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TRABAJO DE INVESTIGACIÓN

Curriculum Development for FAIR Data Stewardship

Francisca Oladipo1,2,3, Sakinat Folorunso4,2†, Ezekiel Ogundepo5,2, Obinna Osigwe1,2,
Akinyinka Akindele6,2

1Directorate of Research, Innovaciones, Consultancy and Extension, Kampala International University, 10102, Uganda

2Virus Over Data Network (VODAN)-Africa and Asia,

3Federal University Lokoja, 260101, Nigeria

4Department of Mathematical Sciences, Olabisi Onabanjo University, P.M.B 2002, Ago-Iwoye, Ogun State, 120005, Nigeria

6School of eLearning Projects, Kampala International University, P.M.B 4000, Ogbomoso, Oyo state, Nigeria, Uganda

5Data Science Nigeria, Lagos 105102, Nigeria

Palabras clave: Data steward; Data science; FAIR Guidelines; FAIR; Digital technology; FDP installation; FAIR Data

Trains; Semantic web; Personal Health Train

Citación: Oladipo, F., Folorunso, S., Ogundepo, E.A., Osigwe, MI., Akindele, A.T.: Curriculum development for FAIR data

stewardship. Data Intelligence 4(4), 991–1012 (2022). doi: 10.1162/dint_a_00183

Submitted: Marzo 10, 2021; Revised: Junio 10, 2022; Aceptado: Julio 15, 2022

ABSTRACTO

The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable
(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, como
the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected.
Como consecuencia, there is a need to develop curricula to foster professional skills in data stewardship through
effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in
FAIR data management training through both formal and informal programmes. This article describes the
experience of developing a digital initiative for FAIR data management training under the Digital Innovations
and Skills Hub (DISH) proyecto. The FAIR Data Management course offers 6 short on-demand certificate
modules over 12 semanas. The modules are divided into two sets: FAIR data and data science. The core subjects
cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to
advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic
datos. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the
resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists

†

Corresponding author: Sakinat Folorunso, Olabisi Onabanjo University (Correo electrónico: sakinat.folorunso@oouagoiwoye.edu.ng;
ORCID: 0000-0003-0584-9145).

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Curriculum Development for FAIR Data Stewardship

and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused
curricula development with diverse participants will build a core of FAIR data scientists for Data Competence
Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.

ACRONYMS

CF-DS
DMP
DS-BoK
DSDA
DSDK
DSDM
DSENG
DSP
DSPP
EDSF
ESCO
FAIR
ICT
ITSM
MC-DS

Data Science Competence Framework
data management plan
Data Science Body of Knowledge
Data Science Analytics
Data Science Domain Knowledge
Data Management and Governance
Data Science Engineering
Data Science Professional
Data Science Professional Profiles
EDISON Data Science Framework
European Skills, Competences, Qualifications and Occupations
Findable, Accessible, Interoperable, Reusable
information and communications technology
information technology service management
Data Science Model Curriculum

1. INTRODUCCIÓN

En 2019, the World Economic Forum estimated that, por 2025, an average of 463 exabytes of data (Tweets,
email messages, Facebook posts, WhatsApp messages, clinical data, and music files, etc.) will be created
every day [1]. This data will be in different formats, like images, texto, or audio, and from different domains.
In response, the big data landscape is redefining requirements for data curation infrastructure, which is
evolving to meet the challenges [2]. By employing data analytics, the metadata of curated health data can
provide insights into solving health problems, gearing the industry toward value-based healthcare and
opening doors to remarkable advancements, while reducing costs. Sin embargo, constraints, como el
misrepresentation of data, privacy issues, siloed data, seguridad, and data not being machine-readable, entre
other things, can lead to false inferences being drawn from data analytics. While the FAIR Guidelines [3]
— that data should be Findable, Accessible, Interoperable and Reusable (FAIR) — tend to mitigate some
of these constraints, these principles are foreign to most of the stakeholders whose devices, infrastructures
and research generate such data. De este modo, there is a need to train data stewards using customised training to
equip them with the skills required to implement the FAIR Guidelines. Respectivamente, an appropriate
curriculum needs to be designed, validated and deployed, which is the subject of this article.

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Curriculum Development for FAIR Data Stewardship

The design of any curriculum has four critical components that address four questions:

·
·
·
·

Why is instruction initiated?
What needs to be taught to achieve the set intent and objectives?
How can we connect all target learning outcomes?
What has been realised and what other actions need to be taken in relation to the instructional
programme, learners, and teachers?

Worldwide, these components are usually addressed differently depending on the philosophy of the
domain curriculum and model on which a design is based [4]. The goal of curriculum development is to
communicate knowledge effectively to learners. This article explores the frameworks implemented in data
stewardship programmes, towards designing a curriculum for training data stewards, in an effort to equip
them with the relevant skills.

2. LITERATURE REVIEW

2.1 Data Stewardship: Descripción, Roles and Goals

Data stewardship is a concept that is deeply rooted in the sciences and should be considered in any
funded research. It relates to the procedure for gathering, sharing, and analysing data and reflects the values
underpinning fair information practices [5]. Principally, data stewardship involves all activities related to
research data management over the research lifecycle. It has the potential to improve research, as it improves
data management approaches for the collection, almacenamiento, aggregation, and de-identification of data, también
as procedures for data release and use [6]. En 2020, Wildgaard [7] posited that the position of a data steward
is trust-based. Data stewards are responsible for the administration, management and manipulation of data
belonging to researchers or enterprises. Sin embargo, the professionalization of data stewardship can only
progress with improved data steward education opportunities [7]. Por lo tanto, as an activity that is part of
performing creative research, data stewardship encompasses the design of all activities to do with (digital)
data throughout the research project lifecycle, with the aim of optimising the usability, reusability, y
reproducibility of the resulting data [8]. The study and practice of data stewardship is necessary for FAIR
and open research. The European Open Science Cloud for Research Pilot Project [9] explains data
stewardship as the shared responsibility of the professional groups involved in data management: datos
management and curation, data science and analytics, data services engineering and domain research [9].
Competences, skills groups, and organisational roles are defined around typical processes and stages in
data management: planning and design, capture and processing, integration and analysis, evaluation and
presentación, publishing and release, exposure and discovery, governance and assessment, scope and
resources, advice and enabling.

Collins et al. [9] point out that transitioning to FAIR data stewardship requires education programmes for
both data scientists and data stewards. De hecho, both pedagogy and curricula are needed. Some of the

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Curriculum Development for FAIR Data Stewardship

popular existing curricular frameworks for digital curation and data science are EDISON [10], EOSCPilot[9]
and DigCurV [11]. These curricular frameworks could be implemented as postgraduate degree programmes
in universities [12] to increase the accessibility of professional data science and stewardship programmes.

Wildgaard et al. [12] explain that the major roles for a data steward are administrator, analyst, developer
and agent of change. Like the roles of the data system developer, the role of the data steward is to optimise
the data through good project management, advise on FAIR Guidelines, create a data plan, facilitate
collaboration and knowledge sharing to raise business intelligence, innovate, and develop procedures and
pautas. These authors also propose three models for data steward education: The first model is for
students with bachelor degrees. This model spans one year for students with programming skills and two
years for students without. The second model consists of PhD students or equivalent from any university
faculty. And the third model is for students with professional studies or technical education and vocational
training. Some of the other training options that could be explored for data stewardships as part of continuing
professional development are: summer schools, on-the-job training, workshops, training-of-trainers, y
online learning [9]. FAIR-themed programmes, like workshops, conference sessions, conferencias, webinars,
hackathons, workshops, visiting scholar programmes and so forth, could also be adopted to enhance FAIR
data stewardship. All of these methods have proven to be effective in training students from all disciplines
on the foundational data skills they need to be professional data stewards. For examples, CODATA-RDA[13]
organised a short course programme in the form of a summer school in 2019 to upskill the research
community for professional FAIR data stewardship. Some of the subjects taught were research data science,
research data management, software and data carpentry, aprendizaje automático, visualisation and computational
infrastructure.

This requires universities and other data-rich facilities to invest in Data Competence Centers (DCCs). En
the FAIR Data Science environment, these are called Data Stewardship Competence Centers (DSCCs),
which are established to embed professional, institution-wide research data stewardship and its related
infrastructure, and which collaborate with the data processors in their institutions to enable better data
management and comply with the FAIR Guidelines (Go-FAIR). Rosenbaum [6] agrees that the majority of
data stewards have good research data management and domain-specific knowledge, but notes that it
would be beneficial to provide pedagogical training to impart the soft skills required to efficiently engage
with researchers and meet their needs [6]. Respectivamente, this article proposes designing a digital skills
curriculum for FAIR data stewardship. The proposed curriculum is divided into three main courses:
computing and information technology, analytics, and FAIR data.

2.2 EDISON Data Science Framework

The EDISON Data Science Framework (EDSF) provides a basis for the definition of data science and
enables the definition of other components related to data science education, training, organisational roles,
and skills management, as well as professional certification. This framework contains five main components:

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Curriculum Development for FAIR Data Stewardship

·
·
·
·
·

Data Science Competence Framework (CF-DS) [10]
Data Science Body of Knowledge (DS-BoK) [14]
Data Science Model Curriculum (MC-DS) [15]
Data Science Professional Profiles (DSPP) and occupations taxonomy [16]
Data Science Taxonomy and Scientific Disciplines Classification

The CF-DS provides the overall basis for the EDSF. The core CF-DS competences and skills groups
identified by the EDISON Community [10] as essential for data scientists in different workplaces include:

Data Science Analytics (DSDA) — which uses suitable statistical methods and predictive analytics
(such as statistical analysis, aprendizaje automático, data mining, and business analytics, etc.) on presented
data to deliver insights and discover new relations.
Data Science Engineering (DSENG) — which uses engineering principles to research, diseño, develop
and implement new instruments and applications for data collection, analysis and management.
Data Management and Governance (DSDM) — which relates to the development and implementation
of a data management approach (using techniques such as software and applications engineering,
data warehousing, big data infrastructure and tools for data stewardship, curation, and preservation)
for data collection, almacenamiento, preservación, and availability for further processing.
Data Science Research Methods and Project Management (DSRMP) — which relates to the research
domain, and Data Science Business Process Management (DSBPM), which creates new understandings
and capabilities by using scientific methods (such as hypothesis, test/artefact, and evaluation) o
similar engineering methods to discover new approaches to create new knowledge and achieve
research or organisational goals.
Data Science Domain Knowledge (DSDK) — which uses the domain knowledge (scientific or business)
to develop relevant data analytics applications and adopt general data science methods for domain
specific data types and presentations, data and process models, organisational roles and relations.

The DS-BoK defines the knowledge areas (EL) required for building a data science curriculum that
supports identified data science competences. The DS-BoK is organised by knowledge area groups (KAG)
that correspond to the CF-DS competence groups. These are Data Science Analytics, Data Science
Ingeniería, Data Management, Research Methods and Project Management, and Business Analytics [14]

The MC-DS is built based on CF-DS and DS-BoK, for which learning outcomes are defined based on
CF-DS competences and learning units are mapped to knowledge units in DS-BoK. Three mastery (o
proficiency) levels are defined for each learning outcome to allow for flexible curricula development and
profiling for different data science professional profiles.

The DSPP is defined as an extension of the European Skills, Competences, Qualifications and Occupations
(ESCO) to the ESCO occupations taxonomy, using the ESCO top classification groups. The definition of
DSPP provides an important instrument for defining effective organisational structures and roles related to

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Curriculum Development for FAIR Data Stewardship

data science positions — and can be also used for building individual career paths and corresponding
competences and skills transferability between organisations and sectors.

The Data Science Taxonomy and Scientific Disciplines Classification serves to maintain consistency and

links between the four core components of EDSF (CF-DS, DS-BoK, MC-DS, and DSPP).

2.3 ESCO Framework and Platform

The ESCO classification identifies and categorises skills, competences, qualifications and occupations
relevant for the European Union labour market, education and training. It systematically shows the
relationships between the different concepts [17]. The ESCO Data Science Professional Profiles (DSPP)
occupation hierarchy is: managers, professionals, technicians, and associate professionals, and clerical
support workers. The ESCO DSPP taxonomy can be extended to situations where proposed profile
competences and organisational roles are similar to CEN Workshop Agreement (CWA) 16458 ICT profile
definitions, such as to:

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Managers who are production and specialised services managers (data science/big data infrastructure
managers) whose role spans DSP01–DSP03
Professionals from three major groups:
– Science and engineering professionals (data science professionals) whose roles span DSP04–

DSP09)

– Information and communication technology (ICT) professionals (data science technology

professionals) whose roles span DSP10–DSP13

– Science and engineering professionals (database and network professionals) whose roles span

DSP14–DSP16

Technicians and associate professionals, such as science and engineering associate professionals (datos
science technology professionals) whose roles span DSP17–DSP19
Clerical support workers, such as general and keyboard clerks (data handling and support workers)
whose roles span DSP20–DSP22

Cifra 1 illustrates the existing ESCO hierarchy and the proposed new data science classification groups
and corresponding new data science related profiles. The table in this figure shows competence groups
relevant to each profile by indicating competence relevance from 0 a 5 (0 — not relevant, 5 — very
important). The profile definitions for specific roles for DSP01–DSP22 are detailed on the EDISON
Community website [16]. Por ejemplo, the profile for data steward is DSP10 under the hierarchy of data
science technology professionals. Mapping ‘data steward’ with CF-DS competences and skills groups, el
relevance level with DSDA, DSENG, DSRM and DSDK is 3. Data steward is most relevant to DSDM. Datos
steward is well mapped with the CF-DS competency groups with an average value of 3.

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Cifra 1. Proposed data science related extensions to the ESCO classiﬁ cation hierarchy and corresponding DSPP
by classiﬁ cation groups [16].

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Curriculum Development for FAIR Data Stewardship

Profile title

Mission

Deliverables

Main tasks

Mesa 1. DSP proﬁ les deﬁ nition [16].

Data steward (DSPP10)

Plans, implements and manages (investigación) data input, almacenamiento, buscar, presentación; creates
data model for domain speciﬁ c data; supports and advises domain scientists/researchers;
interacts with the data analytics team; does data preparation, inspection, visualisation;
prepares data for archiving and publication

Accountable
• Data model
• Data management plan

Responsible
• Data collection/ingest

Contributor
• Domain related models
• Data analytics result inspection

• Deﬁ ne/build/optimise data model and schemas
• Use existing or deﬁ ne new metadata framework
• Publish research data to existing scientiﬁ c data archives
• Manage organisational or project-related data
• Search and promote research data
• Assist main domain researcher/scientist in selecting right data analytics methods
• Monitor application of FAIR (Findable, Accessible, Interoperable, Reusable) and open

data principles to data created by organisation or project

Competences
(from CF-DS)

SDSDM02: Use data storage systems, data archive services, digital
libraries, and their operational models

SDSDM05: Implement data lifecycle support in organisational
workﬂ ow, support data provenance and linked data

SDSDM06: Consistently implement data curation and data quality
controls, ensure data integration and interoperability

SDSDM08: Use and implement metadata, Persistent Identiﬁ er (PID),
data registries, data factories, standards and compliance

SDSDM09: Adhere to FAIR Guidelines for open data, open science,
open access, use ORCID based services

Nivel 1

Nivel 2

Nivel 3

Key performance
indicators (KPI)
área

Consistent data management workﬂ ow
Compliance with FAIR Guidelines

The importance of the role of the data steward is recognised in the European Commission’s High Level
Expert Group report on European Open Science Cloud (Octubre 2016) [18], which identifies the critical
need for core data experts and data stewards in particular. The definition of data steward competences and
training in these is an important component of the GO FAIR initiative [19, 20], as well as the Horizon 2020
EOSCPilot project activity [21, 8].

3. METHOD: NUFFIC DATA STEWARDSHIP CURRICULUM

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NUFFIC (the Dutch organisation for internationalisation in education) Digital Innovations and Skills Hub
(DISH) is a distance education programme sponsored by the Dutch Ministry of Foreign Affairs under the
Orange Knowledge Program in conjunction with 12 partners from different countries in East Africa. El
project targets learners with low opportunities, such as marginalised youth, including refugees and displaced
persons from the Tigray region (Ethiopia), Garowe and Mogadishu (Somalia), Kassala and Khartoum (Sudan),
Wau and Juba (South Sudan), and other conflict affected areas from East African region.

3.1 Course Curriculum: Topics and Description

Given the demography of the targeted learners, the training curriculum for this data stewardship
specialisation programme is designed with the assumption that the students have little or no prior computer
science skills. De este modo, the training curriculum starts from a beginner’s perspective and is divided into three
courses of five to seven modules, with each course being a prerequisite for the next. The course details are
given in Tables 2–4.

Mesa 2. Course I — Introduction to Computer Science I — Communication and Information Technology (CS1).

Course

Module Module title Week

Topics

Module description

CS1.1

Peace
Building and
Conﬂ ict
Resolution
Diplomacy
(PBCRD)

Course 1
Computadora
Science I
(CS 1) —
comunal-
cation and
Informa-
ción
Technol-
ogia (CS1)

Peace
Communica-
ción, ICT and
Media
(PCICTM)

Week 1 Introduction to peace, conﬂ ict

and violence
– Conﬂ ict analysis
– Conﬂ ict resolution and

peace

– Understanding peace

building

– Peace building diplomacy
– Peace building and conﬂ ict

in the African context

Introduction to peace building
and conﬂ ict resolution
– Conﬂ ict resolution and

reconciliation

– Communication for peace

building

– Media and peace building
– Peace building communica-
tion and attitude change

– Peace building process

Peace building and conﬂ ict
resolution are key to building
prosperous communities that
are stable and at peace. Este
course focuses on how to
resolve conﬂ ict and negotiate
peacefully when conﬂ ict
emerges in order to create
stability in the community.

Resolving conﬂ ict requires
effective communication. En esto
curso, students learn how to
communicate effectively about
peace. This includes learning
how to write, engage with
tecnología, and communicate
with policymakers and the
público.

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Curriculum Development for FAIR Data Stewardship

Course

Module Module title Week

Topics

Module description

Mesa 2. Continuado

CS1.2

Introducción
to Digital
Tecnología

Weeks
2, 3, 4

– Overview of computers and

operating systems

– The Internet, redes sociales,
email and web browsers
– Cyber security and cloud

computing

– Digital literacy — creating,
sharing and editing digital
content using ofﬂ ine tools

– Computer shortcuts
– Multimedia design —

animation, videos and skits

– Creating digital content
using online tools, p.ej.,
Google apps

– Remote work tools and tips

CS1.3

Introducción
to Computer
Networks

Weeks
5, 6, 7

– Introduction to computer

networking

– Layer architecture (OSI &

TCP/IP)

– Network hardware,

software, y
standardisation
– Network medium,
– IP addressing
– Building small to medium
level networks including
cabling

– Conﬁ guring TCP/IP
– Peer-to-peer networking
– Sharing resources
– Client-server networking

The ﬁ rst unit of this module
introduces learners to the basic
concepts and gives an overview
of computers such as operating
sistemas, the Internet, social
media, cloud computing, y
cyber security, among other
cosas.
Week 2 is on digital literacy,
es decir., how to create and edit
digital content using both ofﬂ ine
and online tools. This includes
how to create textual content
using word processing software
and how to create multimedia
— graphics, videos, skits, y
animation, etc..
Week 3 focuses on remote work
tools such as Google Workspace
and Google apps, like G.Slide.
G.Doc., G.Form and G.Sheet,
among others. This unit also
provides tips on how to be
productive and manage time in
remote work situations.

This module explores the
concept of computer networks
including their evolution,
application, deployment, y
standardisation. It focuses on
how to set up a computer
network and the deﬁ nition and
identiﬁ cation of different types
of networks.
In subsequent study units,
network layer architecture is
explored, with emphasis on
how to identify different
computer networking devices.
Learners will be taught about
the application of several
network protocols, network
software, network standards,
data transmission media, IP
addressing and network
protocols.

1000

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Curriculum Development for FAIR Data Stewardship

Course

Module Module title Week

Topics

Module description

Mesa 2. Continuado

Week 8 Business and Business

CS1.4

Negocio
Administra-
ción,
Entrepre-
neurship and
Leadership

CS1.5

Weeks
9, 10

Información
Tecnología
Support
Management

Strategies
– Entrepreneur mindset,

innovation and competitive-
ness

– Business ﬁ nancing: use and

sources

– Business management
practices and marketing

– Basic operational tasks

involved in using personal
computers

– Managing software applica-
ciones: installing, updating
and uninstalling a software
application

– Managing hardware:

assembling or coupling a
computer, installing network
devices

– Personal computer perfor-
mance, maintenance and
diagnostics

– External system manage-

ment tools — use of Team
Viewer

– Troubleshooting, y

documentation
– Ticketing system
– Customer service in IT

support role

– Health and wellbeing of IT

users

– ITSM processes

This module covers the intro-
ductory part of business
estrategias, businesses ﬁ nancing
and costs, business communica-
tion and operating a business,
and basic employability skills. Él
seeks to prepare young people
to run their own businesses, ser
successful at work, and lead
healthy and productive lives.

This course is designed to
introduce learners to the role of
an IT support specialist in an
organisation. It intends to
prepare them for an entry level
role with an IT help desk or
apoyo.
Learners are introduced to how
to identify and verify installed
software, and how to update
and/or uninstall computer
software.
Learners are introduced to the
hardware components of a
computer system. Esto es
followed by an explanation of
how the components are
arranged and interact within the
sistema.
In this module, learners are also
introduced to how to resolve
slow boot times, device failures,
and other machine issues using
‘Task Manager’, ‘Device
Manager’, ‘Windows Defender’,
and ‘System Performance’ tools.
Other aspects covered are the
roles performed by information
tecnología (IT) help desks such
as ticketing systems and
customer service, etc..
Information technology service
management (ITSM) procesos
and components are explored
también.

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1001

Curriculum Development for FAIR Data Stewardship

Course

Module Module title Week

Topics

Module description

Mesa 2. Continuado

CS1.6

Week
11

Información
Tecnología
Proyecto
Management

– Overview of project

management and related
terms

– Phases and processes of
project management
– Project methodologies
– Importance and advantages

of project management

– Project management

standards
– PRINCE2
– PIMBOK
– Contemporary issues in
project management
– Human resources and

stafﬁ ng

– IT project risk management
– IT project cost management
– Change management

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1002

Data Intelligence

Curriculum Development for FAIR Data Stewardship

Mesa 3. Course II — Introduction to Computer Science II — Data Analytics (CS2).

Course Module Module title Week Topics

Module description

CS2.1

Weeks
1, 2, 3

Introducción
to Python
Programming
Idioma

Course 2
Computadora
Science II
— Data
Analytics
(CS2)

Programming in Python
– Working with Jupyter Lab
– Expression, data type,

and variable assignment

– String manipulation in

Python

– Basic string methods
– String formatting
Data structure in Python
– List, tuple, colocar
– Dictionary
Python ﬂ ow control
– If else statement
– For loop and while loop
– Continue and break

statements
Python function
– Function arguments
– Anonymous function
– Global and local

variables

– Python packages

The course context will be contextu-
alised for business and agriculture,
es decir., how Python programming can
be used to build systems that make
it easier for businesses and modern
farms to operate efﬁ ciently.
Examples will be based on different
problems that occur within the daily
operations of a small business and
how to creatively solve these
problems with programming.
It will also cover examples of how
programming can be applied in an
agricultural context and give a big
picture overview of how technology
powered by Python has been able
to improve agricultural systems.
At the end, students should under-
stand how to frame business/process
questions and how to solve these
problems using Python.

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Data Intelligence

1003

Curriculum Development for FAIR Data Stewardship

Course Module Module title Week Topics

Module description

Mesa 3. Continuado

CS2.2

Introducción
to Data
Science I

Weeks
4, 5, 6,
7

Introduction to data and
data science
– Deﬁ nition, types and

sources of data

– Lifecycle of data science
– Python lists and NumPy

arrays

– NumPy use cases
Introduction to data
analysis with Pandas
– Series and data frames
– Importing and exporting
conjunto de datos, data cleaning
and pre-processing

– Exploratory data analysis
– Computing descriptive

Estadísticas

– Combining and merging

conjuntos de datos

Introduction to data visuali-
estación
– Data visualisation with

Seaborn

– Plotting continuous data
and categorical data with
Seaborn

CS2.3

Introducción
to Business
Inteligencia

Week
8

– Overview of business

intelligence

– Data base management

sistema

– Data modelling with
Entity Relationship
diagrama

– Relational model
– SQL
– OLAP
– Descriptive and predic-

tive analytics
– Visualisation
– Dashboard creation
– Power business intelli-

gence

The course context will be contextu-
alised for business and agriculture,
es decir., how Python programming can
be used to build systems that make
it easier for businesses and modern
farms to operate efﬁ ciently.
Examples will be based on different
problems that occur within the daily
operations of a small business and
how to creatively solve these
problems with programming.
It will also cover examples of how
programming can be applied within
an agricultural context and give a
big picture overview of how
technology powered by Python has
been able to improve agricultural
sistemas.
At the end, students should under-
stand how to frame business/process
questions and how to solve these
problems using Python.

This module introduces trainees to
business intelligence and basic SQL
conceptos.
The module is aimed at equipping
learners with the skills to mine data
from a relational database, extract
valuable information and create
meaningful dashboards that can be
used by business owners to make
day-to-day decisions.
Además, the module will give an
introduction to some of the open-
source business intelligence
software and how to quickly set up
and use it.

1004

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Curriculum Development for FAIR Data Stewardship

Course Module Module title Week Topics

Module description

Mesa 3. Continuado

CS2.4

Tech Skills:
Option
Basado

Weeks
9, 10,
11

Option 1:
Digital
Marketing

Option 2:
React (Web)

Option 3:
Angular

Option 4:
Docker
(needed for
CS3)

Option 5:
React Native
(mobile)

Digital marketing
– Search engine optimisa-

ción

– Search engine marketing
– Content marketing
– Email marketing
– Social media marketing
– Web analytics
React (Web)
– React UI
– Routing
– Form helpers
– Type checkers
– State management
– API clients
– Testing static generators
Angular
– Angular components
– Angular templates
– Angular directives
– Angular depending

injection

– Angular routing; Angular

formas; Angular HTTP

– Angular animation
– Angular best practices
– Angular project and

revisar
Docker
– Docker overview and
environment setup

– Docker Images
– Docker Networks and

Containers

– Docker Compose and
– Troubleshooting Docker
React Native (Mobile)
– Introduction and

environment setup
– Knowledge in React
– Android and IoS compo-

nents

The digital marketing option
exposes learners to the inherent
possibilities of a digital economy
through digital marketing. El estudio
sessions are designed to expose
learners to the various aspects of
digital marketing. These include
search engine optimisation (SEO),
keyword research, redes sociales
marketing, email marketing, contenido
marketing and web analytics. En el
end of the course learners are
expected to be able to create
effective integrated digital marketing
strategies for businesses.
The React (Web) option teaches
learners how to use React JavaScript
library to develop an interactive
user interface on the website.
Different components of React are
explored, such as the React User
Interface (UI), routing, form helpers,
type checkers, state management,
application programming interface
(API) clientela, and testing static
generators.
The Angular option introduces
learners to Angular, a platform and
framework for building single-page
client applications using HTML and
TypeScript. Learners are expected to
use Angular features to create
dynamic web applications.
The Docker option teaches learners
how to use, create, deploy, and run
applications by using containers.
They will be taught how to use
Docker Images, Docker Networks,
Docker Containers, Docker
Compose and how to troubleshoot
Docker.
The React Native option teaches
learners how to build mobile apps
using JavaScript. Learners should be
able to deploy simple mobile apps
on Android and IoS platforms.

Data Intelligence

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Curriculum Development for FAIR Data Stewardship

Mesa 4. Course III — FAIR Data Management (CS3).

Course Module Module title Week Topics

Module description

Course 3
Computadora
Ciencia
III —
FAIR Data
(CS 3)

CS3.1

Introducción
to Data
Science II

Weeks
1, 2, 3

Introduction to statistical
thinking
– Population data vs sample

datos

– Parameter vs statistics
– Descriptive statistics
– Scale of measurement
– Inferential statistics
Introduction to machine
aprendiendo
– Supervised learning methods
— regression, classiﬁ cation

– Unsupervised learning

methods — clustering, factor
análisis, principal compo-
nent analysis

– Training and evaluating mod-

los

– Regression metrics
– Classiﬁ cation metrics

– Key concepts of data
regulatory framework
– General Data Protection

Regulation (GDPR) and its
principios

– Data regulation speciﬁ c to

Sudan, South Sudan,
Somalia and Ethiopia

– FAIR Guidelines

CS3.2

Regulatory
Framework

Week
2

This module builds on the
previous introduction to data
ciencia. Learners will be taught
how to make statistical inferences
to draw clear conclusions from
datos. It also introduces machine
aprendiendo, supervised and
unsupervised learning. Learners
should be able to create machine
learning models and discover
underlying clusters in a dataset.

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The emergence of the Internet as
a global telecommunications
network has had a huge impact
on how we view and apply data
protection and regulations.
Before the massive expansion of
the Internet, data was of minor
interest and did not generate
signiﬁ cant global interest.
This module provides participants
with an understanding of what a
regulatory framework is and what
it is used for. Learners will
understand general data protec-
tion principles, national data
regulations, and the basics of
FAIR Guidelines, as well as be
able to explain why we need
FAIR Guidelines and the beneﬁ ts
for their country.

1006

Data Intelligence

Curriculum Development for FAIR Data Stewardship

Course Module Module title Week Topics

Module description

Mesa 4. Continuado

CS3.3

FAIR Data
Management

Week
3

– FAIR Guidelines
– Data management plan
– FAIR data management
– Platforms for creating FAIR

DMPs

CS3.4

FAIR Data
Punto
Installation

Weeks
4, 5

– Metadata
– Catalogues
– Dataset
– Distribution
– Docker installation
– Deployment of FDPs
– Open Reﬁ ne
– Open Reﬁ ne installation
– A semantic data model
– FAIRiﬁ cation process for a

conjunto de datos

CS3.5

Semántico
Datos

Weeks
6, 7

– Semantic web and linked
– data
– Semantics modelling
– Ontologies
– eCRF as a FAIR tool
– CEDAR as a FAIR tool

This module exposes learners to
the FAIR Guidelines and FAIR
data management plans (DMPs).
What kind of questions make a
good DMP and which tools
should be used to create a DMP?
Además, learners will be able
to practise creating a FAIR DMP.

This module describes FAIR Data
Points (FDPs), their objectives
and elements. The main purpose
of this module is to illustrate how
an FDP can be deployed on a
local machine and provide
detailed steps for a successful
installation. It also aims to
explain how to publish machine-
actionable (meta)data to an FDP.
Another objective of this module
is to illustrate how non-FAIR data
can be assigned machine-
readable metadata to enable
them to be discoverable by
individuals and machines. En
addition, leaners will be taught
how to work with Open Reﬁ ne
and how to create RDF triplets.
Learners will be presented with a
simulated cancer dataset shown
how to FAIRify it by building a
semantic data model from the
conjunto de datos.

The module introduces learners
to semantic web and linked data,
and shows them how to use
eCRF and CEDAR to create and
explore metadata and as a FAIR
tool.

Data Intelligence

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Curriculum Development for FAIR Data Stewardship

Course Module Module title Week Topics

Module description

Mesa 4. Continuado

CS3.6

FAIR Data for
Salud

Weeks
8, 9

– FDPs and their role in
research and medicine

– FAIR Guidelines in research

and healthcare
– FAIR Data Trains

CS3.7

Internship Weeks
10, 11

– On-the-job training for learn-

ers

In this module students will learn
the importance of FAIR Guide-
lines in healthcare research
including how FAIR Guidelines
can facilitate knowledge
discovery from health data and
how linked health data drives
investigación, better use and learning
from data, and contributions to
patient care.

This internship focuses on the
knowledge gained in the
previous modules and provides
on-the-job training where
students can gain experience and
knowledge and learn how to
apply their skills.

3.2 Mode and Duration

This programme will span 36 semanas (12 weeks per course) with a total of 3 courses: Computer Science
I (CS1), Computer Science II (CS2) and FAIR Management Principles (CS3). The core topics that pertain to
data stewardship will be Introduction to Data Science I and II, Regulatory Framework, FAIR: Datos
Management, Data Point Installation, and Data for Health and Semantic Data.

The weekly activities summary for each course is as follows:

• Week 1 — Registration and Orientation
• Weeks 2 a 11 — Learning Activities and Interaction
• Week 12 — Examination

Considering the possible locality of the target participants and the limited infrastructure available in such
lugares, the distance education model will take a blended learning approach, in which online learning is
combined with face-to-face interaction at partner universities. Each participant is expected to devote a
minimum of 12 hours a week, of which 4 hours is for self-study of the provided learning resources, 4 horas
for online activities and interactions, y 4 hours for assessments and assignments.

3.3 Expected Learning Outcomes, Activities and Assessments

In addition to registration in week 1, learners are mandated to participate in two short modules: Peace
Building and Conflict Resolution (to expose them to the skills needed to coexist and resolve conflicts in

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Curriculum Development for FAIR Data Stewardship

order to maintain peace in their communities) and Trauma and Mental Health (to help them to cope with
the violence and trauma that they might have experienced in times past). To enable them to access the
enormous opportunities inherent in the IT world, the modules on Digital Technologies, Computer Networks,
IT Service Management, and Project Management will be designed to teach a wide range of skills on digital
tecnología, contents creation, software installation, basic cyber security, IT productivity tools, hardware
coupling and troubleshooting, maintenance of local area networks, and other relevant topics. The learners
will be facilitated via a learning management system using activities such as video conferencing, chats,
online forums and so forth for interaction between teachers and learners and also for peer-to-peer
comunicación. Practical sessions will be organised for students to demonstrate the skills acquired. Quizzes
and assignments will also be given to gauge outcomes and these will be graded. It is expected that the
course will not only qualify learners for IT-related jobs, but that they will also be capacitated to perform
exceedingly well in other areas using the skills acquired.

The Computer Science Level 2 (CS2) modules were developed to teach the learners intermediate skills
such as computer programming with Python, Introduction to Data Science, Business Intelligence, Digital
Marketing, Front End Web Development with Angular, Docker and React (Web), and React (Native).
Learners are expected to be able to write Python programs, as this is essential for data science. The Data
Science and Business Intelligence modules will groom learners in the world of machine learning, datos
analytics and business analytics. Tech skills, which can provide a career path, are also taught. Marketable
skills will be taught, such as skills in using digital marketing concepts to manage the digital platforms of
business organisations and create digital advertising campaigns for small and medium scale businesses;
skills in JavaScript to teach front end web development; and skills in Angular, Docker, and React to expose
learners to software engineering. Similar activities of facilitation, engagement, practical and assessment as
in CS1 will be introduced to teach, assess and encourage learners.

Computer Science Level 3 (CS3) modules are an extension of Computer Science Level 2 (CS2). Learners
will be exposed to statistical thinking, supervised and unsupervised learning, and regression. Otro
interesting topic in FAIR Data is called FAIR Data Trains. The students will be exposed to FAIR Data for
Salud, which explores how linked health data drives research, better use and learning from data, y
further contributions to patient care. Además, learners will be taught the FAIR Guidelines for data
management as well as FAIR Data Point installation, Docker installation, the creation of machine-readable
metadata, catalogues, conjuntos de datos, and distribution. Students will also be shown how to FAIRify existing
datasets using linked data and semantics modelling. The main objective of the course at this level is to
understand the role of a data scientist in the industry and become acquainted with different data presentation
formats, understand basic statistical thinking, understand machine learning techniques (such as supervised
and unsupervised learning), understand basic concepts such as (sensitive) personal data and FAIR Guidelines,
apply the FAIR Guidelines, know what data management and a data management plan (DMP) son, saber
the content elements that make up a DMP, be able to develop a FAIR DMP, and learn tools and techniques
for the FAIRification of data.

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Curriculum Development for FAIR Data Stewardship

4. CONCLUSION AND FURTHER DEVELOPMENTS

This article reviewed existing curriculum, such as the EDISON framework, for Data Science Professionals.
The presented profiles are defined based on the ESCO taxonomy and include the following groups: managers
(DSP01–DSP03), professionals (DSP04–DSP09), professional data management/handling (DSP10–DSP13),
professional (database) technical (DSP14–DSP16), professional technicians (DSP17–DSP19), and support
and clerical workers (DSP20–DSP22). This framework defines data steward relevance and profile as DSP10.
It is anticipated that all educational requirements of a data steward were met in the curriculum provided,
which blends the skills involved in data stewardship and the FAIR Guidelines. A student that has satisfied
all requirements will be certified as a FAIR data scientist and will be able to serve as both a FAIR data
steward and analyst. In-depth and focused curricula development with diverse participants will build a core
of FAIR data scientists. This will encourage the rapid adoption of FAIR Guidelines for data for research and
desarrollo.

ACKNOWLEDGEMENTS

We would also like to thank Misha Stocker for managing and coordinating this Special Issue (Volumen 4)
and Susan Sellars for copyediting and proofreading. We also acknowledge VODAN-Africa, the Philips
Base, the Dutch Development Bank FMO, CORDAID, and the GO FAIR Foundation for supporting
esta investigación.

AUTHORS’ CONTRIBUTIONS

All of the authors contributed to the writing and provided critical feedback to help shape this article.
Francisca Oladipo (francisca.oladipo@kiu.ac.ug, 0000-0003-0584-9145): conceptualization, diseño,
revisar, version control, project administration. Sakinat Folorunso (bamidelekeke@gmail.com, 0000-
0002-7058-8618): ideation, data collection, writing – review and editing. Ezekiel Ogundepo (gbganalyst@
gmail.com, 0000-0003-3974-27339): data collection, data analysis and interpretation. Obinna Osigwe
(obinna.osigwe@kiu.ac.ug, 0000-0001-7825-3591): drafting, critical revision, quality assurance of
courseware. Akindele Akinyinka
(akindele.akinyinka@kiu.ac.ug, 0000-0002-7027-466X): diseño
conception, article drafting, critical revision.

CONFLICT OF INTEREST

All of the authors declare that they have no competing interests.

ETHICS STATEMENT

Tilburg University, Research Ethics and Data Management Committee of Tilburg School of Humanities
and Digital Sciences REDC#2020/013, Junio 1, 2020–May 31, 2024 on Social Dynamics of Digital Innovation
in remote non-western communities

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Curriculum Development for FAIR Data Stewardship

Uganda National Council for Science and Technology, Reference IS18ES, Julio 23, 2019–July 23, 2023

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