#  1\. Introduction and background

The main focus of the Beacon Project is modelling, and mainly existing data
from ongoing or previous experiments will be (re)used. This data has been
assembled and evaluated in work package 2 and will be used in work packages 3
and 5. The database with the assembled data is a part of Beacon deliverable
D2.2.

It is envisaged that there will be need to complement existing data and
therefore Beacon has a work package (WP4) for experimental work. The
experiments performed in the project will be

  * complementary where the existing data is not sufficient 

  

  * only laboratory scale 

  

This Data Management Plan concerns both experimental data, models and input
and output data.

Good research data management is a key conduit leading to knowledge discovery
and innovation, and to subsequent data and knowledge integration and reuse.
Instructions from the Commission regarding data management can be found here
_http://ec.europa.eu/research/participants/docs/h2020-fundingguide/cross-
cutting-issues/open-access-data-management/data-management_en.htm_ and here
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-
oa-datamgt_en.pdf_

#  2\. Purpose

The purpose with this plan for the management of Beacon Project data is to
support traceability, availability and quality assured handling of the data
produced within the frames of the Beacon project, and to the extent it is
possible, realistic and relevant it should support in making the research data
in the Beacon project “ _findable_ , _accessible_ , _interoperable_ and
_reusable_ ( **FAIR** )” as it is formulated in the H2020 online manual.

#  3\. Delimitations

This DMP does not concern the data assembled in WP2 from other projects for
reuse in the project. This reused data is documented in an excel database,
published together with the Beacon deliverable D2.2.

#  4\. Summary of the guidelines

 Data and models produced, generated and/or developed in Beacon have to be
saved, stored and backed up in a safe way for _at least 5 years_ after the
Beacon project is finalised.

 The Commission requires that data is kept “ _Findable_ , _Accessible_ ,
_Interoperable_ and _Reusable_ ( **FAIR** )”, to the extent it is possible,
during and after the project. (Explanations to these concepts available
further down)

 Partners should normally use their own procedures and management systems to
manage data and models.

 If such procedures are lacking use the below guidelines.

 In case the dataset cannot be shared, the reasons for this should be
mentioned (e.g. ethical, rules of personal data, intellectual property,
commercial, privacy-related, security-related).

#  5\. Guidelines

##  5.1 General

Data from experiments, models, the input data used and output data generated
in the Beacon project need to be stored, findable, accessible, and as far as
possible interoperable and reusable (FAIR) for at least 5 years after the
Beacon project is finalised.

Data types in Appendix 3.

The **participants that have a management system for handling data and
information** that lives up to the relevant applicable parts of the guidelines
in section 4 should follow that and inform the rest of the consortium how the
data of the experiments and modelling tasks are managed through filling out
the essential parts appendix 1 (modified if appropriate) and uploading it to
the relevant folder in the Beacon Projectplace. The Beacon Projectplace can
also be used to store project data.

The **participants that do not** have a management system for handling data
and information that lives up to the relevant parts of the guidelines in
section 4 will follow the below described guidelines and can use attachment
1(modified if appropriate) to describe data. Attachment 2 can be used as a
template when planning experiments and modelling tasks where data will be
produced.

In an experiment or modelling plan with an appropriate scope the relevant and
applicable parts of the following issues should be addressed. Appendix 1 and 2
can be used as templates. The partner(s) performing the experiments or
modelling and thereby producing the data are responsible for managing the
data.

_**Generally, the procedures normally applied within the organisation can and
should be used.** _

##  5.2 Experiment data summary

State/explain/specify:

  * the purpose of the data collection/generation 

  

  * the relation to the objectives of the project 

  

  * the types and formats of data generated/collected 

  

  * if existing data is being re-used (if any) and if so the origin of the data 

  

  * the expected size of the data (if known) 

  

  * to whom will the data be useful 

  

##  5.3 Modelling, Input Data and Output data

It is the responsibility of the Party performing the modelling that the models
and codes used are quality assured, validated and traceably described.

Modelling files will be

  * (compressed if appropriate) saved and stored in a safe and findable way 

  

  * backed up, 

  

  * together with o a description of which version of the model/calculation code has been used for which modelling/calculation 

    * Input Data 

  

    * Output Data 

  

    * (if relevant: which compression tool, inc version, has been used) 

  

  

##  5.4 FAIR data according to H2020 guidelines

(also assembled in appendix 4)

**FAIR=** Findable, Accessible, Interoperable and Reusable

###  Making the data Findable (inc. provisions for metadata (information about
the data) [FAIR] The discoverability of data should be outlined (metadata
provision)

The identifiability of data should be outlined and standard identification
mechanism referred to. Do you make use of persistent and unique identifiers
such as **Digital Object Identifiers** ? Naming conventions used should be
outlined. The approach towards search keyword should be outlined. The approach
for clear versioning should be outlined. Standards for metadata (information
about the data) creation (if any) should be specified.

_Guidance_ :

The Research Data Alliance provides a  _Metadata Standards Directory_ that can
be searched for discipline-specific standards and associated tools.

###  Making data openly Accessible [FAIR]

It should be specified which data will be made openly available, and how. If
some data is kept closed provide rationale for doing so.

_Guidance_ : Follow the principle " **as open as possible, as closed as
necessary** ". The Commission recognises that there are good reasons to keep
some or even all research data generated in a project closed. Where data need
to be shared under restrictions, explain why, clearly separating legal and
contractual reasons from voluntary restrictions. It is possible for specific
beneficiaries to keep their data closed if relevant provisions are made.

The  _Registry of Research Data Repositories_ provides a useful listing of
repositories that you can search to find a place of deposit. If you plan to
deposit in a repository, it is useful to explore appropriate arrangements with
the identified repository in advance. 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)?

Specify where the data and associated metadata (information about the data),
documentation and code are deposited.

Specify how access will be provided in case there are any restrictions. For
example is there a need for a data access committee.

###  Making data Interoperable [FAIR]

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

_Guidance_ :

Interoperability means allowing data exchange and re-use between researchers,
institutions, organisations, countries, etc. (i.e. adhering to standards for
formats, as much as possible compliant with available (open) software
applications, and in particular facilitating re-combinations with different
datasets from different origins.

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?

##  _Increase data Re-use (through clarifying licenses) [FAIR]_

Specify how the data will be licenced ( **if applicable** ) to permit the
widest reuse possible. Specify whether the data produced and/or used is
useable by third parties, in particular after the end of the project. If the
re-use of some data is restricted, explain why. Specify the length of time for
which the data will remain re-usable.

_Guidance_ :

The  _EUDAT B2SHARE_ tool includes a built-in license wizard that facilitates
the selection of an adequate license for research data. Specify when the data
will be made available for re-use. If applicable, specify why and for what
period a data embargo is needed. Reasons for embargoes may include time to
publish or seek patents. If an embargo is sought, specify why and for how
long, bearing in mind that research data should be made available as soon as
possible.

**6\. Quality assurance of data**

Data in the project should be quality assured. Describe or refer to the
processes used in

#  7\. Allocation of resources for open access of data

Costs related to open access to research data are eligible as part of the
Horizon 2020 grant (if compliant with the Grant Agreement conditions). Costs
are eligible for reimbursement during the duration of the project under the
conditions defined in the H2020 Grant Agreement, in particular  _Article 6_ a
nd  _Article 6.2.D.3_ but also other articles relevant for the cost category
chosen. If applicable describe costs and potential value of long term
preservation

#  8\. Data security

Address:

  * data recovery 

  

  * if applicable: secure storage and transfer of sensitive data 

  

Open data can be stored in Beacon Projectplace. Regarding sensitive data;
bring the issue to the Executive board for discussion. If necessary it should
be safely stored in certified repositories for long term preservation and
curation.

#  9\. Ethical aspects

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.

_Guidance_ :

Consider whether there are any ethical or legal issues than can have an impact
on data sharing. For example, is informed consent for data sharing and long
term preservation included in questionnaires dealing with personal data?

#  10\. National/funder/sectorial/departmental procedures

(If Applicable) Refer to other national/funder/sectorial/departmental
procedures for data management that you are using (if any).

##  APPENDIX 1: Data management description template

Upload to the Beacon Projectplace  
  
<table>  
<tr>  
<th>

**Metadata (information about the data)**

</th>  
<th>

**Description**

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

**Dataset reference and/or name**

</td>  
<td>

_Unique name identifying the dataset._

_Identifier should start with EU-Beacon-WPx where “x” is the relevant work
package number followed by a three digit number. Example: “EU-BeaconWP4-001”_

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

**Datatype**

</td>  
<td>

_Choose one or more of the relevant data types: Experimental Data,_

_Observational Data, Raw Data, Derived Data, Physical Data (samples), Models,
Images, Protocols, Input Data and Output Data. Data types are further
described in Appendix 3._

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

**Source**

</td>  
<td>

_Source of the data. Reference should include work package number, task number
and the main project partner or laboratory which produced the data._

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

**Dataset description**

</td>  
<td>

_Provides a brief description of the data set along with the purpose of the
data and whether it underpins a scientific publication. This should allow
potential users to determine if the data set is useful for their needs._

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

**Standards and information about the data**

</td>  
<td>

_Provides a brief description of the relevant standards used and list relevant
metadata (information about the data) in accordance with the description in
Appendix 3._

_The usage of the Directory Interchange Format is optional._

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

**Science Keywords**

</td>  
<td>

_List relevant scientific key words to ensure that the data can be efficiently
indexed so others may locate the data._

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

**Data sharing**

</td>  
<td>

_Description of how data will be shared both during and after the Beacon
project. Include access procedures, embargo periods (if any), outlines of
technical mechanisms for dissemination and necessary software and other tools
for enabling re-use, and definition of whether access will be widely open or
restricted to specific groups._

_Information should include a reference to the repository where data will be
stored._

__In case the dataset cannot be shared_ , the reasons for this should be
mentioned (e.g. ethical, rules of personal data, intellectual property,
commercial, privacy-related, security-related). _

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

**Archiving and preservation**

</td>  
<td>

_Description of the procedures that will be put in place for long-term
preservation of the data._

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

##  APPENDIX 2: Data set template

**Metadata (information about the data)**

**Dataset reference** **and/or name**

**Source**

**Dataset description**

**Standards and**

**metadata (information about the data)**

**Data sharing**

**Archiving and**

**preservation**

**APPENDIX 3: Data types**

#  1 Experimental Data

##  Dataset description

The experimental data originate from measurements performed in a laboratory
environment, be it _in situ_ or _ex situ_ . The data comprise point or
continuous numerical measurements.

The data will be collected either on a sample basis (sampling an experiment at
a certain point in time) or on an experiment scale (without sampling the
experimental set-up). Data can be derived from either destructive of
preservative analyses. Experimental data collection can occur automatically or
manually, and will be available in a digital or a hard copy format. In the
case of the latter, experimental data will first be copied to e.g. a lab book
and then digitized.

Experimental data are supposed to be unique, in the way that new experiments
will be set-up, producing fresh data. In some cases, similar data will be
available from previous/other experiments within the project, within the
partners’ institution or from overlapping projects, allowing comparison and
integration of the newly obtained data.

##  Standards and information about the data

Experimental data are obtained using standardized laboratory techniques which
are calibrated when applicable. Positive and negative controls are used and
standards, internal or external, are introduced.

Summary information about the data (metadata) can optionally be provided
according to a Directory Interchange Format (DIF). A DIF allows users of data
to understand the contents of a dataset and contains those fields which are
necessary for users to decide whether a particular dataset would be useful for
their needs.

#  2 Observational Data

##  Dataset description

Observational research (or field research) is a type of correlational (i.e.,
non-experimental) research in which a researcher observes ongoing behaviour.

##  Standards and information about the data

The information (metadata) regarding observational data should include any
standards used and the necessary information so that an external researcher
has the possibility to analyse how the data was gathered.

#  3 Raw Data

##  Dataset description

_Raw data_ are primary data collected from a source, not subjected to
processing or any other manipulation.

Raw data are derived from a source, including analysis devices like a
sequencer, spectrometer, chromatograph etc. In most cases, raw data are
digitally available. In some cases (e.g. sequencing), the raw data will be
very extensive datasets.

Raw data has the potential to become information after extraction,
organization, analysis and/or formatting. It is therefore used as input for
further processing.

##  Standards and information about the data

Raw data are obtained using standardized laboratory techniques which are
calibrated when applicable. Positive and negative controls are used and
standards, internal or external, are introduced.

Metadata (information about the data) should at least include standards,
techniques and devices used. It can optionally be provided according to a DIF.
A DIF allows users of data to understand the contents of a dataset and
contains those fields which are necessary for users to decide whether a
particular dataset would be useful for their needs.

#  4 Derived Data

##  Dataset description

Derived data are the output of the processing or manipulation of raw data.
Derived data originate from the extraction, organization, analysis and/or
formatting or raw data, in order to derive information from the latter. In
most cases, derived data are digitally available, as are the raw data. Derived
data will allow for the interpretation of laboratory experiments, e.g. through
statistical analysis or bioinformatics processing.

##  Standards and information about the data

Manipulation of data will be performed using a ‘scientific code of conduct’,
i.e. maintaining scientific integrity and therefore not falsifying the output
or its representation.

Information about the data (metadata) should include any standard or method or
best practice used in the analysis. It can optionally be provided according to
a Directory Interchange Format. A DIF allows users of data to understand the
contents of a dataset and contains those fields which are necessary for users
to decide whether a particular dataset would be useful for their needs.

#  5 Physical Data (samples)

##  Dataset description

Physical data are samples that have been produced by an experiment or taken
from a given environment. Sampling of an environment or experiment is
performed in order to obtain information through analyses. As such,
experimental, raw and or derived data will be obtained from physical data.
When the analyses are destructive, the samples cannot be stored for later use.
When the analyses are preservative, samples can be stored for later use, but
only for a limited time.

##  Standards and information about the data

When sampling an environment or experiment, blank samples are taken as well,
as a reference. Information that should be included about the data (metadata)
are description of the origin of the sample, age, processing, storage
conditions and expected viability of the sample (as some sets of samples can
only be stored for a limited time, due to their nature).

#  6 Images

##  Dataset description

Imaging data are optical semblances of physical objects.

Objects of macro- and microscopic scale can be imaged in a variety of ways
(e.g. photography, electron microscopy), enabling the optical appearance to be
captured for later use or for sharing. When required, the optical appearance
can be magnified (e.g. microscopy) and manipulated to enable the
interpretation of the objects (mostly samples from an environment or
experiment). Imaging data support the interpretation of other data, like
experimental data. Some imaging data will be raw data (3.3), which need to be
derived through image processing to enable interpretation.

##  Standards and information about the data

Advanced imaging devices are calibrated to ensure prospering visualization.

Information about the data (metadata) which are provided are time of imaging,
device settings and magnification/scale when appropriate. In addition,
information will be provided about the object that is being imaged.

#  7 Protocols

##  Dataset description

A protocol is a predefined written procedural method in the design and
implementation of experiments or sampling. In addition to detailed procedures
and lists of required equipment and instruments, protocols often include
information on safety precautions, the calculation of results and reporting
standards, including statistical analysis and rules for predefining and
documenting excluded data to avoid bias.

##  Standards and information about the data

Protocols enable standardization of a laboratory method to ensure successful
replication of results by others in the same laboratory or by partners’
laboratories.

Information (metadata) that should be included for Protocols are the purpose
of the protocols, references to standards and literature.

#  8 Models, Input Data and Output Data

It is the responsibility of the Party performing the modelling that the models
and codes used are quality assured, validated and traceably described.

Modelling files will be zipped and saved, stored in a safe and findable way,
and backed up, together with a description of which version of the
model/calculation code has been used for which modelling/calculation, as well
as Input Data and Output Data.

**APPENDIX 4:**

**FAIR Data Management at a glance: issues to cover in your Horizon 2020 DMP**

This table provides a summary of the Data Management Plan (DMP) issues to be
addressed, as outlined above.  
  
<table>  
<tr>  
<th>

**DMP component**

</th>  
<th>

</th>  
<th>

**Issues to be addressed, can be very short. State N/A when relevant**

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

**1\. Data summary**

</td>  
<td>



</td>  
<td>

State the purpose of the data collection/generation

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

</td>  
<td>



</td>  
<td>

Explain the relation to the objectives of the project

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

</td>  
<td>



</td>  
<td>

Specify the types and formats of data generated/collected

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

</td>  
<td>



</td>  
<td>

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

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

</td>  
<td>



</td>  
<td>

Specify the origin of the data

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

</td>  
<td>



</td>  
<td>

State the expected size of the data (if known)

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

</td>  
<td>



</td>  
<td>

Outline the data utility: to whom will it be useful

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

  2. **FAIR Data**

  

  

2.1. Making data findable, including provisions for metadata

(information about the data)

</td>  
<td>







</td>  
<td>

Outline the discoverability of data (metadata (information about the data)
provision)

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?

Outline naming conventions used

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

</td>  
<td>



</td>  
<td>

Outline the approach towards search keyword

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

</td>  
<td>



</td>  
<td>

Outline the approach for clear versioning

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

</td>  
<td>



</td>  
<td>

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

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

2.2 Making data openly accessible

</td>  
<td>



</td>  
<td>

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

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

</td>  
<td>



</td>  
<td>

Specify how the data will be made available

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

</td>  
<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>



</td>  
<td>

Specify where the data and associated metadata (information about the data),
documentation and code are deposited

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

</td>  
<td>



</td>  
<td>

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

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

Beacon

D8.1 – Beacon Data Management Plan

Dissemination level: PU

Date of issue: **19/03/2018**  
  
<table>  
<tr>  
<th>

2.3. Making data interoperable

</th>  
<th>



</th>  
<th>

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

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

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

2.4. Increase data re-use (through clarifying licences)

</td>  
<td>





</td>  
<td>

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

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>



</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>



</td>  
<td>

Describe data quality assurance processes

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

</td>  
<td>



</td>  
<td>

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

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

3\. Allocation of resources

</td>  
<td>



</td>  
<td>

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

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

</td>  
<td>



</td>  
<td>

Clearly identify responsibilities for data management in your experiment

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

</td>  
<td>



</td>  
<td>

Describe costs and potential value of long term preservation

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

4\. Data security

</td>  
<td>



</td>  
<td>

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

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

5\. Ethical aspects

</td>  
<td>



</td>  
<td>

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

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

6\. Other

</td>  
<td>



</td>  
<td>

Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any)

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

Beacon

D8.1 – Beacon Data Management Plan

Dissemination level: PU

Date of issue: **19/03/2018**