#  1\. DATA SUMMARY

**What is the purpose of the data collection/generation and its relation to
the objectives of the EVO-NANO?**

Long term vision of EVO-NANO project is to create an integrated platform for
the artificial evolution and validation of novel DDS for cancer treatment
using NP. Within EVO-NANO we defined four specific objectives:

**Objective 1** : To develop a new class of open-ended evolutionary algorithms
to creatively assess different cancer scenarios and autonomously engineer
effective NP-based solutions to them in a novel way.

**Objective 2:** To implement a computational platform for the autonomous
generation of new strategies for targeting CSC surface receptors using
functionalized NPs. In its final form our model will simulate all the main
aspects of NP dynamics: their travel via blood streams, extravasation, tumour
penetration and endocytosis.

**Objective 3:** To streamline synthesis of functionalized NPs suggested by
the computational platform.

**Objective 4:** To develop an integrated platform for validation of efficacy
of the artificially evolved nanoparticle designs. It will be composed of (i)
tumour microenvironments on microfluidic chips that will mimic major
physiological barriers for NP tumour delivery and (ii) in vivo pre-clinical
tests.

Reaching any of these objectives will require generation and/or collection of
specific data:  
  
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Collection of available data on physico-chemical NP properties, nonspecific
chemical interactions of functionalized NPs in the bloodstream, extravasation
of NPs, interaction of NPs with tumour cells, behaviour of NPs within tumour
cells (Objective 1);

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Source code data (Objective 1 & 2) ;

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Simulation output files (Objective 2) ;

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Analysis of simulation output files (Objective 2) ;

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Characterization of synthesized NPs (Objective 3) ;

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− Results of _in vitro_ and _in vivo_ tests (Objective 4).

In summary, data collection/generation follows different procedures for each
of the proposed objectives and within EVO-NANO we will create at least 7
separate datasets. Since reusability of datasets between different research
groups is essential for the success of the project, development of DMP is
equally important.

**What types and formats of data will the project generate/collect?**

  * Data from computer models, represented as text, binary or graphics files and videos. 

  

  * Data from clinical assessment of the treatment. 

  

  * Measurements in biological matrices/tissues. 

  

  * Molecular and chemical data on nanoparticles developed, including core and coating . 

  

  * Exposure data: internal biomarkers of exposure. 

  

The Open Research Data Pilot applies to two types of data:

  1. the data, including associated metadata, needed to validate the results presented in scientific publications as soon as possible. 

  

  2. other data, including associated metadata, as specified and within the deadlines laid down in the data management plan–that is, according to the individual judgement by each project group. 

  

  

According to the “Guidelines on Data Management in Horizon 2020” (2015) the
DMP describes the handling of numerical datasets processed or collected during
EVO-NANO lifetime. The DMP include clear descriptions and rationale for the
access regimes that are foreseen for collected data sets. Thus the DMP leaves
explicitly open the handling, use and curation of products like tools,
software and written documents, which could also be subsumed under the generic
term “data”; we restrict the focus of our DMP to numerical data products like
produced model data or observation data.

**Formats of the data:**

  * Data and metadata will be requested, stored and transferred (across partners and in EVONANO) in a comma separated values (CSV) format. 

  

  * To facilitate the data exchange, MS Excel compatible files including comma separated and .xls(x) format will be also accepted. 

  

  * For statistical purposes, other formats include .sas7bdat (SAS), .RData (R), .SAV (SPSS), .mat (matlab). 

  

  * Where applicable data formats may be migrated when new technologies become available and are proved robust enough to ensure digital continuity and continued availability of data 

  

We will follow the following guidelines:

  * Guidelines on Data Management in Horizon 2020, Version 2.0, 30 October 2015: 

  

_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h202_
_0-hi-oa-pilot-guide_en.pdf_

  * Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020, 

  

Version 2.0, 30 October 2015:

_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h202_
_0-hi-oa-pilot-guide_en.pdf_

  * Webpage of European Commission regarding Open Access:  _http://ec.europa.eu/research/science-society/open_access_

  

**Will you re-use any existing data and how?**

To develop NP simulations we will re-use available published data on:  
  
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physico-chemical NP properties,

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nonspecific chemical interactions of functionalized NPs in the bloodstream,

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extravasation of NPs,

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interaction of NPs with tumour cells,

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behaviour of NPs within tumour cells,

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and use them to define model boundary conditions.

**What is the origin of the data?**  
  
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Written source code;

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Published scientific articles;

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Outputs of _in silico_ experiments;

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Outputs of tools for mathematical analysis;

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Experimental _in vivo_ and _in vitro_ test;

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Characterization results of synthesized NPs.

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**What is the expected size of the data?**

  * Numerical data related to optimisation c. 250 Gb per project’s lifetime; 

  

  * Full history of the 3D models of nano-particle swarm: c. 5Tb; 

  

  * Videos of the computer models and animation of results: c. 10Tb; 

  

The above are estimates. To be evaluated during the course of the project. The
expected size depends on the extend and the nature of the data that are made
available.

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

  * EVONANO consortium; 

  

  * European Commission services and European Agencies; 

  

  * EU National Bodies; 

  

  * The general public including the broader scientific community 

  

  * Manufacture of nanoparticle based treatment 

  

  * Clinicians using the nanoparticles 

  

#  2\. FAIR DATA

##  2\. 1. MAKING DATA FINDABLE, INCLUDING PROVISIONS FOR METADATA

**Are the data produced and/or used in the project discoverable with metadata,
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object Identifiers)?**

Yes. All EVO-NANO generated data are stored at Zenodo repository (
_https://zenodo.org/_ ). Zenodo closely follows FAIR principles:  
  
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Each uploaded dataset gets DOI;

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Metadata for individual records are retrievable by their identifier using a
standardized communications protocol;

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Metadata are publicly accessible and licensed under public domain. No
authorization is ever necessary to retrieve it;

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Metadata use a formal, accessible, shared, and broadly applicable language for
knowledge

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representation

All data will be discoverable via medata provision. All data will be
identifiable and referable via standard identification mechanism (DOI). A
unique online naming conventions are adopted. The data will be searchable by
keywords. Clear versioning will be in place.

**What naming conventions do you follow?**

To be able to clearly distinguish and identify data sets, each data set is
assigned with a unique name. To design the data set names, we use the
following procedure:

_FieldIdentifier.CountryCode.PartnerName.DatasetName_ , where

  1. _FieldIdentifier_ defines subfield within which data are produced 

  

  2. The _CountryCode_ part represents the country associated with the dataset using ISO Alpha-3 country codes: 

    1. FIN for Finland 

  

    2. POL for Poland 

  

    3. SRB for Serbia _iv._ ESP for Spain 

  
  

  

  

_v._ GBR for United Kingdom

  3. The _PartnerName_ part represents the name of the organization associated with the dataset: 

    1. UNSPF for Univerzitet u Novom Sadu, Poljoprivredni fakultet Novi Sad 

  

    2. UNIVBRIS for University of Bristol 

  

    3. UWEBRISTOL for University of the West of England iv. AAU for Abo Akademi 

  
  

    5. IMDEANANO for Fundacion IMDEA Nanociencia 

  

    6. PCS for Prochimia Surfaces SP. ZO.O. vii. VHIR for Fundacio Hospital Universitari Vall D’Hebron – Institut de Recerca 

  
  

  

  4. The _DatasetName_ represents the full name of the dataset. 

  

  

**Will search keywords be provided that optimize possibilities for re-use?**

Yes.The dataset information reported into the metadata fiche will be published
in EVO-NANO, where specific filters, based on the metadata elements, will
allow to refine the search across datasets (e.g. search dataset by chemical or
chemical group, by temporal or spatial coverage of the data, by key words,
etc.

**Do you provide clear version numbers?**

Yes. The versioning management of the data, metadata template and in general
the files stored into the Repository will be applied at two levels:

  1. Via the naming convention and the use of the date as suffix, indicating the last version of the file uploaded into the Repository; 

  

  2. As capabilities of the Zenodo Repository sets up for the project, since the solution supports the simple version control system for the uploaded files **What metadata will be created?**

  

  

We will adopt a metadata scheme used to describe chemical monitoring data
collections, e.g.:

chemical occurrence data collected as result of legal obligations on adhoc or
regular basis for reporting /monitoring at European or national levels;

data generated as result of targeted research on the presence of known or
unknown chemical substances in specific media in a European country/region.

Metadata is compliant with two European metadata standards, namely:

  1. INSPIRE metadata elements for spatial data sets and services (see these elements in the INSPIRE Metadata Regulation: http://data.europa.eu/eli/reg/2008/1205/oj#d1e600-14-1) 

  

  2. The "DCAT application profile for European data portals" (DCAT-AP), developed in the framework of the EU ISA Programme. 

  

  

The European Data Portal is implementing the DCAT-AP as the common vocabulary
for harmonising descriptions of datasets harvested from several data portals
of 34 countries. The DCAT-AP specification is available at:
https://joinup.ec.europa.eu/asset/dcat_application_profile/

###  2.2. MAKING DATA OPENLY ACCESSIBLE

**Which data produced and/or used in the project will be made openly available
as the default?**

When no embargo period applies and a data package related to a case study has
been marked as public, it will be made openly available. Only data gathered by
partners outside of the project work plan and protected by IPR, or inside the
work plan but containing confidential information (e.g. patent application)
will be kept closed until those results are necessary to protect, in
accordance to

Articles 27, 29, 36, 37 and 39 of the EVO-NANO Grant Agreement number 800983\.
These principles will apply to the following inclusive but not an exhaustive
list of sets of data produced by the EVONANO:

  * Material science data on nanoparticles 

  

  * Results of computer modelling 

  

  * Optimisation analysis and results 

  

  * Results of experimental laboratory trials 

  

  * Results of clinical studies 

  

**How will the data be made accessible (e.g. by deposition in a repository)?**

All generated datasets within EVO-NANO will be uploaded to Zenodo repository

( _https://zenodo.org/_ ).The data sharing should occur in a timely
fashion.This means that the data resulted from the research conducted in the
project should become available close to the project results themselves.
Furthermore, it is reasonable to expect that the data will be released in
waves as they become available or as main findings from waves of the data are
published.

**What methods or software tools are needed to access the data?**

Since Zenodo stores data as publicly accessible, the only requirement is
internet access. With regards to open software, all the data needed to create
and maintain the marketplace is being made openly accessible through the
GitHub repository, along with the corresponding technical documentation.

**Is documentation about the software needed to access the data included?**

No documentation is required. Online help provided with existing browser is
sufficient.

**Is it possible to include the relevant software (e.g. in open source
code)?**

Software will be shared via GitHub, which is directly linked with Zenodo.

**Where will the data and associated metadata, documentation and code be
deposited?** _Preference should be given to certified repositories which
support open access where possible._

The consortium agreed to deposit the data generated by the project in Zenodo,
publications in arXiv, and software in GitHub unless for a specific project
there is a subject specific repository that is considered more relevant.

**Have you explored appropriate arrangements with the identified repository?**

Yes, the arrangement are tested by the Partners in their other projects.

**If there are restrictions on use, how will access be provided?**

There are not restrictions to us. To access data no registration at arXiv,
Zenodo or GitHub is required.

**Is there a need for a data access committee?**

There is no need for a data access committee because sharing of data is agreed
straightforwardly.

**Are there well described conditions for access (i.e. a machine readable
license)?**

Potential users will find out about the data through publications and the
website. Data will be made available on publication of the associated paper
and will be made accessible on request, under conditions agreed on a case-by-
case basis, and after agreement of the project consortium.

**How will the identity of the person accessing the data be ascertained?**

The identity of the person accessing the date will not be ascentain because
their access is anonymous.

###  2.3. MAKING DATA INTEROPERABLE

**Are the data produced in the project interoperable, that is allowing data
exchange and re-use between researchers, institutions, organisations,
countries, etc. (i.e. adhering to standards for formats, as much as possible
compliant with available (open) software applications, and in particular
facilitating re-combinations with different datasets from different
origins)?**

All data produced will have transparent formats: publications in PDF, compute
code in Phython/C++/Processing/Java, results of numerical simulation in CSV,
animations and videos in AVI/MP4.

**What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?**

Other types of data have been registered following internal codifications,
clearly specified within the file.

**In case it is unavoidable that you use uncommon or generate project specific
ontologies or vocabularies, will you provide mappings to more commonly used
ontologies?**

Not applicable.

###  2.4. INCREASE DATA RE-USE (THROUGH CLARIFYING LICENCES)

**How will the data be licensed to permit the widest re-use possible?**

The deliverables associated to the dataset are licensed through an All rights
reserved license as they are working papers not intended to be re-used.
Nevertheless the database should be shared as a possible reusable dataset. For
this reason, when deposited to the repository, an AttributionNonCommercial
license (by-nc) will be requested. The data is currently available for re-use
from the project website and will also be findable and reusable through the
final depositing repository (the institutional one or Zenodo) and from
OpenAire, the latest by the end of the project.

**When will the data be made available for re-use?** _If an embargo is sought
to give time to publish or seek patents, specify why and how long this will
apply, bearing in mind that research data should be made available as soon as
possible._

The data will remain re-usable after the end of the project by anyone
interested in it, with no access or time restrictions.

**Are the data produced and/or used in the project useable by third parties,
in particular after the end of the project?** _If the re-use of some data is
restricted, explain why._

Each archived data set will have its own permanent repository ID and will be
easily accessible. We expect most of the data generated to be made available
without restrictions and only data sets subject to IPR and confidentiality
issues will be restricted. Where this is going to be the case, agreements will
be made based on the individual data sets. Requests for the use of the data by
externals will be approved by the project consortium.

**How long is it intended that the data remains re-usable?**

Data and metadata will be retained for the lifetime of the Zenodo repository.
This is currently the lifetime of the host laboratory CERN, which currently
has an experimental programme defined for the next 20 years at least.

**Are data quality assurance processes described?**

The data quality is ensured by different measures. These include validation of
the sample, replication and comparison with results of similar studies and
control of systematic distortion.

#  3\. ALLOCATION OF RESOURCES

**What are the costs for making data FAIR in your project?**

Exact costs estimated will be known and adjusted dynamically during the
project’s lifetime.

**How will these be covered?** _Note that costs related to open access to
research data are eligible as part of the Horizon 2020 grant (if compliant
with the Grant Agreement conditions)._

The costs for depositing the dataset with the project, and subsequent
resources required to make the dataset publicly available have been included
within specific WPs within the project.

**Who will be responsible for data management in your project?**

The project coordinator has the ultimate responsibility for the data
management in the project and so, for the Marketplace platform management.

**Are the resources for long term preservation discussed (costs and potential
value, who decides and how what data will be kept and for how long)?**

Due to the data being shared via public repositories, the preservation beyond
lifetime of the project does not involve any costs.

#  4\. DATA SECURITY

**What provisions are in place for data security (including data recovery as
well as secure storage and transfer of sensitive data)?**

Due to the data volume, zenodo also hold a copy of their own processed data,
effectively acting as a second distributed database and additional backup.
Locally, within each partner, all data will be stored at backup external hard-
disks.

**Is the data safely stored in certified repositories for long term
preservation and curation?**

The digital signature of the whole dataset, or the storage of the dataset in a
git repository could provide support for the correct duplication and
preservation. In addition zenodo operates with 12hourly backup cycle with one
backup sent to tape storage once a week.

#  5\. ETHICAL ASPECTS

**Are there any ethical or legal issues that can have an impact on data
sharing?** _These can also be discussed in the context of the ethics review.
If relevant, include references to ethics deliverables and ethics chapter in
the Description of the Action (DoA)._

The ethical aspects related to the personal data collected in this dataset are
addressed in the Ethics Requirements document of the original proposal.
Regarding the protection of personal data of the research participants, the
Consortium will meet the following conditions:

  * To submit to the REA the copies of ethical approvals for the collection of personal data by each of the competent University Data Protection Officers or National Data Protection authorities. 

  

  * To justify (if necessary) the collection and/or processing of personal sensitive data. 

  

  * To follow and accomplish the national and EU legislation on the procedures that will be implemented for data collection, storage, protection, retention and destruction. 

  

**Is informed consent for data sharing and long term preservation included in
questionnaires dealing with personal data?**

Not applicable in this project.

#  6\. OTHER ISSUES

**Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?**

Through the use of the institutional repository, we are also following these
procedures for data management Partner countries, including the Partner
countries Research Council’s common principles on data policy provide an
overarching framework for individual Research Council policies on data policy.

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