#  1 Introduction

This deliverable presents the first version of the Data Management Plan (DMP)
for the GrowBot project. This document provides a preliminary analysis of the
data management policy to be applied by the Partners to datasets generated
within the Project. In particular, the DMP identifies the main data to be
generated within GrowBot, outlining the handling of research data during the
project as well as how and what parts of the datasets will be openly shared.

This document is intended for consortium internal use, aiming to provide
guidance to Project Partners on data management. The DMP is indeed a useful
tool to agree on data processing of the GrowBot project, facilitating the
creation of a common understanding and, where possible, common practices.

This deliverable is submitted to the European Commission in M7 of the first
project year (July 2019, D1.1) and represents a preliminary plan. The document
will be further detailed, updated, and corrected in line with the project life
cycle.

The document follows the EC guidelines and templates for project participating
in the open Research Data Pilot:

  * H2020 Programme – AGA Annotated Model Grant Agreement - Open access to research data  1 

  

  * Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020  2 

  

  * Guidelines on FAIR Data Management in Horizon 2020  2 

  

  * Template for the Data Management Plan  3 

  

  * OpenAIRE Research Data Management Briefing Paper  4 

  

  * DCC Checklist for writing a DMP  5 

  

The present Data Management Plan also reflects the provisions established by
the project contracts and complements the project exploitation, dissemination
and IPR procedures and decisions defined in different deliverables.

The relationship between the DMP and each key document are described below in
Table 1.

##  1.1 Objectives

According to the EC Guidelines on Data Management in Horizon 2020, scientific
research data should be findable, accessible, interoperable and re-usable
(FAIR):

  * **Findable:** Are the data produced and/or used in the project discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)? 

  

  * **Accessible:** Are the data and associated software produced and/or used in the project accessible and in what modalities, scope, licenses (e.g. licencing framework for research and education, embargo periods, commercial exploitation, etc.)? 

  

  * **Interoperable:** Are the data produced and/or used in the project interoperable, that is 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)? 

  

  * **Re-usable:** Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? 

  

###  Table 1\. Relation to project key documents and deliverables

###  Document Access  7  Availability Relationship to GrowBot DMP  
  
<table>  
<tr>  
<th>

Grant Agreement: core text

</th>  
<th>

Confidential

</th>  
<th>

  * Participant portal; 

  

  * GrowBot 

  

repository  8

</th>  
<th>

  * Article 27 details the obligation to protect results (27.1) and of providing information on EU funding (27.3) 

  

  * Article 29 details the obligation to disseminate results, defines open access to research data (29.3) as well as the obligation to provide information on EU funding (29.4) and to exclude Commission 

  

responsibility via a disclaimer (29.5)

  * Article 36 details confidentiality obligations 

  

  * Article 37 details security-related obligations 

  

  * Article 39 details obligations to protect personal data 

  

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

Consortium Consortium  GrowBot Chapter 4.1 on the General principles:

Agreement repository

“Each Party undertakes to take part in the efficient implementation of the
Project, and to cooperate, perform and fulfil, promptly and on time, all of
its obligations under the Grant Agreement and this Consortium Agreement as may
be reasonably required from it and in a manner of good faith as prescribed by
Belgian law.

Each Party undertakes to notify promptly, in accordance with the governance
structure of the Project, any significant information, fact, problem or delay
likely to affect the Project.

Each Party shall promptly provide all information reasonably required by a
Consortium Body or by the Coordinator to carry out its tasks.

Each Party shall take reasonable measures to ensure the accuracy of any
information or materials it supplies to the other Parties.”

This is a general declaration of the partners to abide by the rights and
obligations set out in the Grant Agreement.

  7. Confidential: limited to Consortium, European Commission, appointed external evaluators and other EU bodies; Consortium: originally conceived as consortium but can be made available to European Commission, appointed external evaluators and other EU bodies if necessary; Public: public and fully open availability 

  

  8. _https://www.growbot.eu/login_

  

  
  
<table>  
<tr>  
<th>

Dissemination plan (D11.2)

</th>  
<th>

Public

</th>  
<th>

  * Participant portal; 

  

  * GrowBot 

  

repository

</th>  
<th>

The deliverable deals with a detailed definition of the strategy, the planned
activities outlined in Dissemination, Communication, and Exploitation WP
(WP11), and their expected impact. The CoDE plan will be periodically updated
according to the progress and emerging results of the project, considering
changes in the stakeholders, work context and potential use of results during
the project lifetime.

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

##  1.2 DMP management and update

Four DMP deliverables have to be submitted to the European Commission in M6
(June 2019, D1.1), M18 (June 2020, D1.4), M36 (December 2021, D1.7), and M48
(December 2021, D1.10).

Different versions will be identified by a version number and a date. The
version number will be composed of two digits separated by a period: the digit
before the period represents in ascending orders the official versions
submitted to the European Commission as deliverables; digits after the period
represents the periodic internal revisions of such official versions.

Official versions will be stored on the project online repository as PDF
files. An editable word copy of the latest version will also be stored to
facilitate revision and update of the already identified datasets and
policies. If during the project life cycle, a new dataset is identified,
partners can submit a new form through the online tool, automatically
notifying the coordinator. IIT will then be in charge of updating the document
and its annexes, uploading them on the repository and notify the consortium
through the project mailing list system.

#  2 Data summary

##  2.1 GrowBot datasets

For the first version of the project DMP, the analysis is based on eleven
datasets whose key details are summarized in Table 2. The descriptions of each
data set are provided in Annex 2.

###  Table 2. Preliminary list of GrowBot datasets  
  
<table>  
<tr>  
<th>

**REF**

</th>  
<th>

**TITLE**

</th>  
<th>

</th>  
<th>

**PARTNER**

</th>  
<th>

**DATA TYPE**

</th>  
<th>

**WP &TASK **

</th>  
<th>

**~ SIZE**

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

**DS1**

</td>  
<td>

Biomechanical characterization selected climbing plants

</td>  
<td>

of

</td>  
<td>

ALU-FR,

CNRS, IIT-

CMBR

</td>  
<td>

Experimental

</td>  
<td>

WP3: T3.1, T3.3

</td>  
<td>

5 GB

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

**DS2**

</td>  
<td>

Bioinspired robot control

</td>  
<td>

</td>  
<td>

TAU,

SSSA, IIT-

CMBR,

CNRS,

ALU-FR,

GSSI

</td>  
<td>

Results/analysis

</td>  
<td>

WP3: T3.2

WP6: T6.1, T6.2,

T6.3

</td>  
<td>

5 GB

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

**DS3**

</td>  
<td>

Networking information model

</td>  
<td>

</td>  
<td>

GSSI

</td>  
<td>

Results/analysis

</td>  
<td>

WP3: T3.4

</td>  
<td>

5 GB

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

**DS4**

</td>  
<td>

Microfabricated spinner of responsive materials with

attachment capabilities

</td>  
<td>

Linari, IIT-

POLBIOM, HZG

</td>  
<td>

Results/analysis

</td>  
<td>

WP4: T4.1, T4.3 WP5: T5.1

</td>  
<td>

5 GB

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

**DS5**

</td>  
<td>

Multi-filament deposition mechanism

</td>  
<td>

IIT-CMBR

</td>  
<td>

Results/analysis

</td>  
<td>

WP5 : T5.2

</td>  
<td>

500

MB

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

**DS6**

</td>  
<td>

Micro-extrusion prototype

</td>  
<td>

HZG, IITPOLBIOM

</td>  
<td>

Results/analysis

</td>  
<td>

WP4: T4.2

WP5: T5.3

</td>  
<td>

500

MB

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

**DS7**

</td>  
<td>

Soft “searcher-like” robot

</td>  
<td>

IIT-CMBR, SSSA

</td>  
<td>

Results/analysis

</td>  
<td>

WP5: T5.4

</td>  
<td>

1 GB

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

**DS8**

</td>  
<td>

Microbial fuel cells (MFCs)

</td>  
<td>

Bioo

</td>  
<td>

Results/analysis

</td>  
<td>

WP7: T7.1

</td>  
<td>

500

MB

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

**DS9**

</td>  
<td>

Plant-robot interfaces for energy harvesting

</td>  
<td>

IIT-CMBR

</td>  
<td>

Results/analysis

</td>  
<td>

WP7: T7.2

</td>  
<td>

500

MB

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

**DS10**

</td>  
<td>

Robot integration

</td>  
<td>

IIT, All

</td>  
<td>

Experimental

</td>  
<td>

WP8: T8.1, T8.2

</td>  
<td>

10 GB

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

**DS11**

</td>  
<td>

Robot validation

</td>  
<td>

CNRS, All

</td>  
<td>

Experimental

</td>  
<td>

WP9: T9.1, T9.2,

T9.3, T9.4

</td>  
<td>

10 GB

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

##  2.2 General data purpose and utility

The gathered data within the GrowBot project can be useful for several
purposes.

Summarising:

  * Biological research activities aim at deeply investigating the selected biological models of climbing plants in terms of morphology, physiology, anatomy, attachment capability, and biomechanical features (WP3 - Task 3.1 and Task 3.3). These characteristics are needed for identifying key functional “attributes” for the definition of strategic features of robotic artefacts. At the same time, the accurate investigation of biological models will be important for shedding light on unknown biological issues. 

  

  * The research activities on plant behaviour and communication aim at studying and analysing plant behaviour (WP3 - Task 3.2) and communication abilities (WP3 - Task 3.4) in order to design innovative control architecture and networking information models for robots (WP6 - Task 6.1, Task 6.2, and Task 6.3). As in the previous topic, the outcome is twofold because the plant abilities can inspire innovative control algorithm and networking information models; and a rigorous biological investigation will contribute to solving biological questions. 

  

  * The research activities on the climbing plants’ attachment strategies will also inspire new technological solutions able to perform reversible or permanent attachment on external supports. The artefacts can work as a single attachment device or as attachment components of a more complex robotic system (WP4 - Task 4.3). 

  

  * The research activities on the development of smart materials (e.g. responsible materials, multifunctional materials, printable materials, etc.) are crucial for the generation and characterization of innovative materials that can be applied in several different fields (e.g. robotics, architecture, environmental monitoring, etc.) (WP4 - Task 4.1, and Task 4.2). 

  

  * The research activities in manufacturing aim at designing innovative 3D additive manufacturing techniques able to manage functional materials (4D printing), multi-materials, and microfibers (WP5 – Task 5.1, Task 5.2, and Task 5.3). 

  

  * The research activities on soft “searcher-like” robot are focused on the design and development of a searcher robotic probe able to explore the surrounding environment, find an external supports, and perform grasping/attachment tasks (WP5 - Task 5.4). The developed device can be potentially useful as monitoring and grasping components of different robotic platforms. 

  

  * The research activities on plant energy harvesting aim at investigating the possibility to gather energy from the aerial and underground structure of the plants (WP7 – Task 7.1 and Task 7.2). In this case, the potential spin-off activities can be several in terms of plant energy characterization and technological outcomes. 

  

  * The research activities on characterization and validation of materials and prototypes (WP8 – Task 8.1 and Task 8.2; and WP9 – Task 9.1, Task 9.2, Task 9.3, and Task 9.4) represent an amazing source of data for other similar researches and stakeholders. These activities aim to provide standard protocols for the evaluation of the systems’ performances. 

  

GrowBot datasets will be a corollary to the scientific publications related to
the project. Datasets will be accessible through Zenodo and, when possible,
scientific publications will be directly linked to relevant software and data.
All these links will be explicitly maintained through the use of digital
object identifiers (DOI) associated with scientific papers, datasets and
software versions.

A detailed description of each dataset can be found in Annex 2.

GrowBot datasets are expected to have long-term value and utility. They are
fundamental for guaranteeing reproducible research and re-use in similar
research studies.

Moreover, the gathered data may be potentially useful to several external
entities and stakeholders interested in one or more research activities. A
preliminary list of third parties that can find fruitful the access to our
data:

  * Research and scientific community 

    * Botany and functional biology 

  

    * Robotics 

  

    * Artificial Intelligence 

  

    * Material Science 

  

    * Computer Science 

  

    * Architecture  Rescue 

  

    * Archaeology 

  

  

  * Industry 

    * Manufacturing 

  

    * Environmental monitoring 

  

    * Health-care 

  

    * Engineering 

  

    * Attachment product design 

  

    * Design 

  

  

Last but not least, our results may be potentially interesting as raw data for
producing usable education and formative materials. In this case, GrowBot
datasets can contribute to both school and higher education of future
generations.

##  2.3 Data technical details: origin, type, formats, and size

In the majority of GrowBot’s research activities, the partners will tend not
to re-use existing data in the literature, due to the need to address specific
project questions, but rather to carry out _ad hoc_ experiments and
measurements for generating the needed information.

Although several previous studies, especially in the biological field, have
already examined and carried out similar GrowBot’s investigations, additional
and new data are necessary to provide results and information that are
directly relevant to the GrowBot objectives.

The data will be gathered by various researchers and different partners as
detailed in Table 2 and Annex 2.

The data generated within the project will be both experimental and
theoretical, both quantitative and qualitative. Datasets will be generated
through various data collection techniques: field work in natural habitats,
experiments, observations, and modelling systems.

More in details, GrowBot will generate different categories of data:

  * **Raw collected data** – not yet subjected to quality assurance or control 

  

  * **Validated collected data** – raw data which have been evaluated for completeness, verified for compliance with the standard operating procedure (data protection included) and validated for specific quality 

  

  * **Analysed collected data** – validated data which have been processed and analysed through statistical operations 

  

In order to maximise the dataset interoperability, management and re-use, the
GrowBot consortium agreed to use when possible formats that are non-
proprietary, unencrypted, uncompressed and in common usage by the research
community. Since there are no unique recommendations on best data formats and
neither the selected data repository  6  provides such indication, GrowBot
partners have agreed to follow - when possible - the indications of the UK
Data Archive  7  , recommended by OpenAIRE, as indicated in **Table 3** .

###  Table 3. Data recommended format

###  Type of data Recommended formats Acceptable formats  
  
<table>  
<tr>  
<th>

Tabular data with

extensive metadata

(variable labels, code labels, and defined

missing values)

</th>  
<th>



</th>  
<th>

SPSS portable format (.por)

</th>  
<th>

Proprietary formats of statistical packages:

  * SPSS (.sav), 

  

  * Stata (.dta), 

  

  * MS Access (.mdb/.accdb) 

  

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

Tabular data with  comma-separated values (.csv)  delimited text (.txt) with
minimal metadata  tab-delimited file (.tab) characters not present in data

(column headings,  delimited text with SQL data used as delimiters variable
names) definition statements  widely-used formats:

  * MS Excel (.xls/.xlsx), 

  

  * MS Access (.mdb/.accdb), 

  

  * dBase (.dbf), 

  

  * OpenDocument Spreadsheet 

  

(.ods)  
  
<table>  
<tr>  
<th>

Textual data

</th>  
<th>

</th>  
<th>





</th>  
<th>

Rich Text Format (.rtf)

plain text, ASCII (.txt)

Adobe Portable Document Format

(PDF/A, PDF) (.pdf)

</th>  
<th>





</th>  
<th>

Hypertext Mark-up Language

(.html)

widely-used formats: MS Word

(.doc/.docx)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>



</td>  
<td>

some software-specific formats: NUD*IST, NVivo and ATLAS.ti

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

Image data  TIFF 6.0 uncompressed (.tif)  JPEG (.jpeg, .jpg, .jp2) if
original

created in this format

  * GIF (.gif) 

  

  * TIFF other versions (.tif, .tiff) 

  

  * RAW image format (.raw) 

  

  * Photoshop files (.psd) 

  

  * BMP (.bmp) 

  

  * PNG (.png) 

  

  
<table>  
<tr>  
<th>

Audio data

</th>  
<th>

</th>  
<th>



</th>  
<th>

Free Lossless Audio Codec (FLAC)

(.flac)

</th>  
<th>



</th>  
<th>

MPEG-1 Audio Layer 3 (.mp3) if original created in this format

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>



</td>  
<td>

Audio Interchange File Format

(.aif)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>



</td>  
<td>

Waveform Audio Format (.wav)

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

Video data  MPEG-4 (.mp4)  AVCHD video (.avchd)

  * OGG video (.ogv, .ogg) 

  

  * motion JPEG 2000 (.mj2) 

  

  
<table>  
<tr>  
<th>

Documentation

scripts

</th>  
<th>

and

</th>  
<th>









</th>  
<th>

Rich Text Format (.rtf)

PDF/UA, PDF/A or PDF (.pdf)

XHTML or HTML (.xhtml, .htm)

OpenDocument Text (.odt)

</th>  
<th>





</th>  
<th>

plain text (.txt) widely-used formats:

  * MS Word (.doc/.docx), 

  

  * MS Excel (.xls/.xlsx) 

  

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

The project will generate a very large amount of data with an overall size of
approximately 48 GB. The Zenodo platform recommends a maximum upload limit of
50 GB. All the 12 datasets should fit into this limit.

The consortium will not intend to upload copies of the same data in order to
avoid the creation of multiple persistent identifiers and thus making
references and citation difficult.

#  3 FAIR data

##  3.1 Making data Findable

Each GrowBot dataset will be identified with a Digital Object Identifier (DOI)
so that it can be findable and easily citable.

GrowBot consortium has chosen Zenodo as repository for the storage of the
datasets. Zenodo provides DOI to all publicly available uploads. In
particular, the DOI versioning allows users to update the datasets and
maintain a right citing of the dataset.

Zenodo adopts a linear versioning rule  8  , whereas GrowBot data versioning
will follow the “Major.Minor numbering” rule (e.g. v2.1). An increase of the
number before the period (Major) indicates a substantial change in the
structure and/or content of the dataset. An increase of the number after the
period (Minor) indicates a minimal revision, namely a quality improvement over
existing version. During the project life, dataset will be characterized by
mainly minor revisions, although major revisions will be possible beyond the
end of GrowBot.

The consortium has defined a naming convention for the project datasets,
namely:

  1. A prefix "GrowBot" 

  

  2. "DATA" (short for dataset) followed by a unique chronological number of the project datasets 

  

  3. Letter indicating sub-dataset (if applicable) 

  

  4. The short title of the dataset 

  

  5. Version number 

  

  

For instance, the first project dataset identified in Annex 2 will be named:
"GrowBot_DATA1_ **Error! Reference source not found.** _v1.0"

To increase the findability of each dataset and consequent use, search
keywords will be provided once the dataset is uploaded to Zenodo.

Each project records will be annotated with metadata in order to increase data
reuse.

Zenodo follows the JSON metadata schema  9  and Data Cite metadata standards
and already provides key data documentation such as:

  * Creators and their affiliation 

  

  * Data location and persistent identifier 

  

  * Chosen license 

  

  * Funding 

  

  * Related/alternate identifiers 

  

  * Contributors 

  

  * References 

  

  * Related journals, conferences, books and/or thesis 

  

  * Subjects 

  

Moreover, the consortium will provide documentation as complete as possible to
allow third parties to properly understand the data and eventually replicate
the experiments. This will include:

  * **Dataset overview** – number of sub-datasets; status of documented data (complete or in progress); eventual plan of the future update 

  

  * **Methodological information** – methods used for experimental design, data collection and data processing; instruments and software used; experimental conditions; quality assurance procedures performed on data 

  

  * **Software and tools information** – Name of tool/software; reference version; reference URL; optional DOI. 

  

##  3.2 Making data openly Accessible

As a general rule, datasets will not be released before the publication date
of the scientific paper, patents, reports, etc. in which the data are reported
the first time. It is the intention of the GrowBot consortium to make the
datasets publicly available as early as possible after the publication date.
Potential restrictions or embargo periods of the scientific journal will have
to be respected in accordance with what set out in Grant Agreement (art 29.2).

In accordance with what just claimed about the Intellectual Property Right IPR
& Exploitation, GrowBot consortium has planned different levels of data
confidentiality:

  * _Beneficiary institution access_ : The data are not disclosed at all. The partner that chooses this option believes that the dataset contains information that would lose their value if disclosed. This choice aims at protecting the information from any external access in order to exploit data for patents, publications, etc. The confidentiality must be ensured beyond the clauses agreed in the Consortium Agreement. 

  

  * _Confidential to the consortium (including EC services and GrowBot Advisory Board):_ This option is applied for data containing confidential information (e.g. exploitable results) requiring IP protection, aimed at eventual exploitation. Confidential to consortium datasets will be deposited on specific repositories (private area of project website www.growbot.eu). These repositories will be accessible uniquely by the Consortium members. 

  

  * _Open Access_ : This option is applied when data have no IP restrictions and will be openly available and re-usable. 

  

Although the embargoed or closed access option provided by Zenodo could be a
valid option, the consortium agrees that research data linked to exploitable
results will not be deposited to avoid compromising their protection or
commercialisation prospects. As clearly specified on Zenodo security
provisions, "closed access is not suitable for secret or confidential data"
since these are "stored unencrypted and may be viewed by Zenodo operational
staff"  10  . In this case, the consortium will store the data in the private
area of the project website or institutional repository (if any) with a proper
cybersecurity certificate.

Visibility and access to publicly shared datasets will be facilitated by
Zenodo metadata and search facility as well as to the automatic link to both
OpenAIRE  11  and project Cordis project page  15  .

##  3.3 Making data Interoperable

The consortium will strive to collect and document the data in a standardized
way to ensure that datasets can be correctly understood, interpreted, and re-
used.

Documentation describing the main variables included in the datasets will be
provided in order to support the interpretation and re-use.

Standard vocabulary will be used for all data types present in the dataset to
allow inter-disciplinary interoperability. In addition, the documentation will
include a general glossary used to share information about the vocabulary and
general methodologies employed for the generation of the dataset.

##  3.4 Increase data Re-use

In order to clarify the possibility to re-use GrowBot data, the consortium
will provide a specific license for each deposited dataset that claims if the
data have open or restricted access.

Zenodo automatically offers five different licensing options among Creative
Commons Licenses, all foreseeing the attribution requirement to appropriately
credit the authors for the original creation (credit, link to license and
changes indications).

When possible, the consortium proposed licence is **Creative Commons
Attribution 4.0 International (CC BY 4.0)** 12  allowing third parties to
share and adapt data with no restrictions as long as attribution is provided.

In case the partner would like to further limit access to the uploaded data,
alternative licenses will be selected also through the CC license chooser
among the Zenodo offered options:

  * **Creative Commons Attribution Share-Alike 4.0 International (CC BY-SA 4.0)** 13  – allowing adaptation for any purpose to the work to be shared as long as it is distributed under the same original licence (or a license listed as compatible); 

  

  * **Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)** 14  – allowing sharing for any purpose, but forbidding the distribution of derivative work; 

  

  * **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** 15  – allowing sharing and adaptation to the work, but limiting the use of the shared work to noncommercial purposes; 

  

  * **Creative Commons Attribution-NonCommercial- NoDerivatives 4.0 International (CC BYNC-ND 4.0)** 16  – allowing sharing but restricting both derivative work and commercial use of data. 

  

Although not directly provided through Zenodo, an additional Creative Commons
Attribution license can be applied upon specific request to Zenodo team:

  * **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)** 17  – allowing adaptation to the work to be shared as long as it is distributed for noncommercial purposes and under the same original licence (or a license listed as compatible). 

  

All data will be stored in Zenodo as soon as possible, at the latest upon
publication of the related scientific publication and will remain re-usable
for the lifetime of the repository, which is currently warrantied for a
minimum of 20 years.

#  4 Specific software provisions

Generally, the consortium agrees to provide full software and tools
information for all dataset within the documentation. Information on tools
name, version, URL and DOI will be thence added to increase dataset
accessibility and re-usability.

Software plays a key role in GrowBot and particular provisions should thence
be considered for software developed as part of the project activities in
addition to provisions for access and rights agreed by partners in the GrowBot
Consortium Agreement (Art 9.8, §1).

The partner(s) involved in software development will evaluate the possibility
to upload the code on GitHub directly linked to Zenodo platform, as indicated
in Annex A3.2.

#  5 Allocation of resources

At this preliminary stage of the project, the only costs foreseen for data
management are related to:

  * the working time needed to set up and perform the data collection, including synchronisation of devices, and analysis activities 

  

  * the working time to setup local and shared data collection devices/servers - the working time needed to write documentation, metadata, etc. 

  

The project coordinator is in charge of the DMP from both the scientific and
technical perspective. IIT role include the first version release as well as
the regular update.

Validation and registration of datasets and metadata, as well as backing up
data for sharing through open access repositories is the responsibility of the
partner that generates the data in the WP. Each partner will identify a
specific responsible person for each dataset. Quality control of these data is
the responsibility of the relevant WP leader, supported by the Project
Coordinator. Each partner should respect the policies set out in this DMP.

Finally, in line with Grant Agreement (art 29.1) and Consortium Agreement (art
8.4.2.1), a beneficiary that intends to disseminate its results must give
advance notice to the other beneficiaries of — unless agreed otherwise — at
least 45 days, together with sufficient information on the results it will
disseminate. Any other beneficiary may object within — unless agreed otherwise
— 30 days of receiving notification, if it can show that its legitimate
interests in relation to the results or background would be significantly
harmed. In such cases, the dissemination may not take place unless appropriate
steps are taken to safeguard these legitimate interests.

#  6 Data security

As previously stated, each partner is in charge of backing up data that will
be openly shared through Zenodo.

Once uploaded on Zenodo, data will also be stored in CERN Data Centre in
multiple online independent replicas. Long-term preservation is guaranteed
even in the unlikely event that Zenodo will cease operations, migration of
content on other repositories is planned.

For the data that cannot be uploaded on Zenodo, because not publicly
shareable; each institutional

ICT infrastructure guarantees preservation and safety of the stored data in
compliance with its

(Information Security) internal policy.