Block 1 – Welcome and Intro

September 21st, 2023

Sabrina Zander
MibiNet

Dominik Brilhaus
CEPLAS Data Science

Welcome

House-keeping

Pad: https://pad.hhu.de/Aem023liTWKyfPysU0H8Gw

Your motivation

• how to organise and handle the data collected from my experiments
• grasp important concepts on research data management
• make data available to the others in the work group
• store the data in a sorted way to allow reproducibilty and not loose any important data
• learn how to store my data correctly
• help others to find important data
• familiarize myself with systems and organizing data in an easy but also time efficient way
• data has to be accessible and usable for other members of the project
• generate data that can be used by the other projects
• know how data (including metadata) should be structured and integrated uniformly in the ARC
• learn to build ARCs in order to have the data produced during the PhD in an organised manner
• ...

Tentative agenda

Introduce yourselves

• UoC / HHU
• CEPLAS / MibiNet
• Used code / programming language before
• Has an ORCID

Goals

• Appreciate FAIR principles
• Tools and services for FAIR data management
• Effectively manage your own research data
• Communication and terminology

In this workshop we focus more on how and less on why

Why Research Data Management (RDM)?

• Increase transparency
• Make data accessible
• Save time (writing, reusing)
• Reduce the risk of data loss
• Optimize the costs
• Facilitate future reuse and sharing
• Improve citations

The Research Data Lifecycle

The Research Data Lifecycle

The Research Data Lifecycle

The Research Data Lifecycle

The Research Data Lifecycle

The Research Data Lifecycle

The Research Data Lifecycle

The Research Data Lifecycle is mutable

Have you ever heard about the
FAIR principles?

FAIR

• Findable
• Accessible
• Interoperable
• Reusable

https://doi.org/10.1038/sdata.2016.18

The FAIR principles

Is your data FAIR?

Findable | Accessible | Interoperable | Reusable

• Where do you store your data?
• How do you share your data?
• What tools do you use to analyse your data?
• How do you reuse other people's data?

Findable

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.

https://www.go-fair.org/fair-principles/

Accessible

Once the user finds the required data, she/he/they need to know how they can be accessed, possibly 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

https://www.go-fair.org/fair-principles/

Interoperable

The data usually need to be integrated with other data. In addition, the data need 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.

https://www.go-fair.org/fair-principles/

Reusable

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 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

https://www.go-fair.org/fair-principles/

FAIR on multiple layers

The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure.

https://www.go-fair.org/fair-principles/

Scattered Data Silos

Scattered Data Silos

FAIR Data for everyone