1 Introduction  5

2 Data management plan  6

2.1 General  6

2.2 Attributes of datasets  7

2.3 Data set reference and name - file naming convention  8

2.4 Quality assurance and validation  8

2.5 Access to data and permissions, facilities for storage, storage after
project end  8

3 Overview / Expected data  9

4 List of data sets, including plan per data-set  10

4.1.1 Numerical input data for software developed within the project  10

4.1.2 Numerical parameters included in software developed within the project
10

4.1.3 Numerical output data of software developed within the project  10

4.1.4 Narrative information  10

4.1.5 Numerical statistics obtained based on narrative information  11

4.1.6 Source code  11

#  1 Introduction

This document concerns the Data Management Plan (DMP) of the H2020 GRACeFUL
project. “The purpose of the Data Management Plan (DMP) is to provide an
analysis of the main elements of the data management policy that will be used
by the applicants with regard to all the datasets that will be generated by
the project. The DMP is not a fixed document, but evolves during the lifespan
of the project. The DMP should address the points below on a dataset by
dataset basis and should reflect the current status of reflection within the
consortium about the data that will be produced” (  1  ).

The DMP has a close relation to the open publishing policy, as can be elicited
from the presentation “Open Access and the Open Research Data Pilot in H2020”
2  : “Aim to deposit at the same time the research data needed to validate the
results” (see copies of slides in figure 1).  
  
<table>  
<tr>  
<th>

</th>  
<th>

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

</td>  
<td>

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

**Figure 1: Key slides of the European Commission on Open Access to data.**

Data is referred to in both GRACeFUL proposal and in the GRACeFUL consortium
agreement. The former refers to this DMP being a formal deliverable. The
latter states for each partner: “No data, know-how or information of [partner]
shall be needed by another Party for implementation of the Project (Article
25.2 Grant Agreement) or exploitation of that other Party’s Results (Article
25.3 Grant Agreement). This represents the status at the time of signature of
this Consortium Agreement.”

However, the project concerns the development of Rapid Assessment Tools
(RATs), and the expectation is that numerical data will be required to
populate the different tools which are developed. As a general rule, the data
to populate RATs will be provided by third parties, e.g. digital maps of the
City of Dordrecht, populated hydrodynamic models, etc.. Graceful will leave
the data disclosure and management with the data owner, ensure that proper
references are made and will, whenever applicable ensure that no data are
published without the owner’s consent. In addition, the project will elicit
data and information from stakeholders in the Climate Resilient Urban Design
case study. This may include responses to questionnaires and recordings of
meetings.

Referring to the slide “Pilot on Open Research Data (3)”, the project may need
to opt out of the pilot due to the aforementioned ownership of data and
privacy of results.

The remainder of this document follows the template provided in Guidelines on
Data Management in Horizon 2020 Version 1.0 (  1  ).

#  2 Data management plan

##  2.1 General

In this GRACeFUL DMP we consider the following items as part of ‘data’:

  1. Numerical input data for software developed within the project. 

  

  2. Numerical parameters included in software developed within the project. 

  

  3. Numerical output data of software developed within the project. 

  

  4. Narrative information 

  

  5. Numerical statistics obtained based on narrative information. 

  

  6. Source code 

  

  

The general GRACeFUL policy for data not owned by third parties is as follows:

  1. Generally speaking, data will be made accessible via the GRACeFUL website in a suitable format, protected by a login facility, including a disclaimer and only after agreeing terms of use. 

  

  2. Numerical data which are made accessible will contain meta-data. Wherever possible /feasible, the INSPIRE meta-data model will be used (  3  ). 

  

  3. Narrative data, in particular results from stakeholder interventions will be published in suitable file formats and respecting the agreements on publishing by stakeholders as will be elicited in using Informed Consent Forms (GRACeFUL Deliverable 2.7). 

  

  4. Numerical external input data, that is data which are not developed by the project are not managed nor distributed without prior consent by GRACeFUL . Reference to the data-source will be made. 

  

  5. Numerical results of pre-processing of data, in other words pre-processed data will be managed internally to the project. Only procedures and reference to source data will be published for re-use and peer-review. 

  

  6. Source code of software will be properly managed. Unless there are weighty arguments, such as, potential commercialisation or inclusion of proprietary code, software will published for inspection and re-use. 

  

  7. Models and tools are defined as populated software, including input data and parameter settings. They will be properly managed. Unless there are weighty arguments models and tools will be published for inspection and re-use. 

  

  8. In case any pre-existing software relevant for the case study is used, data management of data used by this software (input / parameters/ output) will be determined case by case. 

  

  

For purpose of keeping an overview, the data management plan is tabularized on
landscape, from chapter 4 “List of data sets, including plan per data-set”,
page 10 onwards. The items discussed are in the header row, and defined in
chapter 2.2.

##  2.2 Attributes of datasets

“Table 1: Attributes of datasets” provides an overview of items that will be
included for each dataset.

**Table 1: Attributes of datasets**  
  
<table>  
<tr>  
<th>

**Item**

</th>  
<th>

**Description**

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

Data set reference and name

</td>  
<td>

Identifier for the data set to be produced.

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

Data set description

</td>  
<td>

Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale and to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration and reuse.

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

Standards and metadata

</td>  
<td>

Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created.

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

Data sharing

</td>  
<td>

Description of how data will be shared, including 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. Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.). In case the
dataset cannot be shared, the reasons for this

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

</td>  
<td>

should be mentioned (e.g. ethical, rules of personal data, intellectual
property, commercial, privacy-related, security-related).

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

Archiving and preservation (including storage and backup)

</td>  
<td>

Description of the procedures that will be put in place for long-term
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end volume, what the associated costs are and how
these are planned to be covered.

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

##  2.3 Data set reference and name - file naming convention

Data will be uploaded to a repository. The naming convention of files will be

WP##_[Description]_v##

## stands for a numerical value, v for version.

##  2.4 Quality assurance and validation

No explicit quality assurance and validation on third party data will be
carried out. If information on quality of data is readily and publicly
available this information will referred to.

Data and software developed within the project will be subject to internal
review and testing. The methodology will be determined a case-by-case.

##  2.5 Access to data and permissions, facilities for storage, storage after
project end.

The GRACeFUL consortium intends to make data publicly and digitally available
through the most appropriate means, for example on a secure section on the
website or via third party open access servers. The following aspects will be
taken into account when selecting the most appropriate means:

  1. Ability to store data, source codes and publications. 

  

  2. Ability to store data, source codes and publications for some time under embargo, to ensure that GRACeFUL partners have the ability to publish first. 

  

  3. Ability to track downloads / users; 

  

  4. Ability to guarantee the users have read the terms of use (including indemnifying the project partners from consequences of using the data and tools); 

  

  5. Ability to guarantee privacy aspects of the users; 

  

  6. Cost during project lifetime; 

  

  7. Sustainability / cost after project termination. 

  

  

The GRACeFUL consortium is looking into appropriate solutions.

#  3 Overview / Expected data

Table 2 provides an initial overview of data used per WP. Typically data are
either input or output, but in some cases generated input data such as causal
loop diagrams are both an input to other parts of the project and an output.

**Table 2: Overview of data**  
  
<table>  
<tr>  
<th>

**Responsible**

**WP**

</th>  
<th>

**Expected input data (including type and spatio-temporal resolution if
applicable)**

</th>  
<th>

**Input/**

**Output**

**/Both**

</th>  
<th>

**Origin (internal/external)**

</th>  
<th>

**Openness of output data (yes/no/TBD [to be decided])**

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

WP1

</td>  
<td>

_None_

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

WP2

</td>  
<td>

Data included in the

“Climate Adaptation

Support Tool (CAST)”

</td>  
<td>

Input

</td>  
<td>

external

</td>  
<td>

TBD

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

WP2

</td>  
<td>

Stakeholder based causal loop diagrams including individual / group weights

</td>  
<td>

Both

</td>  
<td>

internal

</td>  
<td>

Yes (depersonalized)

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

WP3

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

WP4

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

WP5

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

WP6

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

#  4 List of data sets, including plan per data-set

###  4.1.1 Numerical input data for software developed within the project.  
  
<table>  
<tr>  
<th>

WP/task

</th>  
<th>

Data set reference and name

</th>  
<th>

Data set description

</th>  
<th>

Standards and metadata

</th>  
<th>

Quality assurance status

</th>  
<th>

Data sharing

</th>  
<th>

Archiving and preservation

(including storage and backup)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

###  4.1.2 Numerical parameters included in software developed within the
project.  
  
<table>  
<tr>  
<th>

WP/task

</th>  
<th>

Data set reference and name

</th>  
<th>

Data set description

</th>  
<th>

Standards and metadata

</th>  
<th>

Quality assurance status

</th>  
<th>

Data sharing

</th>  
<th>

Archiving and preservation

(including storage and backup)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

###  4.1.3 Numerical output data of software developed within the project.  
  
<table>  
<tr>  
<th>

WP/task

</th>  
<th>

Data set reference and name

</th>  
<th>

Data set description

</th>  
<th>

Standards and metadata

</th>  
<th>

Quality assurance status

</th>  
<th>

Data sharing

</th>  
<th>

Archiving and preservation

(including storage and backup)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

###  4.1.4 Narrative information  
  
<table>  
<tr>  
<th>

WP/task

</th>  
<th>

Data set reference and name

</th>  
<th>

Data set description

</th>  
<th>

Standards and metadata

</th>  
<th>

Quality assurance status

</th>  
<th>

Data sharing

</th>  
<th>

Archiving and preservation

(including storage and

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

10  
  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

backup)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

###  4.1.5 Numerical statistics obtained based on narrative information  
  
<table>  
<tr>  
<th>

WP/task

</th>  
<th>

Data set reference and name

</th>  
<th>

Data set description

</th>  
<th>

Standards and metadata

</th>  
<th>

Quality assurance status

</th>  
<th>

Data sharing

</th>  
<th>

Archiving and preservation

(including storage and backup)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

###  4.1.6 Source code  
  
<table>  
<tr>  
<th>

WP/task

</th>  
<th>

Data set reference and name

</th>  
<th>

Data set description

</th>  
<th>

Standards and metadata

</th>  
<th>

Quality assurance status

</th>  
<th>

Data sharing

</th>  
<th>

Archiving and preservation

(including storage and backup)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

11