#  1\. PROJECT DESCRIPTION

##  1.1. ABSTRACT

A definitive conclusion about the dangers associated with human or animal
exposure to a particular nanomaterial can currently be made upon complex and
costly procedures including complete NM characterisation with consequent
careful and well-controlled in vivo experiments. A significant progress in the
ability of the robust nanotoxicity prediction can be achieved using modern
approaches based on one hand on systems biology, on another hand on
statistical and other computational methods of analysis. In this project,
using a comprehensive self-consistent study, which includes invivo, in-vitro
and in-silico research, we address main respiratory toxicity pathways for
representative set of nanomaterials, identify the mechanistic key events of
the pathways, and relate them to interactions at bionano interface via careful
post-uptake nanoparticle characterisation and molecular modelling. This
approach will allow us to formulate novel set of toxicological mechanism-aware
endpoints that can be assessed in by means of economic and straightforward
tests. Using the exhaustive list of end-points and pathways for the selected
nanomaterials and exposure routs, we will enable clear discrimination between
different pathways and relate the toxicity pathway to the properties of the
material via intelligent QSARs. If successful, this approach will allow
grouping of materials based on their ability to produce the pathway-relevant
key events, identification of properties of concern for new materials, and
will help to reduce the need for blanket toxicity testing and animal testing
in the future.

##  1.2. PROJECT COORDINATION

Dr. Vladimir Lobaskin, School of Physics, University College Dublin, Belfield,
Dublin 4, Ireland. _vladimir.lobaskin@ucd.ie_ Phone: +353-1-716-2432, Fax:
+353-1-283-7275.

**Data Manager:**

Philip Cotter, Information Manager, Systems Biology Ireland, Science Link
Building, University College Dublin, Belfield, Dublin 4, Ireland.
_philip.cotter@ucd.ie_ Phone: +353-1-716-6310.

##  1.3. LIST OF PARTICIPATING ORGANISATIONS

  1. University College Dublin / National University of Ireland, Dublin, Ireland. 

  

  2. Stockholm University, Sweden. 

  

  3. Helmholtz Zentrum München, Germany. 

  

  4. National Research Centre for the Working Environment, Denmark. 

  

  5. French National Institute for Occupational Health, France. 

  

  6. Jozef Stefan Institute, Slovenia. 

  

  7. Environmental Health Centre, Canada. 

  

  8. Imperial College London, UK. 

  

  9. University of Lorraine, France. 

  

  10. Biovia, 3DS, France. 

  

  11. Victrocell Systems, Germany 

  

  

##  1.2. DEFINITIONS

_**AOP** _ – Adverse Outcome Pathway: representation of biological pathways
leading to adverse effects; _**Data Archive –** _ A device or service where
machine-readable data are acquired, managed, documented, and finally
distributed to the wider scientific community;

_**DOI** _ – Digital Object identifier – a persistent data-resource specific
identifier used to uniquely identify an entity;

_**MIE** _ – Molecular Initiating Events – the interaction if a molecule and
biomolecule/biosystem which can be observed to trigger an AOP;

_**NM, NP** _ – Nanomaterial, nanoparticle;

_**Open Data** _ – Research data made available to the wider scientific
community as evidence to support the interpretation of scientific discoveries.
Open data is described and contextualised for use in reanalysis or re-use in
further experimentation and is hosted by publicly accessible data archives;

_**Data –** _ Fact-based material generally accepted in the scientific
community as necessary to document and support research findings. Research
Data include digitally stored raw experimental data, digital laboratory
notebooks, preliminary datasets and data analysis and draft documentation such
as reports, publications and project-related communications;

_**SNT** _ / _**SmartNanoTox** _ – SmartNanoTox synonyms for the project title
‘Smart Tools for Gauging Nano Hazards’.

#  2\. DATA SUMMARY

##  2.1. PURPOSE OF THE DATA COLLECTION AND GENERATION

A heterogeneous collection of dry and wet lab experimental data has and will
be generated over the four years of the Project. These will form a basis for
mechanism-based tools for nanomaterial (NM) toxicity assessment. The project
aims at prediction of specific pulmonary adverse outcome pathways (AOPs)
caused by NM exposure with a focus on Molecular Initiating Events (MIE) and
Key Events (KE) of these AOPs. The key to predicting these events is in
understanding the interactions at bionano interface. Therefore, a significant
part of the project will be devoted to description of the pathways using
‘omics approaches and statistical models from Systems Biology and to
description of the state of NMs after contact with organisms and tissues.

##  2.2. DATA TYPES & FORMATS

The project will generate multiple data types stored in both standardised and
not yet standardised but community defined set of data formats. A description
of each data generation process within the project is tabulated in Appendix A.
Data from _in vivo_ and _in vitro_ experiments will be recorded in text and
spreadsheet forms before conversion into corresponding NANoREG templates
developed by eNanoMapper (  _http://www.enanomapper.net/_ )  . Due to the
novel nature of this project data, curation and design will be subject to
modification during the data aggregation stage.

Data from _in vivo_ and _in vitro_ NM exposure Transcriptomics and Proteomics
and lipidomics data from analysis of NP corona protein structure will consist
of raw experimental data stored in bespoke formats and manipulated by
Manufacturer developed tooling. Analysis configuration files (typically XML),
machine and laboratory generated metadata files and results, both initial
machine read files and post-processing with analytical software/protocols
(typically coma or tab delimited text formats) will also be provided for use
by project partners and potential reuse by others. Along with Histology data
and reports, these data will also be converted into eNanoMapper-NANoREG
inherited MS Excel templates for incorporation into the eNanoMapper Ontology.

Where eNanoMapper templates do not yet exist, wider community standards will
be applied. Results from Fluorescence Microspetroscopy (FMS) derived images
and their documentation, simulation data, Gene Regulatory Networks and AOP-
representing computer models, descriptors from _in silico_ computed NP
structure and descriptors of measured NP toxicity data will be stored in ISA-
TAB Nano or JSON formats with all raw experimental data and documentation
preserved in the formats produced by their analysis software.

##  2.3. IMPORTED DATA RESOURCES

For NM characterisation, bionano interactions modelling and construction of
the QSAR models, we will use NM characterisation data imported from two
previous projects: NANoREG

( _http://www.nanoreg.eu/_ )  (now succeeded by NanoReg2 (
_http://www.nanoreg2.eu/_ )  ) and NanoGenoTox (
_https://www.anses.fr/fr/node/120284_ )  . Data obtained from NANoREG and
NanoReg2 are made freely usable and shareable for non-commercial applications
vi  a _CC-BY-NC-4.0_ .

Computational construction of biological pathways and Transcriptomics analysis
use entity and interaction identifiers imported from the Gene Expression
Omnibus (GEO)

( _https://www.ncbi.nlm.nih.gov/geo/_ )  and NCBI GenBank. Proteomics data
will use human and mouse protein identifiers derived from the EMBL-EBI UniProt
database (  _http://www.uniprot.org/_ )  . Molecular simulations will use
Protein structure data imported from the Protein Data Bank  (
_http://www.rcsb.org/pdb/home/home.do_ )  , which is made available by  _CC-
BY-4.0_ .  The Chemical Information Ontology, a semantic representation of
relevant NP entities and relationships will also be imported and expanded upon
from the eNanoMapper Ontology (GNU Lesser GPL

( _http://www.gnu.org/licenses/lgpl.html_ )  .

Biological entities will use DOIs inherited from the Bioinformatics databases
used in the analysis of experimental data. Those currently in use are included
in the licence and usage policy table (Appendix B) and described in more
detail in the section “ _**DOI and Nomenclature Used** _ ”.

##  2.4. ESTIMATED DATA VOLUME

The expected size of the data varies depending on the type of experiment. The
largest volume should be reserved for Mass-Spectrometry data (~150 GB raw data
and analytical metadata) and from NP Protein corona analysis and molecular
simulation data (ca. 50-100 GB). All other combined data should not be very
space-demanding.

##  2.5. DATA USE AND RE-USE POTENTIAL

iQSAR data, combining the experimental data accumulated; ontologies and models
developed over the four years over the project may be used to:

  1. provide a transparent Scientific basis for the drafting of regulations on NM use in manufacturing: the data will contribute to the development of robust systems for evaluating the health and environmental impact of engineered NMs by developing a systematic framework into which existing and emerging experimental data can be utilised to predict the interactions of NMs of industrial and economic significance with living systems. 

  

  2. reduce the financial cost of and need for animal testing currently required before a new NM may enter the market: the data and predictive tooling provided by the project may form a robust basis of a future screening strategy to predict the health impact of new NMs. Other approaches, based on learning approaches in databases, though necessary also, would less easily reduce the need for empirical testing for the broad range of entirely novel materials. 

  

  3. contribute to predictive models for designing and engineered NMs that are safe by design: by relating the physicochemical properties of NMs with their biological fate (uptake and localization) and behaviour (functional impacts and potential toxicity) we will enable the categorization of NMs by physicochemical factors that constitute a “risk” either for bioaccumulation or potentially a specific toxicity endpoint. Feeding this information back into the process development will enable manufacturers to quickly screen out particles with physicochemical properties that equate with a risk, and either develop new particles or reengineer their products to modify their properties, thereby designing out the risk factors initially, and in the longer term potentially designing the NMs to be safe. 

  

  

#  3\. FAIR DATA COMPLIANCE

##  3.1. MAKING DATA FINDABLE

It is envisaged that all data collected by the consortium will be collated
together in a flexible data structure accommodating OWL Ontologies, open
source conventional MySQL RDBMS and the JSONlike MongoDB (
_https://www.mongodb.com/_ )  NoSQL database.

Data files, models and other resources used or created will be assigned a
unique and persistent identifier linked to a title, creator and creation data
attributions as well as a brief text description of content, provenance, work
package affiliation, input format and compatible formats for possible export.
Experimental data file content will be indexed and linked to their curated
analysis metadata files. Described NPs, NMs and biological entities will use
reference DOIs inherited from the external ontological and bioinformatics
databases used in the analysis of experimental results. Access, though
restricted during the development stage will be opened to the community
allowing the flow of information both into and out of the SNT database and
iQSAR system.

Though all project data will be made available on UCD School of Physics
Servers and via Zenodo, ‘omics data that has been published or is of
particular interest to the research community will be submitted to community
repositories and annotated as per the requirement of the resources to with
they are submitted, e.g. PRIDE Mass Spec data submissions require a verbose
description of Sample and Data Processing Protocols accompanying the upload of
experimental, configuration, analysis and results files. Keyword indexes built
on these submitted text descriptions are searchable from ProteomeCentral (
_http://proteomecentral.proteomexchange.org/cgi/GetDataset_ )  . Thus, placing
data in resources built for data redistribution and most commonly queried by
domain specialists.

###  3.1.1. METADATA PROVISION

Metadata will be provided to describe the data resources created.
Documentation (READMEs) will accompany any tooling or data resources created
and made available from the SNT and Zenodo file servers.

All experimental datasets added to the curated iQSAR system will be described
in terms derived from and added to the eNanoMapper Ontology. The computational
tools needed to evaluate the NM characteristics will be described according to
EMMC guidance using MODA templates.

Journal Publications and dataset submitted to community repositories will also
provide an annotated community interface for the datasets produced.

###  3.1.2. DOIs & NOMENCLATURE USED

A heterogeneous collection of data sources and types requires a large and
potentially expanding variety of Bioinformatics and Semantic tools for
information representation and disambiguation. A framework will be built
around the core data resources: QSAR (  _https://www.qsartoolbox.org/_ )  ,

NANoREG (  _http://www.nanoreg.eu/_ )  Terminology\eNanoMapper Ontology  (
_http://www.enanomapper.net/_ )  , PDB (
_http://www.rcsb.org/pdb/home/home.do_ )  , UniProt Knowledgebase (
_http://www.uniprot.org/_ )  , GenBank (
_https://www.ncbi.nlm.nih.gov/genbank/_ )  and GeneOntology (
_http://www.geneontology.org/_ )  .

NP and NM identifiers are inherited from QSAR and the eNanoMapper Ontology;
information describing the composition of the Corona which develops around a
NP when NPs are exposed to living material are described using DOIs from PDB
and UniProt (Proteomics). Gene Regulatory Networks will use interaction
relationships described by GEO data using gene DOIs from GenBank
(Transcriptomics). Lipid nomenclature or DOI systems have yet to be
investigated for selection.

‘Omics interaction Network analysis will use these DOIs, which are
bidirectionally linked within each respective database. Coarse-grained network
descriptions garnered from text-data mining of peerreviewed texts will be
screened by domain experts and recorded using _recommended names_ linked to
the GenBank and UniProt Bioinformatics databases.

###  3.1.3. KEYWORD SEARCH

Development of SNT database: The database design will consider the main
challenges of data integration and interoperability in Big Data that have been
identified in the literature and, in particular, will follow the
recommendations of Kadadi, A., Agrawal, R., Nyamful, C. and Atiq, R., 2014,
October. Challenges of data integration and interoperability in big data. In
_Big Data (Big Data), 2014 IEEE_

_International Conference on_ (pp. 38-40).  (
_http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7004486_ )  which uses
algorithmic integration of data from disparate data formats.

Using a hybrid RDBMS and NoSQL database implementation, we will attempt to
solve for the available data the challenges of i) the scope of data; ii) data
consistency; iii) optimised data querying; iv) acquire adequate information
and data processing resources and v) scalability. By having a dedicated data
manager, we also ensure that the database will be properly maintained and that
the users’ needs are taken into account when developing the various tools and
resources required. The data manager will participate in the NanoSafety
Cluster focus groups, tasked with ensuring data standardization, integration,
curation and adherence to current SOPs in the data science community.

###  3.1.4. DEVELOPMENT OF THE SMARTNANOTOX DATABASE

To provide access to project digital data resources for all partners and
facilitate the data analysis and integration the data manager will develop a
portal to the specific requirements of the project. Webcontent, various media
and more advanced project information retrieval will eventually be made public
to facilitate the re-use of the data generated in the consortium by the
scientific community. This will form the basis of the iQSARs.

To facilitate the access of all this data a bespoke user-friendly interface
will be developed. Although the final features of the portal will be
determined by the specified requirements of the consortium partners and the
access restriction imposed upon them by publication. The portal will provide
customisable access to correlation data for different NM, including
physicochemical properties, toxicology data and other meaningful data as
required.

The data will be archived on available storage resources using the
nomenclature following the recommendations of NANoREG final report to ensure a
minimal pre-requisite for system knowledge for research scientists to extract
relevant and detailed information using typical domain nomenclature.

###  3.1.5. VERSIONING

The digital capture of experimental data will typically only be produced and
processed once. Version numbers will not be required as the data resources are
not incrementally built. These resulting datasets will be assigned unique IDs
in the iQSAR system and stored using human interpretable file and folder names
on the SNT and ultimately on the Zenodo file servers.

Simulation data, such as the Gene Regulatory Network will use a Project and/or
experiment name acronym, version number and creation date data combination:

‘SNT[SYM]<EXP>[SYM]<VERSION>[SYM]<CREATION_DATE>’: _SNT_GRN-v2-02052017_ .

All data files shared and stored within the consortium will follow this naming
convention, e.g. ‘ **SmartNanoTox** _Phys-chem proposed dose range_ _upload_
**1.1 final-26072017** .xls **’.**

eNanoMapper Ontology releases - The ontology has been made available in a
public version control repository (GitHub) as an OWL language file. Any
accepted updates and additions to the source material will be managed by the
GitHub system.

###  3.1.6. METADATA STANDARDS USED OR CREATED

Work on specific metadata formats is ongoing and though not uniformly XML
based, it will compliant with DataCite’s Metadata Schema 4.0 (
_https://schema.datacite.org/_ )  . Metadata will be stored in DataCite XSD
(XML) and JSON format variants which allow conversion to ISA TAB Nano.

We will continue a discussion with NanoReg2 (  _http://www.nanoreg2.eu/_ )  IT
team on file template, implemented data formats and maximising
interoperability.

##  3.2. MAKING DATA ACCESSIBLE

###  3.2.1. DATA WHICH WILL BE MADE OPENLY AVAILABLE

Where required, restrictions will be applied to the data in accordance with
the embargo requirements of journal publication. Other results obtained using
confidential information and materials provided by Chiesi Pharmaceuticals (
_http://www.chiesigroup.com/en/_ )  , who will be supplying lung surfactant
for certain _in vitro_ experiments, will be omitted from the final data
release.

In SmartNanoTox, the following data will be made publicly available:  
  
<table>  
<tr>  
<th>

**Type of activity**

</th>  
<th>

**Object of study**

</th>  
<th>

**Type of data produced**

</th>  
<th>

**Type of data shared**

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

In vivo toxicity tests in mice

</td>  
<td>

IT instillation of nanomaterials

</td>  
<td>

Tissue for histological evaluation and gene profiling, analysis of biological
endpoints

</td>  
<td>

Toxicological endpoints

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

In vivo toxicity tests in rats

</td>  
<td>

Nose-only inhalation of nanomaterials

</td>  
<td>

Tissue for histological evaluation and gene profiling, analysis of biological
endpoints

</td>  
<td>

Toxicological endpoints

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

Transcriptomics

</td>  
<td>

Nanomaterials/tissues

</td>  
<td>

Gene expression profile

</td>  
<td>

Gene identification

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

</td>  
<td>

Nanomaterials

</td>  
<td>

Mass-spectrometry data for the protein corona content

</td>  
<td>

List of proteins in the corona

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

Atomistic molecular simulation

</td>  
<td>

Protein, lipid-NP interface

</td>  
<td>

Energy profiles, trajectories

</td>  
<td>

Potentials of mean force for

biomolecules/NM

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

Coarse-grained molecular simulation

</td>  
<td>

Protein, lipid-NP interface

</td>  
<td>

Energy profiles

</td>  
<td>

Protein descriptors, adsorption energies, protein adsorption affinity rankings

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

###  3.2.2. MECHANISMS OF DATA ACCESSIBILITY

(Meta)Data and documentation will be made available via a shared file storage
server administered by SNT and will be mirrored on the Zenodo Open Science
file storage service (  _https://zenodo.org_ )  . Access to the SNT File
Server will initially be restricted to SNT partners. Access to Zenodo will
similarly be restricted until project completion or if requested by other
researchers beforehand.

Where data-specific online storage repositories exist, such as ProteomeXchange

( _http://www.proteomexchange.org/_ )  for Proteomics data, datasets will be
submitted to these to place data in a location where it can be best found,
accessed and reused by researchers.

We have set up the project website ‘  _http://www.smartnanotox.eu_ ’  to
publicise the activities of consortium and the associated projects, and to
provide documentation and access information to the project’s resources. A
member area within the project website has been created where experimental and
computational data may also be stored and shared between the participants and
if required, through the project website.

###  3.2.3. METHODS & SOFTWARE REQUIREMENTS

Raw experimental data may be opened in any of appropriate tool for reading or
analysing the data. For example, Mass Spectrometry data will be stored in
Thermo Xcalibur .RAW format with the accompanying mqpar.xml file which
describes the tool configuration used to run quantitative proteomics analysis
in freely available (registration required) MaxQuant  (
_http://www.biochem.mpg.de/5111795/maxquant_ )  .

Generated Ontological data may be viewed on the free to use Protégé (
_http://protege.stanford.edu/_ )  .

‘Omics data interaction network analysis will be performed on Qiagen’s
Ingenuity Pathway Analysis  (
_https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/_ )
under a pre-existing licence held by partners at UCD and on String-DB (
_https://string-db.org/_ )  (registration required) and the DAVID
Bioinformatics Resource (  _https://david.ncifcrf.gov/home.jsp_ )  (free-to-
use).

###  3.2.4. META(DATA) RESOURCES

A project management site has been created at EMDesk (  _https://emdesk.eu_ )
to assist the project members in management the project and reporting.

In its first year the consortium has already generated a considerable amount
of experimental data that has been shared, as required, between the consortium
partners. The media for these experimental data is currently stored privately
by the groups that produced them and is not yet accessible by all partners but
delivered on request via a shared cloud file sharing services.

From the second year onward, the amount of data produced within the consortium
will increase significantly and so too will the need for secure and flexible
sharing methods that can accommodate large volumes of data. To facilitate this
the SmartNanoTox file server  ( _http://137.43.122.41:8080_ )  has been
established to centralise project data and make it available to all consortium
members. While access to the system requires user authentication, the data
that it will host can be shared by request or uploaded to public repositories
as data processing has been completed (unless otherwise restricted by
publication embargo). The SNT File Server infrastructure offers a resource of
approximately 36 Terabytes of shared disk space which may be accessed globally
from Linux based QTS Operating System’s desktop-emulating browser interface or
via a selection of secured and customisable file transfer protocols e.g.
WebDAV, NFS, CIFS and SSH.

The project’s data will be preserved on this system for a period of five years
under the administration of UCD’s School of Physics. However, data mirrored on
Zenodo servers should be available for a much longer, though indefinite period
(the envisioned minimum duration is 20 years). Most data will be copied to the
Zenodo file service as they are produced, and all that that is to be shared
will be transferred before the end of the project term.

##  3.3. MAKING DATA INTEROPERABLE & REUSABLE

###  3.3.1. DATA CONVERSION FOR INTEGRATION, SEARCH AND EXPORT

As the Project will build on data resources developed for the NANoREG Project
we will continue to convert data into ISA-TAB NANO file formats; using NANoREG
harmonised terminology and eNanoMapper ontology terms and relations in the
description and representation of our _in vivo_ and _in vitro_ data.

Tooling will be adopted or developed as required to transform this data into
open access formats such as XML, ISA-TAB or JSON documents allowing the import
and export of data into formats required for further processing, shared
transfer or archive.

Human-readable text documentation will also be stored in the ISO-standardised
PDF/A format to ensure long-term reusability. Content from tabular datasets
such as MS Excel spreadsheets will also be converted to RFC 4180 defined comma
delimited text files to similarly maximise their potential dispersion and
reuse. Image data will be converted and saved along with ISO-12639 Standard
TIFF formatted duplicates.

We will use a combination of semantic web technologies in conjunction with
conventional relational (MySQL) and document orientated (JSON/MongoDB)
databases to facilitate information storage, indexing and retrieval.

##  3.4. MAKING DATA REUSABLE

###  3.4.1 LICENSING & ACCESS RESTRICTIONS

There will be no restrictions imposed on the data made available by this
project though registration will be required to access the iQSAR system and
tools developed for data processing. Access to data resulting from work
involving Chiesi Pharmaceuticals is protected by a non-disclosure agreement
and will not be provided unless specifically permitted by them.

###  3.4.2 GUIDELINES FOR DATA AVAILABILITY

Data will be stored on closed file systems until fully open or partial access
is requested. Except for where temporary publication embargoes are imposed all
relevant (meta)data resources will be made accessible upon completion of
analysis, verification and integration of information into iQSAR. Mechanisms
by which data will be made accessible have been described in Section 3.2.2.
Where embargoes are imposed, citations and links to supplementary data
resources will be provided until the embargo has expired, releasing the data
to the repositories used by SNT and described in the next section.

###  3.4.3. LONG-TERM DATA AVAILABILITY

As described in section 3.2.2 and 3.2.4 Zenodo File Servers will be used to
store (meta)data and documentation relating to the projected

Where domain-specific online data repositories exist, such as GEO for high-
throughput functional Genomics data and PRIDE, as part of ProteomeXchange for
storing and redistributing Mass Spectrometry Proteomics data, etc will be used
to ensure that data is preserved beyond the lifetime of the project and will
still be freely accessible to the research community.

#  4\. RESOURCE ALLOCATION

A part-time Data Manager was appointed for one year in May 2017. They will be
responsible for implementing as close to a FAIR data framework as is possible
within the current DMP and work with each consortium partner to ensure that
the SOPs for data standardization are uniformly applied and robust enough to
handle the growing volume and spectrum of data produced.

In 2016 a QNAP TS-653A (  _https://www.qnap.com/en/product/model.php?II=213_ )
NAS was purchased to securely store and share all data produced by the project
consortium. This machine is hosted by the School of Physics, UCD and will make
data available to authorised users for at least one year beyond the timespan
allotted for the project. Approximately €3k was spent on securing the NAS and
it’s six 7.28Tb HDDs. Costs of administration will be covered for one year by
the Data Manager appointed in 2017 with subsequent devolution of tasks to
existing SNT staff at UCD.

#  5\. DATA SECURITY

##  5.1. DATA ACCESS & SHARING

With the exception of data based on work involving materials and information
provided by Chiesi pharmaceuticals there are no other data used or created by
the project partners which may be considered commercially or ethically
sensitive.

Where Institutional data sharing policies prohibit the sharing of data on
commercial ‘cloud-based’ file storage services data will be routed to the
secure shared folders of the SNT File Server.

##  5.2. DATA RECOVERY

There are currently no facilities in place for data backup, however most of
the data produced will be duplicated across collaborator infrastructure at
source. For example: all Transcriptomics, Proteomics and (the yet to be
produced) Lipidomics data will be duplicated on Systems Biology Ireland’s  (
_http://www.ucd.ie/sbi/_ )  data storage servers from where it will be backed
up, weekly, to resources hosted in UCD’s Daedalus datacentre.

All data will be mirrored at no supplementary cost to the project at Zenodo
servers  ( _https://zenodo.org/_ )  and opened to public access in accordance
with the guidelines for data availability. Data verification will be carried
out by generated MD5 checksums to ensure no loss in data integrity during the
transfer.

##  5.3. ETHICS COMPLIANCE

This proposal complies with ethical principles (including the highest
standards of research integrity, as set out, for instance, in the European
Code of Conduct for Research Integrity and including, in particular, avoiding
fabrication, falsification, plagiarism or other research misconduct).

#  APPENDICES

##  APPENDIX A – DATA GENERATION BY WORKPACKAGE

As of July 2017  
  
<table>  
<tr>  
<th>

**Contact**

</th>  
<th>

**WP**

</th>  
<th>

**Study**

</th>  
<th>

**Activity**

</th>  
<th>

**Activity Description**

</th>  
<th>

**Type of data for sharing**

</th>  
<th>

**Shared Data Format**

</th>  
<th>

**Archive**

</th>  
<th>

**Vol**

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

UCD

</td>  
<td>

4

</td>  
<td>

Proteins

</td>  
<td>

Molecular simulation

</td>  
<td>

Prediction of 3D structure of proteins using i-Tasser software, calculation of
molecule characteristics

</td>  
<td>

coarse-grained models, sequence descriptors, structure descriptors, adsorption
energies

</td>  
<td>

TDT, eNanoMapper Templates

</td>  
<td>

SNT, Zenodo

</td>  
<td>

10 MB

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

Imperial

</td>  
<td>

4

</td>  
<td>

Nanomaterials

</td>  
<td>

Molecular simulation

</td>  
<td>

Quantum-chemical modelling of NM, calculation of material properties

</td>  
<td>

NM coarse-grained presentation, NM descriptors, Hamaker constants

</td>  
<td>

TDT, eNanoMapper Templates

</td>  
<td>

SNT, Zenodo

</td>  
<td>

10 MB

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

UCD

</td>  
<td>

5

</td>  
<td>

AOP

</td>  
<td>

Statistical Modelling

</td>  
<td>

Computer simulation (CG) of the interaction of the key molecule with the NM

</td>  
<td>

Programs & Computational models

</td>  
<td>

Mathematica data, ISA-TAB, JSON

</td>  
<td>

SNT, Zenodo

</td>  
<td>

TBD

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

UCD

</td>  
<td>

2

</td>  
<td>

Nanomaterials & Tissues

</td>  
<td>

Proteomics

</td>  
<td>

Omics studies for in vivo reaction to inhalation/ instillation of NMs for main
classes of NMs: gene expression, metabolomics, proteomics

</td>  
<td>

MS Protein IDs

</td>  
<td>

MaxQuant Data,

Tables*, eNanoMapper Templates

</td>  
<td>

SNT,

PRIDE,

Zenodo

</td>  
<td>

50 GB

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

UCD

</td>  
<td>

2

</td>  
<td>

Nanomaterials

</td>  
<td>

Lipidomics

</td>  
<td>

Study of the content and evolution of the NM biomolecular corona and lipid
wrap/NM containing complexes from in vivo experiment

</td>  
<td>

MS derived Lipid IDs

</td>  
<td>

eNanoMapper Templates

</td>  
<td>

SNT, Zenodo

</td>  
<td>

10 GB

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

SU

</td>  
<td>

4

</td>  
<td>

Nanomaterials

</td>  
<td>

Molecular simulations

</td>  
<td>

Multiscale modelling of NM corona formation and validation of the modelling
against in vivo studies

</td>  
<td>

bionano interactions descriptors - adsorption free energies; binding
coordination

</td>  
<td>

data sheets / Tables*

</td>  
<td>

SNT, Zenodo

</td>  
<td>

100MB

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

HMGU

</td>  
<td>

1

</td>  
<td>

Lung cells

</td>  
<td>

In vitro tox tests in murine cells

</td>  
<td>

Cell-line exposure experiments using Vitrocell exposure systems

</td>  
<td>

Cytotoxicity and biological endpoints (gene/protein-level)

</td>  
<td>

data sheets / Tables*

</td>  
<td>

SNT, Zenodo

</td>  
<td>

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

HMGU

</td>  
<td>

1

</td>  
<td>

Inhalation / IT instillation of

nanomaterials

</td>  
<td>

In vivo tox tests in mice

</td>  
<td>

Inhalation experiments: defined NMs aerosolisation, and investigation of
interactions of NM within the lung lining fluid,

alveolar epithelial cells and

macrophages

</td>  
<td>

Toxicological endpoints - Tissue for histological evaluation and gene
profiling, analysis of biological endpoints

</td>  
<td>

data sheets / Tables*

</td>  
<td>

SNT, Zenodo

</td>  
<td>

10 MB

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

NRCWE

</td>  
<td>

1

</td>  
<td>

Nanomaterials

</td>  
<td>

Surface Tension Experiments

</td>  
<td>

Investigation of NM interaction with the lung alveolar fluid and lung
integrity

</td>  
<td>

Surface tension profiles, dose-response curves - Effect of nanomaterials on
lung surfactant surface tension, inhibitory dose

</td>  
<td>

data sheets / Tables*

</td>  
<td>

SNT, Zenodo

</td>  
<td>

10 MB

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

_This project has received funding from the_  
  
<table>  
<tr>  
<th>

NRCWE

</th>  
<th>

3

</th>  
<th>

Database for the lungspecific or

respiratory

AOP

</th>  
<th>

Toxicity Pathway

Identification

</th>  
<th>

Data mining in Gene

Expression (GEO) database for the known pathways for lung diseases, library of
biological processes that are perturbed by

NM uptake

</th>  
<th>

Candidate Pathways

</th>  
<th>

data sheets / Tables*

</th>  
<th>

SNT,

GEO,

Zenodo

</th>  
<th>

10 MB

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

NRCWE

</td>  
<td>

1&3

</td>  
<td>

IT instillation of

nanomaterials

</td>  
<td>

In vivo tox tests in mice

</td>  
<td>

Instillation experiments:

Investigation of interactions of NM within the lung lining fluid,

alveolar epithelial cells and

macrophages

</td>  
<td>

Toxicological endpoints - Tissue for histological evaluation & gene profiling,
analysis of biological endpoints

</td>  
<td>

data sheets / Tables*

</td>  
<td>

SNT, Zenodo

</td>  
<td>

10 MB

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

FIOH

</td>  
<td>

3&5

</td>  
<td>

Lung tissue

</td>  
<td>

Histology evaluation

</td>  
<td>

Tissue analysis of in vovio samples

</td>  
<td>

**Verbal** evaluation, Semi-quantitative scores

</td>  
<td>

TDT, Tables*, Images**, MDs

</td>  
<td>

SNT, Zenodo

</td>  
<td>

10 MB

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

JSI

</td>  
<td>

2

</td>  
<td>

Labelled

Nanoparticles

</td>  
<td>

Fluorimetry

microspectroscopy, FMS

</td>  
<td>

NM modification for efficient tracking (fluorescent/isotopic labelling)

</td>  
<td>

fluorescence spectra - procedures for labelling nanoparticles and characterize
the efficiency of labelling

</td>  
<td>

spectra, synthetic pathways, data sheets & Tables*

</td>  
<td>

SNT, Zenodo

</td>  
<td>

50 MB

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

JSI

</td>  
<td>

2

</td>  
<td>

Labelled

Nanoparticles, cell lines

</td>  
<td>

Fluorescence microscopy, FMS, 3D STED with spectral data

</td>  
<td>

NM modification for efficient tracking (fluorescent/isotopic labelling)

</td>  
<td>

2D & 3D stack of 2D images (4D & 5D sets of data) - images, evaluation of
affinity constants, lifetimes of states, characteristic times of membrane
changes

</td>  
<td>

Images**, text based, Video

</td>  
<td>

SNT, Zenodo

</td>  
<td>

20 GB

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

JSI

</td>  
<td>

2

</td>  
<td>

Nanoparticles, Lung-lining fluid, Lipid dispersion

</td>  
<td>

In vitro 'NP'-to-lipid affinity

</td>  
<td>

Analysis of NM-lipid interaction for labelled NMs

</td>  
<td>

autocorrelation curves analysis & stack of images - images, evaluation of
affinity constants

</td>  
<td>

interaction description

</td>  
<td>

SNT, Zenodo

</td>  
<td>

10 MB

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

UL

</td>  
<td>

1&3

</td>  
<td>

Lung cells

</td>  
<td>

In vitro tox tests (rat & human cells)

</td>  
<td>

Analysis of cell responses to

NMs

</td>  
<td>

Cytotoxicity and biological endpoints (gene/protein-level), included
transcriptomics

</td>  
<td>

data sheets / Tables*

</td>  
<td>

SNT, Zenodo

</td>  
<td>

20 GB

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

* archived as CSV text, ** archived as TIFF 

_This project has received funding from the_

##  APPENDIX B – IMPORTED DATA & LICENCING  
  
<table>  
<tr>  
<th>

**Resource Usage Description**

</th>  
<th>

**Licence Type**

</th>  
<th>

**Full description**

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

eNanoMapper Templates:

NANoREG &

NanoGenotox

</td>  
<td>

"The NANoREG – eNanoMapper database and NANoREG deliverables, SOPs and other
documents [hereinafter: sources] may be used by individuals, institutions,
governments, corporations or other business entities so long as the use itself
or any works derived from the use of these sources, are not intended to
generate sales or profit."

</td>  
<td>

CC-BY-

NC-4.0

</td>  
<td>

_http://www.rivm.nl/en/About_RIVM/International_Affairs/_

_International_Projects/Completed/NANoREG/NANoREG_specific_license_
__information_

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

eNanoMapper Ontology

</td>  
<td>

"The content which is created by eNanoMapper (available in the internal/
folder) is licensed under CC-BY

(https://creativecommons.org/licenses/by/3.0/). However, eNanoMapper reuses
content from external ontologies which retain their original licenses."

</td>  
<td>

GNU

Lesser

GPL

</td>  
<td>

_https://github.com/enanomapper_ _/ontologies/tree/master/licenses_

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

GenBank

</td>  
<td>

"no restrictions on the use or distribution of the GenBank data. However, some
submitters may claim patent, copyright, or other intellectual property rights
in all or a portion of the data they have submitted"

</td>  
<td>

Bespoke

</td>  
<td>

_http://hgdownload.soe.ucsc.edu/_ _goldenPath/hg38/bigZips/_

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

_This project has received funding from the_  
  
<table>  
<tr>  
<th>

Gene

Ontology

</th>  
<th>

"You are free to copy and redistribute the material in any medium or format
and remix, transform, and build upon the material for any purpose, even
commercially"

</th>  
<th>

CC-BY-4.0

</th>  
<th>

_http://www.geneontology.org/page/use-and-license_

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

Gene

Expression

Omnibus

</td>  
<td>

"NCBI places no restrictions on the use or distribution of the GEO data.
However, some submitters may claim patent, copyright, or other intellectual
property rights in all or a portion of the data they have submitted."

</td>  
<td>

Bespoke

</td>  
<td>

_https://www.ncbi.nlm.nih.gov/geo/info/disclaimer.html_

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

Protein Data Bank

</td>  
<td>

“are free of all copyright restrictions and made fully and freely available
for both noncommercial and commercial use. Users of the data should attribute
the original authors of that structural data”

</td>  
<td>

Bespoke

</td>  
<td>

_https://www.rcsb.org/pdb/static.do?p=general_information/about__
_pdb/policies_references.html_

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

UniProt

</td>  
<td>

"free to copy, distribute, display and make commercial use of these databases
in all legislations, provided you give us credit"

</td>  
<td>

CC-ND-

3.0

</td>  
<td>

_http://www.uniprot.org/help/license_

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

QSAR Toolbox

</td>  
<td>

Free to use with accreditation

</td>  
<td>

Bespoke

</td>  
<td>

_http://www.oecd.org/env/ehs/risk-assessment/Eula4.pdf_

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

_This project has received funding from the_