#  Executive summary

The following document is describing the data management cycle for the data
sets that will be developed, collected, generated, and processed by the
NextFOOD project. The document provided an overview of the data that will be
generated and processed during the project and/or after its completion. This
current document is the first version of a series and therefore is a starting
point and reference document for the researchers that are going to take part
in the NextFOOD project. As the project will progress more detailed and
elaborate versions of this document will be developed to incorporate the
developments of the projects as far data generation, development, processing,
and storage are concerned.

#  Project abstract

NEXTFOOD will drive the crucial transition to more sustainable and competitive
agrifood and forestry systems development by designing and implementing
education and training systems to prepare budding or already practicing
professionals with competencies to push the green shift in our rapidly
changing society. NEXTFOOD will challenge the linear view of knowledge
transfer as a topdown process from research to advice and practice, and
support the transition to more learner-centric, participatory, action-based
and action-oriented education and learning in agrifood and forestry systems.
In several pioneering case studies, covering agrifood and forestry systems in
Europe, Asia and Africa, farmers solve real challenges related to
sustainability together with researchers, students and other relevant
stakeholders while developing both green technical skills and soft
collaborative competencies. NEXTFOOD will assure quality in research and
education by creating a peer-review system for evaluation of practice-oriented
research outputs focusing on sustainability and practical usefulness. In
addition, we will develop an international accreditation framework for
education and training in fields relevant to sustainable agrifood and forestry
systems. An innovative action research process will guide the NEXTFOOD
project’s development in a cyclical manner, ensuring that the research process
and actual case studies are everimproving. This will exemplify how practice-
oriented research can be instrumental to achieve: better collaboration between
university and society, more innovation in the agrifood and forestry systems
sector, and a progressive agrifood community ready to tackle complex
sustainability challenges of the 21st century.

#  1 Data summary

##  Purpose of the data collection/generation

The NextFOOD Project's aim is to generate an innovative European science and
education roadmap for sustainable agriculture along the value chain from
research via fabrication into the application.

In order to achieve this goal, the project team will engage in several actions
to ensure a meaningful result. These actions are organized in 7 work packages
as follows

  * WP1 Inventory of the skills needed for a transition to more sustainable agriculture, forestry and associated bio-value chains. 

  

  * WP2 Action research facilitation 

  

  * WP3 Future curriculum, education and training system 

  

  * WP4 Policy assessment and recommendations 

  

  * WP5 Quality assured knowledge transfer 

  

  * WP6 Communication, dissemination and exploitation 

  

  * WP7 Management 

  

  * WP8 Ethics requirements 

  

For all the above Work packages and especially for the Work packages 1 to 5
data will be collected and produced during the NextFOOD project for

  * research purposes 

  

  * development of new educational modules 

  

  * development of new educational material 

  

  * communication purposes within project partners and other interested bodies 

  

##  Relation to the objectives of the project

The collection of data is closely related to the objectives of the project
which are summarized as follows.

  * Create an inventory of the skills and competencies needed for a transition to more sustainable agriculture, forestry and associated bio-value chains, 

  

  * Facilitate case studies to identify gaps and needs 

  

  * Test new relevant curricula and training methods 

  

  * Identify policy instruments that support the transition towards action-oriented, and practice-oriented learning methods 

  

  * Peer-review tools for evaluating the quality of the practice-oriented research 

  

  * Create a platform for knowledge sharing 

  

An Annex will be provided by case studies’ leader that will summarizing the
Dataset of the project explaining in more detail the rationale of compiling
the dataset and its purpose within the scope of the project

##  Types and formats of data generated/collected

The NextFOOD project will employ a number of Quantitative and Qualitative
methods in order to realize its objectives.

For Qualitative methods, data created in audio and/or video will be
transcribed and anonymized. Any hardcopy data products, such as participatory
design outputs will be documented in a proper electronic medium (e.g.
photographed).

Notetaking, journals etc. should be produced in an electronic textual data
form. When this is not possible, originals should be documented in a proper
electronic medium (e.g. photographed) and an electronic textual data form
should also be produced.

To ensure the greater possible data sharing, reuse and preservation will
adhere to the UK Data Service guidance on recommended formats summarized in
the following table.

_Table 1: Recommended file formats per type of data to be used in the NextFOOD
project_

###  **Type of data Recommended formats Acceptable formats**

**Tabular data with** SPSS portable format (.por) proprietary formats of
statistical **extensive metadata** Delimited text and command packages: SPSS
(.sav), Stata **variable labels, code** ('setup') file (SPSS, Stata, SAS,
(.dta), MS Access (.mdb/.accdb) **labels, and defined** etc.) **missing
values** structured text or mark-up file of

metadata information, e.g. DDI

XML file

**Tabular data with** comma-separated values (.csv) delimited text (.txt) with
characters **minimal metadata** tab-delimited file (.tab) not present in data
used as **column headings,** delimited text with SQL data delimiters
**variable names** definition statements widely-used formats: MS Excel
(.xls/.xlsx), MS Access (.mdb/.accdb), dBase (.dbf),

OpenDocument Spreadsheet (.ods) **Geospatial data** ESRI Shapefile (.shp,
.shx, .dbf, ESRI Geodatabase format (.mdb) **vector and raster data** .prj,
.sbx, .sbn optional) MapInfo Interchange Format (.mif)

geo-referenced TIFF (.tif, .tfw) for vector data

CAD data (.dwg) Keyhole Mark-up Language (.kml)

tabular GIS attribute data Adobe Illustrator (.ai), CAD data

Geography Markup Language (.dxf or .svg)

(.gml) binary formats of GIS and CAD packages

**Textual data** Rich Text Format (.rtf) Hypertext Mark-up Language plain
text, ASCII (.txt) (.html)

eXtensible Mark-up Language widely-used formats: MS Word

(.xml) text according to an (.doc/.docx) appropriate Document Type some
software-specific formats:

Definition (DTD) or schema NUD*IST, NVivo and ATLAS.ti

**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)

Adobe Portable Document Format

(

PDF/A, PDF) (.pdf

)

**Audio data**

Free Lossless Audio Codec

(

FLAC) (.flac

)

MPEG-1 Audio Layer 3 (.mp3) if

original created in this format

Audio Interchange File Format (.aif)

Waveform Audio Format (.wav)

**Video data**

MPEG-4 (.mp4)

OGG video (.ogv, .ogg)

motion JPEG 2000 (.mj2)

AVCHD video (.avchd)

**Documentation**

**and**

**scripts**

Rich Text Format (.rtf)

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

XHTML or HTML (.xhtml, .htm)

OpenDocument Text (.odt)

plain text (.txt)

widely-used formats: MS Word

(

.doc/.docx), MS Excel (.xls/.xlsx

)

XML marked-up text (.xml)

according to an appropriate DTD or

schema, e.g. XHMTL 1.0

__Existing data to be re-used_ _

No re-use of existing data is envisaged for the project so far.

##  Origin of the data

The data for the NextFOOD project will be generated by the project team
through qualitative and quantitative methods. In particular, the core of the
project is utilizing a participatory action research protocol, especially in
WP2 case studies, in order to guide the NextFOOD project’s development in a
cyclical manner, ensuring that the research process and actual case studies
are ever-improving.

In particular, during the participatory action research the following types of
data collection are expected to be utilized:

participation, observation, recordkeeping, notetaking, surveying and
profiling, semistructured and informal interviewing, analysis of key reports,
running focus groups, photographing and videoing, and journaling.

In addition to the above data generated by the NextFOOD project will include
quantitative surveys through questionnaires.

__Expected size of the data_ _

The expected size of the data is not known.

##  Data utility

Data generated from the NextFOOD project is expected to be useful to the
following entities:

  * Educational Institutions 

  

  * Agricultural Advisory Services 

  

  * Policy and decision makers 

  

  * Agrifood industry 

  

  * Farmers, Farmers organisations 

  

  * Forestry Associations 

  

  * Research community 

  

  * Evaluators 

  

  * Project officers and project administration offices 

  

#  1 Findable, Accessible, Interoperable, Reusable Data for the NextFOOD
Project

##  1.1 Making data findable, including provisions for metadata

###  Discoverability of data and metadata provision

There are various disciplinary metadata standards, the NextFOODProject will
follow OpenAIRE Guidelines concerning the availability of medata.

All datasets will include metadata defining the what, where, when, why, and
how data of the data. Where appropriate, the data creators will assign Digital
Object Identifiers (DOIs).

For all data uploaded in a database/repository, the metadata will be in JSON-
format according to a defined JSON schema. The repository will allow for the
metadata exported in several standard formats such as MARCXML, Dublin Core,
and DataCite Metadata Schema (according to the OpenAIRE Guidelines).

Metadata will be provided in a Project and Dataset level and will include
sufficient information in order to link it to the research
publications/outputs, identify the funder and discipline of the research, and
will include appropriate keywords to help external and internal users to
locate the data.

Final responsibility to comply with EU regulations will be the responsibility
of each partner producing and providing data.

To the extent, possible surveys and coding will be shared between case
studies.

###  Identifiability of data and standard identification mechanism

NextFOOD will use an Internal project Identifier for the data set to be
produced. This will follow the format:

WPNumber_TaskNumber__PartnerName_DataSubset_DatasetName_Version__Dat
eOfStorage, where the project name is NextFOOD, the PartnerName represents the
name of the data custodian (WP Lead/ Task Leader).

An example of this naming format would be:

WP2_T2.1_NMBU_Subset1_UserRequirements_V1.0_20.09.18

Data uploaded in repositories (e.g. Zenodo) will have their own Digital Object
Identifier issued by the Repository that will be utilized. Similarly, all
Journal Articles and the Monograph will have their own digital identifier
issued by the respective publisher.

###  Naming conventions used

The following naming conventions should be used across all different case
studies and project partners and irrespective to whether documents, data sets,
etc. are finalized or not.

###  Folders

Aside from the predefined folder names, new folders only shall be created
after approval by the project coordinator.

Preferably folder names shall be built of a number and a short descriptive
name in order to have a consistent sorting and that the folder can be found in
the same place at any time.

###  Files

File names should be developed in a way such that on the one hand they provide
information about the content of the file, on the other hand, they should
enable a chronological order and the creation of variants.

Repetitions in files and/or folder names shall be avoided if possible.

For maximum compatibility between Windows/Linux/Mac etc. the following characters **must be avoided** in both, folder and file names: < > : " \ / | * ? . 

Following characters **should be avoided** in both, folder and file names: [ ]
= % $ + , ; “-“ at the beginning of a name is not allowed.

The length of the entire path shall not exceed 256 characters including empty
spaces.

###  Naming conventions for Survey Data and Coding schemes

Any surveys that will be conducted during the Project will include a detailed
explanation of the naming convention and a documentation file when needed.
Survey naming conventions should be common across cases to the extent that is
possible.

Qualitative data coding schemes should also be documented and follow clear
naming conventions documented to a separate file when needed. As in the case
of surveys coding schemes naming conventions should be common across cases, to
the extent that this is possible.

###  Approach to search keywords

The NextFOOD project will assign keywords based on the UK Data Archive
_HASSET_ _Thesaurus_ and the  _ELSST multilingual thesaurus_ _._

Outline the approach for clear versioning

###  Versioning

No more than 10 versions of a file should be kept. Versions of previous
Substantive changes should be kept. When a new version due to substantive
changes (milestones versions) is produced minor versions of the previous
edition can be deleted.

Different versions should be identified by numbering v1.0, v.1.1 etc. and any
changes made to a file when a new version is created should be recorded by a
different colour. Moreover, relationships between items (e.g. code and data
files) should be recorded.

Lastly, files saved in different locations should be synchronized, and master
versions should be uploaded in the Project Sharing drive.

Version numbering in file names can be through discrete or continuous
numbering depending on minor or major revisions.

Example:

_Table 2: Versioning rules to be used for the NextFOOD project_  
  
<table>  
<tr>  
<th>

**File name**

</th>  
<th>

**Changes to file**

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

Interviewschedule_1.0

</td>  
<td>

Original document

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

Interviewschedule_1.1

</td>  
<td>

Minor revisions made

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

Interviewschedule_1.2

</td>  
<td>

Further minor revisions

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

Interviewschedule_2.0

</td>  
<td>

Substantive changes

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

###  Standards for metadata creation

The NextFOOD project strongly suggests to the partners to utilize the DDI
standard in the development of their surveys. More Information about the DDI
can be found  _here_ .

##  1.2 Making data openly accessible

###  Data that will be made openly available

The NextFOOD project will facilitate the sharing of results and deliverables,
both within and beyond the consortium. Results will be widely shared with the
interested communities, including but not limited to the scientific community,
policy and decision makers through publications in scientific journals and
presentations at conferences, as well as through open access data
repositories. Overall an open access policy will be applied, following the
rules outlined in the Grant and Consortium Agreements.

All data will be considered by default openly available, with the exception of
datasets that include personalized data. In the latter case data should be
anonymized before being considered to be openly available.

All data will be made available for verification and re-use unless the task
leader can justify why data cannot be made openly accessible.

The Steering Committee will assess the reasoning of the justification and make
the final decision based on examination of the following elements regarding
confidentiality of datasets:

  1. Commercial sensitivity of datasets 

  

  2. Data confidentiality for security reasons 

  

  3. Conflicts between open-access rules and national and European legislation (e.g.data protection regulations). 

  

  4. Sharing data would jeopardise the aims of the project 

  

  5. Other legitimate reasons, to be validated by the IPR Committee 

  

  

###  Availability guarantees

All NextFOOD project datasets will be made available in an online open
repository (e.g. Zenodo) including the relevant metadata to identify the
project, funder, scope etc. of the project unless the Steering committee
accepts that there is sufficient reason and justification not to.

###  Methods or software tools needed to access the data and documentation
about the software

NextFOOD will ensure that it will follow the UK archive guidelines on
recommended file formats to ensure accessibility. The following documentation
on a study and Data level will be also provided for this reason.

###  Study-Level

On a study-level the following documentation should be provided:

  * research design and context of data collection: project history, aims, objectives, hypotheses, investigators and funders 

  

  * data collection methods, data collection protocols, sampling design, sample structure and representation, workflows, instruments used, hardware and software used, data scale and resolution, temporal coverage and geographic coverage, and digitisation or transcription methods used 

  

  * structure of data files, with a number of cases, records, files and variables, as well as any relationships among such items 

  

  * secondary data sources used and provenance, for example, for transcribed or derived data 

  

  * data validation, checking, proofing, cleaning and other quality assurance procedures carried out, such as checking for equipment and transcription errors, calibration procedures, data capture resolution and repetitions, or editing, proofing or quality control of materials 

  

  * modifications made to data over time since their original creation and identification of different versions of datasets 

  

  * for time series or longitudinal surveys, changes made to methodology, variable content, question text, variable labelling, measurements or sampling, and how panels were managed over time and between waves 

  

  * information on data confidentiality, access and any applicable conditions of use 

  

  * publications, presentations and other research outputs that explain or draw on the data 

  

Important data documentation should include original questionnaires,
interviewer instructions, interview topic guides or experimental protocols.

###  Data-Level

On a Data-level for Survey and Transcription data partners should follow the
guidelines of the UK Data Survey available  _here_ and  _here_ respectively.

Based on the acceptable file formats SPSS, Stata, NVivo, etc software might be
needed to access the original data. Nevertheless, partners are encouraged to
provide DDI XML data and metadata.

The above should be also documented in specific "readme" files in the
respective folder.

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

The NextFOOD project will utilize the Zenodo or a similar open Repository for
the data that will make available publicly.

Original non-anonymized data or data that is decided not to be published will
be stored under the responsibility of the task leader in a closed repository
that will adhere to all the necessary legal and ethical requirements.

###  Access provision for restricted datasets

All partners will have access to data produced by the NextFOOD project
(internal), with the exception of on-anonymized data.

All published data will be openly accessible to all internal and external data
requests.

In the case of restricted access, the steering committee will assess external
data requests or internal data requests for non-anonymized access.

Additionally, to the above task leaders should set an embargo period for the
data produced by the project that cannot be longer than 24 months after the
completion of the project.

1.3 Making data interoperable

###  Interoperability of Data

The Datasets produced by the NextFOOD project will have high interoperability,
taking into account the type and discipline of the project.

The project will be using the UK Data Service Guidelines concerning data and
metadata vocabularies, partners are expected to follow the recommended file
formats of the UK data archive and are encouraged to follow the DDI standards
to assess interoperability.

###  Standard vocabularies

To the extent, possible partners are expected to use standard vocabulary for
all data types present in the datasets.

In the case that this won't be possible partners should provide adequate
documentation to allow interoperability.

##  1.4 Data re-use and licenses

###  Data licensing and Permissions

All project data will follow a Creative Commons Attribution Non-Commercial
licenses.

The task leader may opt out proposing a different license by providing the
necessary reasoning and justification to the Steering Committee.

###  Data availability and disclosure

All data sets should be made public before the finalization of the project in
an open repository.

In the case of embargoed data, the embargo time will not exceed a period of
24th months after the completion of the project.

###  Data usability by third parties

Datasets open and usable by third parties, since they are going to be uploaded
under a Creative Commons Attribution Non-Commercial license.

In some cases, task leaders may opt out of publishing and publicly releasing
data under the above-mentioned license. In this case, sufficient justification
will be provided and the Data management Plan will be amended accordingly.

Non-open data will also be reusable (except non-anonymized data), provided
that the data request is reasonable and justifiable.

__Data quality assurance processes_ _

###  **Survey data**

Surveys should be developed collectively with the participation of all
relevant partners that will ensure through internal peer review the quality of
the planning, data collection and documentation of the data.

A common survey and metadata registry in a data-level will be developed for
each survey to the extent that a common survey can be applicable in all the
different case studies.

All surveys common or not should provide sufficient documentation on a Data-
Level (as described above).

###  **Transcripts and participatory research data**

Similarly to the common developed protocols for survey research, specific
guidelines, coding schemes etc will be developed for transcripts and
participatory research data. All of the above should be common across all case
studies of the projects to the extent possible.

For each dataset sufficient documentation should be provided on a Data-Level
(as described above).

The Data Quality assurance processes are the responsibility of each Task
Leader.

###  **Length of time for which the data will remain re-usable**

Published data in the open repository will remain re-usable for as long as the
repository specifies.

#  2 Allocation of resources

No specific costs are allocated for making the NextFOOD data FAIR. Each
partner will be responsible for the data management costs of the data that
they will produce.

##  Responsibilities for data management in your project

**The project coordinator** will be responsible for keeping partners
accountable as far as data management is being concerned.

The steering committee will be responsible to assess whether data should be
deemed public or not, as well as under which licence the data will fall.
**Task leaders** will be responsible to ensure that

  * sufficient documentation is produced for each dataset 

  

  * the quality assurance processes are being implemented 

  

  * the datasets are uploaded to public repositories or for non-public datasets stored safely in the chosen repository by the organization. 

  

**Case study leaders** will be responsible for the collection, documentation,
as well as legal and ethical rules, are being followed as they are specified
in the respective documents

**In the case of external contractors** concerning data collection, data
entry, transcribing, processing or analysis, the organization making the
subcontracting must assure that all the legal and ethical rules are being
followed as they are specified in the respective documents.

A specific folder structure for internal data storage will be developed by the
project coordinator with the support of the AFS.

##  Costs and potential value of long-term preservation

There will not be additional costs for the project for a long-term
preservation of the data.

Data generated from the NextFOOD project is expected to be valuable to the
following entities:

  * Educational Institutions 

  

  * Agricultural Advisory Services 

  

  * Policy and decision makers 

  

  * Agrifood industry 

  

  * Farmers, Farmers organisations 

  

  * Forestry Associations 

  

  * Research community 

  

  * Evaluators 

  

  * Project officers and project administration offices 

  

#  3 Data security

##  Data recovery, secure storage and transfer of sensitive data

Case study leaders and task leaders should keep a back-up of all data and
documentation that they produce.

They are also responsible to upload and update relevant data in the internal
repository of the project.

We recommend that any storage should involve at least two different forms of
storage, for example on a hard drive and on DVD and that the data integrity
should be checked periodically.

Personal information should be removed from data files and stored separately
under more stringent security measures. Any digital files or folders which
contain sensitive information and data should be encrypted.

Cloud data storage should be avoided for high-risk information such as files
that contain personal or sensitive information, information that is covered by
the law. Encryption should be used to safeguard data files to a certain
degree, but partners should keep in mind that in some cases it would still not
meet the requirements of data protection legislation.

#  4 Ethical aspects

Ethical issues concerning the NextFOOD project are tackled in the respective
deliverables.

Additional information concerning ethical aspects such as compliance etc. can
be found  _here_ .