#  Introduction

This report describes the **Initial** **Data Management Plan (DMP)** for the
AMECRYS project, funded by the EU’s Horizon 2020 Programme under Grant
Agreement number 712965. The purpose of the DMP is to set out the main
elements of the AMECRYS consortium data management policy for the datasets
generated by the project. The DMP will present in detail only the procedure
for the management of datasets created during the lifetime of the project and
describes the key data management principles, notably in terms of data
standards and metadata, sharing, archiving and preservation.

This is the third version of the AMECRYS DMP and fulfils the project
deliverable D7.2 month

6 by responsible partner University of Strathclyde (UST). It draws on Horizon
2020 guidance (European Commission, 2016) and guidance of the Research and
Knowledge Exchange Services department, University of Strathclyde (University
of Strathclyde (2014)).

Section 4 below list the project partners and key contacts. Section 5 provides
a summary of the datasets generated during the lifetime of the project,
including the types and format, the expected size of the datasets and the data
utility. The specific description of how AMECRYS will make this research data
findable, accessible, interoperable and reusable (FAIR) is outlined in section
6. Sections 7 to 9 outline the policy in relation to data resources, security
and ethics.

This is a live “active” document to be updated at regular intervals during the
project
_https://strathcloud.sharefile.eu/f/focfc1a4-6516-4951-93f0-4e86dd241d17_ .

4

#  Project Participants  
  
<table>  
<tr>  
<th>

**Full Name**

</th>  
<th>

**Short Name**

</th>  
<th>

**Contacts**

</th> </tr>  
<tr>  
<td>

Consiglio Nazionale delle Ricerche, Italy

</td>  
<td>

CNR

</td>  
<td>

Dr Gianluca DI PROFIO _g.diprofio@itm.cnr.it_

</td> </tr>  
<tr>  
<td>

Imperial College of Science, Technology and Medicine, UK

</td>  
<td>

IMP

</td>  
<td>

Dr Jerry HENG

_jerry.heng@imperial.ac.uk_

</td> </tr>  
<tr>  
<td>

Università della Calabria, Italy

</td>  
<td>

UCAL

</td>  
<td>

Prof Efrem CURCIO _e.curcio@unical.it_

</td> </tr>  
<tr>  
<td>

Centre national de la recherche scientifique, France

</td>  
<td>

CNRS

</td>  
<td>

Dr Jean-Baptiste SALMON _jean-baptiste.salmonexterieur@solvay.com_

</td> </tr>  
<tr>  
<td>

Université libre de Bruxelles, Belgium

</td>  
<td>

ULB

</td>  
<td>

Dr Jim LUTSKO _jlutsko@ulb.ac.be_

</td> </tr>  
<tr>  
<td>

University of Strathclyde, UK

</td>  
<td>

UST

</td>  
<td>

Prof Joop TER HORST

_Joop.terhorst@strath.ac.uk_

</td> </tr>  
<tr>  
<td>

Centre for Process Innovation Limited, UK

</td>  
<td>

CPI

</td>  
<td>

Dr John LIDDELL _john.liddell@uk-cpi.com_

</td> </tr>  
<tr>  
<td>

GVS S.p.a., Italy

</td>  
<td>

GVS

</td>  
<td>

Dr Soccorso GAETA _sng@gvs.com_

</td> </tr>  
<tr>  
<td>

Fujifilm Diosynth Biotechnologies, UK

</td>  
<td>

FDB

</td>  
<td>

Dr James PULLEN _james.pullen@fujifilm.com_

</td> </tr> </table>  
<table>  
<tr>  
<th>

**Coordinator Contact**

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Dr Gianluca DI PROFIO

</td>  
<td>

E: _g.diprofio@itm.cnr.it_

T: +39 0984 492010/492014

</td>  
<td>

Consiglio Nazionale delle Ricerche (CNR), Instituto per la Tecnologia delle
Membrane (ITM), Via P. Bucci Cubo 17/C, I-87036 Rende (CS), Italy

</td> </tr> </table>

5

#  Data Summary

The main purpose of the data collection/generation of this project is to
industrially enable the template-assisted membrane crystallization process
through a thorough scientific understanding of the process.

AMECRYS will produce several datasets during the lifetime of the project. The
data will be both quantitative and qualitative in nature and will be analysed
from a range of methodological perspectives for project development and
scientific purposes. These will be available in a variety of easily accessible
formats, including Post Script (PDF, XPS), Excel (XLSX, CSV), Word (DOC, RTF),
Power Point (PPT), image (JPEG, PNG, GIF, TIFF), Origin (OPJ), compressed
formats (TAR.GZ, MTZ), Program database (PDB).

Within AMECRYS approximately 49 separate datasets will be created (see list in
table below). They are listed under each of the work package deliverables
taken from the GA Annex 1 – Description of Action. The datasets will have the
same structure, in accordance with the guide of Horizon 2020 for the Data
Management Plan. The expected size of the datasets produced will be between
5MB and 1GB.

**Table 5.1 –** Potential datasets  
  
<table>  
<tr>  
<th>

**Data Type**

</th>  
<th>

**Format**

</th>  
<th>

**Volume**

</th>  
<th>

**IPR Owner**

</th> </tr>  
<tr>  
<td>

**Work Package 2 - D2.1: Report on preparation of nanotemplates for mAb
crystallization**

**(lead: IMP)**

</td> </tr>  
<tr>  
<td>

Experimental data – Brunauer–Emmett– Teller (BET)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – scanning electron microscope (SEM)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – transmission electron microscopy (TEM)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – crystal shape/size measurement

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – X-ray diffraction (XRD)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – High performance liquid chromatography (HPLC)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – dynamic light scattering (DLS)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Synthesis protocol

</td>  
<td>

DOC, PDF, JPEG

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Lab notes

</td>  
<td>

DOC, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

**Work Package 2 - D2.2: HEL4 domain fragment & Anti CD20 mAb process
specification report (lead: FDB) **

</td> </tr>  
<tr>  
<td>

Final report

</td>  
<td>

DOC, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

FDB

</td> </tr>  
<tr>  
<td>

USP Protocols and technology transfer package for production of Anti-CD20

</td>  
<td>

DOC, XLSX , PDF, PPT

</td>  
<td>

< 200 MB

</td>  
<td>

FDB

</td> </tr>  
<tr>  
<td>

DSP Protocols and technology transfer package for production of Anti-CD20

</td>  
<td>

DOC, XLSX, PDF, PPT

</td>  
<td>

< 200 MB

</td>  
<td>

FDB

</td> </tr>  
<tr>  
<td>

USP Protocols and technology transfer package for production of HEL4

</td>  
<td>

DOC, XLSX, PDF, PPT

</td>  
<td>

< 200 MB

</td>  
<td>

FDB

</td> </tr>  
<tr>  
<td>

DSP Protocols and technology transfer package for production of HEL4

</td>  
<td>

DOC, XLSX, PDF, PPT

</td>  
<td>

< 200 MB

</td>  
<td>

FDB

</td> </tr>  
<tr>  
<td>

**Work Package 2 - D2.3: Report on the optimised nanotemplates for selective
mAb recognition & crystallization (lead: IMP) **

</td> </tr> </table>

6  
  
<table>  
<tr>  
<th>

Experimental data – Brunauer–Emmett– Teller (BET)

</th>  
<th>

XLSX, JPEG, PDF

</th>  
<th>

< 100 MB

</th>  
<th>

IMP

</th> </tr>  
<tr>  
<td>

Experimental data – scanning electron microscope (SEM)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – transmission electron microscopy (TEM)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – crystal shape/size measurement

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – X-ray diffraction (XRD)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – High performance liquid chromatography (HPLC)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Experimental data – dynamic light scattering (DLS)

</td>  
<td>

XLSX, JPEG, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Synthesis protocol

</td>  
<td>

DOC, PDF, JPEG

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

Lab notes

</td>  
<td>

DOC, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

IMP

</td> </tr>  
<tr>  
<td>

**Work Package 3 - D3.1: Report on pilot lines to prepare membranes available
and debugged (lead: GVS)**

</td> </tr>  
<tr>  
<td>

Experimental results from pilot tests

</td>  
<td>

PDF

</td>  
<td>

< 100 MB

</td>  
<td>

GVS

</td> </tr>  
<tr>  
<td>

Pictures of pilot plant developed

</td>  
<td>

PDF

</td>  
<td>

< 100 MB

</td>  
<td>

GVS

</td> </tr>  
<tr>  
<td>

**Work Package 3 - D3.2: Report on the development of membranes for
heterogeneous mAbs nucleation (lead: CNR)**

</td> </tr>  
<tr>  
<td>

Experimental results - Membranes development data v1

</td>  
<td>

DOC, XLSX, OPJ, PPT, JPEG, TIFF

</td>  
<td>

< 500 MB

</td>  
<td>

CNR

</td> </tr>  
<tr>  
<td>

Experimental results - Membranes development data v2

</td>  
<td>

DOC, XLSX, OPJ, PPT, JPEG, TIFF

</td>  
<td>

< 1 GB

</td>  
<td>

CNR

</td> </tr>  
<tr>  
<td>

Experimental results - Heterogeneous nucleation data v1

</td>  
<td>

DOC, XLXS, OPJ, PPT, JPEG, TIFF

</td>  
<td>

< 500 MB

</td>  
<td>

CNR

</td> </tr>  
<tr>  
<td>

Experimental results - Heterogeneous nucleation data v2

</td>  
<td>

DOC, XLSX, OPJ, PPT, JPEG, TIFF

</td>  
<td>

< 1 GB

</td>  
<td>

CNR

</td> </tr>  
<tr>  
<td>

**Work Package 3 - D3.3: Report on membrane preparation scaleup (lead: GVS)**

</td> </tr>  
<tr>  
<td>

Report about prototype of membrane PDF < 100 MB GVS

</td> </tr>  
<tr>  
<td>

**Work Package 4 - D4.1: Robust microfabrication protocols to embed
hydrophobic fluoropolymers membranes within microfluidic chips (lead: CNRS)**

</td> </tr>  
<tr>  
<td>

Fabrication Protocols of Microfluidic devices

</td>  
<td>

PDF, JPEG

</td>  
<td>

< 10 MB

</td>  
<td>

CNRS

</td> </tr>  
<tr>  
<td>

**Work Package 4 - D4.2: Multilevel microfluidic device for high throughput
crystallization screening (“pharma-on-a-chip” concept) (lead: CNRS)**

</td> </tr>  
<tr>  
<td>

Microfluidic screening of membrane crystallization

</td>  
<td>

PDF, XLSX

</td>  
<td>

< 10 MB

</td>  
<td>

CNRS

</td> </tr>  
<tr>  
<td>

**Work Package 4 -D4.3: Report on selective nucleation and growth kinetics of
mAbs from screening tests (lead: UST)**

</td> </tr>  
<tr>  
<td>

Experimental results - Crystallization Kinetics data v1

</td>  
<td>

XLSX, JPEG

</td>  
<td>

55 MB

</td>  
<td>

UST

</td> </tr>  
<tr>  
<td>

Experimental results - Crystallization Kinetics data v2

</td>  
<td>

XLSX, JPEG

</td>  
<td>

< 100 MB

</td>  
<td>

UST

</td> </tr>  
<tr>  
<td>

**Work Package 5 -D5.1: Simulation code for thermodynamics of course-grained
model of mAbs in confined geometry (lead: ULB)**

</td> </tr>  
<tr>  
<td>

Code written by partners in the project - ftDFT Code

</td>  
<td>

TAR.GZ

</td>  
<td>

< 10 MB

</td>  
<td>

ULB

</td> </tr>  
<tr>  
<td>

**Work Package 5 - D5.2: Open-source MCFFS computational simulation packages
(lead: ULB)**

</td> </tr>  
<tr>  
<td>

Code written by partners in the project \- KMC

Code

</td>  
<td>

TAR.GZ

</td>  
<td>

< 10 MB

</td>  
<td>

ULB

</td> </tr>  
<tr>  
<td>

**Work Package 5 - D5.3: Report on multiscale simulation of mAbs
crystallization on membranes/nanotemplates (lead: UCAL)**

</td> </tr> </table>  
<table>  
<tr>  
<th>

Report on simulation activities - Models/software

</th>  
<th>

PDF, JPEG

</th>  
<th>

< 100 MB

</th>  
<th>

UCAL

</th> </tr>  
<tr>  
<td>

**Work Package 5 - D5.4: Report on structural/ morphological/ bioactivity
properties of mAbs crystals produced by prototype operation crystallizers
(lead: CNR)**

</td> </tr>  
<tr>  
<td>

Results of structural analysis

</td>  
<td>

MTZ, PDB

</td>  
<td>

50 MB

</td>  
<td>

CNR

</td> </tr>  
<tr>  
<td>

Results of crystal morphology

</td>  
<td>

XLSX, TIFF

</td>  
<td>

50 MB

</td>  
<td>

CNR

</td> </tr>  
<tr>  
<td>

Bioactivity tests

</td>  
<td>

XLSX, TIFF

</td>  
<td>

10 MB

</td>  
<td>

CNR

</td> </tr>  
<tr>  
<td>

Regression models & multivariate analysis

</td>  
<td>

XLSX, TIFF, DOC

</td>  
<td>

5 MB

</td>  
<td>

CNR

</td> </tr>  
<tr>  
<td>

**Work Package 6 - D6.1: Design of continuous flow template assisted membrane
crystallizer prototype (lead: UCAL)**

</td> </tr>  
<tr>  
<td>

Design of prototype – Design specifications & Design drawings

</td>  
<td>

PDF, JPEG

</td>  
<td>

< 100 MB

</td>  
<td>

UCAL

</td> </tr>  
<tr>  
<td>

**Work Package 6 - D6.2: Installation and validation of continuous flow
template assisted membrane crystallizer prototype (lead: UCAL)**

</td> </tr>  
<tr>  
<td>

Installation of prototype - Construction schedules

</td>  
<td>

PDF, XLSX, CSV

</td>  
<td>

< 100 MB

</td>  
<td>

UCAL

</td> </tr>  
<tr>  
<td>

Validation of prototype – Lab notes

</td>  
<td>

PDF

</td>  
<td>

< 500 MB

</td>  
<td>

UCAL

</td> </tr>  
<tr>  
<td>

**Work Package 6 - D6.3: Report on prototype’s operation monitoring &
compliance with QS/GMP/CQA regulations (lead: CPI) **

</td> </tr>  
<tr>  
<td>

Experimental results (USP/DSP/Analytics) – Anti-CD20 prototype operation

</td>  
<td>

DOC, XLSX, PPT,

PDF, XPS, JPEG,

TIFF

</td>  
<td>

500 MB

</td>  
<td>

CPI

</td> </tr>  
<tr>  
<td>

Experimental results (USP/DSP/Analytics) – HEL4 prototype operation

</td>  
<td>

DOC, XLSX, PPT,

PDF, XPS, JPEG,

TIFF

</td>  
<td>

500 MB

</td>  
<td>

CPI

</td> </tr>  
<tr>  
<td>

Report summarising operation performance including CQA, comparison to
conventional approaches and GMP compliance

</td>  
<td>

DOC

</td>  
<td>

10 MB

</td>  
<td>

CPI

</td> </tr>  
<tr>  
<td>

**Work Package 6 - D6.4: Techno-economic comparison between conventional batch
and innovative DSP (lead: FDB)**

</td> </tr>  
<tr>  
<td>

Final report

</td>  
<td>

DOC, PDF

</td>  
<td>

< 100 MB

</td>  
<td>

FDB

</td> </tr>  
<tr>  
<td>

Output from BioSolve Simulation

</td>  
<td>

BioSolve, XLSX

</td>  
<td>

< 200 MB

</td>  
<td>

FDB

</td> </tr> </table>

7 8

**Table 5.2 –** Lead Partners for Work Packages  
  
<table>  
<tr>  
<th>

**Lead partner**

</th>  
<th>

**Related WP(s)**

</th> </tr>  
<tr>  
<td>

IMP

</td>  
<td>

WP2: Production of mAb/domain & Nanotemplates synthesis

</td> </tr>  
<tr>  
<td>

GVS

</td>  
<td>

WP3: Membranes development

</td> </tr>  
<tr>  
<td>

CNRS

</td>  
<td>

WP4: Microfluidics for continuous mAbs crystallization

</td> </tr>  
<tr>  
<td>

ULB

</td>  
<td>

WP5: Multi-scale modelling & characterization of mAb crystals

</td> </tr>  
<tr>  
<td>

CPI

</td>  
<td>

WP6: Prototype design, construction & operation

</td> </tr> </table>

#  5 FAIR (Findability, Accessibility, Interoperability, and Reusability) data

##  5.1 Making data findable, including provisions for metadata

A DOI will be assigned to datasets for effective and persistent citation when
it is uploaded to the repository [ _Zenodo_ ]. This DOI can be used in any
relevant publications to direct readers to the underlying dataset.

Each dataset generated during the project will be recorded in an Excel
spreadsheet with a standard format and allocated a dataset identifier, see
tables 6.1.1 and 6.1.2 below. The spreadsheet will be hosted at UST [
_ShareFile_ ]. This dataset information will be included in the metadata file
(see _Section 6.3_ and _Annex B_ ) at the beginning of the documentation, and
updated with each version.

AMECRYS naming convention for project datasets will comprise of the following:

  1. A unique chronological number of the datasets in the project will be added. 

  

  2. The title of the dataset. 

  

  3. For each new version of a dataset it will be allocated with a version number which will be for example start at v1.0. 

  

  4. A prefix "AM" indicating an AMECRYS dataset. 

  

  5. A unique identification number linking with the dataset work package and deliverable/task e.g., "W4_D4.3". 

  

  

**01_ Crystallization Kinetics_v1.0._AM_W4_D4.3.xlsx**

Search keywords will be provided when the dataset is uploaded to Zenodo which
will optimise possibilities for re-use. Zenodo follows the minimum Data Cite
metadata standards.  9

The specific metadata contents, formats and volume are given in table 6.1.1
below and will be further defined in future versions of the DMP.

**Table 6.1.1** : AMECRYS Datasets fields  
  
<table>  
<tr>  
<th>

**Dataset Identifier**

</th>  
<th>

The ID allocated using the naming convention outlined in section 6.1

</th> </tr>  
<tr>  
<td>

**Tile of Dataset**

</td>  
<td>

The title of the dataset which should be easily searchable and findable

</td> </tr>  
<tr>  
<td>

**Responsible Partner**

</td>  
<td>

Lead partners responsible for the creation of the dataset

</td> </tr>  
<tr>  
<td>

**Work Package**

</td>  
<td>

The associated work package this dataset originates

</td> </tr>  
<tr>  
<td>

**Dataset Description**

</td>  
<td>

A brief description of the dataset

</td> </tr>  
<tr>  
<td>

**Dataset Benefit**

</td>  
<td>

What are the benefits of the dataset

</td> </tr>  
<tr>  
<td>

**Dataset**

**Dissemination**

</td>  
<td>

Where will the dataset be disseminated

</td> </tr>  
<tr>  
<td>

**Type Format**

</td>  
<td>

This could be DOC, XLSX, PDF, JPEG, TIFF, PPT etc. (see table 5.1)

</td> </tr>  
<tr>  
<td>

**Expected Size**

</td>  
<td>

The approximate size of the dataset

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

How/why was the dataset generated

</td> </tr>  
<tr>  
<td>

**Repository**

</td>  
<td>

Expected repository to be submitted

</td> </tr>  
<tr>  
<td>

**DOI (if known)**

</td>  
<td>

The DOI can be entered once the dataset has been deposited in the repository

</td> </tr>  
<tr>  
<td>

**Date of Repository Submission**

</td>  
<td>

The date of submission to the repository can be added once it has been
submitted

</td> </tr>  
<tr>  
<td>

**Keywords**

</td>  
<td>

The keywords associated with the dataset

</td> </tr>  
<tr>  
<td>

**Version Number**

</td>  
<td>

To keep track of changes to the datasets

</td> </tr>  
<tr>  
<td>

**Link to metadata file**

</td>  
<td>

</td> </tr> </table>

**Table 6.1.2:** AMEYCRES Completed Dataset example  
  
<table>  
<tr>  
<th>

**Dataset Identifier**

</th>  
<th>

01_ Crystallization Kinetics_v1.0._AM_W4_D4.3.xlsx

</th> </tr>  
<tr>  
<td>

**Title of the dataset**

</td>  
<td>

Crystallization kinetics

</td> </tr>  
<tr>  
<td>

**Responsible Partner**

</td>  
<td>

UST

</td> </tr>  
<tr>  
<td>

**Work Package**

</td>  
<td>

WP4, Deliverable 4.3

</td> </tr>  
<tr>  
<td>

**Dataset Description**

</td>  
<td>

A dataset of protein nucleation rates data measured from probability
distributions of induction times

</td> </tr>  
<tr>  
<td>

**Dataset Benefit**

</td>  
<td>

The developed method for collection, processing and analysis of lysozyme data
will apply for measurement of nucleation kinetics of Anti-CD20 protein.
Furthermore, this data will be used to construct new heterogeneous protein
nucleation theories (Task 5.2) and to enable process scale up (Task 6.1)

</td> </tr>  
<tr>  
<td>

**Dataset Dissemination**

</td>  
<td>

This data will be the basis for a peer reviewed journal paper on the
heterogeneous crystal nucleation of the studied proteins

</td> </tr>  
<tr>  
<td>

**Type Format**

</td>  
<td>

Word, Excel, JPEG

</td> </tr>  
<tr>  
<td>

**Expected Size**

</td>  
<td>

< 100 MB

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

Experimental results

</td> </tr>  
<tr>  
<td>

**Repository**

</td>  
<td>

Zenodo

</td> </tr>  
<tr>  
<td>

**DOI (if known)**

</td>  
<td>

To be inserted once the dataset is uploaded to the repository

</td> </tr>  
<tr>  
<td>

**Date of Repository Submission**

</td>  
<td>

In this example the expected date is 30-9-2020 or before if used for
publication

</td> </tr>  
<tr>  
<td>

**Keywords**

</td>  
<td>

Protein Crystallization, Crystal Nucleation Rate, Induction Time Probability
Distributions, Template Crystallization, Heterogeneous Nucleation

</td> </tr>  
<tr>  
<td>

**Version Number**

</td>  
<td>

V1.0

</td> </tr>  
<tr>  
<td>

**Link to metadata file**

</td>  
<td>

</td> </tr> </table>

10

##  5.2 Making data openly accessible

Research data which is created in the project is owned by the partner who
generates it ( _GA_ _Art. 26_ ). Each partner must disseminate its results as
soon as possible unless there is legitimate interest to protect the results. A
partner that intends to disseminate its results must give advance notice to
the other partners (at least 45 days) together with sufficient information on
the results it will disseminate ( _GA Art. 29.1_ ).

Research data should be deposited in the Zenodo repository as soon as possible
unless a decision has been taken to protect results. Specifically, research
data needed to validate the results in the scientific publications should be
deposited in the data repository at the same time as publication ( _GA Art.
29.3_ ).

During embargo periods, information about the restricted data will be
published in the data repository, and details of when the data will become
available will be included in the metadata. Where a restriction on open access
to data is necessary, attempts will be made to make data available under
controlled conditions to other individual researchers.

There will be three restricted datasets within deliverable D6.3 (lead: CPI):

  * Anti-CD20 CHO cell line 

  

  * HEL4 E. coli strain 

  

  * Growth protocols (although basic data such as final product titre would be available). These datasets are proprietary to Fujifilm (FDB) and may only be used in the restricted application of making material to support the work of this project. As these activities are enabling aspects of the project allowing the production of industry relevant biomolecules 11 which can be used to develop the continuous crystallisation technology, it is not felt that restrictions will impact on eventual dissemination of the project outputs for the continuous crystallisation technology. CPI will archive the data as set out in Section 8: Data Security. 

  

In accordance with GA Art. 25, data must be made available to partners upon
request, including in the context of checks, reviews, audits or
investigations. Data will be made accessible and available for re-use and
secondary analysis.

The AMECRYS project has chosen to use Zenodo.org as the repository for storing
the project data:

  * _Research. Shared_ . — all research outputs from across all fields of research are welcome! Sciences and Humanities, really! 

  

  * _Citeable. Discoverable_ . — uploads gets a Digital Object Identifier (DOI) to make them easily and uniquely citeable. 

  

  * _Communities_ — create and curate your own community for a workshop, project, department, journal, into which you can accept or reject uploads. Your own complete digital repository! 

  

  * _Funding_ — identify grants, integrated in reporting lines for research funded by the European Commission via OpenAIRE. 

  

  * _Flexible licensing_ — because not everything is under Creative Commons. 

  

  * _Safe_ — your research output is stored safely for the future in the same cloud infrastructure as CERN's own LHC research data. 

  

Not all project partners have access to an institutional repository and the
use of Zenodo ensures data management procedures are unified across the
project. A project page (community) has been setup for easy upload of project
datasets _https://zenodo.org/communities/amecrys-project-eu._ Details of how
to access the data will be available on the project web-site _www.amecrys-
project.eu_ . Zenodo.org is open, free, searchable and structured with
flexible licensing allowing for storing all types of data: datasets, images,
presentations, publications and software. In addition, Zenodo allows
researchers to deposit both publications and data, while providing tools to
link them.

All the public data of the project will be openly accessible at the
repository. Non-public data will be archived at the repository using the
“closed access” option.

Data objects will be deposited in ZENODO under:

  * Open access to data files and metadata and data files provided over standard protocols such as HTTP and OAI-PMH. 

  

  * Use and reuse of data permitted. 

  

  * Privacy of its users protected. 

  

Since the data is being deposited in an external repository [Zenodo], a
dataset registry record should also be created in local host institutions
repositories e.g. PURE for UST. The registry record should include relevant
metadata explaining what data exists, and a DOI linking to where the data is
available in the external repository. Any data which is deposited externally
in a closed state, i.e. it is not accessible, should also be deposited in
Pure, so that 12 the University is still able to access the data. Here's an
example of a dataset registry record in Pure, which includes a description of
the dataset, and a DOI linking to where the data is available in the UK Data
Service: _https://pure.strath.ac.uk/portal/en/datasets/humour-stylesand-
bullying-in-schools(fff279ab-3b66-4e25-99f1-39122b58839c).html_

##  5.3 Making data interoperable

The AMECRYS project aims to collect and document the data in a standardised
way to ensure that, the datasets can be understood, interpreted and shared in
isolation alongside accompanying metadata and documentation.

Generated data will be preserved on institutional intranet platforms until the
end of the Project ( _see section 8_ ).

A metadata file will be created and linked within each dataset. It will
include the following information:

###  General Information

  * Title of the dataset 

  

  * Dataset Identifier 

  

  * Responsible Partner 

  

  * Author Information 

  

  * Date of data collection 

  

  * Geographic location of data collection 

  

  * The title of project and Funding sources that supported the collection of the data 

  

###  Sharing/Access Information

  * Licenses/access restrictions placed on the data 

  

  * Link to data Repository 

  

  * Links to other publicly accessible locations of the data • Links to publications that cite or use the data 

  

  * Was data derived from another source? 

  

13

###  Dataset/File Overview

  * This dataset contains X sub-dataset as listed below 

  

  * What is the status of the documented data? – “complete”, “in progress”, or “planned” 

  

  * Are there plans to update the data? 

  

###  Methodological Information

  * Used materials 

  

  * Description of methods used for experimental design and data collection: <Include links or references to publications or other documentation containing experimental design or protocols used in data collection>

  

  * Methods for processing the data: <describe how the submitted data were generated from the raw or collected data>

  

  * Instruments and software used in data collection and processing-specific information needed to interpret the data 

  

  * Standards and calibration information, if appropriate 

  

  * Environmental/experimental conditions 

  

  * Describe any quality-assurance procedures performed on the data 

  

  * Dataset benefits 

  

An example of a metadata file can be found in _Annex B._

##  5.4 Increase data re-use (through clarifying licences)

The datasets will be made available for re-use through uploads to the Zenodo
community page for the project.

In principle, the data will be stored in Zenodo after the conclusion of the
Project without additional cost. All the research data will be of the highest
quality, have long-term validity and will be well documented in order other
researchers to be able to get access and understand them after 5 years.

If datasets are updated, the partner that possesses the data has the
responsibility to manage the different versions and to make sure that the
latest version is available in the case of publically available data. Quality
control of the data is the responsibility of the relevant responsible partner
generating the data.

14

#  Allocation of resources

There are no immediate costs anticipated to make the datasets produced FAIR.
The datasets will be deposited in the Zenodo repository for at least 5 years
after the conclusion of the project. Any unforeseen costs related to open
access to research data in Horizon 2020 are eligible for reimbursement during
the duration of the project under the conditions defined in the Grant
Agreement, in particular Article 6 and Article 6.2.D.3.

Prof Joop ter Horst and Claire Lynch based at the University of Strathclyde
(UST) are responsible for data management within the AMECRYS project,
specifically for D7.2 creation of data management plan and D7.6 updating the
data management plan and ensuring the datasets are recorded. The PI of each
partner will have overall responsibility for implementing the data management
plan.

Each AMECRYS partner should respect the policies set out in this DMP. Datasets
have to be created, managed and stored appropriately and in line with European
Commission and local legislation. Dataset validation and registration of
metadata and backing up data for sharing through repositories is the
responsibility of the partner that generates the data in the WP.

The datasets in Zenodo will be preserved in line with the European Commission
Data Deposit Policy. The data will be preserved indefinitely (minimum of 5
years) and there are currently no costs for archiving data in this repository.

15

#  Data security

For the duration of the project, datasets will be stored on the responsible
partner’s centrally provided storage, detailed in the table below.

**Table 8: Data Storage**  
  
<table>  
<tr>  
<th>

**Short Name**

</th>  
<th>

**Data Storage**

</th> </tr>  
<tr>  
<td>

CNR

</td>  
<td>

All data is stored at the same time in internal servers of CNR-ITM, CNR-IC and
CNR-IAC located in Rende and Bari, 300 km far each other. Data would also be
fully copied in cloudbased repositories once provided by the central ICT
system of CNR. Selected data is also stored in cloud-based repositories
(Dropbox, Google drive) for sharing easily.

</td> </tr>  
<tr>  
<td>

IMP

</td>  
<td>

Non-sensitive data is stored in the “Home directory” of Imperial College
London. Data stored on H: drives is secure and backed up daily, so a deleted
file can be restored within 24 hours. More information can be found at
_https://www.imperial.ac.uk/adminservices/ict/self-service/connect-
communicate/file-storage/home-directory-h-drive/_ . Sensitive data can be
encrypted and then stored in the “Home directory” of Imperial College London.
More information can be found at
_https://www.imperial.ac.uk/adminservices/ict/self-service/be-secure/protect-
college-personal-information/encryption/_

</td> </tr>  
<tr>  
<td>

UCAL

</td>  
<td>

At University of Calabria, data storage is managed by the ICT Center (“Centro
ICT di Ateneo”, c.ict@unical.it). The ICT Center is in charge of Security
Analysis of Applications (AsIA), Virtual Desktop Infrastructure Service
(Cloud-VDIS), S.O. Microsoft Windows/ S.O.

Linux Debian Servers, High Performance Computing (HPC), Institutional Research
Information System (Cineca IRIS, an innovative best-of-breed solution has been
designed to fulfil the needs of academic and research institutions: IRIS is an
IT platform that makes it easy to collect and manage data on research
activities and outputs within an organization,
_https://www.cineca.it/en/content/iris-institutional-research-
informationsystem_ ).

</td> </tr>  
<tr>  
<td>

CNRS

</td>  
<td>

All data are either stored on Solvay’s data repositories which are managed by
the IT service of Solvay (with no access from outside) or on cloud-based
repositories such as MyCore or Google drive for sharing easily the data.

</td> </tr>  
<tr>  
<td>

ULB

</td>  
<td>

All code and resultant data are stored in git repositories that are fully
copied across numerous computers both on site at the ULB and off site.
Selected data is also stored in cloud-based repositories (Dropbox, Google
drive, ...).

</td> </tr>  
<tr>  
<td>

UST

</td>  
<td>

Data stored on the University of Strathclyde’s storage is dual sited and
replicated between two data centres which are physically separated by several
hundred metres. Data links between datacentres are provided by dual disparate
fabrics, providing added resilience. Additionally, the central I.T. service
provides tape based backup to a third and fourth site. Data security is
provided by access controls defined at a user level. The data will be stored
on network drives _http://www.strath.ac.uk/it/filestore/_

</td> </tr>  
<tr>  
<td>

CPI

</td>  
<td>

The company has an IT group who have responsibility for IT infrastructure and
data security. Electronic data is stored locally on network drives and/or data
base systems (IDBS). Data is backed up daily to tape and stored in fireproof
safes.

</td> </tr>  
<tr>  
<td>

GVS

</td>  
<td>

GVS has an internal archive where all proprietary data are stored. Moreover,
all data are stored in secondary secure archives that are backed up every
night.

</td> </tr>  
<tr>  
<td>

FDB

</td>  
<td>

Project data is stored on the internal intranet servers. FDB uses Commvault
software to back up files on a nightly basis. This is a cloud-based solution
and data is backed up to a data centre in London. Data on this server is also
covered with our data recovery procedure which is replicated real time to the
same data centre to enable remote access should we lose the server room.

</td> </tr> </table>

16

Following completion of the project, all the responsibilities concerning data
recovery and secure storage will go to the repository storing the dataset.
Data will be archived and preserved in the Zenodo data sharing repository.
This provides options for making data openly available and other data
restricted access as required.

#  Ethical aspects

AMECRYS partners are to comply with the ethical principles as set out in
_Article 34 of the_ _Grant Agreement_ , which states that all activities must
be carried out in compliance with:

  1. ethical principles (including the highest standards of research integrity — as set out, for instance, in the European Code of Conduct for Research Integrity (European Science Foundation, 2011) — and including, in particular, avoiding fabrication, falsification, plagiarism or other research misconduct) and 

  

  2. applicable international, EU and national law. 

  

  

The AMECRYS project does not involve the use of human participants or personal
data in the research and therefore there is no requirement for ethical review.

**Confidentiality**

AMECRYS partners must retain any data, documents or other material as
confidential during the implementation for the project. Further details on
confidentiality can be found in _Article_ _36 of the Grant Agreement_ along
with the obligation to protect results in _Article 27_ .

17

#  Other issues

As well as European Commission policies on open data management, Project
Partners must also adhere to their own institutional policies and procedures
for data management:

**IMP**

_https://www.imperial.ac.uk/admin-services/ict/self-service/be-secure/protect-
collegepersonal-information/sensitive-info/recommended-options/_
_https://www.imperial.ac.uk/admin-services/ict/self-service/be-secure/protect-
collegepersonal-information/encryption/_

**UCAL**

Source: _http://www.unical.it/portale/ateneo/stat_reg/_

  * Regolamento per la gestione dell'innovazione e della proprietà intellettuale e industriale. 

  

Rectoral Decree n.1597, 19/10/2015

  * Codice di comportamento dell'Università della Calabria. Rectoral Decree n. 2653, 23/12/2014 

  

**UST**

_http://www.strath.ac.uk/staff/policies/informationsecurity/_
_http://www.strath.ac.uk/media/ps/cs/gmap/academicaffairs/policies/research_code-
of_practice_-_

__May_2010.pdf_

_http://www.strath.ac.uk/media/ps/cs/gmap/academicaffairs/policies/Research_Data_Policy_v1.pdf_
_http://www.strath.ac.uk/media/ps/cs/gmap/academicaffairs/policies/Research_Data_Policy_

__for_website.pdf_

18

**CPI**

IT policies for the company are set out in written policies which are subject
to periodic review

**FDB**

FDB has its own set of internal policies and procedures on data management.