#  Initial Data Management Plan

This is the first version of the Data Management Plan following the guidelines
for the Open Research Data Pilot.

#  Data Summary

##  Purpose of the data collection

Data is collected by two end-user company partners (DSM and Biosyntia) in
order to provide real-life data to validate the underlying data
analysis/interpretation methods, developed in this project and to provide test
cases for the full DeCaF platform including data visualization.

##  Relation to the objectives of the project

End-user validation and feedback is crucial for the purpose of developing a
broadly usable cell factory and community design platform. End-user engagement
is best achieved by applying the platform and underlying methods to data
generated by the end-user companies in their own research and development
projects.

##  Types and formats of data collected

The primary large-scale experimental data types collected are:

Genomics: Data collection is achieved by short read sequencing on Illumina
sequencer platforms creating raw reads in fastq format. Reads are aligned to a
reference genome in order to allow identification of genetic variants in
production strains.

Transcriptomics: Data collection is achieved through RNA-seq on Illumina
sequencer platforms creating raw reads in fastq format. These raw reads are
processed into derived tabular data formats that consist of unique transcript
identifies and the corresponding absolute RNA expression level in a particular
condition.

Proteomics: Data collection is achieved through standard massspectrometry
proteomics platforms (vendor depends on the partner) each of which produces
its own proprietary file type. These files will be converted to the standard
mzML format that allows deposition to public databases. The processed data is
in tabular data format that consist of unique protein and peptide identifiers
and the corresponding relative protein expression level in a particular
condition.

Metabolomics: Data collection is achieved through mass spectrometry (LC/MS)
and

HPLC platforms. The raw data is not of primary interest as the file formats
are proprietary to each instrument vendor. Instrument vendor provided software
will be used to obtain abslute metabolite concentration data in a tabular
format that consist of unique metabolite identifiers and the corresponding
absolute metabolite concentration in a particular condition.

Fluxomics: Data collection is achieved through mass spectrometry (GC/MS) and
HPLC platforms. As above the raw data is not of interest, but from the raw
data a series of derived data types will be generated including isotopomer
distributions and final metabolic flux estimates. All the derived data can be
represented in tabular formats consisting of unique identifiers (e.g. the
metabolic reaction specified by the reactant and product metabolite ids and
stoichiometry) and the corresponding measured/estimated data.

In addition to experimental data, this project will include creating,
improving and extending genome-scale models of cellullar processes.These
models will be stored in the SBML (Systems Biology Markup Language) format,
which will allow importing the models to public domain model repositories
(BioModels at EBI). We will follow MIRIAM (Minimum Information Required in the
Annotation of Models) standards to ensure that identifiers used in the models
are consistent with identifiers used with data (e.g. gene/protein/metabolite
identifiers). During Model versioning during the project will be done by
maintaining the models on github. Upon publication, the final versions of the
models will be deposited in BioModels.

##  Existing data being re-used

Existing reference genomics, transcriptomics, proteomics, metabolomics and
fluxomics data may be reused for wild type (i.e. non-engineered) strains in
order to validate the quality of the data produced within the project.
However, new data for wildtype strains will also be produced during the
project in order to ensure that a consistent dataset is overall generated for
all the strains studied.

Existing public domain metagenomics data will be used for mining of novel
enzyme functions and for functionally annotating metagenomic datasets by
partners EMBL and biobyte. These data are also available in public metagenome
repositories, but with minimal annotation. There are no restrictions for the
use of the public domain metagenomics data and the derived data generated from
this data can also be freely shared. The DD-DeCaF project will not generate
any additional metagenomics data.

Existing public domain genome-scale models will be used as basis for
improvement or extension within the project. The majority of these models will
be available free of restrictions, but a few of the models will come with
restrictions for commercial use. These restrictions will be propagated to the
derived models as required by the licenses attached to the original models.

##  Origin of the data

The data will originate from two end user partners who will provide the raw
data in standardized formats (for transcriptomics and proteomics) and the
processed data in tabular format (for all omics data types).

The models will originate from four of the academic partners (DTU, EMBL,
Chalmers and EPFL). DTU partner will be responsible for ensuring that the
models are in formats that allow reuse and integration of experimental data
with the models.

##  Expected size of the data

The raw data for each genomics data set (one strain) is approximately 0.25 Gb.

The raw data for each transcriptomics data set (one strain, condition,
replicate) is approximately 0.5 Gb.

The raw data for each proteomics data set (one strain, condition, replicate)
is approximately 2 Gb.

Each of the processed tabular data sets (all omics data types) is of the order
of 0.5 Mb.

The total number of individual data sets is approximately 20
strains/conditions x 3 replicates = 60 samples total for all other omics data
types except genomics and 20 samples for genomics (no replicates needed). This
gives a total experimental data volume of 160 Gb primarily consisting of
proteomics and transcriptomics raw data.

The genome-scale models will each take few tens of Mb of space and don't
represent a major data management challenge.

##  Data utility

The experimental data will be useful within the project for testing and
validating methods and for testing features of the overall data analysis and
visualization platform. Outside of the project, the data will be useful as
reference data for cell factory design in yeast and E. coli and as part of
compendia of omics datasets for these organisms.

The genome-scale models generated within the project will be useful for cell
factory design as well as a number of other applications related to metabolic
physiology both within and outside of the project.

#  FAIR data

##  Making data findable, including provisions for metadata

There are two types of metadata, that need to accompany the primary omics data
collected within the project: 1) Metadata describing the general experimental
setup (e.g. strain genotype, cultivation conditions, sampling time points) and
2) metadata describing the process of going from a particular microbial
culture to the raw data (e.g. sampling, sample processing, relevant instrument
settings). The metadata within this project will be collected in the ISA-Tab
format, which allows metadata submission together with raw/processed data
submission to relevant databases (e.g. those maintained by the European
Bioinformatics Institute, EBI). We will also utilize the ISATab format as a
metadata exchange format with the data repositories built within the project.
Unique dataset identifiers will be generated in conjunction with submission of
the genomics, proteomics and transcriptomics data to the public domain
databases (ENA, PRIDE and ArrayExpress at EBI respectively). There are not
currently comparable metabolomics or fluxomics databases that would work for
the type of data generated within this project. For metabolomics and fluxomics
the data will be submitted to the general purpose research materials sharing
platform Zenodo. These platforms create the necessary dataset IDs and DOIs for
all the materials. In addition to public domain databases, we will also build
a database within this project that will contain the specific omics data and
metadata generated within this project in order to demonstrate the use of the
data within the platform and allow easy use of the data by academic and
industrial partners working on data analysis and visualization methods.

##  Making data openly accessible

All data generated within the project will be made openly accessible after an
embargo period of maximum of two years from the generation of the dataset
during which the data will only be accessible within the consortium to the
partners that require data access for method or tool development. During the
embargo period the data will be made available to the partners through the
DeCaF platform developed in this project. After the embargo period, the data
will be made available publicly both through the platform developed during the
project and through public data repositories as described above. The embargo
period will end upon public disclosure of the data in the form of a preprint
or conference/journal article if this happens in less than 2 years from the
generation of the data. The project will develop the software tools and APIs
that allow accessing the data within the DeCaF platform. Public data
repositories already provide such tools.

The genome-scale models developed during the project will be subject to the
usual embargo period where they will be available only to consortium partners
until publication (either preprint or conference/journal article). After
publication, models will be publicly available both through the DeCaF platform
and through the BioModels database. Software tools developed by academic
partners during the DD-DeCaF project will be available open source through the
github repository. SME partners in the project may release some of their code
open source, but also reserve the right to maintain proprietary code bases
where it is deemed to be necessary for commercial reasons.

##  Making data interoperable

The DD-DeCaF project will create genomics, transcriptomics, proteomics,
metabolomics and fluxomics data for two organisms - Escherichia coli and
Saccharomyces cerevisiae. We will use standard gene, transcript and protein
identifiers that are specified by the reference databases for these organisms
(EcoCyc and SGD respectively). For metabolites we will use universal unique
chemical identifier (InChI) that allow conversion to other types of commonly
used identifiers such as SMILES, ChEBI, PubChem, CAS. Within the DeCaF
platform we will also provide genome-scale models that utilize these same
standard identifiers (MIRIAM) so that the data can be directly mapped to the
models and used with the methods developed within the DD-DeCaF project. The
models will be made available in the standard SBML format facilitating use of
the models with different types of modeling and visualization software. We
will use standard raw and processed data formats where possible (genomics,
transcriptomics and proteomics) as outlined earlier in the document.

##  Increase data re-use

All omics data and associated metadata will after the embargo period
(described above, maximum of two years from data generation or upon
publication) be freely usable without restrictions. The embargo period will be
to allow seeking patents or to prepare publications. No data will be generated
for commercially sensitive strains or processes within this project. Data will
be quality controlled during initial data analysis within the project, and
poor quality data (with quality standards dependent on the type of data) will
be discarded and new data will be generated.

The modified and extended genome-scale models will be made publicly available
upon scientific publication at the latest. Models will be made available with
the same licensing restrictions that apply to underlying models that are used
as starting point. All models will be free to use and modify for non-
commercial use, but some may require commercial use licenses. Within the DeCaF
platform these licensing restrictions will be made explicit. Models will be
quality controlled by verifying their predictive performance against standard
publicly available benchmark datasets for each organism if these are
available.

#  Allocation of resources

##  Estimation of the costs for making data FAIR

Since this project is focused on building a data analysis and processing
platform with the goal of designing new cell factories and communities, the
costs of making data and models FAIR are already included in the proposed work
and no additional costs will be involved in this process. The project includes
components where data deposition and sharing tools are developed.

##  Responsibilities for data management

Data management of data generated within the project is primarily handled by
the coordinating partner DTU. DTU will develop the data management, analysis
and visualization platform that is used within the project. Partners EMBL and
biobyte will handle development of the metagenomic data mining platform, but
this platform only uses existing public metagenomics data.

##  Costs and potential value of long term preservation

Long term preservation of the primary data and metadata is ensured by
deposition of the data to public domain repositories (ENA, ArrayExpress,
PRIDE, BioModels and Zenodo). The DeCaF platform developed within the project
will also be maintained long term using internal resources available within
the Novo Nordisk Foundation Center for Biosustainability at DTU.

#  Data security

During the embargo period the data and models will be stored in the data and
model repositories developed within the DD-DeCaF project as part of the DeCaF
platform. These repositories will include as a feature the possibility of
restricting data and model sharing to specific partners or making them
entirely public. Access control is handled by the REMS system developed by
CSC. The DeCaF platform will include backup features for all the data as well
as long term archiving (10 years). The same apply to the genome-scale models.

Deposition to public domain databases will be only done after embargo period
is complete. These databases handle backups and archiving internally and are
expected to provide very long term data storage.

#  Ethical aspects

The data generated in this project is for microbial cell factories and
involves no human subjects.

#  Other

DTU has drafted a research data management policy that is aligned with the EC
Horizon 2020 data management policy requirements. DD-DeCaF will follow the DTU
policy once it is made official within the following months.