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Welcome

<script input="button"> alert("Disclaimer: Please note that you are leaving the CAU net once you open this presentation in your browser. This presentation includes links to other third party websites and services. These sites are not under our control. RDM@CAU is not responsible for the content of linked third party websites. Please be aware that the security and privacy policies on these sites may be different than CAU policies. Please read third party privacy and security policies closely.") "Disclaimer" </script>

Thilo Paul-Stüve, Britta Petersen, Linda Zollitsch

Central Research Data Management of Kiel University

To see this document as an interactive LiaScript rendered version, click on the following link/badge:

LiaScript

If you need help, feel free to ask us any questions:

fdm@rz.uni-kiel.de

ccby This work is licensed under a Creative Commons Attribution 4.0 International License with exception of the used material from other copyright holders.

Please be nice!

Some rules for today:

  • Please mute your microphone when you do not have the floor

  • Please hear each other out and let each other finish

  • Draw attention to yourselves when you want to say something

  • Please help each other

  • Please do not do anything on the side

  • Please ask if you have not understood something

  • Please contribute actively

  • Please allow mistakes -> positive culture of mistakes.

  • No racism or sexism

figures hugging Source: Pixabay

Goals today

At the end of the workshop you should…

  • have a basic idea of the general concept of RDM and know some important related terms.

  • can describe what research data and research data management is.

  • can describe the research data lifecycle.

  • can describe the FAIR-principles.

  • can recall some important rules in regard of naming files and folders.

  • can explain the importance of documentation and can describe what metadata are.

  • can identify and assess data formats.

  • can distinguish back up and long-term storage.

  • can describe what a DMP is.

  • have heard of requirements of funders & other important players.

  • know RDM related CAU contacts.

  • had some time to exchange with peers.

  • hopefully also had some fun!

targets Source: Cleo Michelsen

Agenda

Let us have a look at our workload for today:

  • Research data and research data management

  • Research data lifecycle

  • FAIR principles

  • Data organisation

    LUNCH BREAK

  • Documentation & metadata

  • Data formats

  • Back up & long-term storage

  • Openess of data

  • Licenses

  • Data publication

  • Data management plan (DMP)

  • RDM related organisations & funder requirements

  • RDM @ CAU

women Source: Pixabay

Warm up!

image

Let us play a game…

Hide your camera (use a sticker or your finger).

I will read statements to you.

Each time you can agree with the statement show yourself and wave.

That's it !

{{1-2}}

I like to drink coffee in the morning.

{{2-3}}

I am working in the field of nature science.

{{3-4}}

If I have to decide to go to the cinema or to a concert, I decide for the concert.

{{4-5}}

I am working in a field of the humanities.

{{5-6}}

I know the FAIR data principles.

{{6-7}}

I am working in a field of engeneering

{{7-8}}

I have an ORCID.

{{8-9}}

I have a pet (or more than one).

{{9-10}}

I am using open data for my research work.

{{10-11}}

I am working with personal data.

{{11-12}}

I am teaching students next to my PhD.

{{12-13}}

I am writing code for my PhD.

{{13-14}}

I was sent to this workshop by my supervisor and really don't know what am I supposed to do here.

Research data and research data management

Research Data

{{0-1}}

What is reserach data? image

Today you are supposed to learn something about research data management.

What do you think: What is research data? Collect as many examples for research data as you can think of.

https://answergarden.ch/4936688

{{1-2}}

<iframe src="https://answergarden.ch/4936688" style="border:0px;width:100%;height:500px" allowfullscreen="true" webkitallowfullscreen="true" mozallowfullscreen="true"></iframe>

{{2}}

What is research data?

‘In short data means whatever is necessary to validate or reproduce your research findings, or to gain a richer understanding of them.’

University of Edinburgh Research Data Service

{{3}}

‘Any information you use in your research.‘

University of Camebridge PrePARe Project

{{4}}

‘The term “research data” generally refers to all kinds of (digital) data that represent the result of scientific work or that serve as a basis for such work. Research data is generated using a wide variety of methods, such as measurements, source research or surveys. Therefore, it is always subject- and project-specific.’

Uni Giessen

Examples for Research Data

Bild

  • Audio and video recordings
  • Diaries
  • Geographic information system (GIS) data
  • Laboratory and field notebooks
  • Model, script and research software code
  • Pictures and figures
  • Questionnaires and codebooks
  • Samples and artifacts
  • Sensor data
  • Sequence data
  • Spectra
  • Text and spreadsheet documents
  • Text corpora and annotations
  • Topography data
  • Transcripts

Research Data Management

{{0-3}}

What is research data management?

{{1-3}}

‘Research data management is an explicit process covering the creation and stewardship of research materials to enable their use for as long as they retain value.’

DCC Glossary

{{2-3}}

‘Research Data Management (RDM) is the methodical handling of the information produced or re-used during the course of academic research.’

University of Edinburgh Research Data Service

Research data lifecycle

{{0-1}} ************

RD-Lifecycle

{{1-2}}

Planning:

  • How do you plan to create data?
  • Will data be reused? How is the data available?
  • Which data types, in terms of data formats (e.g. image data, text data or measurement data in tables) are created?
  • What volume of data can be expected?
  • What legal and ethical aspects need to be taken into account?
  • Who is responsible (for what)?
  • Which analyses are planned? What requirements must the data meet in order to be analysed as planned? What kind of software environment will you need?

{{2-3}}

Collection and analysis:

  • Which (digital) methods and tools (e.g. software) are required collect and safe the (raw) data?
  • What measures are taken to ensure high quality of the data?
  • What approaches are taken to document all your work in a comprehensible manner?
  • Which digital methods and tools (e.g. software) are required to read, use and analyse the data?
  • How and where will the data be stored during the project?
  • What is your back up strategy?
  • How will the security of sensitive data be guaranteed during the project (access and usage management)?

{{3-4}}

Archiving & publication:

  • What legal conditions need to be considered in regard of publishing your research data?
  • What ethical conditions need to be considered in regard of publishing your research data?
  • Are there any effects or restrictions to be expected with regard to publication or accessibility of the data?
  • How are usage and copyright aspects as well as ownership issues taken into account?
  • Are there any important scientific codes or professional standards that should be taken into account?

{{4-5}}

Re-use:

  • Which data is particularly suitable for re-use?
  • What criteria are used to select research data in order to make it available for re-use by others?
  • Do you plan to archive your data in a suitable infrastructure?
  • Are there embargo periods?
  • When can the research data expected to be used by third parties?

{{5}}

RD-Lifecycle

Individual work:

Think about your own PhD project and add keywords to the stations of the research data lifecycle that describe what steps and procedures at each station are relevant to your research data.

Find a workspace at the Miro-Board

-> Does this research data lifecycle fit to your research project?

-> Are there any deviations? If yes, please mark deviations.

BREAK

Have a break!

Breaktime

FAIR Data Principles

{{0-1}}

targets Illustration: Patrick Hochstenbach in Engelhardt, Claudia et. al. (2021)

{{1-2}}

An important goal of research data management is to keep data

🔍 Findable,

🔐 Accessible,

🔗 Interoperable and

♻️ Reusable

in the long term and independent of individuals.

{{2}}

Findable

{{3-4}}

The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.

F1. (Meta)data are assigned a globally unique and persistent identifier

F2. Data are described with rich metadata (defined by R1 below)

F3. Metadata clearly and explicitly include the identifier of the data they describe

F4. (Meta)data are registered or indexed in a searchable resource

{{2}}

Accessible

{{4-5}}

Once the user finds the required data, the user needs to know how data can be accessed, including authentication and authorisation.

A1. (Meta)data are retrievable by their identifier using a standardised communications protocol

A1.1 The protocol is open, free, and universally implementable

A1.2 The protocol allows for an authentication and authorisation procedure, where necessary

A2. Metadata are accessible, even when the data are no longer available

{{2}}

Interoperable

{{5-6}}

The data usually needs to be integrated with other data. In addition, the data needs to interoperate with applications or workflows for analysis, storage, and processing.

I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (Meta)data use vocabularies that follow FAIR principles

I3. (Meta)data include qualified references to other (meta)data

{{2}}

Reusable

{{6-7}}

The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be understood, replicated and/or combined in different settings.

R1. Meta(data) are richly described with a plurality of accurate and relevant attributes

R1.1. (Meta)data are released with a clear and accessible data usage license

R1.2. (Meta)data are associated with detailed provenance

R1.3. (Meta)data meet domain-relevant community standards

Movie time!📽️😀

{{0-1}}

<iframe width="560" height="315" src="https://www.youtube.com/embed/66oNv_DJuPc " title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

{{1}}

Let's discuss! image You will be assigned to group F, A, I, or R. Think about your own projects and evaluate the handling of research data against the one principle you have been assigned to. Compare with the video. Do scenes in the video look familiar to you?

  • Where do you (or all of us) need to improve?
  • What do you need (e. g. knowledge, tools, methods) in order to comply with the FAIR principles?

Use the Miro-Board to take notes: https://miro.com/app/board/uXjVI5UWK7o=/?moveToWidget=3458764600483701087&cot=14

Data organisation

{{0-1}}

>

**It may seem trivial, but structured folder and file naming is a first step in research data management!**

wild file names

https://xkcd.com/1459. Shared under CC-BY-NC License

****************

{{1}}

Measures in research data management serve to improve findability and traceability of research data and to avoid data loss with the aim of increasing the (person-independent) re-usability of research data.

The first person to want to re-use your own data may be yourself!

Always store, name and document your data in such a way that you minimise the risk of data loss.

Always think of your future self! Store, name and document your own data in such a way that you can find, understand and re-use it as easily as possible.

General notes

{{0-3}}

  • Never touch raw data! Always keep your raw data unchanged in a separate folder.

{{1-3}}

  • Try to find ‘speaking’ names for folders and files ➞ no ‘fantasy names’ 🦄, no random character strings

  • Develop a standardised scheme and a logical structure

    • for both folder and file names.

    • Folders in hierarchical order with the most important first.

    • Limit yourself to a maximum of three folder levels, ensure a maximum path length of 256 characters.

    • Keep your personal preferences in mind during development, e.g. for sorting!

{{2-3}}

  • Follow ISO 8601 for dates and times

    • Date and time, e.g. YYYY-MM-DD-hh-mm-ss or YYYYMMDDhhmmss

{{3-4}}

  • Always avoid spaces and all special characters (including special letters, such as german umlauts).

    • The following characters in particular should NOT be used in folder or file names:

      • less than: <

      • greater than: >

      • colon: :

      • double quotation mark: “

      • slash: /

      • backslash: \

      • vertical bar or pipe: |

      • question mark: ?

      • asterisk: *

    • The only unproblematic special characters in folder or file names are underscore (_) and hyphen/minus (-)

{{4-7}}

  • Prefix consecutive numbers with a sufficient number of zeros (e.g. 001 for numbering from 1 to 100)

{{5-7}}

  • Use only one dot per file name -> between the file name and format suffix (e.g. filename.txt)

{{6-7}}

  • Upper and lower case is considered different by some file systems, but not by others.

{{7}}

  • Document your folder structures as well as the naming conventions and abbreviations used!

    • Readme.md

Examples

{{0-1}}

Example for a folder hierarchy

example folder hirarchy

Provided by Oliver Nakoinz

{{1-2}}

Example for a file name following a naming convention

[Project name]_[Approach]_[Location]_[Person-ID]_[Date].[Format-Suffix]

Rebel-Hunting_Interview_DS-1-Orbital-Battle-Station_Organa_1976-05-25.mp4

{{2}}

Why ISO 8601 for dates and times?

  • Kristall_765_spektr_2016-12-03.csv
  • Kristall_765_spektr_16-12-03.csv

Version Control

{{0-1}}

which version to choose?

{{1-2}}

  • Store versions in separate files

    • can be included in file names

    • ❌❌ Do NOT use unspecific name components, such as final, finished, new or similar ❌❌

    • Semantic versioning (Major.Minor.Patch), e.g.,

      • 0.1.0 (a beta)

      • 1.0.0 (a release version)

      • 1.0.1 (a release with slight corrections)

    • define what you consider to be a "release" or a "slight correction"

  • Document your versioning scheme and constantly document your changes

    • Readme.md

    • Version control table

    • Data dictionary

  • Working in a team? A lot of changes? Use a distributed version control system!

    • Git

    • GitLab, GitHub

{{2-3}}

What a version control table could look like

Versionsnr. Changes Date changed by
1.0 Release 2016-11-02 KL
1.1 Erased spelling mistakes 2016-11-20 KL
1.2 Changed layout 2017-02-20 GN
2.0 Add new chapter (3.1.) 2017-02-20 GN

{{3-4}}

Example for version information within a document:

Version Information inside a document

Have a look here: Principles and best practices in data versioning for all data sets big and small

{{4}}

Example for a documented versioning scheme:

Beispiel Versionierungschema

Have a look here: Zenodo

Develop a naming convention

Individual work or group work for people working on the same project image

What would be a good folder structure and a good file naming convention for the files related to your PhD project?

Please document

  1. to which files your folder structure and your naming convention applies

  2. a scratch of a folder structure

  3. the descriptive aspects to be used in file names and their order

  4. the abbreviations used

  5. the versioning scheme, if applicable

Use your workspace on the miro-Board to take notes.

BREAK

Have a break!

Breaktime

Data documentation

{{0-2}}

Group work:

image

You are working in a research group working on the ecology of forests and take over data from a previous project: average_d.xlsx

  • Speculate what kind of data it could be.

Discuss and take notes

  • Apart from the data itself, what information do you need to be able to work with a dataset?

  • What do you notice in regard of data quality?

{{1-2}}

A good data documentation should include

  • Information on the collection of data

    • Methods, units, time periods, locations, technique used, etc.

  • Structure of the data and their mutual relationships

  • Explanation of variables, labels and codes

  • Differences between different data set versions

  • Measures for data cleaning

  • Information on access and terms of use

    • Licensing

  • Ideal world

    • Description of the research undertaking

      • Goals

    • Hypotheses

figures hugging

{{2-4}}

Short research assignment:

Which guideline of the DFG Guidelines for Good Scientific Practice deals with documentation?

{{3-4}}

**Guideline 12: Documentation**

„Researchers document all information relevant to the production of a research result as clearly as is required by and is appropriate for the relevant subject area to allow the result to be reviewed and assessed.[…]“

> >

Deutsche Forschungsgemeinschaft. (2022). Guidelines for Safeguarding Good Research Practice. Code of Conduct. https://zenodo.org/records/14281892, S. 16f.

Metadata

{{0}}

What is Metadata?

{{1}}

image

Metdata is...

Understanding Data

{{0-9}}

Date: 364.07

{{2-3}}

doi The DOI® System ISO 26324

{{3-4}}

kelvin Temperature in Kelvin 364,07 K ≈ 42,6º C

{{4-5}}

calender

{{5-6}}

wetterwarte

{{6-7}}

ROR
dwd logo

{{1-2}}

Data about Data

  • Identifier: 10.1594/dwd-weather-data
  • Identifier Type: DOI
  • Unit: K
  • Data Type Identifier: 11314.3/0a9062a9cb51995dea9f
  • Date: 2019-07-25T15:00:00Z
  • Location: 52.5178687 7.3057642
  • Creator: Deutscher Wetterdienst
  • ROR: 02nrqs528

{{2-3}}

Origin, Location and Meaning of Data

  • Identifier: 10.1594/dwd-weather-data

  • Identifier Type: DOI

  • Unit: K
  • Data Type Identifier: 11314.3/0a9062a9cb51995dea9f
  • Date: 2019-07-25T15:00:00Z
  • Location: 52.5178687 7.3057642
  • Creator: Deutscher Wetterdienst
  • ROR: 02nrqs528

{{3-4}}

Origin, Location and Meaning of Data

  • Identifier: 10.1594/dwd-weather-data
  • Identifier Type: DOI

  • Unit: K

  • Data Type Identifier: 11314.3/0a9062a9cb51995dea9f

  • Date: 2019-07-25T15:00:00Z
  • Location: 52.5178687 7.3057642
  • Creator: Deutscher Wetterdienst
  • ROR: 02nrqs528

{{4-5}}

Origin, Location and Meaning of Data

  • Identifier: 10.1594/dwd-weather-data
  • Identifier Type: DOI
  • Unit: K
  • Data Type Identifier: 11314.3/0a9062a9cb51995dea9f
  • Date: 2019-07-25T15:00:00Z
  • Location: 52.5178687 7.3057642
  • Creator: Deutscher Wetterdienst
  • ROR: 02nrqs528

{{5-6}}

Origin, Location and Meaning of Data

  • Identifier: 10.1594/dwd-weather-data
  • Identifier Type: DOI
  • Unit: K
  • Data Type Identifier: 11314.3/0a9062a9cb51995dea9f
  • Date: 2019-07-25T15:00:00Z
  • Location: 52.5178687 7.3057642
  • Creator: Deutscher Wetterdienst
  • ROR: 02nrqs528

{{6-7}}

Origin, Location and Meaning of Data

  • Identifier: 10.1594/dwd-weather-data
  • Identifier Type: DOI
  • Unit: K
  • Data Type Identifier: 11314.3/0a9062a9cb51995dea9f
  • Date: 2019-07-25T15:00:00Z
  • Location: 52.5178687 7.3057642

  • Creator: Deutscher Wetterdienst

  • ROR: 02nrqs528

{{7-8}}

Origin, Location and Meaning of Data

  • Identifier: 10.1594/dwd-weather-data
  • Identifier Type: DOI
  • Unit: K
  • Data Type Identifier: 11314.3/0a9062a9cb51995dea9f
  • Date: 2019-07-25T15:00:00Z
  • Location: 52.5178687 7.3057642
  • Creator: Deutscher Wetterdienst
  • ROR: 02nrqs528
  • Description: Air temperature measurement at the weather station Lingen, Germany, on 29 July 2019 in Kelvin

{{8-9}}

Traceability

figures hugging

Mandatory Metadata

Individual work: image

Do you know the Kiel University research data repository?

Go to https://opendata.uni-kiel.de/content/publish/metadata.xml?lang=en

  • Which metadata is mandatory?

  • Is this information sufficient to understand your research data?

File formats

{{0-1}}

Group work: image

Let us collect all file formats you are working with.

Post all file formats you are working with into the answergarden:

https://answergarden.ch/3931685

{{1}}

<iframe src="https://answergarden.ch/3931685" style="border:0px;width:100%;height:500px" allowfullscreen="true" webkitallowfullscreen="true" mozallowfullscreen="true"></iframe>

Choosing file formats

-> Non-Proprietary, unencrypted, uncompressed and commonly used

-> Open-standard-compliant, documented and royalty-free

Data Type Recommended Trade-off Matter Not Recommented
Tabular CSV, TSV, ODS XLSX, SPSS portable XLS, SPSS
Textual TXT, MD, HTML, ODT DOCX, RTF, PDF/A DOC, PDF, PS
Presentation ODP, HTML PPTX PPT
video MP4, MKV, OGG WEBM WMV, MOV, QT, Flash
Audio MP4, FLAC, WAV, OGG MP3, AIF
Image TIFF, PNG BMP, JPG PSD, GIF
Vector SVG AI
Generic XML, JSON, RDF
Container Bagit, Frictionless, Data Package ZIP, TAR

Back up & long-term storage

{{0}} Where do you store your data?

No back up? No mercy!

{{1}}

Recommendations for your back up

  • At least 3 copies of a file
  • On at least 2 different media
  • At least one of which is remote
  • Test data recovery at the beginning and at regular intervals.

{{2}} How do your store your (sensitive) data?

{{3}}

Protect your (sensitive) data:

  • Hardware (e.g. separate lockable room).
  • File encryption
  • Password security
  • At least two people should have access to your data

Back up vs. long-term storage

Back up Long-term storage
Automatic backup of all data Storage of only selected data
All versions Final version only
to prevent data loss
(technical, e.g. defective,
or human, e.g. accidentally deleted)		 Integrity backup
(e. g. regular check for modified or damaged data,
file system consitency)
Long-term storage
Searchability

BREAK

Have a break!

Breaktime

Openess of Data

  • Open Access

  • Open Data

  • Open Educational Resources

  • Open Source Research Software

  • Open Methodology

  • Citizen Science

  • ...

Open Access

"Open access is the term given to access granted to research publications and other materials online free of charge. A research document published under open access conditions may be read, downloaded, saved, linked to, printed and used by anybody at no cost."

Source: DFG

**Open access** contributions are often represented with the help of an open lock.

  • Distribution of research output online, freely available for everyone

    • no restrictions for use und republication

    • In practice: attribution to the original author or creator

  • Formerly only open publication of journal articles, papers, etc.

![Open Access](images\access.png)

Open Access at Kiel University

Information and services @Kiel University:

Open Data

"Open data are data that have been made available for free use, re-use and dissemination. They can comprise any kind of data from learning materials to geographical data, statistics, traffic data, academic publications, medical data, radio and tv broadcasts.

In oder to mark data as "open", different choices of license are available. Data with restrictive licences, which limit use by prohibiting derivatives or commercial use, do not strictly count as "open data" even though they can certainly improve the academic exchange of ideas." (https://forschungsdaten.info/praxis-kompakt/english-pages/open-data-open-access-and-re-using-data/)

  • Open Data

    • Publication of data without legal or technical restrictions
    • for use, re-use and dissemination
    • Indispensable for data science
    • Generic non-proprietary formats
    • different types of data as well as Metadata

{{1}}

What do you think?

$$ OPEN = FAIR, ? $$

Open Data at Kiel University

Information and services @Kiel University:

Licenses

{{0-1}}

  • Licenses regulate conditions of subsequent use of published data.

  • Free licenses allow the use, redistribution and modification of copyrighted works

    • are usually available for free use and only need to be linked to
    • Prerequisite is that you are the copyright holder

Selection of the license depends on the type of data:

  • e.g. Creative Commons (CC) licenses for articles, monographs, images, etc.

  • Open-Database-License (ODbL) for DB or CC starting with version 4

  • General Public License (GNU) for software

  • If no license is granted, the stricter copyright applies, as far as applicable to data

{{1-2}}

CC-Licenses

CC-Licenses

{{2-3}}

ODC-Licenses

odc-licences.png

{{3-4}}

Take care!

$$ no,license \not = free,license $$

Data publication

How to publish and share your data?

{{1}}

Supplement to a peer-reviewed article ("enhanced publication")

{{2-3}}

  • as a supplement to the associated article
  • as a data set in a repository with a link to the corresponding article.

Example:

Example R-R-Article

{{1}}

Independent information object in a research data repository

{{3-4}}

Example:

Example R-R-Article Source: https://www.pangaea.de/, Zugriff 10.02.2021
Example Zenodo Source: https://zenodo.org/, Zugriff 10.02.2021

{{1}}

Data journals

{{4-5}}

  • publish detailed description of data

  • partly peer-reviewed

Example:

Example Data journal Source: https://www.earth-system-science-data.net, Zugriff 10.02.2021

Repositories

{{0-2}}

What is a repository?

{{1-2}}

"A repository (Latin repositorium, 'storehouse') is a managed place for storing ordered documents that are accessible to the public or to a restricted group of users. An archive (Latin archivum, file cabinet'), on the other hand, manages only historical documents.“

"Digital research data repositories are information infrastructures that store and organize digital research data...as permanently as possible...to ensure the discoverability and accessibility of the data...“

^Source: Esther Asef, Katarzyna Biernacka, Elisabeth Böker,Sarah Ann Danker, Juliane Jacob, Janna Neumann, Britta Petersen, Jessica Rex und Ute Trautwein-Bruns (2021): Data Sharing interaktiv vermitteln^

{{2-5}} How to find a repository

{{3-4}}

re3data.org

  • Collection of repositories
  • Worldwide
  • Various disciplines
  • Researchers, funders, publishers and institutions
re3data Source: re3data About. http://service.re3data.org/about. Zugriff 10.02.2021

{{4-5}}

risources.dfg.de

  • Offer of the DFG
  • Information portal
  • Germany-wide
  • Research Infrastructures
  • For researchers

{{5}}

re3data
>__Individual work__ > Search **re3data.org** for a repository that is appropriate for your data. > >Let us discuss your search and selection.

Data management plan (DMP)

What is a data management plan?

{{1-2}}

  • All information that adequately describes and documents the collection, processing, storage, archiving, and publication of research data in the context of a research project.

  • "[...] analysis of the workflow from the generation of the data to their use.“^1^

^[1] J. Ludwig, H. Enke (Hrsg.) Leitfaden zum Forschungsdaten-Management. Handreichungen aus dem WissGrid-Projekt. Verlag Werner Hülsbusch: Glückstadt, 2013.^

Components of a DMP

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  • Administrative information

    • Project name, data originator, other contributors, contact, funding program, etc.

  • Project abstract

    • Data set descriptions
    • Data types, formats, scope
    • Metadata and standards information
    • Data sharing
    • Archiving and backup of data
    • Responsibilities
    • Legal and ethical aspects (e.g., licences, GDPR, Nagoya protocol, CARE)
    • Costs

Length can vary from a few paragraphs to several pages!

Sketching out a DMP

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Download the CAU template for data management plans: CAU_DMP_Template

Have a look at the template and try to sketch out a DMP for your research project.

  • What information do you already have?
  • What information is missing to fill the template?

RDM related organisations & funder requirements

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Research Data Alliance

Nationale Forschungsdateninfrastruktur (NFDI)

Deutsche Forschungsgemeinschaft (DFG)

Horizon 2020 & Horizon Europe

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Research Data Alliance

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  • International organisation founded in 2012

    • Vision: Researchers and innovators openly share data across technologies, disciplines, and countries to address the grand challenges of society

    • Mission: RDA builds the social and technical bridges that enable open sharing of data

  • Bottom-up development of practices, infrastructures, tools, technologies, services, approaches, policies, etc.

  • Practitioners come together in Birds of a Feather-Groups (BoF), Interest Groups (IG) or Working Groups (WG)

  • Regional chapters, e.g., RDA Europe or RDA Deutschland e.V.

  • Strong influence on European Commission, BMBF, DFG, …

rda logo

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Nationale Forschungsdateninfrastruktur (NFDI)

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  • National research data management initiative in Germany

  • Initiated by the German Council for Scientific Information Infrastructures

  • Horizontal linking of existing actors

    • Discipline-specific NFDI consortia with with binding roadmaps

    • Bring into use existing infrastructure

    • Identify and fill gaps

  • Interoperability of data and infrastructure

  • Use of NFDI will probably get mandatory

  • Participation in the work of NFDI consortia possible

nfdi logo

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Deutsche Forschungsgemeinschaft (DFG)

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  • Code of Conduct: Guidelines for Safeguarding Good Research Practice

  • Guideline 7 – Quality assurance

    • Disclosing of origin of data, organisms, materials and software used
    • Reuse of data is clearly indicated; original sources are cited
    • Description of nature and scope of research data generated
    • Handling of research data in accordance with requirements of relevant subject area

  • Guideline 13 – Public access

    • Making available research data where possible and reasonable

    • Making available software programmed by researchers with source code

  • FAIR Principles

nfdi logo

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Horizon 2020 & Horizon Europe

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  • Framework Programme for Research and Technological Development of the European Commission:

  • Horizon 2020 Online Manual – Data Management

  • Horizon Europe – Programme Guide – Open Science

  • Horizon Europe – Data Management Plan Template

  • Open access to research data is applicable by default

    • as open as possible, as closed as necessary

  • Make research data findable, accessible, interoperable and re-usable (FAIR)

  • DMP should include information on:

    • The handling of research data during & after the end of the project

    • What data will be collected, processed and/or generated

    • Which methodology & standards will be applied

    • Whether data will be shared/made open access and

    • How data will be curated & preserved (including after the end of the project)

nfdi logo

RDM @ CAU

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You have 5 minutes to perform an internet search!

Are there any guidelines for handling research data at CAU?

Are there specific guidelines for handling research data at your institute?

Who are the contact persons at CAU and/or at your institute if you have questions about handling your digital research data?

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CAU Guideline on Research Data Management

Kiel University’s Recommendations on Handling Research Data

  • Research data are the basis and result of scientific work and are therefore of particular significance. It is important to handle research data responsibly to ensure that scientific work is traceable, verifiable and reusable
  • Handling research data is subject to constant change due to advances in research
  • Kiel University continuously assists with this ongoing process by implementing appropriate measures

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Principle 1

  • Research data are all data created in the process of research work through observation, collection, experiments, simulation, and further processing for the purpose of producing and validating research results
  • Research data management covers all areas of data administration: data acquisition, data preservation, data security and data integrity, as well as the release and publication of data

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Principle 2

  • Research data management is performed in accordance with current professional standards while respecting the DFG (German Research Foundation) proposals for safeguarding good scientific practice
  • Research data management is documented in a data management plan
  • Responsibility for research data management lies with the head of the particular research activity.

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Principle 3

  • Research data is made accessible to the public in national or international subject-specific archives under appropriate licensing; if no such archives exist, central university services are used

  • Scientific and legal interests of the researchers, the protection of the personal details of participants, patients and other individuals affected by the collected data, as well as other obligations to third parties – for example, cooperation partners – as well as ethical, legal and economic restrictions must be observed

  • Kiel University assists researchers through its central infrastructure facilities

    • Acquisition and preservation of research data

    • Formulating and maintaining standards for handling research data

    • Appropriate training and advisory service offers created in cooperation with the specialist disciplines

  • Kiel University recommends and assists with anchoring the significance of subject-specific, structured research data management in teaching by the particular subject areas

Take-Away Messages

Practical Take-Away Messages

  1. **Document your data**

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  • Use documented naming and versioning conventions

  • document changes

  • think about metadata necessary to understand your data

  1. **Formats**

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  • Generic and open standard file formats last longer than proprietary file formats

    • Open Document Format (ODF)

    • Comma separated values (CSV)

    • Raw text files (TXT, MD)

  • Data container formats for exchange, archival and publication, e.g., BagIt, Frictionless Data

  1. **Storage**

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  • Central infrastructure with backup for storage

  • Desktop and laptop for work on current research data only

  • Systematic file and folder naming and hierarchy

  • Provide Readme files

  • Data Management Middleware for handling data and metadata, e.g., iRODS

  • DFG Guidelines for Safeguarding Good Research Practice require 10 years of preservation at least!

  1. **Publication**

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  1. **Licensing**

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Questions

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Time for questions!

One Minute Paper

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Please take a piece of paper or create an own pad (e.g. https://zumpad.zum.de/).

You have one minute.

Please write down the most important points of our workshop today.

Feedback

Please give us some feedback!

You have a date with some friends tonight.

Your friends remember that you attended a workshop on research data management today and ask: “Well, how was it”?

What do you answer?

| :-) | :-/ | :-( |

CAU contacts

rdmCAU

RDM contacts at CAU:

https://www.fdm.uni-kiel.de/en/team?set_language=en

Thank you! :-)

Please take care of your data! 🌼