18
3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.6 Are there any methods or tools required to access the described data?

Yes

3.1.2.8 Please provide information about the tools needed to access the data.

MATLAB is a tool that will be necessary to access the code for applying the models

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?

after Publication

# 4 Allocation of resources

4.1 Allocation of resources

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

4.1.3 Identify the people or roles that will be responsible for the management of the described data

a. Geoffrey Roudaut (orcid: 0000-0003-3400-1768)

Data producer, data management

b. Eva Balsa-Canto (orcid: 0000-0002-1978-2626)

Data management

# 5 Data security

5.1 Data Security

5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time

# 6 Ethical aspects
## 6.1 Ethical aspects

6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

6.1.2 Are the described data sensitive?

No

6.1.3 Are the described data personal?

No

# 7 Other

## 7.1 Other

7.1.1 Do you make use of other procedures for data management?

No
ESR3 project – Hamza Faquir
AGENCIA ESTATAL CONSEJO
SUPERIOR DE INVESTIGACIONES
CIENTIFICAS

The objective of the first part of this study was to develop a general framework for the control of stochastic gene regulatory network, based on a Partial integro differential equations model that describes the evolution of gene expression of gene regulatory network (GRN), we have first developed an optimal control method to reach a target protein distribution in population at a given final time, moreover we have extended this method to a model predictive framework in order not only reach this target protein expression but also stabilize the GRN around it, we have applied these two methods in silico on submotif regulatory motifs that are common in biological networks such as genetic toggle switches, autoregulatory genes.

The second part of this study was more focused on dynamic modeling and control of metabolic pathways based on ordinary differential equations (ODE), we are developing an ODE model of the central carbon metabolism of Lactococcus lactis, that exhibits a metabolic shift from lactic acid to mixed acids as end products, the goal of model is to primary capture this shift through several factors mainly the NADH/NAD+ ratio, which itself can be controlled by the NADH oxidase level, this modeling framework combined with the optimal control and MPC we have developed were applied to dynamically control this shift.

The last part of this study was about modeling the dynamics of cheese ecosystem, with a focus on the 9 strains used to explore the iron effect on the final composition of cheese, and the goal is to develop an ODE model including growth factors such as carbon source, different initial iron concentrations and the evolution of the concentrations of microorganisms during the cheese production taking into consideration the different interactions happening within this ecosystem

Dataset Description

1 Data Summary

1.1 Data Summary

1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

To obtain information

As a result of this study, we will use the mathematical models and experimental data to predict
and control the biological system studied, namely:

Gene regulation

Metabolite
Microorganisms concentrations

1.1.2 What are the types of the described generated/collected data?

Simulation:

-ODE models simulation of the central carbon metabolism of *Lactococcus lactis*

-ODE model simulation for the evolution of microorganism in cheese fermentation and ripening

First part we generate a control algorithm of gene expression in bacteria (Optimal control, model predictive control

1.1.3 What are the formats of the described generated/collected data?

• Numerical

• Multimedia

• Models

• Other

Mathematical models in matlab with format: .m

Mathematical models in python: .py

Multimedia: videos of the evolution of the concentrations modeled in each of the mentioned frameworks

22

1.1.4 What is the origin of the described data?

Primary data

1.1.5 What is the expected size of the described data?

MB (megabyte)

100

1.1.6 To whom might it be useful ('data utility')?

• Researchers

• Research communities

• Industry

The modeling and control frameworks we develop can both be used for research to obtain
deeper insights on Gene regulatory networks, metabolic pathways, or cheese ecosystem
They can also be used in industry such as metabolic engineering, or for the control of gene expression in bioindustrial processes

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

No

## 3 FAIR Data

### 3.1.1 Making data findable, including provisions for metadata

#### 3.1.1.1 Will you use metadata to describe the data?

Yes

Kinetic parameters for the studied biological systems

#### 3.1.1.3 Will your metadata use standardised vocabularies?

Yes

#### 3.1.1.5 Will you make the metadata available free-of-charge?

Yes

#### 3.1.1.6 Will your metadata be harvestable?

No

#### 3.1.1.7 Will you use naming conventions for your data?

No

#### 3.1.1.9 Will you provide clear version numbers for your data?

Yes

#### 3.1.1.10 Will you provide persistent identifiers for the described data?

No

#### 3.1.1.12 Will you provide searchable metadata for the described data?

No

#### 3.1.1.15 Will you use standardised formats for the described data?

No
### 3.1.1.18 Are the file formats you will use open?

* Yes

* .png

* .gif

* .m

* .py

### 3.1.1.20 Do supported open-source tools exist for accessing the data?

* Yes

* Github

### 3.1.1.21 Please describe if data require proprietary tools to access the data.

* No

### 3.1.1.22 Will you provide metadata describing the quality of the data?

* No

## 3.1.2 Making data openly accessible

### 3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

* No

### 3.1.2.2 Will the described data be openly accessible?

* Yes

### 3.1.2.3 How will the data be made available?

* Project website

* Repository of Archive

* Other

* GITHUB

### 3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

* secure with backup and recovery

### 3.1.2.6 Are there any methods or tools required to access the described data?

24
No

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

after publication

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?

Yes

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

3.1.4.5 Do you have documented procedures for quality assurance of the described data?

No

3.1.4.7 Describe the data quality assurance processes

the models and methods will be shared on GITHUB to be tested by anyone who wants to use
them

3.1.4.8 Will you provide any support for data reuse?

Yes

3.1.4.9 How long do you intend to support data reuse?

More than 10 years

4 Allocation of resources

4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Infrastructure Grant

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

4.1.3 Identify the people or roles that will be responsible for the management of the described data

25
a. Hamza Faquir (orcid:0000-0003-2515-4068)

Data producer

b. Irene Otero-Muras (orcid:0000-0003-2895-997X)

Data management

#### 4.1.4 How do you intend to ensure data reuse after your project finishes?

* Institutional archive

* Data Center Archive Storage

## 5 Data security

### 5.1 Data Security

#### 5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time

## 6 Ethical aspects

### 6.1 Ethical aspects

#### 6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

#### 6.1.2 Are the described data sensitive?

No

#### 6.1.3 Are the described data personal?

No

## 7 Other

### 7.1 Other

#### 7.1.1 Do you make use of other procedures for data management?

No

26
Study on the physiology of *Lactobacillus helveticus* including but not limited to:

-Growth data in different media and carbon sources

-Development of a Chemically Defined Media that allows for robust and reproducible growth

-Understanding the triggers and conditions leading to autolysis of the selected strain(s).

-Generation and curation of a Genome Scale Model (GEM).

-Generation of RNA-seq data in relevant conditions - eg: Casein VS single amino acids, and fitting the results to the GEM.

-Understanding of the antimicrobial ability of *Lb. helveticus* in the context of cheese.

# Dataset Description

## 1 Data Summary

### 1.1 Data Summary

#### 1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

To obtain information

27

*Lb. helveticus* is a Gram-positive bacteria part of the lactic acid bacteria family. As such, it is used during the cheese making process, either as a starter culture to help driving acidification, or as an adjunct culture, which will boost flavour during ripening.

One of the main characteristics by which *Lb. helveticus* helps in the development of flavour is by its high proteolytic activity, meaning it will break down the casein into smaller peptides which, in turn, will be uptaken by the organism and further metabolised into amino acids. Another important feature of this organism is its ability to autolyse in cheese therefore, spilling all its contents into the matrix and accelerating cheese ripening as well as providing nutrient to the surrounding microbial community.

The aim of this project is to study these parameters in isolation and in the context of cheese ripening

#### 1.1.2 What are the types of the described generated/collected data?

**Experimental:**

-Growth data in different carbon sources and media.

-Sequencing data of the relevant strains to study.

-RNA-seq and extracellular metabolite data of *Lb. helveticus* in relevant conditions.
- Microbial interaction data.

**Simulation:**

-Genome Scale Model (GEM): this is a model containing all metabolic reactions contained within an organism. The idea is that, once curated and fitted with experimental data, the model will be able to predict the growth rate of the organism and the fluxes going through each reaction, therefore providing essential information about the organism's growth and metabolism.

1.1.3 What are the formats of the described generated/collected data?

• Text files

• Numerical

• Multimedia

• Models

• Instrument specific formats

Growth data and uptake rates: .csv

Extracellular metabolites measurements: .csv

GEM: .xml, .sbml

28

1.1.4 What is the origin of the described data?

Primary data

1.1.5 What is the expected size of the described data?

GB (gigabyte)

1.1.6 To whom might it be useful ('data utility')?

• Researchers

• Research communities

• Industry

2 Reused Data

2.1 Reused Data

2.1.1 Are you re-using the described data and how?

No
## 3 FAIR Data

3.1.1 Making data findable, including provisions for metadata

3.1.1.1 Will you use metadata to describe the data?

Yes

3.1.1.3 Will your metadata use standardised vocabularies?

Yes

3.1.1.5 Will you make the metadata available free-of-charge?

Yes

3.1.1.6 Will your metadata be harvestable?

Yes

3.1.1.7 Will you use naming conventions for your data?

Yes

3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

3.1.1.11 Persistent identifiers

DOI

3.1.1.12 Will you provide searchable metadata for the described data?

Yes

3.1.1.15 Will you use standardised formats for the described data?

Yes

3.1.1.18 Are the file formats you will use open?

Yes

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

3.1.1.22 Will you provide metadata describing the quality of the data?

Yes
3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.3 How will the data be made available?

• Project website

• Repository of Archive

• Other

3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

No

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

after publication

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?

Yes

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

3.1.4.5 Do you have documented procedures for quality assurance of the described data?

No

3.1.4.8 Will you provide any support for data reuse?

No

# 4 Allocation of resources
## 4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

### Infrastructure Grant

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

4.1.3 Identify the people or roles that will be responsible for the management of the described data

Teresa Osset Ojea ([orcid: 0000-0003-3577-1772](orcid: 0000-0003-3577-1772))

Data producer, Data management

Lokamand Koduru ([orcid: 0000-0001-9223-8742](orcid: 0000-0001-9223-8742))

Data management

4.1.4 How do you intend to ensure data reuse after your project finishes?

* Institutional archive

* Data Center Archive Storage

31

# 5 Data security

## 5.1 Data Security

5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time

# 6 Ethical aspects

## 6.1 Ethical aspects

6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

6.1.2 Are the described data sensitive?

No

6.1.3 Are the described data personal?

No
# 7 Other

## 7.1 Other

### 7.1.1 Do you make use of other procedures for data management?

No
ESR5 project – Rina Mekuli

INSTITUT NATIONAL DE RECHERCHE
POUR L'AGRICULTURE,
L'ALIMENTATION ET
L'ENVIRONNEMENT

Study of the role of iron in the growth, interaction and production of volatile flavour compounds of bacteria from Livarot-type smear-ripened cheeses. Growth data have been collected for five ripening bacteria to evaluate their growth dynamics at different temperatures and iron concentrations. Two strains were selected to investigate their interactions, *Hafnia alvei* and *Brevibacterium aurantiacum*. Omics data have been collected to understand and predict the optimal metabolic behaviour for mutualistic interactions between the two selected species. A genome-scale metabolic model for *Hafnia alvei* has been constructed and curated, and iron-sulphur clusters have been integrated into the metabolic network.

# Datasets Description

1 Data Summary

1.1 Data Summary

1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

To obtain information

The starting point for the study and modelling of microbial communities in cheese production
is to characterize the physiology of relevant species. Species of interest include bacteria in
Livarot-type smear ripened cheeses: *Brevibacterium aurantiacum*, *Hafnia alvei*,
*Staphylcoccus equorum*, *Corynebactium casei* and *Glutamicibacter arilaitensis*. Previous
research has indicated that among other potential interactions in the cheese ecosystem, *B*.
*aurantiacum* and *H. alvei* cooperate by sharing of iron-chelating molecules called
siderophores. Because iron is scarce in cheese, sharing siderophores in this environment
can be advantageous. In addition, these two bacteria are suspected to cooperate in additional
ways. Of special importance to cheese flavour are proteases excreted by one bacterium and
utilized by the other, which can lead to higher availability of amino acids and ultimately, to an
increase in the formation of aromatic compounds.

1.1.2 What are the types of the described generated/collected data?
**Experimental data:**

Growth data of five ripening bacteria in different iron concentrations and temperature ranges

Omics data: Metabolomic, proteomic and transcriptomic data acquisition of two selected ripeing bacteria (B. aurantiacum and H. alvei)

**Simulation:**

Predictive growth models from the growth data of the five ripening bacteria in iron concentration and temperature range.

Genome Scale Metabolic Models (GSMMs): GSMMs are structured representations of the metabolic reactions of a target organism based on existing genetic, biochemical, and physiological information. Omics data acquired will be used to constrain and validate the models.

1.1.3 What are the formats of the described generated/collected data?

* Text files

* Numerical

* Models

* Instrument specific formats

Growth data: csv

Predictive models: Rmd (R notebook)

GSMM: sbml, xml

Metabolomics: instrument specific format, mzml, csv

Proteomics: instrument specific format, mzIdentML, csv

1.1.4 What is the origin of the described data?

Primary data

1.1.5 What is the expected size of the described data?

TB (terabyte)

5TB

1.1.6 To whom might it be useful ('data utility')?

* Researchers
* Research communities

* Industry

Data and models that will be produced will bring new knowledge on bacteria involved in cheese ripening processes, at a fundamental level, and this knowledge will help in the understanding of the function and structure of ripening microbial communities, that will be helpful for the researchers and cheese makers.

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

To follow-up research on a specific area

#### 2.1.2 Where do the described data reside?

https://www.genoscope.cns.fr/agc/microscope/

#### 2.1.3 Which data will be re-used?

Genomes

35

## 3 FAIR Data

### 3.1.1 Making data findable, including provisions for metadata

#### 3.1.1.1 Will you use metadata to describe the data?

Yes

#### 3.1.1.2 Please provide URL/Location describing the used metadata schema

ISA - Tab

#### 3.1.1.3 Will your metadata use standardised vocabularies?

Yes

Growth data: https://fairsharing.org/FAIRsharing.9sza27 (GMO),
https://doi.org/10.25504/FAIRsharing.53gp75 (ISA-Tab)

Predictive models: https://doi.org/10.25504/FAIRsharing.ap169a (MIRIAM)

GSMM: https://doi.org/10.25504/FAIRsharing.ap169a (MIRIAM)

Metabolomics: https://doi.org/10.25504/FAIRsharing.kkdpxe (MTBLS)
3.1.1.5 Will you make the metadata available free-of-charge?

Yes

3.1.1.6 Will your metadata be harvestable?

Yes

If the repositories support the OAI-PMH protocol

3.1.1.7 Will you use naming conventions for your data?

Yes

3.1.1.8 Please provide more details and examples on used naming conversions

yyyymmdd_E-MUSE_ESR5_explicitfilename.extension

3.1.1.9 Will you provide clear version numbers for your data?

Yes

Data: Raw/Processed/Analysed

Model versioning

36

3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

The PID will be provided by the repositories

3.1.1.11 Persistent identifiers

DOI

3.1.1.12 Will you provide searchable metadata for the described data?

Yes

Data uploaded to public repositories will be linked to the metadata following the searchable
standards of the repository.

3.1.1.13 What services will you use to provide searchable metadata?

Other

Searchable metadata of the repositories

3.1.1.15 Will you use standardised formats for the described data?
Yes

3.1.1.16 Provide information about used standardised formats

a. a. https://docs.fileformat.com/spreadsheet/csv/ csv

b. b. https://r markdown.rstudio.com/articles_intro.html Rmd

c. c. https://sbml.org/ sbml

d. d. https://www.w3.org/XML/ xml

3.1.1.18 Are the file formats you will use open?

Yes

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

Data will be uploaded to public repositories.

3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

37

3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.3 How will the data be made available?

• Domain-specific database

• Other

Growth data: https://entrepot.recherche.data.gouv.fr/dataverse/esr5-project

Predictive models: https://entrepot.recherche.data.gouv.fr/dataverse/esr5-project

GSMM: BIGG

Metabolomics: EMBL-EBI Metabolight https://www.ebi.ac.uk/metabolights/

Transcriptomics: NCBI https://www.ncbi.nlm.nih.gov/

Proteomics: EMBL-EBI PRIDE https://www.ebi.ac.uk/training/online/courses/proteomics-an-introduction/proteomics-resources-at-the-ebi/pride/
3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

Yes

3.1.2.7 Please provide information about the method(s) needed to access the data

Data download

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

No

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?

Yes

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

3.1.4.4 What internationally recognised licence(s) will you use for the described data?

Etalab open licence (CC-BY), GNU-GPL for source code

3.1.4.5 Do you have documented procedures for quality assurance of the described data?

Yes

3.1.4.7 Describe the data quality assurance processes

Consistency verified with data models and standards

The LC-MS devices are routinely checked: ionisation, LC columns and mass spectrometer (quadrupole and Orbitrap) performances. Reference samples are also frequently injected in order to check the systems and to perform quantification.

Use of standard range, standard, and controls

3.1.4.8 Will you provide any support for data reuse?

No

# 4 Allocation of resources
## 4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of the institution infrastructure

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

Rina Mekuli, Dominique Swennen

4.1.3 Identify the people or roles that will be responsible for the management of the described data

Rina Mekuli (orcid:0000-0002-0041-6511)

Data producer, data management

Dominique Swennen (orcid:0000-0002-9370-5717)

Data management

4.1.4 How do you intend to ensure data reuse after your project finishes?

* Institutional archive

* National archive

# 5 Data Security

## 5.1 Data Security

5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for a limited time

# 6 Ethical aspects

## 6.1 Ethical aspects

6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

6.1.2 Are the described data sensitive?

No

6.1.3 Are the described data personal?
No

7 Other

7.1 Other

7.1.1 Do you make use of other procedures for data management?

No

40
# WP2 Data-based modelling

WP2 aimed to explore multi-omics and multiscale network-based, statistical and machine learning techniques to identify features (key biochemical compounds, biological components and pathways) predicting fermented food products' properties. This holistic approach has the potential to suggest novel biological hypotheses about molecular pathways and overarching mechanisms involved in a biological system.

## ESR6 project – Mitja Briscik

## UNIVERSITE TOULOUSE III - PAUL SABATIER TOULOUSE III – UNIVERSITE DE TOULOUSE

The recent advancement in high-throughput biotechnologies is making large datasets easily available, posing both opportunities and challenges for biostatistics. In this context, kernel methods, which offer a nonlinear version of any linear algorithm solely based on dot products, are particularly suited for analysing and integrating high-dimensional data. The project addresses the interpretability of kernel methods and the integration of heterogeneous sources.

### Datasets Description

#### 1 Data Summary

##### 1.1 Data Summary

###### 1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

* To obtain information

* To share information

* To keep on record

* To develop a product

Data collected and used to build a benchmark to test new methodologies developed by ESR6.

###### 1.1.2 What are the types of the described generated/collected data?

* experimental
* simulation

* reference or canonical

Experimental and reference data are used to test models (.csv, .txt, tar.gz)

Simulation data are computed during the testing phase. (.R, .ipynb)

### 1.1.3 What are the formats of the described generated/collected data?

Text files, numerical and tabular data are employed in models and softwares.

R library:

Mitja Briscik, Mohamed Heimida, Sébastien Déjean (2025). The Comprehensive R Archive Network (CRAN). “kpcaIG: Variables Interpretability with Kernel PCA” . R package. 10.32614/CRAN/package.kpcaIG.

### 1.1.4 What is the origin of the described data?

Secondary data

### 1.1.5 What is the expected size of the described data?

GB (gigabyte)

### 1.1.6 To whom might it be useful ('data utility')?

* Researchers

* Education

* The public

* Industry

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

* To reproduce and validate findings

* To follow-up research on a specific area

* To develop new products/ services

#### 2.1.2 Where do the described data reside?

42
a. NCBI Gene

b. xenabrowsier

c. Wallomics Data

d. Nutrimouse data

2.1.3 Which data will be re-used?

a. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102079

b. https://github.com/jundongl/scikit-feature/tree/master/skfeature/data

c. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL93

d. https://xenabrowsier.net/datapages/

e. https://cran.r-project.org/web/packages/WallomicsData/index.html

f. https://search.r-project.org/CRAN/refmans/CCA/html/nutrimouse.html

3 FAIR Data

3.1.1 Making data findable, including provisions for metadata

3.1.1.1 Will you use metadata to describe the data?

Yes

The data are already described in NCBI Gene redipositories, R Cran vignettes (Wallomics and Nutrimouse), and on [xenabrowsier.net/datapages/](https://xenabrowsier.net/datapages/) (University of California Santa Cruz)

3.1.1.3 Will your metadata use standardised vocabularies?

No

3.1.1.5 Will you make the metadata available free-of-charge?

Yes, the datasets are all already published

3.1.1.6 Will your metadata be harvestable?

Yes, the datasets are all already published

3.1.1.7 Will you use naming conventions for your data?

No

3.1.1.9 Will you provide clear version numbers for your data?
Yes

3.1.1.10 Will you provide persistent identifiers for the described data?

No

3.1.1.12 Will you provide searchable metadata for the described data?

Yes

3.1.1.13 What services will you use to provide searchable metadata?

Registry/Catalogue

3.1.1.14 Please provide URL/Name for the used searchable metadata

https://xenab browser.net/datapages/

3.1.1.15 Will you use standardised formats for the described data?

Yes, the datasets are already published

3.1.1.16 Provide information about used standardised formats

https://xenab browser.net/datapages/

.csv

3.1.1.18 Are the file formats you will use open?

Yes, R, excel.

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes.

The data can be accessed using the free programming languages such R and Python, in
addition to LibreOffice and Microsoft Office software.

3.1.1.21 Please describe if data require proprietary tools to access the data.

Processed data and some specific data such us models will be in .rds format, which is a
format supported in R.

Also .csv and .xlsx

3.1.1.22 Will you provide metadata describing the quality of the data?

No
### 3.1.2 Making data openly accessible

#### 3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

#### 3.1.2.2 Will the described data be openly accessible?

Yes, datasets already published.

#### 3.1.2.3 How will the data be made available?

* Domain-specific database

* Repository of Archive

#### 3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

#### 3.1.2.6 Are there any methods or tools required to access the described data?

No

#### 3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

after publication

### 3.1.3 Making data interoperable

#### 3.1.3.1 Will you use a controlled vocabulary for the described data?

No

#### 3.1.3.2 Will you provide a mapping to more commonly used ontologies?

No

### 3.1.4 Increase data reuse

#### 3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

Mitja Briscik, Marie-Agnès Dillies, Sébastien Déjean. "Improvement of variables interpretability in kernel PCA", BMC Bioinformatics, 2023, Volume 24, 282. DOI: 10.1186/s12859-023-05404-y

Mitja Briscik, Gabriele Tazza, Laszlo Vidacs, Marie-Agnès Dillies, Sébastien Déjean. "Supervised multiple kernel learning approaches for multi-omics data integration", BioData Mining 17, article 53, 2024. DOI: 10.1186/s13040-024-00406-9

45
3.1.4.1 What internationally recognised licence(s) will you use for the described data?

Creative Commons Attribution 4.0

3.1.4.5 Do you have documented procedures for quality assurance of the described data?

No

3.1.4.8 Will you provide any support for data reuse?

Yes

3.1.4.9 How long do you intend to support data reuse?

More than 10 years

# 4 Allocation of resources

## 4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of institution infrastructure

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

No

Mitja Briscik (orcid 0000-0001-8851-074X)

4.1.3 Identify the people or roles that will be responsible for the management of the described data

Mitja Briscik (orcid: 0000-0001-8851-074X)

Data producer, Data management

Sébastien Déjean (orcid: 0000-0001-9610-5306)

Data management

4.1.4 How do you intend to ensure data reuse after your project finishes?

* Institutional archive

* National archive

* Data Center Archive Storage

46
# 5 Data Security

## 5.1 Data Security

### 5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time

# 6 Ethical aspects

## 6.1 Ethical aspects

### 6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

### 6.1.2 Are the described data sensitive?

No

### 6.1.3 Are the described data personal?

No

# 7 Other

## 7.1 Other

### 7.1.1 Do you make use of other procedures for data management?

No
Microorganisms have specific but complementary and interdependent roles during food-making processes. They create a functional network where different species/strains are related, share many substrates, products, and enzymes, and have different tasks in common. On the other hand, and at the sub-cellular level, each specific microorganism has its own molecular networks that ensure its functions, like signalling networks, regulatory networks and metabolic networks between enzymes and metabolites. In order to study these inter and intra-species interactions and predict their outcome, we are using machine learning and network theory approaches to reconstruct and analyse these networks. These approaches allow the integration of multi-omics data (genomics, transcriptomics, metabolomics, ...) and, thus, allow a more realistic representation of the complex biological processes that govern food making.

# Datasets Description

## 1 Data Summary

### 1.1 Data Summary

#### 1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

* To obtain information

* To share information

* To keep on record

In this dataset, scripts, data and results generated within ESR 7 project are described and stored. The objective of this collection is to make the work done so far reproducible and exploitable.

#### 1.1.2 What are the types of the described generated/collected data?

* experimental

* simulation: machine learning models

* reference or canonical

#### 1.1.3 What are the formats of the described generated/collected data?
* Text files

* Numerical

* Multimedia

* Models

* Software

.txt, .csv and .tsv files.

### 1.1.4 What is the origin of the described data?

* Primary data

* Secondary data

### 1.1.5 What is the expected size of the described data?

MB (megabyte)

around 500 MB

### 1.1.6 To whom might it be useful ('data utility')?

* Researchers

* Research communities

* Industry

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

https://entrepot.recherche.data.gouv.fr/dataverse/e-muse/

To follow-up research on a specific area

#### 2.1.2 Where do the described data reside?

https://entrepot.recherche.data.gouv.fr/dataverse/e-muse/

E-MUSE Intranet Sharepoint

#### 2.1.3 Which data will be re-used?
a. EcoStab (https://entrepot.recherche.data.gouv.fr/dataverse/ecostab), Execo (doi: 10.1371/journal.pone.0124360)
Metatranscriptomics and metametabolomics data, Microbial growth data.

b. Data from public databases STRING (https://string-db.org) and CARD (https://card.mcmaster.ca)

## 3 FAIR Data

### 3.1.1 Making data findable, including provisions for metadata

#### 3.1.1.1 Will you use metadata to describe the data?

Yes, the data are already described.

#### 3.1.1.3 Will your metadata use standardised vocabularies?

No

#### 3.1.1.5 Will you make the metadata available free-of-charge?

Yes, the data are already published.

#### 3.1.1.6 Will your metadata be harvestable?

Yes

#### 3.1.1.7 Will you use naming conventions for your data?

No

#### 3.1.1.9 Will you provide clear version numbers for your data?

Yes

#### 3.1.1.10 Will you provide persistent identifiers for the described data?

No

#### 3.1.1.12 Will you provide searchable metadata for the described data?

No

#### 3.1.1.15 Will you use standardised formats for the described data?

Yes

#### 3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

50
The data can be accessed using the free programming languages such R and Python, in addition to LibreOffice and Microsoft Office software.

3.1.1.21 Please describe if data require proprietary tools to access the data.

Processed data and some specific data such as models will be in .rds format, which is a format supported in R.

3.1.1.22 Will you provide metadata describing the quality of the data?

No

3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

Yes

3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.3 How will the data be made available?

EcoStab (https://entrepot.recherche.data.gouv.fr/dataverse/ecostab)

Execo (doi: 10.1371/journal.pone.0124360)

3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with no backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

Yes

3.1.2.7 Please provide information about the method(s) needed to access the data

The .rds files require the usage of R.

3.1.2.8 Please provide information about the tools needed to access the data.

Most of the data can be accessed using the different text and datasheet editors (Word, Excel...), except for .rds which require the usage of R.

3.1.2.9 Please provide information about the tools needed to access the data.

after publication
### 3.1.3 Making data interoperable

#### 3.1.3.1 Will you use a controlled vocabulary for the described data?

Yes

We'll use a common language used in bioinformatics field.

### 3.1.4 Increase data reuse

#### 3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

#### 3.1.4.5 Do you have documented procedures for quality assurance of the described data?

Yes

The data resulting from the analysis (models, calculations...) will be processed and error-free.

#### 3.1.4.7 Do you have documented procedures for quality assurance of the described data?

Consistency verified with data models and standards.

#### 3.1.4.8 Will you provide any support for data reuse?

Yes.

We'll provide different channels (emails...) allowing contact with the data users in case of inquiry.

#### 3.1.4.9 How long do you intend to support data reuse?

More than 10 years

# 4 Allocation of resources

## 4.1 Allocation of resources

### 4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

* Use of national infrastructure

* Collaboration with other Projects

### 4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes
4.1.3 Identify the people or roles that will be responsible for the management of the described data

Anis Mansouri (orcid: 0000-0001-5764-3427)

Data producer, Data management

Daniel Remondini (orcid: 0000-0003-3185-7456)

Data management

4.1.4 How do you intend to ensure data reuse after your project finishes?

Institutional archive

National archive

# 5 Data Security

## 5.1 Data Security

5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time

# 6 Ethical aspects

53

6.1 Ethical aspects

6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

Yes

6.1.2 Are the described data sensitive?

No

6.1.3 Are the described data personal?

No

# 7 Other

7.1 Other

7.1.1 Do you make use of other procedures for data management?

No
ESR8 project - Gabriele Tazza
SZEGEDI TUDOMANYEGYETEM

Due to the growing availability of multi-omics data, supported by advancements in high-
throughput technologies, Machine Learning (ML) and Deep Learning (DL) have become key tools
for analysing complex biological systems. These methods are increasingly applied to tasks such
as multi-omics data integration, biomarker discovery, and predictive modelling, especially in
scenarios where sample sizes are limited and data dimensionality is high. The project is situated
within this context. The main objective was to explore and develop ML/DL techniques for multi-
omics integration, feature selection, and the support of mechanistic models using a data-driven
approach.

**Datasets Description**

1 Data Summary

1.1 Data Summary

1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

54

* To obtain information

• To share information

* To keep on record

* To combine with other data

All the data in the ESR8 project are managed using an open source distributed version control
system, in order to have all the experiments produced reproducible.

1.1.2 What are the types of the described generated/collected data?

* experimental (e.g. gene sequencing data)

* simulation (e.g. climate modeling data)

* reference or canonical (e.g. static, peer-reviewed data sets, likely published or curated, such as gene sequence databases or chemical structures)

1.1.3 What are the formats of the described generated/collected data?

* Text files
* Numerical

* Multimedia

* Software

CSV: Abundance data, DNA-methylation, miRNA expression, mRNA expression.

.py, .R: script

.png: transformed data, plot

### 1.1.4 What is the origin of the described data?

* Secondary data

* Other

### 1.1.5 What is the expected size of the described data?

GB (gigabyte)

5 GB

### 1.1.6 To whom might it be useful ('data utility')?

* Researchers

* Research communities

* Industry

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

- https://github.com/txWang/MOGONET
  https://github.com/minoh0201/DeepMicro/tree/master/data

* To reproduce and validate findings

* To follow-up research on a specific area

* To develop new products/ services

#### 2.1.2 Where do the described data reside?

a. AMP-AD Knowledge Portal
b. The Cancer Genome Atlas Program (TCGA)

2.1.3 Which data will be re-used?

a. deepmicro: abundance

b. ROSMAP

c. BRCA

3 FAIR Data

3.1.1 Making data findable, including provisions for metadata

3.1.1.1 Will you use metadata to describe the data?

Yes

3.1.1.3 Will your metadata use standardised vocabularies?

No

3.1.1.5 Will you make the metadata available free-of-charge?

Yes

3.1.1.6 Will your metadata be harvestable?

Yes

3.1.1.7 Will you use naming conventions for your data?

No

3.1.1.9 Will you provide clear version numbers for your data?

Yes

3.1.1.10 Will you provide persistent identifiers for the described data?

No

3.1.1.12 Will you provide searchable metadata for the described data?

No

3.1.1.15 Will you use standardised formats for the described data?

Yes

3.1.1.18 Are the file formats you will use open?
Yes

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

3.1.1.22 Will you provide metadata describing the quality of the data?

Yes

3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.3 How will the data be made available?

Other

GitHub

3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

57

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

No

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

after publication

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?

No

3.1.3.2 Will you provide a mapping to more commonly used ontologies?

No

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?
after article publication

Mitja Briscik, Gabriele Tazza, Laszlo Vidacs, Marie-Agnès Dillies, Sébastien Déjean.
"Supervised multiple kernel learning approaches for multi-omics data integration",
BioData Mining 17, article 53, 2024. DOI: 10.1186/s13040-024-00406-9

MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling. Gabriele Tazza, Francesco Moro, Dario Ruggeri, Bas Teusink, László Vidács.

bioRxiv 2025.05.14.653943; doi: https://doi.org/10.1101/2025.05.14.653943

GitHub: https://github.com/gabrieletaz/MINN

Preprint: https://www.biorxiv.org/content/10.1101/2025.05.14.653943v1

### 3.1.4.5 Do you have documented procedures for quality assurance of the described data?

No

### 3.1.4.8 Will you provide any support for data reuse?

Yes

### 3.1.4.9 How long do you intend to support data reuse?

More than 10 years

58

## 4 Allocation of resources

### 4.1 Allocation of resources

#### 4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

* Use of institution infrastructure

* Infrastructure Grant

#### 4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

#### 4.1.3 Identify the people or roles that will be responsible for the management of the described data

Gabriele Tazza (orcid:0000-0001-5263-2278)

Data producer, Data management

László Vidács (orcid:0000-0002-0319-3915)
Data management

4.1.4 How do you intend to ensure data reuse after your project finishes?

Other

Web repository

5 Data Security

5.1 Data Security

5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time

6 Ethical aspects

6.1 Ethical aspects

6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

6.1.2 Are the described data sensitive?

No

6.1.3 Are the described data personal?

No

7 Other

7.1 Other

7.1.1 Do you make use of other procedures for data management?

No

59
ESR9 project – Dario Ruggeri
SZEGEDI TUDOMANYEGYETEM

This project explored the application of machine learning and deep learning techniques to genomics and metabolomic data, with a focus on real-world use cases in agronomy, food processing, and biomedical research. It included activities ranging from data cleaning and preprocessing to model development, evaluation, and MLOps integration. The work also aimed to improve the reproducibility and scalability of ML workflows and contributed to several collaborative research outputs.

Data cleaning was performed in collaboration with Alina Kyrylenko on a fermentation dataset provided by the NIZO partner. The goal was to detect outliers based on acidification measurements to support further machine learning applications.

A research article titled "Introducing MLOps to Facilitate the Development of Machine Learning Models in Agronomy: A Case Study" was published in IEEE Access. The work presents a complete MLOps pipeline using ClearML, Docker, and version control to improve reproducibility and deployment of ML models in agronomy.

GitHub: https://github.com/DarioRugg/MLOps-for-ML-in-Agronomy

DOI: https://doi.org/10.1109/ACCESS.2025.3586691

A preprint titled "MINN - Multi-omics Integration with Neural Networks" was developed in collaboration with Gabriele Tazza and Francesco Moro. In this work, I contributed by proposing a multi-task learning model for integrating more effectively mechanistic knowledge in the neural network model's training.

GitHub: https://github.com/gabrieletaz/MINN

Preprint: https://www.biorxiv.org/content/10.1101/2025.05.14.653943v1

A conference contribution titled "Adopting MLOps in Agronomic Machine Learning: Challenges and Benefits" at FOODSIM 2024 discusses the lack of MLOps adoption in FoodOmics and shows the potential improvements it can bring to research and production pipelines.

Conference record: https://m2.mtmt.hu/gui2/?mode=browse&params=publication;35759409

A study titled "Investigating the Impact of Autoencoder Overfitting on Downstream Classification in High-Dimensional Low-Sample Size Context" explores how overfitting in autoencoders affects classification performance on gut microbiome datasets. The results challenge the common assumption that minimizing reconstruction error leads to better features. This work is currently under review.
GitHub: https://github.com/DarioRugg/auto-encoder-classifier-overfitting

A study titled "Advancing Wheat SNP Data Analysis with Explainable Deep Learning Models" presents a high-performing artificial neural network for SNP-based prediction tasks in foodomics. The model includes enhancements like data augmentation and Bayesian optimization, and integrates SHAP for interpretability. This work is currently under review.

Zenodo: https://doi.org/10.5281/zenodo.15656244

# Datasets Description

## 1 Data Summary

### 1.1 Data Summary

#### 1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

* To develop a product

* To improve a product

We used datasets from consortium partners and publicly available sources to develop deep learning models. These models use multi-omics and metabolomic data. The work focuses mainly on food processing, and in some cases where data was limited, we also used data in the fields of microbiology and biomedicine.

#### 1.1.2 What are the types of the described generated/collected data?

In our work, we used both primary and secondary data. The primary data came from consortium partners, while the secondary data came mainly from datasets already published in the literature. These public datasets were reused and allowed us to advance the state of the art in applied deep learning.

#### 1.1.3 What are the formats of the described generated/collected data?

Models.

We used data in different formats, mainly tabular data (e.g. multi-omics and genomic data in CSV format) and spectral data from GC-IMS. No proprietary formats were used; instead, we relied on standard formats like CSV to ensure long-term accessibility and reuse.

For the modelling part, all code was developed in Python. We used widely adopted machine learning frameworks such as PyTorch, TensorFlow/Keras, and scikit-learn. Model configuration was managed using Hydra, and ClearML was used to support experiment tracking and reproducibility. All code was versioned with Git and shared in a public repository to promote transparency and reuse.

#### 1.1.4 What is the origin of the described data?

61
* Primary data

* Secondary data

### 1.1.5 What is the expected size of the described data?

GB (gigabyte).

The project code is expected to occupy around 25 MB and is managed through a Git repository. The datasets used are not stored in the repository and must be downloaded separately.

The size of the datasets varies depending on the source. Public and consortium datasets used in the project range from tens of megabytes (for small tabular genomic or omics data) up to a few gigabytes (for metabolomic spectral data such as GC-IMS). Typically, tabular datasets include hundreds of rows and a few thousand columns, while spectral data may include larger file sizes due to higher-resolution measurements.

### 1.1.6 To whom might it be useful ('data utility')?

* Researchers

* Research communities

* Decision makers

* Industry

The data and methods are useful for researchers, research communities, industry, and decision makers. The project provides practical guidance on applying machine learning and deep learning to real-world multi-omics and metabolomic data, supporting both research and production use cases.

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

* To follow-up research on a specific area

* To develop new products/ services

#### 2.1.2 Where do the described data reside?

a. https://github.com/minoh0201/DeepMicro

b. https://github.com/Sandhu-WSU/DL_Wheat
# 3 FAIR Data

## 3.1.1 Making data findable, including provisions for metadata

### 3.1.1.1 Will you use metadata to describe the data?

Yes, we will use metadata following good practices in the field. Metadata will be provided to describe how to set up the environment, run the code, and reproduce the results. This includes clear documentation of dependencies, model configurations (via Hydra), experiment tracking (via ClearML), and code versioning (via Git). These elements ensure transparency, reproducibility, and easier reuse by others in both research and production contexts.

### 3.1.1.3 Will your metadata use standardised vocabularies?

No

### 3.1.1.5 Will you make the metadata available free-of-charge?

Yes, metadata will be made available free of charge. Articles are published in open access, and both code and data are shared through public repositories to ensure full accessibility and reuse.

### 3.1.1.6 Will your metadata be harvestable?

Partially. Metadata for the code repositories shared on Zenodo will be harvestable through standard protocols and include DOIs. Other repositories will be available on GitHub, which does not support automatic metadata harvesting, but they will include human-readable metadata in the repository (e.g. README files and configuration files) to support reuse.

### 3.1.1.7 Will you use naming conventions for your data?

No

No specific naming convention was applied, but files and variables in the code are named appropriately to ensure clarity and ease of understanding for other researchers who may reuse the project.

### 3.1.1.9 Will you provide clear version numbers for your data?

Yes

Yes, versioning is managed through Git for code and configuration files.

### 3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

### 3.1.1.12 Will you provide searchable metadata for the described data?
Yes, metadata will be provided through repositories like Zenodo and GitHub. Zenodo ensures that metadata is indexed and searchable by web crawlers and search engines. GitHub repositories will include structured README files and documentation to support findability.

### 3.1.1.14 Please provide URL/Name for the used searchable metadata

https://zenodo.org/

Zenodo.

### 3.1.1.15 Will you use standardised formats for the described data?

No

### 3.1.1.17 Please describe the formats you plan to store the described data in, including any URLs to documentation.

python, csv, yaml

Commonly used format

### 3.1.1.18 Are the file formats you will use open?

Yes

### 3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

### 3.1.1.22 Will you provide metadata describing the quality of the data?

No

## 3.1.2 Making data openly accessible

### 3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

### 3.1.2.2 Will the described data be openly accessible?

Yes

### 3.1.2.3 How will the data be made available?

Repository of Archive.

Code, configuration files, and documentation will be available on GitHub.
3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

No

The code will be available on an open repository (GitHub), with no passwords or special tools required to access it.

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

never

No, auxiliary data will not be made available.

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?

No

3.1.3.2 Will you provide a mapping to more commonly used ontologies?

No

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling. Gabriele Tazza, Francesco Moro, Dario Ruggeri, Bas Teusink, László Vidács.

bioRxiv 2025.05.14.653943; doi: https://doi.org/10.1101/2025.05.14.653943

Introducing MLOps to Facilitate the Development of Machine Learning Models in Agronomy: A Case Study. Dario Ruggeri, Gabriele Tazza, and László Vidács. in IEEE Access, vol. 13, pp. 122059-122070, 2025. DOI: 10.1109/ACCESS.2025.3586691

3.1.4.4 What internationally recognised licence(s) will you use for the described data?

Creative Commons Attribution 4.0

The data and related outputs will be shared under a Creative Commons license, specifically CC BY 4.0, to allow reuse with attribution.

3.1.4.5 Do you have documented procedures for quality assurance of the described data?
No

3.1.4.8 Will you provide any support for data reuse?

Yes

3.1.4.9 How long do you intend to support data reuse?

Less than 2 years.

# 4 Allocation of resources

## 4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of institution infrastructure

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

4.1.3 Identify the people or roles that will be responsible for the management of the described data

66

Dario Ruggeri ([orcid: 0000-0002-2981-8450](orcid: 0000-0002-2981-8450))

Data Producer

László Vidács ([orcid: 0000-0002-0319-3915](orcid: 0000-0002-0319-3915))

Data management

4.1.4 How do you intend to ensure data reuse after your project finishes?

Other.

GitHub repository.

To ensure long-term reuse, all code and documentation will be stored in a public GitHub repository. GitHub is a widely used online platform for hosting and sharing code. It supports version control, collaboration, and public access, making it easier for other researchers to reuse and build upon our work.

For any data or outputs requiring more formal archiving and citation, repositories like Zenodo will be used, which assign DOIs and support long-term preservation.

# 5 Data Security
## 5.1 Data Security

### 5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for a limited time.

## 6 Ethical aspects

### 6.1 Ethical aspects

#### 6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

#### 6.1.2 Are the described data sensitive?

No

#### 6.1.3 Are the described data personal?

No

## 7 Other

### 7.1 Other

#### 7.1.1 Do you make use of other procedures for data management?

No
ESR10 project – Mahtab Shoukat

INSTITUT NATIONAL DE RECHERCHE
POUR L'AGRICULTURE,
L'ALIMENTATION ET
L'ENVIRONNEMENT

Understand the effect of iron on the cheese ecosystem. Dynamic data have been collected during the ripening process of a model cheese produced with/without iron supplementation. Several macro-scale data have been collected: microorganisms' growth, substrate degradation, physico-chemical data, and organoleptic properties. The ESR has studied biochemical pathways involved in iron homeostasis, oxidative stress, sulphur metabolism and volatile sulphur compounds production to understand the effect of iron on the ecosystem's equilibrium. These metabolisms are interconnected and largely contribute to important organoleptic properties of fermented foods through the release of odour-active compounds.

# Datasets Description

# 1 Data Summary

## 1.1 Data Summary

### 1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

* To obtain information

* To share information

Microbial communities' interaction is among the main drivers of cheese quality, safety, nutritional, and aromatic properties. These microorganisms require certain crucial nutrients for their functionality, and iron is at the top of the nutrient list. Iron is involved in various metabolic, physiological, and biochemical processes like respiration, gene regulation, enzyme cofactor, DNA, fatty acids, and amino acids syntheses. However, cheese is a highly iron-restricted habitat (2.9 mg/kg) due to its low iron quantity, iron-chelating proteins, and low iron diffusion. Conversely, iron competition is one of the mediators of microbe-microbe interactions in cheese rinds. Iron is a limiting factor for surface ripening bacteria in cheese. Iron is critical in the cheese microbial community's structuration and resilience. An adequate amount of iron may enhance positive interactions with optimal microbial functionalities.

### 1.1.2 What are the types of the described generated/collected data?
* experimental

### Experimental Data:

- Growth Data (9 microorganisms)

- Physiochemical data (pH, Colour, dry matter, moisture loss, Fe & Na quantification, Lactose, lactate, volatile fatty acids)

- Proteolysis

- Metabolomics (GC-MS, LC-MS)

### 1.1.3 What are the formats of the described generated/collected data?

* Text files

* Numerical

* Instrument specific formats

Growth data: CSV

Physiochemical data: CSV

Metabolomics: instrument-specific format, mzml, csv

### 1.1.4 What is the origin of the described data?

Primary data

### 1.1.5 What is the expected size of the described data?

TB (terabyte)

1TB

### 1.1.6 To whom might it be useful ('data utility')?

* Researchers

* Research communities

* Education

* Industry

Data and models that will be produced will bring new knowledge on bacteria and yeasts involved in cheese ripening processes at a fundamental level, and this knowledge will help in understanding the function and structure of ripening microbial communities, which will be helpful for researchers and cheese makers.
## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

To follow-up research on a specific area

#### 2.1.2 Where do the described data reside?

https://www.genoscope.cns.fr/agc/microscope/

https://www.ncbi.nlm.nih.gov/genome/

http://bioinformatics.psb.ugent.be/

#### 2.1.3 Which data will be re-used?

Genomes

## 3 FAIR Data

### 3.1.1 Making data findable, including provisions for metadata

#### 3.1.1.1 Will you use metadata to describe the data?

Yes

ISA-Tab

#### 3.1.1.3 Will your metadata use standardised vocabularies?

Yes

Growth data: https://fairsharing.org/FAIRsharing.9sza27 (GMO),
https://doi.org/10.25504/FAIRsharing.53gp75 (ISA-Tab)

Physiochemical data: https://fairsharing.org/FAIRsharing.9sza27(GMO),

Metabolomics: https://doi.org/10.25504/FAIRsharing.kkdpxe (MTBLS)

#### 3.1.1.5 Will you make the metadata available free-of-charge?

Yes

#### 3.1.1.6 Will your metadata be harvestable?

Yes

#### 3.1.1.7 Will you use naming conventions for your data?

Yes

70
3.1.1.8 Please provide more details and examples on used naming conversions

yyyymmdd_E-MUSE_ESR10_explicitfilename.extension

3.1.1.9 Will you provide clear version numbers for your data?

Yes

Data: Raw/Processed/Analysed

3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

The PID will be provided by the repositories

3.1.1.11 Persistent identifiers

DOI

3.1.1.12 Will you provide searchable metadata for the described data?

Yes

Data submitted to a public repository will be linked to the metadata in accordance with
the repository's searchable criteria.

71

3.1.1.13 What services will you use to provide searchable metadata?

Other

3.1.1.15 Will you use standardised formats for the described data?

Yes

3.1.1.16 Provide information about used standardised formats

a. https://docs.fileformat.com/spreadsheet/csv/

csv

3.1.1.18 Are the file formats you will use open?

Yes

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

Data will be uploaded to public repositories.

3.1.1.22 Will you provide metadata describing the quality of the data?
Yes

3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.3 How will the data be made available?

* Domain-specific database

* https://entrepot.recherche.data.gouv.fr/dataverse/esr10-project

3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

Yes

3.1.2.7 Please provide information about the method(s) needed to access the data

Data download

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

after publication

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?

Yes

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

Shoukat M, Hervé V, Sarthou AS, Peron AC, Danel A, Swennen D, Bonnarme P, Dugat-Bony E. Iron fortification modifies the microbial community structure and metabolome of a model surface-ripened cheese. Int J Food Microbiol. 2025 Jan 16;427:110971. doi: 10.1016/j.ijfoodmicro.2024.110971. Epub 2024 Nov 14. PMID: 39557002.

3.1.4.4 What internationally recognised licence(s) will you use for the described data?
3.1.4.5 Do you have documented procedures for quality assurance of the described data?

Yes

3.1.4.7 Describe the data quality assurance processes

Consistency verified with data models and standards

The performances of the GC-MS and LC-MS devices are routinely checked: ionization, LC columns and mass spectrometer (quadrupole and Orbitrap). Reference samples are also frequently injected to check the systems and perform quantification.

3.1.4.8 Will you provide any support for data reuse?

No

# 4 Allocation of resources

## 4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of institution infrastructure

73

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

Mahtab Shoukat, Eric Dugat-bony, Vincent Hervé

4.1.3 Identify the people or roles that will be responsible for the management of the described data

Eric Dugat-Bony (orcid:0000-0002-5182-0063)

Data Management

Vincent Hervé (orcid:0000-0002-3495-561X)

Data Management

Mahtab Shoukat (orcid:0000-0002-3094-1579)

Data producer, Data Management

4.1.4 How do you intend to ensure data reuse after your project finishes?
* Institutional archive

* National archive

# 5 Data Security

## 5.1 Data Security

### 5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for a limited time

# 6 Ethical aspects

## 6.1 Ethical aspects

### 6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

### 6.1.2 Are the described data sensitive?

No

### 6.1.3 Are the described data personal?

No

74

# 7 Other

## 7.1 Other

### 7.1.1 Do you make use of other procedures for data management?

No
# WP3 Predictive modelling

The aim of work package 3 was to generate predictive in silico models based on in vitro experimental data obtained during cheese production. Predictive modelling aimed to integrate individual agent-based models (exploiting experimental data collected at multiple scales) within partial differential equation models and identify cheese-makers-oriented process models, with specific emphasis on in vitro-in silico cheese model systems as a case study throughout WP3.

## ESR11 project - Jian Wang

## KATHOLIEKE UNIVERSITEIT LEUVEN

ESR 11 will design in silico individual-based models (IbMs) to accurately represent the growth behaviours of microbial communities within cheese. These IbMs need to encapsulate the intricate mechanical and dynamical attributes of cheese ecosystems, additionally integrating data from metabolic networks at a cellular level as produced in WP1. The IbMs, in expectation, ought to provide in silico simulation results for comparison with empirical findings. Results for such comparison may include, but are not limited to, cell numbers, cell states, colony radius, and colony patterns

### Datasets Description

#### 1 Data Summary

##### 1.1 Data Summary

###### 1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

To share information

The simulation data produced by IbMs and PDEs serve as benchmarks against the growth behaviours of microbial communities in cheese. Notable discrepancies and inaccuracies between experimental observations and simulation outcomes will provide feedback for model calibration. Post-calibration, there will be opportunities to adapt these constructed models for investigating cheese-related scenarios.

###### 1.1.2 What are the types of the described generated/collected data?

* simulation

Individual-based models (IbMs) represent in silico replicas of cheese microbial communities, encapsulating intricate biological processes through the integration of physical and chemical submodels that span across multiple scales. IbMs, by integrating this multi-scale information,
provide comprehensive insights into microbial communities, including, but not limited to, total cell counts, cell states, colony radii, and colony patterns.

### 1.1.3 What are the formats of the described generated/collected data?

* Numerical

* Models

Growth data: csv

Growth patterns: mp4

IbMs and PDEs: models

### 1.1.4 What is the origin of the described data?

* Primary data

* Secondary data

### 1.1.5 What is the expected size of the described data?

GB (gigabyte)

### 1.1.6 To whom might it be useful ('data utility')?

* Research communities

* Decision makers

Calibrated Individual-based models (IbMs) and Partial Differential Equations (PDEs) will become *in silico* replicas of cheese microbial communities. Utilizing these two types of models will facilitate the investigation of cheese-related scenarios.

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

No

## 3 FAIR Data

### 3.1.1 Making data findable, including provisions for metadata

#### 3.1.1.1 Will you use metadata to describe the data?

No

#### 3.1.1.2 Will your metadata use standardised vocabularies?
No

3.1.1.7 Will you use naming conventions for your data?

No

yyyymmdd_E-MUSE_ESR11_explicitfilename.extension

3.1.1.9 Will you provide clear version numbers for your data?

Yes

yyyymmdd_E-MUSE_ESR11_explicitfilename.version 1

3.1.1.10 Will you provide persistent identifiers for the described data?

No

3.1.1.12 Will you provide searchable metadata for the described data?

No

3.1.1.15 Will you use standardised formats for the described data?

No

3.1.1.17 Please describe the formats you plan to store the described data in, including any URLs to documentation.

77

https://github.com/JianWang1123

lbMs

3.1.1.18 Are the file formats you will use open?

Yes

.csv

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

3.1.1.22 Will you provide metadata describing the quality of the data?

No

3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No
3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.3 How will the data be made available?

Repository of Archive

3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

Yes

3.1.2.7 Please provide information about the method(s) needed to access the data

Github or git

https://github.com/JianWang1123

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

after publication

78

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?

No

3.1.3.2 Will you provide a mapping to more commonly used ontologies?

No

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

Ihab Hashem, Jian Wang, Jan F.M. Van Impe. A Discretized Overlap Resolution Algorithm (DORA) for resolving spatial overlaps in individual-based models of microbes.
PLoS Comput Biol 21(4): e1012974. https://doi.org/10.1371/journal.pcbi.1012974

Jian Wang, Ihab Hashem, Satyajeet Bhonsale, Jan F.M. Van Impe. Signaling dynamics in microbial communities: A reduced-complexity mathematical analysis. Chemical Engineering Science, Volume 319, 2026, 121962.
https://doi.org/10.1016/j.ces.2025.121962
Jian Wang, Ihab Hashem, Satyajeet Bhonsale, Davy Verheyen, Huabin Luo, Jan F.M. Van Impe. Individual-based modelling (IbM) in food microbiology: A comprehensive guideline. Food Research International, Volume 213, 2025, 116408. https://doi.org/10.1016/j.foodres.2025.116408

Jian Wang, Ihab Hashem, Satyajeet S. Bhonsale, Jan F.M. Van Impe. Vulnerability of Microbial Communities to Dishonest Signals: A biology driven complexity reduced model. Computer Aided Chemical Engineering, Elsevier, Volume 53, 2024, Pages 769-774. https://doi.org/10.1016/B978-0-443-28824-1.50129-0

#### 3.1.4.5 Do you have documented procedures for quality assurance of the described data?

No

#### 3.1.4.8 Will you provide any support for data reuse?

Yes

#### 3.1.4.9 How long do you intend to support data reuse?

More than 10 years

## 4 Allocation of resources

### 4.1 Allocation of resources

79

#### 4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of institution infrastructure

#### 4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

Jan Van Impe

#### 4.1.3 Identify the people or roles that will be responsible for the management of the described data

Jian Wang (orcid:0000-0002-2113-7415)

Data producer, Data management

Jan Van Impe (orcid:0000-0002-5904-1638)

Data management

#### 4.1.4 How do you intend to ensure data reuse after your project finishes?
# 5 Data Security

## 5.1 Data Security

### 5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time

# 6 Ethical aspects

## 6.1 Ethical aspects

### 6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

### 6.1.2 Are the described data sensitive?

No

### 6.1.3 Are the described data personal?

No

# 7 Other

## 7.1 Other

### 7.1.1 Do you make use of other procedures for data management?

No

80
# ESR12 project - Muhammad Ahmed Ihsan

## UNIVERSITÀ TA MALTA

The aim of the E-MUSE project is to analyze fungal/bacterial ecosystem and understand their functioning and impact on the ripening of fermented food products. The experimental data will be analyzed with models describing either the kinetics, probability of growth/no growth in function of time or preselected conditions. This information will be integrated, through correlation of omics indexes with the population dynamics. Data will be generated at multiscale levels from single-cells, to microbial population, and to macroscopic properties.

## Datasets Description

### 1 Data Summary

#### 1.1 Data Summary

##### 1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

* To obtain information

* To share information

Experimental data will be generated in vitro with Culture dependent and independent methodologies. Isolating the microbial species from raw milk, selecting the starter culture for the production of hybrid cheese, the ripening conditions impact on the spoilage and flavor development will be assessed.

##### 1.1.2 What are the types of the described generated/collected data?

* sample or specimen data experimental

* simulation

Experimental data: Microbial CFU log data, Metgenome sequencing data, Metatranscriptome data for hybrid cheese at different time and temperature

Simulation data: Predictive growth model will be used and validated for the prediction of starter culture growth in the cheese and flavor features of cheese.

##### 1.1.3 What are the formats of the described generated/collected data?

* Text files
* Numerical

* Models

* Discipline specific formats

Numerical: CFU log values

Models: Predictive growth model on MATLAB.

Instrument specific: GC-MS/LC-MS instrument specific format

#### 1.1.4 What is the origin of the described data?

Primary data

#### 1.1.5 What is the expected size of the described data?

GB (gigabyte)

Total storage varies based on the metagenomic and transcriptomics files, which includes both processed and unprocessed.

#### 1.1.6 To whom might it be useful ('data utility')?

* Researchers

* Research communities

* Education

* Industry

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

* To reproduce and validate findings

* To compare and combine with other data

* To follow-up research on a specific area

#### 2.1.2 Where do the described data reside?

a. GenBank Nucleotide Sequence Database

b. GC flavour data
2.1.3 Which data will be re-used?

DNA and RNA sequencing data

3 FAIR Data

3.1.1 Making data findable, including provisions for metadata

3.1.1.1 Will you use metadata to describe the data?

Yes

3.1.1.3 Will your metadata use standardised vocabularies?

Yes

3.1.1.5 Will you make the metadata available free-of-charge?

Yes

3.1.1.6 Will your metadata be harvestable?

No

3.1.1.7 Will you use naming conventions for your data?

Yes

83

3.1.1.8 Please provide more details and examples on used naming conversions

yyyymmdd_E-MUSE_ESR12_explicitfilename.extension

3.1.1.9 Will you provide clear version numbers for your data?

Yes

3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

3.1.1.11 Persistent identifiers

DOI

3.1.1.12 Will you provide searchable metadata for the described data?

No

3.1.1.15 Will you use standardised formats for the described data?

Yes
3.1.1.18 Are the file formats you will use open?

Yes

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

3.1.1.21 Please describe if data require proprietary tools to access the data.

MATLAB and R studio

3.1.1.22 Will you provide metadata describing the quality of the data?

No

3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.3 How will the data be made available?

• University repository

• Repository of Archive

3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

Yes

3.1.2.7 Please provide information about the method(s) needed to access the data

data download

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

no auxiliary data

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?
Yes

### 3.1.4 Increase data reuse

#### 3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

Muhammad Ahmed Ihsan, Vasilis P. Valdramidis, Sholeem Griffin. Bacterial and fungal profiling of Maltese sheep cheese with amplicon metabarcoding. International Dairy Journal, Volume 170, 2025, 106362. https://doi.org/10.1016/j.idairyj.2025.106362

#### 3.1.4.4 What internationally recognised licence(s) will you use for the described data?

Academic Free License v1.1

#### 3.1.4.5 Do you have documented procedures for quality assurance of the described data?

No

#### 3.1.4.8 Will you provide any support for data reuse?

No

## 4 Allocation of resources

### 4.1 Allocation of resources

#### 4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of national infrastructure

#### 4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

#### 4.1.3 Identify the people or roles that will be responsible for the management of the described data

Muhammad Ahmed Ihsan (orcid: 0000-0002-9608-4813)

Data producer, Data management

Vasilis Valdramidis (orcid: 0000-0001-6486-3890)

Data management

#### 4.1.4 How do you intend to ensure data reuse after your project finishes?

Institutional archive

85
# 5 Data Security

## 5.1 Data Security

### 5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time

# 6 Ethical aspects

## 6.1 Ethical aspects

### 6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

### 6.1.2 Are the described data sensitive?

No

### 6.1.3 Are the described data personal?

No

# 7 Other

## 7.1 Other

### 7.1.1 Do you make use of other procedures for data management?

No
ESR13 project - Alina Kyrylenko

NIZO FOOD RESEARCH BV

The production/manufacture of traditional cheeses, which are based on milk fermentation, is a well-studied and standardised process. This is mostly determined by the fact, that milk as the raw ingredient, is quite consistent in terms of its nutritional composition, physical characteristics, and microbial contaminants. Plant-based proteins, on the other hand, come from many different sources and can be available in different forms and qualities. This also relates to the application of the lactic acid bacteria (LAB), traditionally used in dairy fermentations, mostly due to the different carbon and nitrogen sources available for bacterial growth in plant substrates. The ESR13 project's main task was to study fermentation characteristics of a large set of lactic acid bacteria (LAB) on different plant-based matrices and use this data to predict the key properties of starter culture strains to produce plant-based cheese analogues with optimal fermentation and flavour profiles.

# Datasets Description

1 Data Summary

1.1 Data Summary

87

1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

• To obtain information

• To make informed decisions

• To combine with other data

A range of fermentation experiments were conducted in a high-throughput system to collect the phenotypic data (acidification, growth/no growth, carbon sources utilization profiles, aromatic compounds development) for 44 lactic acid bacteria grown in 7 different plant-based substrates.

1.1.2 What are the types of the described generated/collected data?

• experimental

The collected data included raw numerical data about:

- GC_MS data: aromatic volatiles measured in 7 plant-based ingredients supplemented with 2.5% of glucose and fermented with 44 different strains of lactic acid bacteria.
- Acidification data: acidification curves, carbon sources utilization profiles and growth/no growth profiles for 5 plant-based ingredients supplemented with 2.5% of glucose, sucrose or raffinose and fermented with 44 different strains of lactic acid bacteria.

- WGS data: whole genome sequencing data for 44 lactic acid bacteria strains.

### 1.1.3 What are the formats of the described generated/collected data?

- Text files

- Numerical

- Instrument specific formats

Growth data: csv

Acidification data: instrument specific format, csv, rmd (T notebook)

Aromatic volatiles data: instrument specific, csv

Protocols for the experiments: word

### 1.1.4 What is the origin of the described data?

Primary data

88

### 1.1.5 What is the expected size of the described data?

TB (terabyte)

### 1.1.6 To whom might it be useful ('data utility')?

- Researchers

- Research communities

- Education

- Industry

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

To follow-up research on a specific area

To compare and combine with other data
2.1.3 Which data will be re-used?

Bacterial DNA sequencing data

3 FAIR Data

3.1.1 Making data findable, including provisions for metadata

3.1.1.1 Will you use metadata to describe the data?

Yes

3.1.1.3 Will your metadata use standardised vocabularies?

Yes

3.1.1.5 Will you make the metadata available free-of-charge?

Yes

3.1.1.6 Will your metadata be harvestable?

No

3.1.1.7 Will you use naming conventions for your data?

Yes

89

3.1.1.8 Please provide more details and examples on used naming conversions

yyyymmdd_E-MUSE_ESR13_explicitfilename.extension

3.1.1.9 Will you provide clear version numbers for your data?

Yes

Data: Raw/Processed/Analysed

3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

The PID will be provided by the repositories

3.1.1.11 Persistent identifiers

DOI

3.1.1.12 Will you provide searchable metadata for the described data?

No
3.1.1.15 Will you use standardised formats for the described data?

Yes

3.1.1.16 Provide information about used standardised formats

a. https://docs.fileformat.com/spreadsheet/csv/

csv

b. https://r markdown.r studio.com/articles_intro.html

rmd

3.1.1.18 Are the file formats you will use open?

Yes

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

Data will be uploaded on public repositories.

3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

3.1.2.2 Will the described data be openly accessible?

Yes

3.1.2.3 How will the data be made available?

* Domain-specific database

3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

Yes

3.1.2.7 Please provide information about the method(s) needed to access the data

Data download

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?
No auxiliary data

### 3.1.3 Making data interoperable

#### 3.1.3.1 Will you use a controlled vocabulary for the described data?

Yes

### 3.1.4 Increase data reuse

#### 3.1.4.1 When do you plan to make the described data available for reuse?

The first part of the data is already available as a part of the publication:

Kyrylenko A, Eijlander RT, Alliney G, de Bos EL, Wells-Bennik MHJ. Levels and types of microbial contaminants in different plant-based ingredients used in dairy alternatives. Int J Food Microbiol. 2023 Dec 16;407:110392. doi: 10.1016/j.ijfoodmicro.2023.110392. Epub 2023 Sep 9. PMID: 37729802.

The rest of the data will be made available after publication.

#### 3.1.4.4 What internationally recognised licence(s) will you use for the described data?

CC-BY 4.0, Academic Free License v1.1

### 3.1.4.5 Do you have documented procedures for quality assurance of the described data?

Yes

### 3.1.4.7 Describe the data quality assurance processes

All devices used to conduct experiments (GC-MS, spectrophotometers, pH meters, etc.) were routinely checked and calibrated. The range of standards was always used to verify the precision and accuracy of the produced results. The sterilization procedures were done when necessary to eliminate the possibility of cross-contamination of the equipment with other microorganisms (not used in the research).

### 3.1.4.8 Will you provide any support for data reuse?

No

## 4 Allocation of resources

### 4.1 Allocation of resources

#### 4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of institution infrastructure

#### 4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?
Yes

Alina Kyrylenko, Marjon Wells-Bennik

### 4.1.3 Identify the people or roles that will be responsible for the management of the described data

Alina Kyrylenko (orcid:0000-0002-0467-848X)

Data producer, Data management

Marjon Wells-Bennik (ResearchGate)

Data management

### 4.1.4 How do you intend to ensure data reuse after your project finishes?

* Institutional archive

* National archive

## 5 Data Security

### 5.1 Data Security

#### 5.1.1 Where do you plan to keep the described data?

Kept on insecure, unmanaged storage for limited time

## 6 Ethical aspects

### 6.1 Ethical aspects

#### 6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

#### 6.1.2 Are the described data sensitive?

No

#### 6.1.3 Are the described data personal?

No

## 7 Other

### 7.1 Other

#### 7.1.1 Do you make use of other procedures for data management?

No
ESR14 project – Huabin Luo
KATHOLIEKE UNIVERSITEIT LEUVEN

Flavour is one of the major sensory attributes affecting the acceptance and preference of foods by consumers. Sensory evaluation is a traditional flavour characterisation approach that relies on well-trained and experienced experts, but is often subjective. With rapid advances in analytical techniques, it has become more accessible to explore the flavour characteristics of foods objectively at the molecular level. Flavour data obtained from analytical techniques is vast, but also complex. The objective of this work is to generate a predictive model for the impact of ripening conditions on the flavour development of semi-hard cheese and hybrid cheese.

Datasets Description

1 Data Summary

1.1 Data Summary

1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

To obtain information

93

Experimental data will be generated *in vitro* with high throughput methodologies. Using selected starter cultures for production of laboratory-scale hybrid cheese, the effect of various ripening conditions on flavor development will be assessed.

1.1.2 What are the types of the described generated/collected data?

* experimental (e.g. gene sequencing data)

* simulation (e.g. climate modeling data)

Experimental data:

GC-MS data: flavor profiles of semi-hard cheese or hybrid cheese ripened at different time and temperature

Simulation data:

Predictive model: a regression model based on a machine learning method (e.g., Artificial Neural Networks, Random Forest, Support Vector Machine) will be trained and validated for the prediction of the flavor features of cheese

1.1.3 What are the formats of the described generated/collected data?

* Numerical
* Models

GC-MS: instrument specific format, csv

Predictive models: m (MATLAB)

### 1.1.4 What is the origin of the described data?

Primary data

### 1.1.5 What is the expected size of the described data?

GB (gigabyte)

### 1.1.6 To whom might it be useful ('data utility')?

* Researchers

* Research communities

* Industry

## 2 Reused Data

### 2.1 Reused Data

#### 2.1.1 Are you re-using the described data and how?

Yes

* To compare and combine with other data

* To follow-up research on a specific area

#### 2.1.2 Where do the described data reside?

Bitter Compounds Database

#### 2.1.3 Which data will be re-used?

GC-MS

## 3 FAIR Data

### 3.1.1 Making data findable, including provisions for metadata

#### 3.1.1.1 Will you use metadata to describe the data?

Yes

#### 3.1.1.3 Will your metadata use standardised vocabularies?
Yes

3.1.1.5 Will you make the metadata available free-of-charge?

Yes

3.1.1.6 Will your metadata be harvestable?

No

3.1.1.7 Will you use naming conventions for your data?

Yes

3.1.1.8 Please provide more details and examples on used naming conversions

yyyymmdd_E-MUSE_ESR14_explicitfilename.extension

3.1.1.9 Will you provide clear version numbers for your data?

No

3.1.1.10 Will you provide persistent identifiers for the described data?

Yes

3.1.1.11 Persistent identifiers

DOI

3.1.1.12 Will you provide searchable metadata for the described data?

No

3.1.1.15 Will you use standardised formats for the described data?

Yes

3.1.1.18 Are the file formats you will use open?

Yes

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

3.1.1.21 Please describe if data require proprietary tools to access the data.

MATLAB

3.1.1.22 Will you provide metadata describing the quality of the data?

95
No

### 3.1.2 Making data openly accessible

#### 3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No

#### 3.1.2.2 Will the described data be openly accessible?

Yes

#### 3.1.2.3 How will the data be made available?

Repository of Archive

#### 3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

#### 3.1.2.6 Are there any methods or tools required to access the described data?

Yes

#### 3.1.2.7 Please provide information about the method(s) needed to access the data

data download

#### 3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

no auxiliary data

### 3.1.3 Making data interoperable

#### 3.1.3.1 Will you use a controlled vocabulary for the described data?

Yes

### 3.1.4 Increase data reuse

#### 3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

Huabin Luo, Simen Akkermans, Davy Verheyen, Jian Wang, Monika Polanska, Jan F. M. Van Impe. Tuning and modeling cheese flavor. Comprehensive Reviews in Food Science and Food Safety. Volume 23, Issue 5, e13420. https://doi.org/10.1111/1541-4337.13420

Huabin Luo, Simen Akkermans, Jan F.M. Van Impe. Demystifying food flavor: Flavor data interpretation through machine learning. Food Chemistry. Volume 483, 2025, 144000. https://doi.org/10.1016/j.foodchem.2025.144000
3.1.4.4 What internationally recognised licence(s) will you use for the described data?

Academic Free License v1.1

3.1.4.5 Do you have documented procedures for quality assurance of the described data?

No

3.1.4.8 Will you provide any support for data reuse?

No

# 4 Allocation of resources

## 4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of national infrastructure

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

Huabin Luo, Jan Van Impe

97

4.1.3 Identify the people or roles that will be responsible for the management of the described data

Huabin Luo (orcid: 0000-0002-9786-0976)

Data producer, data management

Jan Van Impe (orcid: 0000-0002-5904-1638)

Data management

4.1.4 How do you intend to ensure data reuse after your project finishes?

Institutional archive

# 5 Data Security

## 5.1 Data Security

5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for limited time
# 6 Ethical aspects

## 6.1 Ethical aspects

6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

6.1.2 Are the described data sensitive?

No

6.1.3 Are the described data personal?

No

# 7 Other

## 7.1 Other

7.1.1 Do you make use of other procedures for data management?

No
ESR15 project – Michèle Bou Habib

INSTITUT NATIONAL DE RECHERCHE
POUR L'AGRICULTURE,
L'ALIMENTATION ET
L'ENVIRONNEMENT

The production of fermented dairy products, particularly cheese, is a complex process that heavily relies on microbial dynamics. Therefore, it is relevant from both a scientific and economic perspective to better understand the functioning of these particular ecosystems. In particular, lactic acid bacteria play a crucial role in milk acidification. This step is essential for milk coagulation and the development of the texture and flavour profile of the final product. However, the fermentation process is vulnerable to various disruptions, one of the most significant being bacteriophage attacks. Phages, which are viruses that specifically attack bacteria, can infect and lyse lactic acid bacteria. Between 0.1% and 10% of industrial milk fermentations are negatively affected by bacteriophages. When fermentation halts, economic losses and environmental problems arise due to milk waste and the need for cleaning. In less severe cases, the presence of phages can slow production, leading to lower cheese quality and longer production times. By simulating the dynamics of phage attacks on bacterial populations during milk fermentation, it is possible to anticipate potential disruptions and optimise fermentation conditions. Such models provide a framework to understand how phages impact the acidification process and help design strategies to mitigate these effects..

Datasets Description

1 Data Summary

1.1 Data Summary

1.1.1 What is the purpose of the data collection/generation and its relation to the objectives of the project?

To obtain information

1.1.2 What are the types of the described generated/collected data?

* experimental

• simulation

**Experimental data:**
Acidification data : pH evolution as a function of time. There are 2 runs with 3 replicates. Each experiment consists of 48 acidification curves : 12 initial phage titers x 8 initial lactic acid bacteria concentrations. pH measurements are taken every 4 minutes for 20 hours.

Simulation data:

Predictive mechanistic model: to predict pH and relevant state variables (lactose, lactic acid, susceptible and infected bacteria concentrations, and phage titers) as a function of time for different initial conditions of bacteria and viruses.

1.1.3 What are the formats of the described generated/collected data?

• Text files

• Models

1.1.4 What is the origin of the described data?

Secondary data

1.1.5 What is the expected size of the described data?

MB (megabyte)

200

100

1.1.6 To whom might it be useful ('data utility')?

• Researchers

• Research communities

• Industry

2 Reused Data

2.1 Reused Data

2.1.1 Are you re-using the described data and how?

No

3 FAIR Data

3.1.1 Making data findable, including provisions for metadata

3.1.1.1 Will you use metadata to describe the data?

Yes

3.1.1.3 Will your metadata use standardised vocabularies?
Yes

3.1.1.5 Will you make the metadata available free-of-charge?

Yes

3.1.1.6 Will your metadata be harvestable?

No

3.1.1.7 Will you use naming conventions for your data?

Yes

3.1.1.8 Please provide more details and examples on used naming conversions

yyyymmdd_E-MUSE_ESR15_explicitfilename.extension

3.1.1.9 Will you provide clear version numbers for your data?

No

3.1.1.10 Will you provide persistent identifiers for the described data?

No

3.1.1.12 Will you provide searchable metadata for the described data?

No

3.1.1.15 Will you use standardised formats for the described data?

Yes

3.1.1.18 Are the file formats you will use open?

Yes

3.1.1.20 Do supported open-source tools exist for accessing the data?

Yes

3.1.1.22 Will you provide metadata describing the quality of the data?

No

3.1.2 Making data openly accessible

3.1.2.1 Are there ethical or legal issues that can impact sharing the described data?

No
3.1.2.2 Will the described data be openly accessible?

No

3.1.2.3 How will the data be made available?

Domain-specific database

3.1.2.5 Is the storage sufficiently secure for the data and does the storage provide backup and recovery procedures?

secure with backup and recovery

3.1.2.6 Are there any methods or tools required to access the described data?

No

3.1.2.9 Will you also make auxiliary data that may be of interest to researchers available?

no auxiliary data

3.1.3 Making data interoperable

3.1.3.1 Will you use a controlled vocabulary for the described data?

No

3.1.3.2 Will you provide a mapping to more commonly used ontologies?

No

3.1.4 Increase data reuse

3.1.4.1 When do you plan to make the described data available for reuse?

after article publication

https://entrepot.recherche.data.gouv.fr/dataverse/esr15-project

Michèle Bou Habib, Emmanuel Bernuau, Benjamín José Sánchez, Dominique Swennen, Ahmad A. Zeidan, Ioan-Cristian Trelea, Jannik Vindeloev. "Mechanistic modeling of the dynamics of phage attack during milk acidification in the cheese-making process", Journal of Food Engineering, Volume 387, 2025, 112329. DOI: 10.1016/j.jfoodeng.2024.112329

3.1.4.4 What internationally recognised licence(s) will you use for the described data?

Etlab Open License 2.0, GNU GPL

3.1.4.5 Do you have documented procedures for quality assurance of the described data?

No
3.1.4.8 Will you provide any support for data reuse?

No

# 4 Allocation of resources

## 4.1 Allocation of resources

4.1.1 How will the cost of making the described data findable, accessible, interoperable and reusable be covered?

Use of institution infrastructure

4.1.2 Will you identify a data manager to manage the described data? If not who will be responsible for the management of the data?

Yes

Michèle Bou Habib, Cristian Trelea

4.1.3 Identify the people or roles that will be responsible for the management of the described data

Michèle Bou Habib (orcid: 0000-0002-4816-5977)

Data producer, data management

103

Cristian Trelea (orcid: 0000-0001-6727-0716)

Data management

4.1.4 How do you intend to ensure data reuse after your project finishes?

National archive

# 5 Data Security

## 5.1 Data Security

5.1.1 Where do you plan to keep the described data?

Kept on secure, managed storage for a limited time

# 6 Ethical aspects

## 6.1 Ethical aspects

6.1.1 Are there any ethical or legal issues that can have an impact on data sharing?

No

6.1.2 Are the described data sensitive?
No

6.1.3 Are the described data personal?

No

7 Other

7.1 Other

7.1.1 Do you make use of other procedures for data management?

No
E-MUSE

Life sciences and
Artificial intelligence

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E-MUSE project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 956126.

Copyright and legal notice: The content reflects only the author's view. The Research Executive Agency and Commission are not responsible for any use that may be made of the information that it contains.
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