#  1\. Introduction

The ImageInLife project sprang from European scientists in both academia and
industry who identified a common challenge: setting up a training frame to
educate the next generation of imagers in complex biological systems (healthy
& pathological), so they are able to master all major aspects of this
competitive field and bring important innovations to universities and
companies. The long-term goal of any initiative to image biological processes
is reaching cellular or subcellular resolution in a complete organism. This is
now possible using vertebrate embryos as models and the most recent
technological advances as tools. Young researchers will be trained by
addressing the following scientific bottlenecks and challenges:

  * Preparing vertebrate embryos (rodent & zebrafish) for optimal imaging ; 

  

  * Fine-tuning sensors, reporters and actuators to track cell types, cellular processes and behaviours in living organisms ; 

  

  * Developing and implementing new imaging instruments ; 

  

  * Analysing complex sets of big-data images to extract relevant information ; 

  

  * Using processed images to design computational and mathematical models of development and pathologies ; 

  

  * Comparing these models with experimental data and create a feedback loop improving the whole work chain from sample preparation to instrumentation and analysis. 

  

This interdisciplinary training is based on an intersectoral organisation of
the consortium with partners from academia and companies that need these
future experts to develop new instruments, screen drugs and chemicals in
living systems and develop software to analyse and model medical images. The
full training programme is based on an optimal balance between training
through research and many network-wide training events, including conferences
with physical presence, digital conferences and monthly videolink events.
Consortium members are keen to implement both classical and original outreach
activities (e.g. MOOCs, serious games, Lego designs) to bring state-of-the-art
microscopy to the classroom.

##  2\. Data summary

**The following issues will be presented in this section:**

  * **the purpose of the data collection/generation**

  

  * **the relation of the data collection/generation to the objectives of the project**

  

  * **the types and formats of data that will be generated/collected**

  

  * **the origin of the data**

  

  * **the expected size of the data (if known)** ▪ **the data utility**

  

The main objective of the ImageInLife project is to set up a training frame to
educate the next generation of imagers in complex biological systems (healthy
and pathological), so they are able to master all major aspects of this
competitive field and bring important innovations to universities and
companies. In this context, it is very important for the ImageInLife
consortium to establish a suitable Data Management Plan that would help all
the partners of the project to adopt the right policy concerning the
collection, the generation and sharing of the data.

ImageInLife is a large consortium (it includes 11 partners) and this is why it
is necessary for all partners to agree on a common treatment of the project
data.

Thus, the objective is to gather the data under the FAIR principles: the data
that would be Findable, Accessible, Interoperable and Reusable.

The purpose of data/collection/generation in the framework of the ImageInLife
project is to store and share the project data that might be useful for the
project partners and that would be reusable by the researchers in the field
(while respecting the ImageInLife data sharing policy). This goes hand in hand
with the main objective of the project - to educate the next generation of
imagers in complex biological systems that would be able to select, classify
and store useful data in order to share it with the scientific community.

In regard to the types of the generated/collected data - due to the scientific
objective of ImageInLife, this project will mainly generate and collect
**images** . Another important part of the generated data will be **software**
. In a small part, the project will also generate **biological samples** and
**simulation movies** .

The formats of the data depend on the type of the data collected/generated:

For _image files_ , the formats will be the following:

  * TIFF 

  

  * Proprietary formats from microscope companies (Nikon/Zeiss/Leica) 

  

  * DICOM 

  

  * HDF5 

  

  * Uncompressed AVI movies 

  

  * Compressed image formats (e.g., JPEG, MPEG4, AVI) 

  

For _software packages_ , ImageInLife expect to work with these formats:

  * Text (for source code) 

  

  * Binary (executable software) 

  

  * Fiji macros 

  

  * LabVIEW files 

  

  * Text and binary data (measurements and scores) 

  

For _biological samples_ , the data formats will be the following:

  * Plasmids 

  

  * Transgenic or mutant zebrafish 

  

  * Recombinant viruses 

  

On the long term, the policy will be to store these samples in external
repository facilities for distribution (Addgene, EZRC). At first, samples
would be distributed by the partners themselves upon request.

While no external data will be reused for the project, some partners may reuse
some of their own pre-existing data.

We expect that the total volume of data generated by the consortium over the
course of the project could reach around 200 terabytes.

As far as the data utility is concerned, the ImageInLife data will be useful
first of all to the consortium and to the project partners. However, the
objective is also to share the data that might be useful to the international
imaging scientific community.

##  3\. FAIR Data

**3.1 Making data findable, including provisions for metadata:**

  * **Discoverability of data (metadata provision)**

  

Metadata will be associated to all data files archived during the ImageInLife
project. Because of the diversity of data types (images generated by
microscopes from different companies, software produced in different
environments) it will be difficult to generate a universal metadata format.

However, we will strive to define a common metadata standard, particularly for
image files.

  * **Identifiability of data and standard identification mechanism** All archived publications will be associated with a DOI. 

  

  * **Naming conventions**

  

A common naming convention will be adopted for the data files. The name will
be as selfexplanatory as possible but will have to be partially coded to keep
the name length within limits manageable by different computer operating
systems (255 characters, but less would be more convenient).

Names will begin with "ImageInLife" followed by: date of file generation
(yyyymmdd), a letter for data type (I for image, S for software, R for report,
etc.), source partner number in the consortium (P01 to P11, P00 for the
management team, P99 for an external source), source person’s family name, and
a 32-letter summary of the content of the file.

The exact naming scheme will be discussed at the first consortium meeting to
achieve a consensus.

▪ **Approach towards search keyword**

A "keywords" category will be systematically included in the metadata file.

  * **Approach for clear versioning**

  

For archived objects that may exist in different versions (typically, software
and models), all versions will be archived, including the metadata of the
newer versions indicating the location of the previous one and containing a
summary of the changes. Versions will be numbered according to standard
conventions (e.g. with a suffix "v" followed by a number and possibly a letter
for minor changes).

  * **Standards for metadata creation**

  

A standardized metadata format will be defined for images. A web-based
interface will be created to facilitate generation of PDF-format metadata for
image files in a common format for the whole ImageInLife consortium. This will
be used for all image files, although some files will also be associated to
another metadata file generated by the software. The standard metadata will
include: keywords, lab of origin, user(s) who generated the file, microscope
specifications (brand, type, objective mag and NA), organism name, strain, and
developmental stage, experimental manipulations (injection of tracers,
infections, and the like), imaging conditions (temperature, duration, time
between frames, size of imaged area in X-Y-Z, Z-step, excitation methods,
recorded wave lengths); other information will be included as needed. The
interface will be evolutive according to the users' needs.

**3.2 Making data openly accessible:**

  * **Data openly available**

  

Whether the data will be made fully open immediately will be decided case-by-
case. In any case, all data will be made fully accessible after publication of
the corresponding article. In addition, the data will be openly shared among
partners of the consortium, except in some specific cases

(confidential medical imaging, projects involving third parties with specific
agreements). The DOI corresponding to the data will be provided in
publications. For data made open prior to publications, the corresponding
links will be found on the partners’ institutional websites.

  * **Methods or software tools needed to access the data**

  

Whenever possible, the data will be converted into formats that can be opened
using public domain software (e.g. ImageJ for imaging data). If not, the
specific tools required will be indicated in the metadata file (itself a PDF
file, thus openly accessible). When a specific software will be generated by
consortium members to access/visualize the data, this software and its source
code will be available as any other data file.

  * **Emplacement of data, associated metadata, documentation and code**

  

A specific ImageInLife archive will be created within the open Zenodo archive
repository. Data will be deposited there associated with their metadata and
any software required to access it. In addition to the Zenodo archive, each
partner will archive their own data on their institutional servers and/or
external storage media, and may allow direct access via their laboratory
homepage.

▪ **Access in case of restrictions**

For restricted datasets (e.g. medical image files), access will be managed by
the PI responsible for the data generation. In general, efforts will be made
to curate the data file in order to provide the relevant source data for other
researchers who wish to reproduce the image analysis without compromising
private information of patients.

**3.3 Making data interoperable:**

▪ **Interoperability of data ( the use of data and metadata vocabularies,
standards or methodologies in order to facilitate interoperability)**

Because of the diverse types of data and of tools, it will not be possible
(nor necessarily desirable) to attain a full interdisciplinary
interoperability within the project.

However, as specified above, efforts will be made, notably regarding imaging
data, to convert all files into a common format (readable with public domain
software, e.g., ImageJ) and have a common PDF-based metadata format.

**3.4 Data re-use (through clarifying licenses):**

  * **Licensing of data in order to permit the widest reuse possible**

  

In general, data will be licensed according to a Creative Commons Attribution
License (  _http://creativecommons.org/licenses/by/3.0_ ), which permits
unrestricted use, distribution and reproduction in any medium provided that
the original work is properly attributed.

The only exceptions will be cases where the data contains confidential medical
information. In that case the files will be curated, in order to make
available for reuse only data which cannot be traced back to a specific
patient.

  * **Availability of data for re-use**

  

See the section "Availability of Data" above. Data will be fully available for
reuse after publication of the corresponding article. Decisions on making the
data immediately available will be made by PIs on a case-by-case basis.

  * **Data quality assurance processes**

  

Data quality will be the responsibility of each partner. However, a collegial
evaluation of the archived data will be made during consortium meetings to
ensure a shared minimal quality standard.

  * **The length of time for which the data will remain re-usable**

  

We will not impose limits on the length of reuse of the data. The main issue
will be the continuity of data archival on the Zenodo platform, which is so
far impossible to predict.

##  4\. Allocation of resources

**In this section, the allocation of resources will be exposed by examining
the following issues:**

  * **Estimation of costs for making the data FAIR**

  

  * **Responsibilities for data management in your project**

  

  * **Costs and potential value of long term preservation**

  

The estimated costs for making the ImageInLife data FAIR are mainly those of
the time (estimated to be about 1 week/year for each partner) that the
researchers will dedicate to the activities connected with this issue. These
costs are thus covered by the project funds.

Every collaborator involved in the project will be responsible for the
management of the data that he/she will collect/generate. The Data Manager and
the Project Manager will make sure that the common agreed principles of
management of the project data are respected. Both of them will be contact
persons in a case any of the project partners has a request concerning the
management of the data.

Regarding the costs of preservation of the digital data, ImageInLife will use
a free data repository, Zenodo. The costs of long term preservation of data
using a chargeable repository will be discussed later in the course of the
project.

Preservation of biological samples will be the responsibility of the partners
that generated them. To ensure long-term preservation, valuable samples will
be transferred to third-party repositories that do not charge for such
archiving (Addgene for plasmids, EZRC for zebrafish).

#  5\. Data security

▪ **Data recovery, secure storage and transfer of sensitive data**

ImageInLife will make sure that the all the data collected/generated is safely
stored for long term preservation. As stated above, the general principle will
be a central archival on the Zenodo platform for the whole consortium plus a
storage of each partner's data on their institutional servers. This issue will
be discussed later in the project and the proper measurements and actions will
be taken toward this objective.

Some of ImageInLife partners will collect sensitive data (e.g. medical images
and patient data). Ensuring their secure storage and restricting access will
be their responsibility. Data that will be transferred will be the same as the
one made publicly open, i.e. data curated to remove information allowing
patient identification.

##  6\. Ethical aspects

One of the partners will collect personal data from patients and exploit
images obtained from medical imaging devices. Consent from participants will
be obtained by the partner. However, the data that will be shared with other
members of the consortium or with the research community will include only
curated data to remove any possibility to identify the patients.

##  7\. Use of another national/funder/sectorial/departmental procedures for
data management

A few partner institutions require that articles be deposited into their own
open archives (HAL format). This will be one way to ensure fully open access
in the cases where publications do not appear in Open Access journals.

In general, Open Access journals will be preferred for publications; however
this will have to be balanced with the need for high impact.