#  SUMMARY

This report describes the first updated version of the data management plan
(DMP) for the OpenRiskNet e-infrastructure projects. The current DMP covers
the general aspects of the OpenRiskNet data management based on the FAIR
(findable, accessible, interoperable and reusable) guidelines, ethics
considerations for re-sharing of public datasets and the first examples of
shared data sources including diXa, BridgeDb, WikiPathways, AOP-Wiki and
ToxCast/Tox21. More specific data source and clearly-defined measures will be
added in parallel to their integration into the infrastructure, which will
follow the time plan enforced by the case study requirements on data
availability.

#  INTRODUCTION

The European Commission is running a flexible pilot under Horizon 2020 called
the Open Research Data Pilot (ORD Pilot). The ORD pilot aims to improve and
maximise access to and re-use of research data generated by Horizon 2020
projects and takes into account the need to balance openness and protection of
scientific information, commercialisation and Intellectual Property Rights
(IPR), privacy concerns, security as well as data management and preservation
questions  [1  ​  ]  .​ Open data is data that is free to access, re-use,
repurpose, and redistribute. The Open Research Data Pilot aims to make the
research data generated by selected Horizon 2020 projects accessible with as
few restrictions as possible, while at the same time protecting sensitive data
from inappropriate access  [2  ​  ]  .​ Projects starting from January 2017
are by default part of the Open Data Pilot, including the Research
infrastructures (including e-Infrastructures) are required to participate in
the ORD Pilot. Since one of the main aims of OpenRiskNet is to allow for a
simpler, more harmonised access to public, open data sources and workflows and
enrich these with semantic annotation to improve their interoperability
between each other and with predictive toxicology and risk assessment
software, OpenRiskNet is fully supporting the ORD Pilot, is developing best-
practice approach and trying to act as a role model for data management and
sharing,

To help optimising the potential for future sharing and re-use of data, the
OpenRiskNet Data Management Plan (DMP) helps the partners to consider any
problems or challenges that may be encountered and helps them to identify ways
to overcome these. This DMP is a “living” document developed using the online
tool described above and given as a snapshot of the current status (November
2018) in this document, It outlines how the research data collected or
generated, including redistribution of existing data sources as well as
results from the i​ n silico investigations performed as part of the case
studies, are handled during and after a research project. It follows the
Guidelines on FAIR Data Management in Horizon 2020  [1  ​  ]  and is based
around the resources available to the project partners in a realistic way
taking the current knowledge into account. The ongoing activities to keep the
DMP up to date follow an online, distributed approach as outlined in the
Guidelines for creating an online DMP (see Figure 1)  [3  ​  ]  .​ Here, we
summarise the concepts for the description of data sets as well as data
sharing and archiving approaches adopted in the DMP first followed by the DMP
in relevant version of the time of this writing. Since the OpenRiskNet case
studies are under ongoing development, which define the integrated data
sources, the current DMP covers the general aspects of the

OpenRiskNet data management but also specific and clearly-defined measures for
the first data sources integrated. Additional sources will be added in
parallel to the data integration.

**Figure 1** . DMP tool interface used to create the OpenRiskNet plan ​  [4  ​
]

##  DATA SET DESCRIPTION

This section give the general concepts of what is considered data in
OpenRiskNet and a listing of what kind of datal the project collects, makes
available for redistribution and sharing oor generates as part of the case
studies (i​ n silico only), and to whom might they be useful later. More
information and specific details are given in the DMP below.

Data refers to:

  * Data generated in i​ n vivo​, i​ n vitro​, i​ n chemico and i​ n silico experiments broadly related to toxicology and risk assessment in the form of raw, processed and summary data as well as metadata to describe the type of data and how it was produced (protocols and method descriptions for the experimental, processing and analysis procedures); 

  

  * More specifically, data and metadata needed to execute the case studies and validate results in scientific publications, and 

  

  * Other curated and/or raw data and metadata that may be required for validation purposes or with reuse value. 

  

The metadata provided with the datasets allow to answer questions to enable
data to be found and understood, ideally according to the particular standards
applied. Such questions include but are not limited to:

  * What is the data about? 

  

  * Who created it and why? 

  

  * In what forms it is available? 

  

  * Standards applied. 

  

Finally the **metadata** ​ ,​ **documentation** ​ and **standards** ​ will
help in making the data FAIR (Findable, Accessible, Interoperable and Re-
usable) by not only provide the technical requirements like global, persistent
identifier and clear access protocols (data application programming interfaces
(APIs)) and licenses but by harmonizing and improving the scientific
interoperability of the data by semantic annotations and allowing combination
and enrichment of data sets using linked-data approaches (Combined OpenAPI and

JSON-LD description of data APIs).

Data integrated, for which the integration is in progress or produced can be
grouped into the following areas:

  * Existing toxicology, chemical properties and bioassay databases for redistribution 

  

  * Existing omics databases for redistribution 

  

  * Existing knowledge bases for redistribution and information extracted by data mining 

  

  * Intermediate or final results of i​ n silico studies performed as part of the case studies 

  

##  DATA SHARING

According to the ORD Pilot programme, by default as much of the resulting data
as possible should be archived as Open Access. Most data handled in
OpenRiskNet is is provided by international publicly funded projects, not-for-
profit consortia or governmental and regulatory agencies. This data sources
are already available under open-data licenses and will be redistributed by
OpenRiskNet in a restructured and enriched form under the same license. Newly
generated data are results from the improved processing, analysis and
modelling workflows developed as examples in the case studies and OpenRiskNet
is fully committed to make these publicly available either as part of the
publicly shared workflows or, if they have value outside the case studies, as
a separate information and knowledge source. Working together with associated
partners especially from the commercial sector (service providers and end
users from SMEs and larger industry) might put some restriction on the sharing
of data generated in these collaborations. Such legitimate reasons for not
sharing resulting data will be explained in the DMP in the rare cases they
have to be applied. Additionally, OpenRiskNet is committed to protect personal
data and IPR agreements and to responsible data sharing and is taking all
steps reasonably necessary to ensure that data is treated securely and in
accordance with the OpenRiskNet privacy policy (see section below on the
Privacy Policy). No personal data will be transferred to an organization or a
country unless there are adequate controls in place including the security of
data and personal information. Complementing these general data sharing
policies, the DMP describes any ethical or legal issues that can have an
impact on data sharing. Since in many cases, the data production is not under
control of the OpenRiskNet partners and is only redistributed by them, the
obligation to guarantee that the data is generated from high quality, ethical
research and can be shared under an open license is in the hand of the
original data provider or the primary data distributor. This includes the
obligation to operate in conformity with the requirements of their own
institution, and fulfil all necessary national and international regulatory
and ethical requirements. OpenRiskNet is working together with the original
data providers as well as ethical experts on producing workflows and checklist
(see attachments) for the ethics evaluation and on documenting the measures
adopted during the data generation process (ethical approval of the i​ n vivo
and i​ n vitro experiments by the relevant authorities) as well as for
protection personal data e.g. by anonymization of data before sharing.
Whenever agreed on by the data provider and technical feasible, this data is
made available to the OpenRiskNet user as part of the data service
description.

##  ARCHIVING AND PRESERVATION

To ensure that publicly funded research outputs can have a positive impact on
future research, for policy development, and for societal change, it is also
important to assure the availability of data for a long period beyond the
lifetime of a project. This does not refer only to storage in a research data
repository, but also to consider the usability of the data. One of the main
goals of the infrastructure created by the OpenRiskNet project is to harmonise
data, make it interoperable and sustainable and in some cases even enable data
sharing or replace existing data sharing solutions. Therefore, the project has
a special obligation for preserving data not only produced in the project but
also from other projects redistributed by OpenRiskNet and software or any code
produced to perform specific analyses or to render the data as well as being
clear about any proprietary or open source tools that will be needed to
validate and use the preserved data. OpenRiskNet is build upon software
engineering and infrastructure components developed, supported and adopted by
a large community guaranteeing, on one hand, some stability and sustainability
of the data sharing, accessing and processing solutions provided even in the
relatively quickly changing field of microservice architectures and
deployments. On the other hand, the containerization approach adopted by
OpenRiskNet allows for the storage of the data and software in the version
used during the execution of the analysing and modelling workflows allowing
for complete and exact repeatability using the same code and improved
reproducibility due to better documentation.

It has to be noted here that many of the data sources are only redistributed
by OpenRiskNet. The primary data provider for e.g. diXa and ToxCast are big
European infrastructures or US agencies, ELIXIR and US EPA, respectively. For
these, data archiving and preservation have to be guaranteed by these
institutions. However, OpenRiskNet and more specifically the OpenRiskNet
partner responsible for the integration into the OpenRiskNet infrastructure is
in charge of maintaining and updating the alternative method to access the
data (OpenRiskNet-compliant data API), guaranteeing that the data available
within OpenRiskNet is on the same technical and curation level and at the same
version as in the primary source, and sustaining the solution beyond the
OpenRiskNet project. The same is true for data sources, where OpenRiskNet also
takes the responsibility of hosting the data and thus, becomes the primary
data source. In the later case, archiving and preservation of the data source
containers is of uttermost importance since otherwise there is the danger that
the data is lost completely. Negotiations with the Birmingham Environment for
Academic Research (BEAR) are underway to provide archiving and preservation
facilities for containerised data and software services for at least the next
5 years, for which this cannot be guaranteed by the service provider.

#  DATA MANAGEMENT PLAN (DMP)

This data management plan will address all data-related problems or challenges
that may be encountered by partners during the execution of the project. It
consists of general guidelines and project-internal rules and regulations
dealing with the type of data collected, data sharing following the FAIR
principles, hard- and software resources as well as with data security,
privacy and ethics. Additionally, more details for specific data sources on
all these aspects will be provided whenever necessary.

##  1\. DATA SUMMARY  
  
<table>  
<tr>  
<th>

**Summary of the data addressing the following issues:**

  * State the purpose of the data collection/generation 

  

  * Explain the relation to the objectives of the project 

  

  * Specify the types and formats of data generated/collected 

  

  * Specify if existing data is being re-used (if any) 

  

  * Specify the origin of the data 

  

  * State the expected size of the data (if known) 

  

  * Outline the data utility: to whom will it be useful 

  

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

###  1.1 Purpose of the data collection

The main purpose in collecting and use of data and metadata in the OpenRiskNet
project is to fulfill its main objectives in providing and improving solutions
on data availability to the risk assessment scientific community, data
quality, interoperability, standardization and sustainability and overcome
some of the data-related issues, e.g.:

  * Fragmentation of data across different databases; 

  

  * Low quality due to insufficient data curation; 

  

  * Poor explanation and insufficient details on experimental design and protocols applied; 

  

  * Data available in different formats and with different annotations. 

  

Another goal is to generate guidelines and templates for data exchange,
harmonise the use of ontologies as well as develop criteria and solutions for
controlling the quality of a dataset or i​ n silico tool, for quantifying the
uncertainty of predictive models and for improving the repeatability and
reproducibility of processing, analysis and modelling workflows.

###  1.2 Relation to the objectives of the project

The OpenRiskNet project aims to establish the infrastructure and services
functions providing a centralised and standardised set of data and computing
resources, accompanied by standardised operating procedures and guidance:

  * Provision of quality sources of data to facilitate more accurate evaluation of toxicity; 

  

  * Data infrastructure will offer a centralised repository for data created during other research programs, including the import of relevant research i​ n vitro​, i​ n vivo ​and human data from other sources. 

  

  * Well-designed data import facilities to support ongoing data collection according to quality guidance. 

  

  * Use and further development of data annotation and exchange standards for describing toxicity data based on application programming interfaces in order to reduce errors and enable data integration from different laboratories, including data sources outside the program 

  

  * Integrate regulatory reporting requirements with respect to metadata and documentation details and completeness as well as to export options into file formats like ISA, ToxML and OECD harmonised templates. 

  

The OpenRiskNet project aims also to develop and optimise computational models
and automated and reliable analysis workflows in order to increase the
mechanistic understanding of toxicity:

  * The models will permit identification of mechanistic links between omics data at different levels of functional organisation; 

  

  * The models will help to advance the understanding of the relationship between toxicity, architecture, function and risk; 

  

  * Computational sensitivity analyses components will aid in identifying most sensitive parameters relevant to toxicity and guide further data acquisition and experiments towards increased chemical safety. 

  

The data sources integrated during the project are highly relevant to the
predictive toxicology and risk assessment community and therefore, are used to
showcase and evaluate the concepts and solutions provided by OpenRiskNet and
how these are addressing the aims just mentioned. Additionally, they are used
in the case studies to provide the example workflows on how to apply and
combine the different tools for effective problem solving for the different
aspects of risk assessment.

###  1.3 Types and formats of data

OpenRiskNet is structured around the concept of semantic-annotated application
programming interfaces, which will also be used to search and access data from
OpenRiskNet-compliant data sources. As serialised exchange format, JSON or the
semantically annotated form JSON-LD is recommended and enforced whenever
possible. These formats will cover mainly the metadata associated to the data
and in the case of small numbers of readouts (experimental toxicology
endpoints) per sample also the data. Especially for omics data or imaging
techniques, these files will be accompanied by data in standard file formats
to keep the compatibility and interoperability with tools developed in these
areas like gene- and pathway-enrichment approaches or image recognition
software, respectively. Additionally, to be able to integrate legacy data and
provide the final results in the format required for e.g. regulatory
reporting, OpenRiskNet is supporting import and export of standard file
formats like ISA (-tab or -json), ToxML and OECD harmonised templates.
However, such technical file format conversion solutions require a scientific
harmonization of the metadata completeness and data description levels, which
are better defined for summary data used for regulatory purpose than for raw
data, where the reporting style is very dependent on the individual data
provider and application area. Therefore, we are working together with other
big projects (EU-ToxRisk and NanoCommons) to define the amount and content of
metadata, which has to be provided for each experimental assay or
computational investigation, and data formats if no standards exist so far as
well as providing the means for future additions and adaptations based on
flexible data schema specifications. However, only the strict usage of
ontologies in this data and metadata descriptions can guarantee that the
information is easily understood by the user or automatically transferred
between services.

###  1.4 Reuse of data

For data and computational models, we use, as much as possible, existing data,
software tools, open and readily available to all partners. We will aim at re-
usable and extensible tools. OpenRiskNet is not producing any new experimental
data but is reusing data from publicly available data sources or if absolutely
needed for interaction with an associated partner or a case study, data from
other projects not yet under an open license. It is the clear goal of the
project, to provide all input data independent of the original source as well
as the results from the processing, analysis and modelling workflows under an
open-data license and provide it in a easy way for reuse by others. On one
hand, making sharing, accessing and reusing of data easier is the main goal of
the data solutions provided by OpenRiskNet and the integration of the
reference data sources. On the other hand, results from the i​ n silico
investigations are considered as equally valuable for sharing and reuse
especially with the goal to improve the evaluability, repeatability and
reproducibility of these computational studies. Full documentation of the
workflows including intermediate results and permanent storage of the final
outcomes highly annotated by metadata describing the procedures is, therefore,
another central goal of the OpenRiskNet infrastructure.

###  1.5 Origin of the data

As mentioned before at many places, the main data integrated, used and
provided for easy reuse by OpenRiskNet are coming from other publicly funded
research and infrastructure projects or institutions and are already in the
public domain or will be made public available soon. However, users might also
want to access commercial data services provided by associated partners or and
use their in-house data as part of the infrastructure and partly share them
with a selected users under a specific license. These considerations lead to
three different classes grouping the origins of the data:

  * Data and models owned and provided by OpenRiskNet Partners and Associated Partners as part of the project work under an open-data license; 

  

  * Open Source data and models provided under the license mentioned by the owners; 

  

  * Data from third parties including associated partners and commercial services of OpenRiskNet partner, and not yet available in existing open databases used provided under the conditions specified by the data owner and included in a formal agreement. 

  

For all these data sources, the original license of data usage as to be
considered and also applied (in the original or more restricted form) to the
version integrated in OpenRiskNet environments. To prevent unauthorised data
access even in virtual environments shared by multiple users like the
reference environment, an authentication and authorisation service is
integrated in OpenRiskNet infrastructure, which also handles the license
management. In the same way, commercial software or free software requiring a
registration is handled.

###  1.6 Expected size of the data

As described above the idea of the OpenRiskNet infrastructure is not to
combine data from different sources into one data warehouse but to access the
data from its original source and use the interoperability layer added to the
data services to harmonise them. In this way, no additional capacity for
storage of the original data is needed. However, two aims of the OpenRiskNet
project might lead to additional requirements on data storage.

  1. Some of the data sources considered for integration are not yet available in open-accessible databases, these cannot be accessed via application programming interfaces or don’t comply with the FAIR principles. In such cases, OpenRiskNet will negotiate with the data owners if the data should be either transferred to standard data repositories or if the existing solution should be improved within the framework of the associated partner programme. 

  

  2. Most if not all of the data sources should also be provided in a form suitable for in-house deployment. Even if the user system administrator setting up the in-house virtual research environment (VRE) is responsible for providing the required resources for such deployments, the data sources have to be containerised and provided to the users for download via the OpenRiskNet service catalogue. To assess the needed storage space for the containers and to give the users guidance on the needed computational resources for the VRE, expected sizes for all data services are given in section 1.8 below. 

  

  

###  1.7 Utility of data and models

OpenRiskNet solutions will work towards making data available to its main
stakeholders, researchers, risk assessors and regulators, in an easy
accessible, standardised and harmonised way in order to be able to base
conclusions and recommendations about the safeness of a chemical, drug,
cosmetic ingredient and nanomaterial on all available evidence. The same
principles are applied also to data processing, analysis and modelling tools
involved in risk assessment.

The access to the data infrastructure part of OpenRiskNet by industry has the
merit of providing a wide spectrum of data, with which industry could perform
parts of research and development activities and to lower the barriers to real
innovation resulting in new products, processes and services. Close
cooperation with the regulatory agencies is also key to push the regulatory
acceptance of the integrated tools and workflows.

Possible beneficiaries of the data, computational models and e-infrastructure:

  * Industry represented by chemicals, pharma, food, cosmetics or other consumer products companies which are required use all available information and to address the ‘3Rs’ principles and report on alternative methods used (including i​ n silico​); 

  

  * Regulatory agencies (e.g. ECHA, EMA, EFSA); 

  

  * SMEs as they frequently do not have in-house tools and knowledge resources for the regulatory risk assessment requirements; 

  

  * R&D community: the translation of these methods to industrial and regulatory science will result in a deeper understanding of biological response to perturbations supporting e.g. better designed and safer drugs and clinical practice; 

  

  * Consumers: OpenRiskNet aims to support integration of apps that can be used by consumers on their mobile phones supporting everyday activities, such as obtaining knowledge on ingredients in products they are purchasing or using. 

  

###  1.8 Specific information on individual shared data sources

In this section, we will address specific information and requirements of
individual data sources provided by OpenRiskNet with respect to their purpose,
origin, relationship to the project, data type and format, size and potential
users. Section 2.5 and 5.3 below are fulfilling the same purpose for issues on
FAIR data sharing and ethics. These are meant as additions or clarifications
to the general descriptions relevant only for the specific data set /
database. Points completely covered by the general remarks will not be
repeated here again and thus some of the subsections above will not appear in
the database descriptions

####  1.8.1 diXa

Like OpenRiskNet, diXa was an e-infrastructure project for collecting data
from different research projects and make them publicly available via a common
interface. Thus, its goals and objectives fit those of the OpenRiskNet
project. As part of the integration of diXa Data Warehouse into the
OpenRiskNet infrastructure, the data access will be semantically annotated and
further harmonised to other OpenRiskNet services.

1.8.1.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project

The Data Infrastructure for Chemical Safety (diXa) project (
_http://www.dixa-fp7.eu_ ​  _/_ )  _​_ was funded by EU FP7 to provide a
single resource for the capture of toxicogenomics data produced by past,
present and future EU research projects, and to ensure sustainability of such
a resource for use by the wider research community. Therefore, the diXa Data
Warehouse was established (  _http://wwwdev.ebi.ac.uk/fg/dixa/index.htm_ ​
_l_ ).​

Data from the diXa Data Warehouse as well as other sources (i.e. NCBI GEO and
EBI ArrayExpress) is currently being used in a meta-analysis for genotoxicity
prediction using data from multiple i​ n vitro cell models as part of the TGX
case study. The results using only the human data have been presented as
poster at EUROTOX 2018

(  _https://doi.org/10.1016/j.toxlet.2018.06.60_ ​  _8_ )  _​_ .

1.8.1.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data

The diXa Data Warehouse comprises of 95 studies, 29609 samples and 469
compounds (including solvents). I​ n vitro human and rodent data and i​ n vivo
rodent data were collected from the EU FP6 projects carcinoGENOMICS, PredTox,
NewGeneris, Predictomics, the EU FP7 projects ESNATS, Predict-iv, the Dutch
project of the Netherlands Toxicogenomics Centre, the Japanese project Open
TG-GATEs and the US project DrugMatrix. Most studies consist of
transcriptomics data, whereas a few also contain metabolomics and/or
proteomics data. It should be noted that the 2 i​ n vivo ​human studies of the
EU FP7 Envirogenomarkers project do not contain data as these have been
retracted based on objections from the Swedish biobank with regard to personal
data protection.

In addition, 188 human disease transcriptomics data sets have been added to
the data warehouse.

Metadata for all studies and disease data sets are captured in the ISA-tab
format.

In addition to this ‘omics data collection links to other globally-available
chemical/toxicological databases were provided.

The diXa Data Warehouse has been further used in the EU FP7 project HeCaToS
coordinated by UM. In this project the data warehouse has become part of EBI’s
BioStudies (  _https://www.ebi.ac.uk/biostudies_ ​  _/_ )  _​_ and the data
generated in HeCaToS were directly uploaded to BioStudies. Upon public release
of these data they can also be used within OpenRiskNet.

1.8.1.3 Additional details to 1.6 Expected size of the data

The raw data of the ~30,000 samples are at least 400 GB in size.

####  1.8.2 BridgeDb

The BridgeDb project was set up to provide both identifier mapping data and a
general framework that provides an API to access identifier mapping data  [5
​  ]  ​. BridgeDb is used in smaller and larger projects, the latter including
WikiPathways, Cytoscape and Open PHACTS  [6  ​  ]  .​ It is available in
various forms, including an Open API web service, Java library,

Docker image, and BioConductor package. The platform supports two kinds of
identifiers.

The first are simple identifier-data source combinations. The second is
Internationalised Resource Identifiers, for use in semantic web technologies.

1.8.2.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project

Data interoperability requires identifier mappings. The mapping data is
collected by the BridgeDb project and reshared in OpenRiskNet (possible
because of the open licenses).

Availability of identifier mappings allows simplifications of workflows. Data
is part of the BridgeDb Docker services, and either preloaded (as in the
current OpenRiskNet services) or loaded when the service is fired up (this
approach is currently not actively used in OpenRiskNet).

1.8.2.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data

BridgeDb identifier mapping databases are commonly available in two formats:
Derby data files and as link sets. Both formats have been developed for
different use cases. BridgeDb identifier mapping databases are available under
open licenses or CC-Zero.

Identifier mapping is essential to data set interoperability. Existing
identifier mappings databases suffice for the current needs, but mapping
databases are expected to be needed for other entities, like nanomaterials and
AOP entities (e.g. stressors, key events, outcomes).

1.8.2.3 Additional details to 1.6 Expected size of the data

Identifier mappings databases are released under the data management plan of
the

BridgeDb project. Data is shared in different ways depending on the type of
entity. Metabolics identifier mappings databases are released on Figshare, and
gene-variant databases are planned to be released on Figshare or Zenodo. The
gene/protein and interaction mapping databases are currently still released
using a custom approach, using a download server and not actively archived
yet. The sizes of these databases vary, but typically are in the order of
500MB to 1GB in size. Exception are the gene-variant databases which are much
larger. All sizes are still well within the scope of what archival websites
allow.

1.8.2.4 Additional details to 1.7 Utility of data and models

Identifier mapping is essential to data set interoperability since there are
multiple competing identifier systems available for labeling e.g. chemical
compounds, genes and pathways. Existing identifier mappings databases suffice
for the current needs, but mapping databases are expected to be needed for
other entities, like nanomaterials and AOP entities (e.g. key events,
outcomes). Additionally, access to these tools from other services for e.g.
cross-database searches and data curation and enrichment will be facilitated
by the OpenRiskNet integration.

####  1.8.3 WikiPathways

WikiPathways is a molecular pathway database, established by the WikiPathways
team, a collaboration between the Department of Bioinformatics of Maastricht
University and the Gladstone Institute, San Francisco. Its purpose is to
facilitate the contribution and maintenance of pathway information by the
biology community by utilizing the open, collaborative platform of
WikiPathways.

1.8.3.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project

The contents of WikiPathways comprise of molecular pathways, consisting of
nodes that are annotated for genes, proteins, and metabolites, which can be
utilised for omics data analysis through pathway analysis in PathVisio. The
WikiPathways database captures the biological knowledge in biological pathway
diagrams, supported by scientific literature. Because molecular pathways can
describe processes in any field of biology, it is relevant for toxicological
risk assessment workflows. Pathways describe the connections between
biological entities and show how a disturbance by a chemical or nanomaterial
could cause downstream effects.

1.8.3.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data

The molecular pathways in WikiPathways are developed and curated by
researchers, and are based on scientific literature. Pathways are available in
multiple formats, including but not limited to the original Graphical Pathway
Markup Language (GPML), Resource Description Framework (RDF), gene lists (GMT
format), and nanopublications. The CC-Zero license puts no restrictions on
reuse.

1.8.3.3 Additional details to 1.6 Expected size of the data

The complete collection of GPML files is less than 100MB.

1.8.3.4 Additional details to 1.7 Utility of data and models

Biological pathways are used for data analysis, biological interpretation of
omics data, and data integration.

####  1.8.4 AOP-Wiki

The AOP-Wiki is the primary repository of qualitative, mechanistic Adverse
Outcome Pathway (AOP) knowledge. It was developed by the Organisation for
Economic and Co-operation and Development (OECD), representing a collaboration
between the

European Commission DG Joint Research Centre and US Environmental Protection
Agency. The AOP-Wiki is part of the AOP-Knowledge Base, which was launched by
the OECD to allow everyone to build AOPs.

1.8.4.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project

The AOP-Wiki data comprises of mechanistic toxicological knowledge relevant
for risk assessment. While most of the knowledge is present as free-text,
literature-supported descriptions, essential aspects, such as biological
processes, objects, cell types, and stressor chemicals that cause a
disturbance, among other things are annotated with ontologies and chemical
identifiers. Therefore, the AOP-Wiki serves as a knowledge base for
toxicological effects related to a variety of chemicals, which summarises
relevant literature.

1.8.4.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and

1.5 Origin of the data

Knowledge in the AOP-Wiki data is stored partly as free text and partly as
ontology annotations and chemical identifiers. The data originates from the
AOP-Wiki database, and is supported by scientific literature that is gathered
and written by researchers. The contents of the AOP-Wiki are reviewed by the
OECD Extended Advisory Group on Molecular Screening and Toxicogenomics
(EAGMST). Nightly exports of the AOP-Wiki contents are available, but only
quarterly downloads are stored and maintained permanently on the Wiki which
allows citation when the information is reused.

1.8.4.3 Additional details to 1.6 Expected size of the data

While the contents of the AOP-Wiki are increasing rapidly on a daily basis,
the latest permanent download of the data (October 2018) does not exceed 12Mb.

1.8.4.4 Additional details to 1.7 Utility of data and models

In order to perform risk assessment, one has to gather all relevant knowledge
about the mechanistic effects of a compound that requires assessment. The AOP-
Wiki allows for reusing mechanistic knowledge of toxicological events upon
disturbance by a stressor, often a chemical. As the AOPs are developed in a
way that knowledge is separated in biological events (called Key Events) and
are chemical-agnostic, their major purpose is the re-usability of
toxicological knowledge. Therefore, the contents of the AOP-Wiki can be
relevant for each risk assessment workflow, providing mechanistic information
about biological processes and linking these together.

####  1.8.5 ToxCast

The United States Environmental Protection Agency Toxicity forecaster
(ToxCast)  1  has generated toxicity screening data on thousands of chemicals
in commerce and of interest to the agency and general public. The project also
uses computational approaches to prioritise and rank chemicals for risk
assessments and regulatory decision making. The data is publicly available,
widely distributed and can be annotated to fit into the OpenRiskNet data
harmonisation and integration framework.

1.8.5.1 Additional details to 1.1 Purpose of the data collection and 1.2
Relation to the objectives of the project

The ToxCast research project data is generated on high-throughput in vitro
toxicity screens for a variety of chemicals and biological targets. One of the
goals of the project is to prioritise and evaluate the potential human health
risk of chemicals in a cost efficient way. The data generated also includes
computational and predictive models to predict toxicity potential of the
chemicals in humans. The results of these analysis are being used actively to
inform the context of decision making such as endocrine disruptor screening.
The data can be integrated with other data services of the OpenRiskNet
infrastructure such as ontology mapping, pathway identification and mapping,
and AOP development tools that. Users of the OpenRiskNet service will be able
to take advantage of the information gaps filled through the integration of
these datasets and models to develop predictive toxicology and risk assessment
models e.g. read-across models. Examples of such uses are created in the case
studies collecting evidence from all available data sources to create profiles
of specific compounds, complementing omics data in bioinformatics workflows,
data-driven developing and validating AOP, and model building based on
chemical and biological data. The US EPA is also making the data publicly
available and accessible through various means. As the project progresses, and
more chemicals are screened, the agency makes periodic updates to the public
release as more data is generated.

1.8.5.2 Additional details to 1.3 Types and formats of data, 1.4 Reuse of data
and 1.5 Origin of the data

The data currently available on the ToxCast dashboard includes over 9076
chemicals tested in 1192 assays (as at November 26, 2018) that map to hundreds
of genes in both humans and rats. The chemicals screened span various uses
including industrial, individual, food additive and potentially safer
alternatives to already existing older chemicals. The assays tested are
usually of two types: i.) cell-based assays which measure changes in cellular
response to the test substances; and ii) Biochemical assays which measure the
activity of a biological macromolecule. The cell typically used may be human
or rat primary cells and cell lines. To inform chemical safety decisions, the
computational toxicology research group at the US EPA makes both the archived
and current versions of the data available to the public through 1.) a
database called invitroDB which is a MySQL download of all the data, 2.)
summary data in flat-file format (e.g. comma-separated value files and tab
separated files), 3.) concentration response plots in pdf format, and 4.) a
ToxCast dashboard web application which serves as a portal for users to search
and query the data. No personal data is collected during the data generation
process as all data in this set is in vitro and commercial cell lines are used
for the testing.

1.8.5.3 Additional details to 1.6 Expected size of the data

The current version of the invitroDB database download is less than 8GB. The
accompanying summary files and analysis pipeline and concentration response
plots have a cumulative size of less than 30GB.

1.8.5.4 Additional details to 1.7 Utility of data and models

The high-throughput screening toxicity data and models available in ToxCast
cover a wide chemical and biological space useful for risk assessment and as
such of great value to the OpenRiskNet stakeholders. Integrating this dataset
to the OpenRiskNet infrastructure will allow for easier access to the data by
users who may not have background or expertise to setup and run the local
databases and modelling pipelines. In addition being able to access this data
from the OpenRiskNet service will also create greater utility for the data as
it can be directly cross-referenced and used for modelling or analysis with
other data in the service.

##  2\. FAIR DATA

###  2.1 Making data findable, including provisions for metadata  
  
<table>  
<tr>  
<th>

_●_

</th>  
<th>

Outline the discoverability of data (metadata provision)

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

_●_

</td>  
<td>

Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?

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

_●_

</td>  
<td>

Outline naming conventions used

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

_●_

</td>  
<td>

Outline the approach towards search keyword

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

_●_

</td>  
<td>

Outline the approach for clear versioning

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

_●_

</td>  
<td>

Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how

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

OpenRiskNet is integrating existing data sources and make them easier
findable, accessible and interoperable. This is based, on one hand, on the
metadata provided by the data sources and, on the other hand, on the
interoperability layer, which harmonises these metadata into service
descriptions and data schemata, which can be queried through the OpenRiskNet
discovery service.

The description of the capabilities of a database and the data schema allows
for:

  * Accessing specific search functionality, and 

  

  * Identify the data fields to be searched (e.g. where information on the biological assays are stored); 

  

  * Finding the best format for data exchange; 

  

  * Understanding all the data and tools, with transparent access to metadata describing the experimental setup or computational approaches. 

  

In the case that the original data sources don’t provide all features required
by the FAIR principles as e.g. unique identifiers, the interoperability layer
added to the service in the context of OpenRiskNet can add these features and
in this way improve the quality of the data source.

###  2.2 Making data accessible  
  
<table>  
<tr>  
<th>

_●_

</th>  
<th>

Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so

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

_●_

</td>  
<td>

Specify how the data will be made available

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

_●_

</td>  
<td>

Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?

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

_●_

</td>  
<td>

Specify where the data and associated metadata, documentation and code are
deposited

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

_●_

</td>  
<td>

Specify how access will be provided in case there are any restrictions

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

The OpenRiskNet approach will enable the early and transparent sharing and
analysis of data between organisations involved in many sectors and programs.
OpenRiskNet APIs and the used transfer formats were openly released
immediately after their definition had reached a first stable form and updates
will be made available throughout the project. In the prioritization of
services to be integrated, open source tools are favoured for the use in the
case studies and the reference workflows targeting specific question but
commercial services will be equally important to sustain the infrastructure in
the long run. However, also these commercial services are required to openly
share their API definitions and data formats to allow for integration and
combination with other tools.

Open Standards applied:

  * Data and models are stored and served using well-developed and widely applied standards and technologies that promote data reuse and integration, such as JSON-LD, RDF and related semantic web technologies; 

  

  * OpenRiskNet resources are aligned with activities of toxicology communities like OpenTox in developing open standards for predictive toxicology resources; 

  

  * Tools to access study data and metadata description in standards file formats such as ISA, already in use in a number of omics, toxicogenomic and nanosafety resources (e.g. ToxBank, diXa, eNanoMapper), further simplify the integration; 

  

  * Model descriptions are provided encoded guided by suitable open standards (e.g. QMRF, BEL, SBML) and annotated advancing appropriate minimal information standards (MIRIAM) for dissemination through appropriate repositories (e.g. BioModels) to cover the extended requirements of the semantic interoperability layer of OpenRiskNet. 

  

OpenRiskNet does not propose to create new file standards rather to employ the
existing approaches as to define a core set of information, on which the
scientific community agrees that they are important to document, but which can
also be modified and extended if necessary for a specific application. For
defining this core set, regulatory files formats like **OECD** ​ **harmonised
templates (OECD HT)** [7  ​  ]  and **Standard** ​ **for Exchange of
Nonclinical Data (SEND)** [8  ​  ]  are considered. Even if these file formats
are too limited and do not have the flexibility to be used outside regulatory
purposes and especially for early stage research and method development, the
OpenRiskNet partners developing the guidelines are including as much
information needed for these reports as possible in the data transfer
templates.

###  2.3 Making data interoperable  
  
<table>  
<tr>  
<th>

_●_

</th>  
<th>

Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability

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

_●_

</td>  
<td>

Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?

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

OpenRiskNet interoperability layer opens possibilities to provide data
schemata, which describe the format of the data using a controlled vocabulary:

  * Metadata standards and data documentation approaches consider the existing standards that can be consolidated and the equivalent data that can be retrieved independent of the file format; 

  

  * Developments towards the integration of ontologies under a single framework are ongoing together with partner projects mainly from the EU NanoSafety Cluster, which will contribute to the goal of automatic harmonization and annotation of datasets. The goal is not to develop new ontologies. Instead, already existing ontologies (e.g. OBI, ChEBI, PATO, UO, NCIT, EFO, OAE, eTOX, eNanoMapper, MPATH, etc.) are consolidated and integrated into applications ontologies for the toxicology community and specifically for the requirements of OpenRiskNet service annotation. 

  

  * Another requirements to establish the comprehensive use of ontologies and in this way foster the interoperability not only of the major data sources but also user-provided data are user-friendly capturing frameworks supporting the selection of ontology terms during data curation and an ontology mapping service resolving issues of using synonyms from different ontologies (e.g. CAS numbers can be annotated using the National Cancer Institute Thesaurus, the EDAM ontology or even the Chemical Information Ontology reused in the eNanoMapper ontology, where it is available under the term “CAS registry number”). OpenRiskNet is working with experts in the field to integrate such tools in the infrastructure. 

  

  * Allowing mapping between related items in different database (e.g. different gene-identifiers, linking genes to proteins or RNA identifiers, or mapping between equivalent chemical structures in different databases. BridgeDb, which can perform such mappings and is already part of the OpenRiskNet Services, is thus a core interoperability service. 

  

Additionally, we provide guidelines and training on the usage of standard data
transfer/sharing formats and ontologies in the context of OpenRiskNet:

  * Best-practice examples like diXa and ToxBank are used to create templates for data storage and sharing; 

  

  * Data schemata for different endpoints as already available in file formats like ISA and ToxML will be transformed into more flexible data transfer approach able to accompany modifications needed due to changed and enhanced experimental and computational protocols. 

  

An important part of these procedure is searching and accessing data from
different sources supported by the semantic annotation of the data sources
based e.g on the

Bioschemas and BioAssays ontology:

  * The databases will be accessible by the OpenRiskNet APIs (similar like the computational tools) including the interoperability layer; 

  

  * Searches throughout multiple databases will be possible, removing the need to search in everyone independently 

  

  * The interoperability layer can be used to inspect the data schema and find out if the needed information is available from the databank and if it can be provided in a form for further analysis. 

  

OpenRiskNet provides to its potential data managers or developers complete
control over the provided data and associated functionalities but requires
from them to describe the interfaces and transfer formats in a generally
understandable OpenRiskNet-compliant way through the interoperability layer.
This work is based on and extends:

  * OpenTox APIs, which were designed to cover the field of QSAR-based predictive toxicology with dataset generation, model building, prediction and validation; 

  

  * Open PHACTS APIs, which handle knowledge collection and sharing; 

  

  * Various other APIs for accessing databases like BioStudies, EGA, ToxBank, and PubChem. 

  

###  2.4 Increase data re-use (through clarifying licenses)  
  
<table>  
<tr>  
<th>

_●_

</th>  
<th>

Specify how the data will be licenced to permit the widest reuse possible

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

_●_

</td>  
<td>

Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed

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

_●_

</td>  
<td>

Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why

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

_●_

</td>  
<td>

Describe data quality assurance processes

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

_●_

</td>  
<td>

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

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

Most of the data sources are already available in the public domain.
OpenRiskNet will redistribute the data using the same license as the original
data provider or, if this is demanded by the data provider, in a more
restricted form. New data will be made available through the OpenRiskNet
access methods as soon as it is released by the original data provider, i.e.
no additional embargo period will be enforced by the OpenRiskNet project.
Thus, users can access the same data with respect to the number of datasets
and version of each dataset either from the original service provider, via
e.g. a web interface specifically design for the data warehouse, or through
the OpenRiskNet mechanism, where the latter has the advantage of easy
integration into workflows and interoperability with other data sources and
software tools. Besides this simpler access, OpenRiskNet aims to improve the
quality of the data with the following measures:

Data quality assurance processes:

  * Tools for performing automatic validation, analysis and (pre)processing are developed to find inconsistencies in the data and databases and, in this way, improve the quality of the source and are made available in OpenRiskNet, e.g.  _http://arrayanalysis.org/_ and _ https://github.com/BiGCAT-U  ​  M  _ .​ Additionally, efforts to establish a general, cross-database data curation framework, in which users can flag possible errors in the data and semantic annotation, is supported. 

  

  * Some partners (e.g. UM) developed their own pipelines for quality control and analysis of sequencing data (RNA-seq and MeDIP-seq). 

  

  * We also integrate tools for manual curation of datasets. The modified dataset are stored (similar to the pre-reasoned datasets) in the original databases as a new version of the dataset or in other OpenRiskNet-compliant databases with a link to the original source. 

  

Quality assurance in the processing, analysis and modelling tools:

  * Protocolling of the performed calculations increasing the repeatability and reproducibility of the studies, is supported by the automatic logging and auditing functionalities of modern microservices frameworks as well as the integrated workflow management systems. 

  

  * Validation of the services are enforced by the consortium and appropriate measures of uncertainty are requested for all models. 

  

###  2.5 Specific information on individual shared data sources

####  2.5.1 diXa

The diXa Data Warehouse is open access. Data retrieval as well as upload takes
place via the diXa Data Warehouse webpage
_http://wwwdev.ebi.ac.uk/fg/dixa/index.htm_ ​  _l_ using the ISA-tab format
for the metadata. There is no API available for diXa.

Since the diXa Data Warehouse has become part of BioStudies diXa’s data can
also be accessed here. Data uploaded via BioStudies using the PageTab format
(Page layout Tabulation format) will be part of BioStudies and therefore, will
not be visible via the diXa Data Warehouse. The same is true the other way
around.

BioStudies also provides other formats, such as JSON and XML. Furthermore,
BioStudies is part of the ELIXIR infrastructure and an Rest API  2  is
available.

####  2.5.2 BridgeDb

The BridgeDb software is available under the OSI-approved Apache License 2.0.
Identifier mappings files are available under open licenses too, following the
open licenses of the upstream resources (Ensembl, Rhea) or CCZero in case of
the metabolite mapping database. The BridgeDb web service and data for
identifier mappings is made available on the OpenRiskNet cloud using an
OpenAPI specification wrapped around a REST services.

####  2.5.3 WikiPathways

All contents of WikiPathways are licenced with the Creative Commons CC0
waiver, which states that all contents of the database are free to share and
adapt. WikiPathways adopts a customised quality assurance protocol to curate
the database, which is done on a weekly basis.

####  2.5.4 AOP-Wiki

The AOP-Wiki provides quarterly downloads for the complete database, which are
permanently maintained by the OECD. The AOP-Wiki does not provide licence
information, but states that the data can be reused. All AOPs undergo review
by EAGMST to ensure the quality of the contents of the AOP-Wiki.  
  
<table>  
<tr>  
<th>

**FAIR Principles**

</th>  
<th>

**WikiPathways**

</th>  
<th>

**AOP-Wik i**

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

F1. (Meta)data are assigned and globally unique and persistent identifiers

</td>  
<td>

2

</td>  
<td>

1

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

F2. Data are described with rich metadata

</td>  
<td>

1

</td>  
<td>

1

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

F3. Metadata clearly and explicitly include the identifier of the data they
describe

</td>  
<td>

2

</td>  
<td>

2

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

F4. (Meta)data are registered or indexed in a searchable resource

</td>  
<td>

2

</td>  
<td>

2

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

A1.1. The protocol is open, free and universally implementable

</td>  
<td>

2

</td>  
<td>

2

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

A1.2. The protocol allows for an authentication and authorization where
necessary

</td>  
<td>

2

</td>  
<td>

2

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

A2. Metadata are accessible, even when the data are no longer available

</td>  
<td>

2

</td>  
<td>

2

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

I1. (Meta)data use a formal, accessible, shared, and broadly applicable
language for knowledge representation

</td>  
<td>

2

</td>  
<td>

2

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

I2. (Meta)data use vocabularies that follow FAIR principles

</td>  
<td>

1

</td>  
<td>

1

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

I3. (Meta)data include qualified references to other (meta)data

</td>  
<td>

2

</td>  
<td>

1

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

R1.1 (Meta)data are released with a clear and accessible data usage license

</td>  
<td>

2

</td>  
<td>

1

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

R1.2. (Meta)data are associated with detailed provenance

</td>  
<td>

1

</td>  
<td>

1

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

R1.3. (Meta)data meet domain-relevant community standards

</td>  
<td>

1

</td>  
<td>

2

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

**Table 1.** Compliance to FAIR principles ​  [9  ​  ]  by AOP-Wiki and
WikiPathways. Score meanings:​

1 = partial compliance, 2 = compliance

####  2.5.5 ToxCast

All data produced by the U.S EPA including ToxCast is by default in the public
domain (U.S. Public Domain license) and is not subject to domestic copyright
protection under 17 U.S.C. § 105. This allows to reproduce the work in print
or digital form, create derivative works, perform the work publicly, display
the work and distribute copies or digitally transfer the work to the public by
sale or other transfer of ownership, or by rental, lease, or lending. The​
release currently used in OpenRiskNet is a REST API developed by Douglas
Connect of the official summary data release made available as downloadable
CSV files. These were restructured to fit the OpenRiskNet concept and to make
them fit for semantic annotation.

##  3\. ALLOCATION OF RESOURCES  
  
<table>  
<tr>  
<th>

**Explain the allocation of resources, addressing the following issues** ​:

  * Estimate the costs for making your data FAIR. Describe how you intend to cover these costs 

  

  * Clearly identify responsibilities for data management in your project 

  

  * Describe costs and potential value of long term preservation 

  

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

Making data FAIR is a central task of the integration of data source in the
OpenRiskNet infrastructure. Many of the sources considered for integration
already follow the FAIR principles and no additional cost are foreseen other
than the effort needed to make the sources OpenRiskNet-compliant. For data
sources not at a sufficient high level, the OpenRiskNet partners owning the
data or responsible for its integrating covers the costs of the integration
from their allocated budget. In the case of third-party data sources, the
integration will be performed in collaboration of the associated partners
partly financially supported through the Implementation Challenge and an
OpenRiskNet partner designated as main contact point of the associated
partner.

##  4\. DATA SECURITY  
  
<table>  
<tr>  
<th>

_●_

</th>  
<th>

Address data recovery as well as secure storage and transfer of sensitive data

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

The OpenRiskNet approach on data recovery, secure storage and transfer of
sensitive data includes:

  * Responsible and secure management processes for personal data including anonymisation, encryption, logging of data usage as well as data deletion after usage are implemented; 

  

  * To ensure that all ethical guidelines are followed by all OpenRiskNet Partners and Associated Partners and implemented in every step of the infrastructure, a p​ rivacy by design approach is followed in the project, documented in the OpenRiskNet 

  

privacy policy (see below) and controlled by an independent Data Protection

Officer;

  * The most sensible way to protect sensitive data offered by the OpenRiskNet infrastructure is to bring the virtual environment and all data sources behind a company’s firewall by in-house deployment. 

  

##  5\. ETHICAL ASPECTS  
  
<table>  
<tr>  
<th>

_●_

</th>  
<th>

To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former

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

The ethical aspects are covered by the Protection Of Personal Data (POPD)
requirements and submitted deliverables:

  * D6.1 - NEC - Requirement No. 3: Statements regarding the full compliance of third country participants to the H2020 rules 

  

  * D6.2 - POPD - Requirement No. 4: Copies of the previous ethical approvals of data to be collected and used in the project, approvals which must also allow the possible secondary use of data 

  

  * D6.3 - POPD - Requirement No. 5: Consent forms and information sheets before interviews and surveys and any other data and personal data collection activity in the project 

  

  * D6.4 - POPD - Requirement No. 6: A statement by third party testers that they will comply with the applicable EU law and H2020 rules 

  

  * D6.5 - POPD - Requirement No. 7: Data Protection Officer Report 1 

  

And the two future deliverables:

  * D6.6 - POPD - Requirement No. 8: Data Protection Officer Report 2 \- D6.7 - POPD - Requirement No. 9: Data Protection Officer Report 3 

  

Based on the ethics report received as part of the evaluation of the proposal
and the first report of the Data Protection Officer summarizing the relevant
regulations and legislation for the different data types and test models,
OpenRiskNet has worked together with an external expert to develop elements on
an ethics review framework and ethics requirements checklist, which have to be
considered in the project and the infrastructure implementation before
integrating a data source.

One important component for working on the project case studies and the future
usage of the e-infrastructure is access to existing data provided by other
European projects in this field or from international consortia. The
OpenRiskNet platform is based on a data management system that will make
existing data sources mainly from i​ n vitro ​human and animal and i​ n vivo
animal experiments available to all stakeholders in a harmonised way. In this
context, one important additional task of the DMP, becoming even more relevant
with the new EU General Data Protection Regulation (GDPR) in effect since May
2018, is to support and give recommendations in achieving the highest impact
without jeopardising the ethics integrity of the OpenRiskNet infrastructure.

To fulfil the requirements from the ethics review, a step-by-step decision
process was developed addressing how important legacy data sources need to be
handled by the project. On top of the workflows provided in the first review
of the Data Protection Officer, a hierarchical data source analysis and
evaluation of the ethical implications for OpenRiskNet was performed.
Different categories of data sources have been analysed, including references
to the legislation in place and the conditions for primary, secondary and
tertiary data collection and use. Also special measures that need to be
considered for some specific cases are included.

Data sources considered:

I. Human Biomaterial Use, Collection or Storage (Donors) II. Primary Results
Data processing (Clinics)

  3. a) Compound Storage/Processing (Commercial cell lines data providers), and b) Secondary Results Processing (Experimentalists) 

  

  4. Tertiary Use - Storage/Provider - No Processing (Database) 

  

  

Based on the assessment of each of these categories, a checklist is proposed
for each data source to be included and/or used in the OpenRiskNet platform (