(Open) Research Data Management in H2020 (ISERD – Tel Aviv, Oct 31, 2016)

(Open) Research Data Management in
H2020
ISERD – Tel Aviv, Oct 31, 2016
@openaire_eu
Natalia Manola
OpenAIRE managing director
Athena Research & Innovation Centre
Credits
The OpenAIRE team
Sara Jones, Data Curation Center (DCC), UK
Marjan Grootveld, DANS, NL

Outline
• OpenAIRE – who are we?
• H2020 policies – what’s involved?
• Research data management – what about?
• Data Management Plan (DMP) – how to?
• Lessons learnt – what to avoid?
• OpenAIRE – what do we offer?
RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 2

Who we are
• EU project
• In 24x7 operation since Dec
2010
• OpenAIRE
• OpenAIREplus
• OpenAIRE2020
• Consortium of 50 partners
• One of 5 key EU e-
Infrastructures
• Institutional, national and
international perspectives on OA
policies & e-Infrastructures
Open Access experts
• Building efficient e-Infra technologies
• State of the art technologies (big
data, linked data)
Information & Computer
Science experts
• Legal &policy recommendations
Legal experts
• Best practices for data
• Linking to data infrastructures
Data communities

Human
Network
50 Partners from every EU country, and beyond
Data centers, universities, libraries, repositories, legal experts
Digital
Network
… fosters the social and technical links
that enable Open Science in Europe and beyond
RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016
National Open Access
Desks (NOADs)
33 OA expert nodes in all
Europe
• (OA) Policy aligning
• Technical assistance
• Training 4

Integrated Scientific Information System
17.2 mi unique publications
760 validated data providers
370Κ publications linked to
projects from 6 funders
28 K datasets linked to
publications or funders
3.5K links to software

H2020 Policies

The following European Commission branded slides come
from the EC’s open access team and provide an overview
to the key points. Content from Jean-Francois Dechamp
and colleagues.
Mail: RTD-open-access@ec.europa.eu
Web: http://ec.europa.eu/research/openscience/index.cfm

H2020 Open Access
policies
• Publications
• Openly accessible and minable.
Eligible costs for APCs.
• Research data
• Openly accessible research data
can typically be accessed,
mined, exploited, reproduced
and disseminated free of charge
for the user.

Publications

Three top reasons to opt out
Whether a (proposed) project participates
in the ORD or chooses to opt out does
not affect the evaluation of that project.
Proposals will not be penalised for opting
out

The EC Open Research Data pilot
Key sources of information
• Guidelines on Open Access to Scientific Publications and Research Data in
Horizon 2020
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-
hi-oa-pilot-guide_en.pdf
• Guidelines on Data Management in Horizon 2020
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-
hi-oa-data-mgt_en.pdf
• Annotated model grant agreement, clause 29.3
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/amga/h2020-
amga_en.pdf
• New infographic summarising key policy points
http://ec.europa.eu/research/press/2016/pdf/opendata-infographic_072016.pdf

Good research data
management practices

A FAIR approach to data
• Findable
– Assign persistent IDs, provide metadata, register in a searchable
resource...
• Accessible
– Retrievable by their ID using a standard protocol, metadata remain
accessible even if data aren’t...
• Interoperable
– Use formal, broadly applicable languages, use standard vocabularies,
qualified references...
• Reusable
– Rich metadata, clear licences, provenance, use of community
standards...
www.force11.org/group/fairgroup/fairprinciples

Findable
• Use metadata and specify standards for metadata creation (if
any). If there are no standards in your discipline describe what
type of metadata will be created and how
• Search keywords
• Persistent and unique identifiers such as DOIs or other
handles
• File and folder naming conventions
• Versioning of the datasets and clear version numbers

Metadata and documentation
• Metadata and documentation is needed to find and
understand research data
• Think about what others would need in order to find,
evaluate, understand, and reuse your data
• Get others to check the metadata to improve quality
• Use standards to enable interoperability

Where to find metadata standards
Metadata Standards Directory
Broad, disciplinary listing of
standards and tools
Maintained by RDA group
http://rd-alliance.github.io/metadata-directory
Biosharing
A portal of data standards,
databases, and policies for life,
environmental and biomedical
sciences
https://biosharing.org

Accessible
• Explain which data can’t be shared openly, if any
• Specify how access will be provided in case of restrictions,
e.g., through a data committee, a license, or arranged with the
repository
• Will methods or software tools needed to access the data (if
any) be included or documented?
• Deposit the data and associated metadata, documentation and
code preferably in certified repositories which support Open
Access

Where to find a repository?
More information: https://www.openaire.eu/opendatapilot-repository
What to deposit?
a. the data needed to validate the results
presented in scientific publications, including the
metadata;
b. any other data, including the metadata, as
specified in the DMP;
c. plus for a-b the documentation and the tools
that are needed to validate the results, e.g.
specialised software or software code,
algorithms and analysis protocols (when
possible, these instruments themselves).
22

Shop around
http://databib.org
http://service.re3data.org
Re3data is a registry of data repositories
Searching with Re3data.org
www.fosteropenscience.eu/content/re3data-demo
23

How to select a repository?
• Look into your community, university, publisher, funder etc.
• Check they match your particular data needs: e.g. formats
accepted; mixture of Open and Restricted Access
• See if they provide guidance on how to cite the deposited data
• Do they assign a persistent & globally unique identifier for
sustainable citations and to links back to particular researchers
and grants?
• Look for certification as a ‘Trustworthy Digital Repository’ with
an explicit ambition to keep the data available in long term.
www.openaire.eu/opendatapilot-repository

Zenodo
Multi-disciplinary repository used for the long-tail of research
data
• An OpenAIRE-CERN joint effort
• Multidisciplinary repository accepting
– Multiple data types
– Publications
– Software – link to Github
• Assigns a Digital Object Identifier (DOI), up t 50GB per
dataset
• Links funding, publications, data & software
www.zenodo.org

Choose appropriate file formats
If you want your data to be re-used and sustainable in the long-term, you
typically want to opt for open, non-proprietary formats.
Type Recommended Avoid for data
sharing
Tabular data CSV, TSV, SPSS portable Excel
Text Plain text, HTML, RTF
PDF/A only if layout matters
Word
Media Container: MP4, Ogg
Codec: Theora, Dirac, FLAC
Quicktime
H264
Images TIFF, JPEG2000, PNG GIF, JPG
Structured data XML, RDF RDBMS
Further examples:
www.data-archive.ac.uk/create-manage/format/formats-table

File format considerations
• No clearcut definitions of “sustainable file format”
• Each archives has its own expertise, relative to its designated
community Examples:
4TU.ResearchData DANS
Level 1 Level 2 or 3 Preferred Accepted
audio .wav .ra, .mp3, .wma .wav, .flac .aiff, .mp3, .aac
chemistr
y
NMR, ChemDoodle,
… .pdb, .xyz
database
s
delimited flat file
w/DDL .mdb, .dbf, .acdb .sql, .siard, .csv
.mdb, .dbf, .hdf5
…
video
.mp1, .mp2, .mp4,
.mov …
.mpg2, .mpg4, .avi,
.mov .mkv
27

Interoperable
• Interoperability on data and metadata, on data exchange
formats and protocols
• Specify what data and metadata vocabularies, standards or
methodologies you will follow to facilitate interoperability
• Standard vocabulary to allow inter-disciplinary interoperability
or a mapping from your vocabulary to more commonly used
ontologies?
Aim for compliance to globally accepted practices

• Clarify licences early on
• License the data to permit the widest reuse possible
• Specify a data embargo, if needed
• If data re-use by third parties is restricted, explain why
• How long will the data remain reusable?
• Describe data quality assurance processes
Reusable

www.dcc.ac.uk/resources/how-guides/license-research-data
License research data openly
DCC guide outlines the pros and cons of
each approach and gives practical advice
on how to implement your licence
CREATIVE COMMONS LIMITATIONS
NC Non-Commercial
What counts as commercial?
ND No Derivatives
Severely restricts use
These clauses are not open licenses
Horizon 2020 Open Access
guidelines point to:
or

What should be preserved and shared?
• The data needed to validate results in scientific publications
(minimally!).
• The associated metadata: the dataset’s creator, title, year of publication,
repository, identifier etc.
• Follow a metadata standard in your line of work, or a generic
standard, e.g. OpenAIRE or DataCite, and be FAIR.
• Documentation: code books, lab journals, informed consent forms –
domain-dependent, and important for understanding the data and
combining them with other data sources.
• Software, hardware, tools, syntax queries, machine configurations –
domain-dependent, and important for using the data. (Alternative:
information about the software etc.)
Basically, everything that is needed to replicate a study should
be available. Plus everything that is potentially useful for others.
31

Rules of thumb for how long
RDNL Selection criteria: http://www.researchdata.nl/en/services/data-management/selecting-research-
data/
DCC How-to guide: http://www.dcc.ac.uk/resources/how-guides/appraise-select-data
• When regenerating data is cheaper than archiving, don’t
archive. Select what data you’ll need and want to retain.
• 10 years is often stated in data policies and academic codes,
but data can be valuable for ages, in climatology, sociology,
health sciences, astronomy, linguistics, … Look beyond
minimal retention periods where relevant.
• “The lifetime of software is generally not as long as that of
data” (Daniel Katz e.a. http://bit.ly/2eScCKp)

How much does it cost? Who pays?
• What are the costs for making data FAIR in your project?
• Resources needed for long term preservation
• Check the UK Data Service Costing model
• The High Level Expert Group on the European Open Science Cloud
recommends that “well budgeted data stewardship plans should be
made mandatory and we expect that on average about 5% of
research expenditure should be spent on properly managing and
stewarding data”
• Who pays? How?UKDS model http://www.data-archive.ac.uk/create-manage/planning-for-sharing/costing
HLEG report
http://ec.europa.eu/research/openscience/pdf/realising_the_european_open_science_cloud_2016.pdf#view=fit&pagemode=none

RDM in a nutshell
Develop a
DMP
Select which
data to make
open
License data
openly for the
widest reuse
Use established
community
standards for
interoperability
Provide
metadata for
data discovery
Deposit in
a data
repository
Share details
about the tools
and instruments
used to allow
verification

Managing and sharing data –
A best practice guide
http://data-archive.ac.uk/media/2894/managingsharing.pdf

Data Management
Plan
ToolsRDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 36

What is a data management plan?
A brief plan written at the start of a project to define:
• how the data will be created?
• how it will be documented?
• who will access it?
• where it will be stored?
• who will back it up?
• whether (and how) it will be shared & preserved?
DMPs are often submitted as part of grant applications, but are useful
whenever researchers are creating data
The DMP is a living document.
You are not required to provide
detailed answers to all the
questions in the first version of
the DMP (due M6)
37
Explain any selection criteria in the DMP

DMPonline
A web-based tool to help researchers write DMPs
Includes a template for Horizon 2020 – FAIR principles
https://dmponline.dcc.ac.uk

How the tool works
Click to write a
generic DMP
Or choose your
funder to get their
specific template
Pick your uni to add
local guidance and
to get their template
if no funder applies
Choose any
additional optional
guidance

DMPonline for H2020
Focus on how you will
ensure your data are
“FAIR”
KEEP IT UP TO
DATE
40

Example DMP plans

Example plans
• 108 DMPs from the National Endowment for the Humanities
www.neh.gov/divisions/odh/grant-news/data-management-plans-successful-
grant-applications-2011-2014-now-available
• 20+ scientific DMPs submitted to the NSF (USA) provided by UCSD
• http://libraries.ucsd.edu/services/data-curation/data-management/ dmp-
samples.html
• Example DMP collection from Leeds University
• https://library.leeds.ac.uk/research-data-tools
• Further examples:
• www.dcc.ac.uk/resources/data-management-plans/guidance-examples

Example H2020 DMPs in Zenodo
• Helix Nebula – High Energy Physics example
• https://zenodo.org/record/48171#.WATexnriF40
• Tweether – engineering (micro-electronics) example
• https://zenodo.org/record/55791#.WATei3riF40
• AutoPost – ICT example
https://zenodo.org/record/56107#.WATefXriF40

Different DMP for different needs
• No strict rules – Every project has different
requirements
• Approach the DMP in whatever way best fits your project
• EC template is intended as a service, not an obligation
• Read the background information and the guidance, and use it
as a checklist.

Example: OpenMinTeD
OpenMinTed aims to create an
infrastructure for Text and Data
Mining (TDM) of scientific and
scholarly content
Have adopted own structure to
create a ‘Data and Software
Management Plan’
http://openminted.eu

Example: OpenMinTeD – Data chapter
Six types of datasets
identified
1. Scholarly publications
2. Language and knowledge resources
3. Services and workflows
4. Automatically and manually
generated annotations
5. Consortium publications
6. Metadata
Described in a table per
dataset
E-Infra structure: data in
transit

Example OpenMinTeD – Software
Lots of software components
Background vs. foreground

Example: CAPSELLA
CAPSELLA aims to develop ICT
solutions for farmers and other
actors engaged in
agrobiodiversity
Devised a questionnaire to
collate datset information from
project partners
Identified 13 datasets, 6 of which
are imported as is, 3 aggregated,
3 transformed and 1 generatedwww.capsella.eu
Focus on data produced

Example dataset record

Example: Autopost

Example: tweether
Travelling wave tube based w‐band
wireless networks with high data rate
distribution, spectrum
& energy efficiency
The academic institutions participating in TWEETHER have
available appropriate depositories which in fact are linked to
OpenAIRE.
Apart from these repositories, TWEETHER will also use
ZENODO to ensure the maximum dissemination of the
information generated in the project (research publications and
data)…
51

Example metadata description
Minimal metadata
More than one dataset? Describe generically what is
possible and dataset-specific what is necessary.
52

Data description examples
The final dataset will include self-reported demographic and
behavioural data from interviews with the subjects and laboratory
data from urine specimens provided.
NIH data sharing statements
Every two days, we will subsample E. affinis populations growing
under our treatment conditions. We will use a microscope to identify
the life stage and sex of the subsampled individuals. We will document
the information first in a laboratory notebook and then copy the
data into an Excel spreadsheet. The Excel spreadsheet will be saved
as a comma separated value (.csv) file.
DataOne – E. affinis DMP example

Metadata examples
Metadata will be tagged in XML using the Data Documentation Initiative (DDI)
format. The codebook will contain information on study design, sampling
methodology, fieldwork, variable-level detail, and all information necessary for
a secondary analyst to use the data accurately and effectively.
ICPSR Framework for Creating a DMP
We will first document our metadata by taking careful notes in the laboratory notebook that refer to
specific data files and describe all columns, units, abbreviations, and missing value
identifiers. These notes will be transcribed into a .txt document that will be stored with the
data file. After all of the data are collected, we will then use EML (Ecological Metadata
Language) to digitize our metadata. EML is one of the accepted formats used in ecology, and
works well for the types of data we will be producing. We will create these metadata using Morpho
software, available through KNB. The metadata will fully describe the data files and the context of
the measurements.
DataOne – E. affinis DMP example

Data sharing examples
We will make the data and associated documentation available to users under a data-sharing agreement
that provides for: (1) a commitment to using the data only for research purposes and not to identify any
individual participant; (2) a commitment to securing the data using appropriate computer technology; and
(3) a commitment to destroying or returning the data after analyses are completed.
The videos will be made available via the bristol.ac.uk website (both as streaming media and
downloads) HD and SD versions will be provided to accommodate those with lower bandwidth. Videos
will also be made available via Vimeo, a platform that is already well used by research students at
Bristol. Appropriate metadata will also be provided to the existing Vimeo standard.
All video will also be available for download and re-editing by third parties. To facilitate this Creative
Commons licenses will be assigned to each item. In order to ensure this usage is possible, the required
permissions will be gathered from participants (using a suitable release form) before recording
commences.
University of Bristol Kitchen Cosmology DMP

Restriction on use examples
Because the STDs being studied are reportable diseases, we will be collecting
identifying information. Even though the final dataset will be stripped of identifiers
prior to release for sharing, we believe that there remains the possibility of
deductive disclosure of subjects with unusual characteristics. Thus, we will make
the data and associated documentation available to users only under a data-
sharing agreement.
1. Share data privately within 1 year.
Data will be held in Private Repository, but metadata will be public
2. Release data to public within 2 years.
Encouraged after one year to release data for public access.
3. Request, in writing, data privacy up to 4 years.
Extensions beyond 3 years will only be granted for compelling cases.
4. Consult with creators of private CZO datasets prior to use.
Pis required to seek consent before using private data they can access
Boulder Creek Critical Zone Observatory DMP

Archiving examples
Data will be provided in file formats considered appropriate for long-term access, as recommended by the UK
Data Service. For example, SPSS Portal format and tab-delimited text for qualitative tabular data and RTF
and PDF/A for interview transcripts. Appropriate documentation necessary to understand the data will also
be provided. Anonymised data will be held for a minimum of 10 years following project completion, in
compliance with LSHTM’s Records Retention and Disposal Schedule. Biological samples (output 3) will be
deposited with the UK BioBank for future use.
Writing a Wellcome Trust Data Management and Sharing Plan
The investigators will work with staff at the UKDA to determine what to archive and
how long the deposited data should be retained. Future long-term use of the data will
be ensured by placing a copy of the data into the repository.
ICPSR Framework for Creating a DMP

Lessons learnt and
a few planning
tricks

Start early
• Negotiation on licenses and consent agreement may
preclude later sharing if not careful
• Costs cannot be included retrospectively
• Useful to consider data issues at the consortium negotiation
stage to make sure potential issues are identified and sorted
asap
• Involve all work packages and partners to get a coherent
plan.
• Focus effort on datasets you’ll create rather than reuse
Decisions made early on affect what you can do laterRDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 59

Think backwards
What data organisation would a re-user like?
CREATING
DATA
PROCESSING
DATA
PRESERVING
DATA
GIVING
ACCESS TO
DATA
RE-USING
DATA
Think about the desired end result and plan for thi
“Sharing” means “outside the consortium”
60

Institution
RDM policy
Facilities
€$£
Research
funders
Publishers
Data Availability
policy
Commercial
partners
Find who is responsible for what
https://www.openaire.eu/briefpaper-rdm-infonoads 61

Note: It’s more than open data
CC-BY Andreas Neuhold
https://commons.wikimedia.org/wiki/File:Open_Science_-_Prinzipien.png
• Access to research facilities
• Access to processing
capabilities
• Communication at all levels
of research life cycle

Limitations on current DMPs
• DMP valuation is project based
• Too much text – Text and data mining to get
aggregate knowledge
• Machine readability
• Linking to infrastructures
• Semi-automated evaluation and comparison

How can Open AIRE
help?

OpenAIRE support
materials
Briefing papers, factsheets,
webinars, workshops, FAQs
Information on
• Open Research Data Pilot
• Creating a data
management plan
• Selecting a data repository
• Personal data
Developing guidance to add
to DMPonline
https://www.openaire.eu/opendatapilot
https://www.openaire.eu/support

OpenAIRE services
• Researchers
• Zenodo for all types of publications, data and software
• Claiming – linking research results
• Amnesia, an anonymization tool for all
• Data providers – Interoperability Guidelines, validation,…
• Project coordinators – reporting
• Funders and institutions – monitoring
• Research communities – gathering, monitoring all research
DASHBOARDS

Literature
Repositories
OA Journals
Funding Info
Validation
Cleaning
De-duplicating
Inferring
Linking
Organiz
ations
Projects
Authors
Dataset
s
Publicat
ions
Data
Provider
s
…
Monitoring
Reporting
Evaluation
Impact
Classification
Clustering
Analysis
CRIS systems
An EU-CRIS system
Data
Repositories
Metadata
Full text
Usage data
Discovery
Crowdsourcing
APIs
Trends
Aggregators
Enriching
ServicesOpenAIRE PlatformData Providers
Routing
Archives
Guidelines

Integrated Scientific Information System
17.3 mi unique publications
760+ validated data providers
370Κ publications linked to
projects from 6 funders
28 K datasets linked to
publications
3.5K links to software
repositories
33K organizations
Organization
s
Projects
AuthorsDatasets
Publications
Data
Providers
Software Facilities Methods
Research
Communities
OpenAIRE-Connect
From January 2017
68

World-wide alignment &
synergies
Interoperability alignment, sharing
technologies & services
• La Refencia: Latin America repository
network
• JAIRO – Japanese Institutional
Repositories Online
• REMERI – Mexican Network of
Institutional Repositories
• …
• ICSU/World Data Service – A network of
70 certified data centers

Monitoring & reporting
• Funders
• Institutions
+
NSF, NIH (US)
NOW (NL)
MSES, CSF (HR)
SFI (IR)
DFG (DE)
…..Towards an EU-CRIS system
70

Project level– Automatically export results
Project’s web site
EC front-end system
71

…to the EC's participant portal
EC back-end reporting
72

www.openaire.eu
@openaire_eu
facebook.com/groups/openaire
linkedin.com/groups/OpenAIRE-3893548
Thank you!
natalia@di.uoa.gr
Questions?
73

(Open) Research Data Management in H2020 (ISERD – Tel Aviv, Oct 31, 2016)

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