WEBVTT - generated by VCS

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How can you start with writing your data management
chapter?

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We have some suggestions. You don't have to
follow them.

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It's just a way of

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collecting all the information and what to
keep in mind while discussing this

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with your colleagues. We suggest to

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use a holistic approach to research data management,

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that reflects all the issues

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I just introduced. So don't

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only think about the IT landscape,

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without the conceptual

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measures. The IT landscape

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might not be useful.

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First, think about what you really want to
do in your project,

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what is needed for your project,

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like how closely you work together,

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what exactly is your research plan?

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What do you really need for research data handling?

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And, after that, choose the suitable IT landscape
for

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that. Don't forget to address that Somebody

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has to do the work when it comes to research
data management and that

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you need resources for that and you have to
allocate responsibilities

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for that. And also think about that you might
have

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to shape the competences and skills for research
data management and

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also generate awareness for research data management

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and also for some legal applications that come
with

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data. You might

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have already a lot of experience with research
data management because

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you have a lot of experience with research.

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So just get in a conversation with your collaborators

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about the strengths you already have as a consortium.

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What is already going well

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while working together? What already went very

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well in previous projects when it comes to
data handling? And then

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just use this and keep those measures and strategies
and describe

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those strategies in this chapter about research
data management.

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Also, you might have experiences what went
wrong when it comes to data exchange,

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for instance, when it comes to making data
understandable,

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for later,

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documented data, maybe. So,

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be frank about what went wrong before and,

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maybe, introduce some changes,

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some measures that fix those problems you ran
into,

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before. And think about what risks

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might there be, so what requirements might
not

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be met, because you are not introducing

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specific strategies and think about how you
can reduce those risks.

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Mostly, it's because no responsibilities have
been allocated

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or no resources for research data management
have been planned.

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So calculate what could go wrong in the future
and how you can

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meet those risks, mostly with planning for
some resources for

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research data management as well. And also
think about

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what changes

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you can implement and how these changes could
be

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a sustainable solution for all the partners
in the future.

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So maybe you can introduce some research data
management procedures that

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might stay for the chair, for the faculty,

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and so on.

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This could be a starting point for your conversation
with your collaborators,

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this is a so-called SWOT analysis about strengths,

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weaknesses, risks

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and threats and opportunities.

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To collect all information that might go into
a data management plan.

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So maybe for the data management plan for the
European Commission or the data

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management plan that you want to use for from
the start

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of your project, there is a

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a template that's called Research Output Management
Planning - ROMPi.

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It's published via Zenodo,

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if you want to have a look at this.

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I used this to

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build a help template for

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research data management planning for European
Commission proposals.

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Basically,

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this template uses the

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work project, the project planning, project
management with work

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packages, and analyzes from which work package
which

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research output follows. The research output
could be

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data, or processed data,

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or a method, or a protocol, or a literature
summary,

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or something like that. So, first have a look
at each work package,

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which research output might arise from those
work packages,

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and then try to come up how you want to make
this research output

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FAIR, again for you,

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for your future self, for the whole project
group,

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or for the community. And if you are doing
that

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for each relevant

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research output, in each work package where
a relevant research

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output is produced, you

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will have an overview of what measures for
research data management

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you have to implement for your project.

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This is very detailed. I admit you don't might
not need this for

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the proposal stage of your

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data management planning, but I think you will
need this

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during project planning later on.

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So at least for a data management plan

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that the European Commission is requesting
in the first six

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months of your project. You can see here that

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here's a table, a project management table
that collects

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all the information you have to produce for
the data description.

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Will you reuse data?

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Do you produce sensible data? Which data data
types will you produce

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in the different work packages? What data format
will the data

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have for the research output in the different
work packages? That might

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be the same for all of the work packages or
might be different for the

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work packages. Again, if you are using some
software that produces specific

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data formats, name the software.

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Then estimate which data volume

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you will produce in the different work packages,

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and for which target group this data or this
research output

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may be relevant. You can just put it into the
last column

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here. The next table

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is summarizing how you make the data

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Findable for each of the work packages.

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Here I listed

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some examples,

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what you have to put in the different columns,

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to make it findable. This could be done with
metadata.

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So if you're putting some specific metadata
alongside the data,

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it might be easier to find specific data.

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Maybe you have already some metadata standards
you're using.

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This can also make the data more findable,

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for you, for the project group,

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also for the community later on. A specific
data organization,

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which is meant by

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developing some file naming convention,

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or a folder structure

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to your data can also make the data findable.

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So if you're having a specific naming convention,

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you agree up on with all your collaborators,

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then of course the data will be better findable

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on your central data storage for all of your
collaborators.

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Later on, you have to put a persistent identifier
on

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the data when you publish it.

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Most of the repositories do this automatically.

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For instance, there is also the possibility
of getting a DOI,

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a digital object identifier, if you're publishing
data on a specific

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repository. And this might be the same for
all work packages or might be

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different for the work packages.

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Making data accessible

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you can describe with this table if you want.

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So who is responsible to give access to the
people

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in your project to the data?

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Who is responsible for giving access,

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for instance, to a network drive for the people
who need the access or

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for giving access to a cloud store?

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Will the data be open access maybe later in
this repository

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you just named before or you name the repository
again

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here. The European Commission wants you to
choose a trusted

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repository and there are websites where they
describe what they mean with trusted

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repository. I just put some examples here.

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Will there be a standard access protocol? Most
of the repositories have this but

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your network drive may not - that's no problem.

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Just describe, how will be the access managed
within the project and

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later on. For making

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the data interoperable, the same issues might

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solve this as the issues

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you already described in the findable part,

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but maybe for making data interoperable

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other metadata will be used. Just describe
this here.

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Again, if you're using some standards,

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just name this here, maybe you're using the
DataCite standard,

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maybe you're using a discipline specific standard
for engineering or

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for the social sciences. There are standards
you can

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have a look there.

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We can also guide you to some websites where
you will find standards.

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Are you using a standard vocabulary?

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Or have you developed a vocabulary,

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a glossary, or something like that

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within your project? Then just name it here.

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Are you using an ontology that has already
been published by

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a certain institution? Or maybe you are using
a vocabulary

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that comes with your measurement device, then
just name it here.

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If you know this at the state of the proposal,

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you can name this in your proposal.

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If not, then you don't have to already describe
this in this detail,

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but you can say, for instance, "We will

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have a look for standard vocabularies or for
standards.

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And if not, we will develop our own standards
and describe those standards."

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For making the data reusable,

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a good documentation helps:

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Will you put readme files or protocols or codebook
scripts and

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so on alongside your data? Just name it here
or describe it here.

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What data quality procedures will you develop?

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Maybe you have meetings from time to time on
data quality,

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or review the data with some colleagues?

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Maybe you have some checklists that help each
member of the project with

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data quality? Again, here,

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put in, which repository you are planning to
use later on so

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that people can download the data there for
reuse,

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people from your community. And here the license
is important.

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You have to put alongside your data a license
that describes

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who is allowed to use the data and for

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which purposes. The European community wants
you to put a CC -

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Creative Commons - CC license or CC by license
alongside

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your data. Most of the repositories offer this

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possibility to put a certain license alongside
the data.

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You can just click it there

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and then,

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the license is automatically attached to your
data.

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Storage: here you would just describe for each
work package,

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where you will put the data during the project,

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is there a backup to this secure storage, an
automatic backup

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or do you have to do the backup manually?

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How will you transfer data? Maybe this is already,

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described in all the other

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tables, maybe not. Because you have to transfer
data

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in a different way because you only have interfaces
between

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two partners or between two or three subprojects.

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And here you should describe how you are planning
for long term preservation of

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the data. If you follow the good scientific
practice,

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you are requested to archive the data for at
least 10 years.

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And here you can describe how you will do that.

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This might be the data management plan if

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you are filling in all those tables,

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as you go within your project,

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this might be the data management plan for
your

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group. And

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the data management plan you hand in for the
funding body might be

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a summary that you can create out of filling
in those tables,

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but it would also be okay to hand in those
tables as a

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data management plan for the European Commission.

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If everything is described in an understandable

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way for the European Commission in here.