Report on reusable data visualisation tools to support data-driven policymaking.
Date: 2016-09-19
Institutions: Publications Office of the European Union
Subjects: EU policy, access to EU information, catalogue, data processing, decision-making, documentary tool, on line data service, report

Publications Ofﬁce of the European Union

Reusable data visualisation 
tools to support data-driven 
policymaking in the EU

REPORT 2016. ISA 1.1. D04.08.

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Report on reusable data  
visualisation tools to support  
data-driven policymaking

Study prepared for the ISA programme by PwC EU services

Publications Office

Disclaimer:

The views expressed in this report are purely those of the authors and may not, in any 
circumstances, be interpreted as stating an official position of the European Commission.

The European Commission does not guarantee the accuracy of the information included 
in this study, nor does it accept any responsibility for any use thereof.

Reference  herein  to  any  specific  products,  specifications,  process,  or  service  by  trade 
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Contents

Executive summary 

1. Introduction 

1.1. Objectives and scope 

1.2. Background 

1.3. Context 

1.4. Structure of this document 

2. Approach 

3.  Types of data and types of visualisations 

3.1. A classification of data types 

3.2. A typology of data visualisations 

4.  User requirements for data visualisation tools 

5.  Specification of a catalogue of reusable data visualisation tools 

5.1. High-level description of the catalogue of reusable data visualisation tools 

5.2. Faceted search and associated user stories 

5.3. High-level architecture options 

5.4. Documentation of data visualisation tools 

6. Conclusions and next steps 

Annex I. Glossary 

Annex II. Sample inventory of data visualisation tools 

References 

List of figures 

List of tables 

Contents

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Executive summary

While visual representations of data used to be something out of scientists’ or experts’ toolsets, 
in  recent  years  they  have  become  increasingly  common  in  areas  frequented  by  laymen, 
such  as  newspapers  and  journalism,  television,  and  social  media.  In  the  context  of  public 
administration work, data visualisation is being used to transmit and communicate information 
or results both internally to decision-makers and to external stakeholders, to help them arrive 
at conclusions and to take well-informed decisions. Thus, data visualisation facilitates decision-
making based on evidence. Business cases for  data  visualisation tools include the  usage  of 
such tools to communicate or explain known results quickly and effectively, or to explore the 
data for previously unnoticed trends and patterns. Data visualisation also has an impact on the 
area of (big) data analytics, as it can help explore complex data coming from different sources 
and  communicate  it  to  others  effectively,  illustrate  a  network  or  process,  or,  interestingly, 
examine the data to look for the cause of problems or track evolution over time.

Professionals working in the EU institutions are facing ever-increasing amounts of data that 
need  to  be  processed  and  often  published  in  intuitive  ways.  Making  data  understandable 
to  the  general  public  or  non-specialised  staff  is  made  possible  through  the  use  of  data 
visualisation. In 2015, the European Commission’s Information Systems Project Management 
Board  asked  the  Publications  Office  of  the  European  Union  to  conduct  a  survey  regarding 
visualisation tools and the needs of EU institutions. The end goal was to find out what are 
the  possibilities  of  sharing  data  visualisation  tools  or  developing  new  useful  ones  that  are 
shareable  and  would  be  useful  to  data  consumers.  One  salient  result  of  the  survey  is  that 
approximately 70 % of the participants indicate that they publish data for which they do not 
have appropriate visualisation tools yet. Some 37 % of the survey respondents indicate that 
they have already carried out research on data visualisation tools that may be suitable for their 
data.  Some  EU  institutions  have  independently  developed  or  purchased  data  visualisation 
tools, which could also be used by others. Thus, there is a need to create a holistic view on the 
data visualisations landscape and to disseminate this knowledge within the EU institutions and 
the Member States (MSs).

The principal objective of this study, which is funded under Action 1.1 of the ISA programme 
of  the  European  Commission,  is  to  carry  out  preparatory  work  for  an  online,  interactive 
catalogue of reusable data visualisation tools that would allow users to identify easily the most 
appropriate tool corresponding to their needs, taking into account the nature and structure 
of data as well as the internal capacity to handle it. Another long-term objective is to work 
towards commonly agreed definitions, for data structure requirements for the different types 
of visualisation. This will facilitate the aggregation and sharing of data through common tools. 
The identification, study and analysis of tools that are used in the data management lifecycle 
for preparing data for visualisation, such as data processing, transformation or cleansing tools, 
are outside the scope of this work.

4

Reusable data visualisation  tools to support data-driven  policymaking in the EUExecutive summary

In order to create a catalogue of reusable data visualisation tools for the EU institutions, the 
following work was carried out.

 ■ Created a list of user requirements for data visualisation tools for the EU institutions 
referring  to  the  Publications  Office’s  report  on  data  visualisation  tools,  to  feedback 
received  at  the  workshop  ‘Data  visualisation  tools  for  the  EU  institutions’,  and  to 
hands-on experience with data.

 ■ Defined  a  classification  of  data  types  (i.e.  numerical  data,  geospatial  data,  network 
data, textual data) that will provide some of the dimensions by which the content of 
the catalogue will be organised.

 ■ Defined  a  typology  of  data  visualisation  types  (i.e.  maps,  relations/dependencies, 
lines, dots and bubbles, bars, grids, shapes and proportions, infographics) associated 
with a rich library of data visualisations that will provide some of the dimensions by 
which the content in the catalogue will be organised.

 ■ Specified high-level information and user requirements for the catalogue of reusable 
data visualisation tools by means of wireframes based on the European Open Data 
Portal (EU ODP — http://data/europa.eu/euodp) and by means of user stories.

 ■ Provided  an  analysis  of  the  high-level  architecture  of  the  catalogue  and  examined 
four alternatives regarding the implementation of the catalogue (i.e., Drupal multisite 
platform of the European Commission, EU ODP, Joinup, standalone service).

 ■ Identified and documented some of the data visualisation tools currently in use by the 
EU institutions as well as other relevant visualisation tools used by other organisations.

Next steps following this work include:

 ■ agreeing on the implementation options for the catalogue of visualisation tools (e.g., 
hosting of the catalogue on the EU ODP, hosting the catalogue as a Drupal multisite 
platform of the European Commission, Joinup and as a standalone service);

 ■ creating a working pilot of the catalogue;

 ■ selecting the best-fit tools to be included in the catalogue; 

 ■ creating sample visualisations for high-value datasets published on the EU ODP;

 ■ creating comparative analysis tables for the best-fit tools to help EU staff decide which 

one best fits their needs; and

 ■ defining  support  services  for  developing  data  visualisation  capabilities  in  the  EU 

institutions.

5

1. Introduction

1. Introduction

1.1. Objectives and scope
The main objective of this study — funded under Action 1.1 of the ISA programme of the 
European Commission — is to carry out preparatory work for an online, interactive catalogue of 
reusable data visualisation tools that would allow users to identify easily the most appropriate 
tool corresponding to their needs, taking into account the nature and structure of data as well 
as the internal capacity to handle it. 

Another long-term objective is to work towards a commonly agreed data structure definition 
for the different types of visualisation, to facilitate the aggregation and sharing of data through 
common tools. 

The present report does not handle libraries and APIs, or the tools used to prepare data for 
visualisation (such as data processing, transformation or cleansing tools), or the development 
of the pilot catalogue, and does not provide a complete inventory of data visualisation tools. 

The  scope  of  this  work  only  takes  into  account  the  visualisation  tools  (out-of-the-box, 
commercial, open source or other) that are used, can be used, and/or are developed by the 
EU institutions. 

1.2. Background
‘By harnessing data mining and visualisation tools, extremely large datasets that would otherwise 
be impenetrably complex can be converted into carefully crafted visual representations that 
can be effectively processed by the brain itself.’ [1].

Data  visualisation  is  defined  as  the  visual  representation  of  information  abstracted  in 
some  schematic  form  [2].  Visualising  data  is  a  sensory  experience  that  can  encourage  the 
engagement  of  an  audience  and  the  understanding  of  the  information  being  transmitted, 
for laymen as well as for experts [3]. All visualisations have something in common: a set of 
mappings between data properties and visual attributes; any interactivity allows the user to 
direct the layout [4].

Different  forms  of  visualising  data  exist,  including  linear  (one-dimensional),  2D  and  3D, 
temporal, multidimensional, hierarchical and network visualisations [5]. More often than not, 
the visual representation of data implies that the reader has some domain knowledge and 
is  provided  with  some  context  about  the  visualised  data.  When  the  intention  is  to  present 
findings  without  making  assumptions  about  the  readers’  knowledge,  explaining  results 
through a combination of visuals and knowledge in the form of textual information in a single 
graphical representation, a new type of data visualisation known as infographics, is the best 
choice [6].

Selecting a type of data visualisation largely depends on the insight needed from the data, 
on the scale of the data and on the target audience [1]. Furthermore, data visualisation can be 
used for different purposes. For instance, Unwin et al. [7] differentiate between presentation 

7

and  exploratory  graphics,  stating  that  they  are  not  only  different  in  practice,  but  also  in 
form.  Presentation  visualisations  are  drawn  to  summarise  the  information  to  be  presented, 
while exploratory ones are used to look for results. Andy Kirk, on the other hand, identifies 
three  objectives  for  data  visualisation:  exploratory,  explanatory  and  exhibitory.  Exploratory 
visualisation is used to interrogate and manipulate the data, exhibitory visualisation is used to 
display results, while explanatory visualisation is used to annotate and describe results in a self-
sufficient way. All visualisations can be exploratory or exhibitory, while explanatory visualisation 
is infographics. Thus, it can be concluded that business cases for data visualisation include the 
communication of known results to quickly and effectively convey a message, the detailed 
explanation of findings using a combination of visuals and text (i.e. in infographics), and the 
identification of trends and patterns through data exploration. Finally, and importantly, data 
visualisation facilitates evidence-based decision-making.

In the context of public administration work, data visualisation is being used to transmit and 
communicate  information  or  results  to  external  stakeholders  (such  as  the  general  public, 
readers of institutions’ websites), to help them arrive at conclusions and to take well-informed 
decisions.

Data visualisation also has an impact on the area of big data analytics, as it can help explore 
complex data coming from different sources and communicate it to others effectively, illustrate 
a network or process, or, interestingly, ‘rewind’ the data to look for the cause of problems or 
track evolution over time [8].

Data  visualisations  are  often  limited  by  the  functionalities  of  their  supporting  software.  An 
effective  software  solution  should  be  flexible  enough  to  accommodate  user  requirements, 
achieve  maximum  data  manipulation  with  minimum  user  configuration,  and  be  able  to 
produce  meaningful  and  intuitive  abstractions  of  the  data  [9].  One  of  the  most  popular 
examples of data visualisation tools are dashboards, which provide multiple visual summaries 
of large sets of information in a limited amount of space (e.g. a single web page).

Technology  left  aside,  the  skills  required  for  displaying  information  effectively  as  data 
visualisation  are  not  intuitive  and  to  a  great  extent  depend  on  learned  principles  [10].  As 
explained  below,  creating  an  effective  data  visualisation  can  be  achieved  by  combining 
knowledge from the sciences and the arts.

Data  visualisation  is  most  frequently  used  in  the  ‘communication  of  results’  phase  in  the 
data analytics lifecycle. The data analytics lifecycle is commonly described in the following 
sequence:  discovery,  data  preparation,  model  planning,  model  building,  communication  of 
results and operationalisation through the setting up of pilots to deploy the work [11]. Naturally, 
feedback loops between these workflow phases are advisable and constructive.

In view of the described data analytics workflow, visualisation creation requires a number of 
skills  [11]  (also  see  Andy  Kirk’s  presentation  8  hats  of  data  visualisation  (1)).  If  the  user  has  to 
deal  with  unexplored  raw  data,  creating  effective  visualisations  of  this  data  starts  with  the 
data  discovery  phase,  the  data  preparation  phase  and  the  model  planning  phase,  which 
allow framing of the problem, developing initial hypotheses, performing data quality checks 
(e.g.  cleaning,  normalising  and  transforming  data,  optionally  exploratory  data  visualisation), 

(1)  http://www.slideshare.net/visualisingdata/the-8-hats-of-data-visualisation

8

Reusable data visualisation  tools to support data-driven  policymaking in the EU1. Introduction

identifying  and  defining  the  variables  to  be  analysed,  and  selecting  candidate  models 
for  clustering,  classifying  or  finding  relationships.  The  model  building  phase  involves  the 
development of the analytical model and the testing of its performance.

The  discovery,  the  data  preparation,  the  model  planning  and  the  model  building  phases 
usually require knowledge in statistics and experimental design, which is possessed by data 
analysts and scientists. Nowadays, IT skills are not critical for carrying out a data visualisation 
project successfully, because many of the existing software packages are user-friendly, with a 
steep learning curve and not requiring programming skills.

Once results of the data analytics project are obtained, a definition has to be established of 
how data visualisation can be used to tell a pertinent story about the project, and to help the 
readers derive a correct interpretation of results. This skill is  usually possessed by scientists, 
data  journalists,  communication  officers  and  designers  who  have  domain  knowledge.  The 
communication of results should take into account cognitive science findings and knowledge 
on the human visual system. Knowledge from these domains might help in choosing the right 
visualisation type (e.g. scatter plots, bubble charts, etc., see Section 3.2. A typology of data 
visualisations) as well as customising the visualisation in terms of layout, colours and labels, to 
ease grasping and the interpretation of the results.

1.3. Context
Professionals working in the EU institutions are facing ever-increasing amounts of data that 
need  to  be  processed  and  often  published  in  intuitive  ways.  Making  data  understandable 
to  the  general  public  or  non-specialised  staff  is  made  possible  through  the  use  of  data 
visualisations.

In 2015, the European Commission’s Information Systems Project Management Board asked 
the Publications Office to conduct a survey regarding visualisation tools and the needs of EU 
institutions. The end goal was to find out what the possibilities are of sharing data visualisation 
tools or developing new useful ones that are shareable and would be useful to data consumers. 
One salient result of the survey is that approximately 70 % of the participants indicate that they 
publish data for which they do not have appropriate visualisation tools yet. Some 37 % of the 
survey respondents indicate that they have already carried out research on data visualisation 
tools which may be suitable for their data. The selection of the best solution depends on the 
capabilities of the organisation.

Some  EU  institutions  have  independently  developed  or  purchased  data  visualisation  tools, 
which  could  also  be  used  by  others.  Thus,  there  is  a  need  to  create  a  holistic  view  on  the 
data visualisation landscape and to disseminate this knowledge within the EU institutions but 
also to the MSs. The potential to use common data visualisation tools in the EU institutions 
is confirmed by the survey. EU institutions have indicated common visualisation needs. For 
example,  data  visualisation  solutions  for  the  EU  institutions  should  be  cost-effective,  work 
with tools and standards used in the institutions and, to the extent possible, build on existing 
experiences.  Additionally,  respondents  said  that  the  development  of  common  visualisation 
tools should be complemented with usage guidelines, consultancy, assistance, training and 
awareness-raising activities.

9

1.4. Structure of this document
This document is structured in several sections. After describing the background, the context 
and the scope of this work in Section 1, Section 2 describes the approach taken to create an 
interactive catalogue of reusable data visualisation tools. Section 3 presents a classification of 
datatypes and a typology of data visualisation tools. Section 4 presents a collection of user 
requirements  for  the  tools  to  be  included  in  the  catalogue,  which  was  compiled  from  the 
report on data visualisation tools, existing literature and the workshop on data visualisation 
tools.  Section  5  lays  the  foundations  for  the  catalogue  describing  user  stories,  a  high-level 
architecture for the catalogue and proposing a common way of documentation for the tools. 
Section 6 concludes on the work and identifies next steps that have to be taken.

10

Reusable data visualisation  tools to support data-driven  policymaking in the EU2. Approach

2. Approach

In order to create the catalogue of reusable data visualisation tools for the EU institutions, we 
propose the following approach.

 ■ Define a classification of data types and a classification of data visualisation 
types that will provide the dimensions by which the content in the catalogue will be 
organised.

 ■ Create  a  list  of  user  requirements  for  data  visualisation  tools  for  the  EU 
institutions referring to the existing literature, previous experience, the Publications 
Office’s report on data visualisation tools and a workshop with stakeholders from the 
EU institutions.

 ■ Specify  the  information  and  user  requirements  for  the  catalogue  of  reusable 
data visualisation tools. The metadata of this catalogue should link the various types 
of data, the various types of tools, and the various types of user requirements. This 
catalogue  will  provide  a  knowledge  base  that  will  support  the  EU  institutions  and 
public administrations in the choice of data visualisation tools.

 ■ Identify data visualisation tools currently in use by the EU institutions as well 
as other relevant visualisation tools used by other organisations, specifying their 
reusability, their technical requirements, their requirements for data structure, their 
visualisations  capabilities,  their  customisability  and  their  limitations,  among  other 
characteristics and using the Publications Office report on data visualisation tools as 
a starting point.

11

3.  Types of data and  

types of visualisations

3.1. A classification of data types

A  data  type  refers  to  the  nature  of  the  content  of  the  data.  There  is  no  commonly  agreed 
classification of data types. Thus, the classification of data types defined below refers to the 
classification  most  agreed  upon  on  at  a  workshop  on  ‘Data  visualisation  tools  for  the  EU 
institutions’ organised by the Publications Office.

Table 1   Types of data

Type of data

Description

Numerical data

Numerical data is quantitative information stored in an organised form (i.e. observations corresponding 
to variables, with or without statistical processing). Numerical data can consist of real numbers and/or of 
categories. Numerical data is continuous data when it contains real numbers deriving from measurements 
of variables in samples, but it can also contain processed data such as means, variances, etc. Examples of 
continuous data include numbers, counts, percentages, ratios, etc. Categorical data is data that is divided into 
discrete groups [12]. Examples of categorical variables include binary data (e.g. presence/absence), rankings, 
gender, age groups, etc.

A subcategory of numerical data is shape or coordinate data. Shape data is geometric data consisting of 2D or 
3D coordinates involved in shape and design analysis (e.g. models of objects or anatomical parts).

Domain in which numerical data is used: Any (e.g. economy and finance; science and technology; justice, legal 
system and public safety; population and society, etc.).

Geospatial data

Data that can be characterised by a specific location in space and time [13]. It is information about a physical 
object that can be represented by numerical values in a geographic coordinate system. This data describes 
the location, size and shape of an object [14] (i.e. a region in a country characterised by some specificity). 
Examples of geospatial data include vector data, raster data, etc.

Network data

Domain in which geospatial data is used: regions and cities (e.g. distribution of values across countries or regions); 
science and technology; population and society, economy and finance and environment.

Data representing hierarchical or non-hierarchical relationships and interactions between entities (e.g. 
persons, webpages, domains, products). The data structure of network data can contain labels for 
nodes and edges expressed as the number of the source and the number of the target or matrices of real 
numbers that need further statistical processing. Included in network data are graphs representing semantic 
relationships (e.g. triples), interactions (e.g. communication traces in social networks) and hierarchies (e.g. 
dendrograms and taxonomies).

Domain in which network data is used: Any.

Textual data

Data consisting of text. Textual data is usually analysed to produce text categorisation, text clustering, concept 
extraction, patterns, document summaries, sentiment analysis, etc. Textual data has to be ‘mined’ before 
analysis. 

Domain in which textual data is used: Any.

12

Reusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

3.2. A typology of data visualisations

A visualisation is a graphical representation of data (e.g. maps, relations/dependencies, lines, 
dots  and  bubbles,  bars,  grids,  shapes  and  proportions,  infographics).  Visualisations  depict 
qualitative or quantitative data, without a well-defined reference system [1] (e.g. scatter plots, 
pie charts, word clouds, etc.).

In  Table  2  Types  of  data  visualisations,  the  different  high-level  categories  along  with  their 
descriptions are presented, while in the following tables each category is presented in detail 
with its different types of visualisations. The large majority of the images presented in Table 3 
to Table 9 are linked to their reusable source. If a visualisation is not linked to a reusable source, 
this is an indication that this visualisation was made in the framework of this study.

Table 2 

Types of data visualisations

Data visualisation

Description

Maps

Relations/ 
dependencies

Lines

Dots/bubbles

Bars

Grids

Maps display data according to spatial relations, representations showing how data is distributed 
spatially [1] (e.g. choropleths, cartograms, dot distribution maps, proportional symbol maps, contour/
isopleth/isarithmic maps, dasymetric maps, self-organising maps [15]).

Data visualisations using nodes to represent data elements and links that connect nodes to represent 
relations between such elements [1] (e.g. hierarchies and non-hierarchical networks and specifically 
matrices, node-link diagrams, dependency graph/circular hierarchies, hive plots, alluvial diagrams, 
subway/tube maps, dendrograms, radial trees, hyperbolic trees, tree maps, wedge stack graphs, partition 
charts, etc.).

A type of graph representing the relation between two or more variables as a single line or area.

Dot or bubble graphs are a graphical display of data, representing the relation between two or more 
variables using dots or bubbles.

Bar graphs are visualisations representing the relation between a categorical variable and a continuous 
variable.

Representations of entities in a matrix.

Shapes/proportions

Visualisations showing proportions without reference to a coordinate system.

Infographics

An infographic is a visual representation containing visuals, in some cases data visualisation, and text, 
aimed to convey complex information to an audience in a manner that can be quickly consumed and 
easily understood [6].

13

Table 3 

Types of data visualisations — Maps

Types of data visualisations — Maps below lists the different types of data visualisations related 
to maps.

Types of maps

Description

Example

Choropleth map

A thematic map in which areas are shaded or patterned in 
proportion to the measurement of the statistical variable 
being displayed on the map, such as population density 
or per capita income. The choropleth map provides an 
easy way to visualise how a measurement varies across a 
geographic area or shows the level of variability within a 
region.

Contour/ isopleth/ 
isarithmic map

They differ from choropleth maps in that the data is not 
grouped to a predefined unit like a city district. These maps 
can take two forms:

Choropleth map

— lines of equal value are drawn such that all values on 
one side are higher than the ‘isoline’ value and all 
values on the other side are lower; or

— ranges of similar value are filled with similar colours or 

patterns.

This type of map is ideal for showing gradual change over 
space and avoids the abrupt changes which boundary lines 
produce on choropleth maps. Temperature, for example, is 
a phenomenon that should be mapped using isoplething, 
since temperature exists at every point (is continuous), yet 
does not change abruptly at any point (like population 
density may do as one crosses into another census zone).

A contour map is a topographic map on which the shape 
of the land surface is shown by contour lines, the relative 
spacing of the lines indicating the relative slope of the 
surface. [16]

A map in which some thematic mapping variable, such 
as population, is substituted for land area or distance. The 
geometry or space of the map is distorted in order to convey 
the information of this alternate variable.

Contour/isopleth/isarithmic map

Cartogram/area cartogram

Cartogram/ area 
cartogram

14

Reusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

Types of maps

Description

Example

Dot distribution 
map

Also known as dot density map, this is a map type that 
uses a dot symbol to show the presence of a feature or 
phenomenon. Dot maps rely on a visual scatter to show 
spatial patter.

Proportional 
symbol map 

A type of map that uses the visual variable of size to 
represent differences in the magnitude of a discrete, abruptly 
changing phenomenon, e.g. counts of people. The area of 
the symbols are proportional to the values of the attribute 
being mapped. [17]

Dot distribution map

Dasymetric map

This is a method of thematic mapping, which uses areal 
symbols to spatially classify volumetric data.

Proportional symbol map

Prism map

Prism maps draw each boundary object as a raised prism, 
where the height of the prism is relative to the associated 
data value. Prisms can also be assigned fill colours to 
represent data ranges or classes of data for each area on a 
map so they look like a 3D hatch map. [18]

Dasymetric map

Grid map

A map on which a network of horizontal and vertical lines 
are superimposed, for locating points. [19]

Prism map

Grid map

15

Table 4 

Types of data visualisations — Relations/dependencies

Types of data visualisations — Relations/dependencies below lists the different types of data 
visualisations related to relations or dependency graphs.

Types of 
dependency 
graphs

Tree hierarchy

Description

Example

This is a way of representing the hierarchical nature of a 
structure in a graphical form. It is named a ‘tree structure’ 
because the classic representation resembles a tree, even 
though the chart is generally upside down compared to an 
actual tree, with the ‘root’ at the top and the ‘leaves’ at the 
bottom.

Dendrogram 
(cluster 
dendrogram)

This is a tree diagram frequently used to illustrate the 
arrangement of the clusters produced by hierarchical 
clustering.

Tree hierarchy

Sankey diagram

A specific type of flow diagram, in which the width of the 
arrows is shown proportionally to the flow quantity. Sankey 
diagrams are typically used to visualise energy or material 
or cost transfers between processes. They are helpful in 
locating dominant contributions to an overall flow. Often, 
Sankey diagrams show conserved quantities within defined 
system boundaries.

Dendrogram

Sankey diagram

16

Reusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

Types of 
dependency 
graphs

Hyperbolic tree

Description

Example

This is an information visualisation and graph-drawing 
method inspired by hyperbolic geometry. Displaying 
hierarchical data as a tree suffers from visual clutter as the 
number of nodes per level can grow exponentially.

Parallel 
coordinates chart

A common way of visualising high-dimensional geometry 
and analysing relations of multiple variables at a time. To 
show a set of points in an n-dimensional space, a backdrop 
is drawn consisting of n-parallel lines, typically vertical 
and equally spaced. A point in n-dimensional space is 
represented as a polyline with vertices on the parallel axes; 
the position of the vertex on the i-th axis corresponds to the 
value, for that variable, of the corresponding observation.

Hive plot

Slope graph

Hive plots (2D – nD) are a unique method of displaying 
networks of many types in which node properties are 
mapped to axes using meaningful properties rather than 
being arbitrarily positioned. The hive plot concept was 
invented by Martin Krzywinski. [20]

Nodes are ordered on a segment based on properties such 
as connectivity, density or some quantitative measure. 
The user is free to choose whatever rules fit their data and 
visualisation requirements. Edges are drawn as Bezier curves, 
which can be annotated with colour, thickness or label to 
communicate additional information. Visualisations of two 
networks are directly comparable because they assess 
network structure.

A slope graph is a lot like a line graph, in that it plots change 
between points. However, a slope graph plots the change 
between only two points, without any kind of regard for the 
points in between. It is based on the idea that humans are 
fairly good at interpreting changes in direction. Decreases 
and quickly rising increases are easily detected.

Slope graphs are often used to show change over time but 
have also been used to compare two groups, such as boys 
and girls.

It is useful for showing the hierarchy of the data elements, 
how the data has changed over time, and most significantly, 
the rate of change. [21]

Hyperbolic tree

Parallel coordinates chart

Hive plot

Slope graph

17

Types of 
dependency 
graphs

Alluvial diagram

Network diagram

Description

Example

A type of flow diagram originally developed to represent 
changes in network structure over time. In an alluvial 
diagram, blocks represent clusters of nodes, and stream 
fields between the blocks represent changes in the 
composition of these clusters over time. The height of 
a block represents the size of the cluster and the height 
of a stream field represents the size of the components 
contained in both blocks connected by the stream field. 
Alluvial diagrams can also be used to illustrate patterns of 
flow on a fixed network over time.

A visual representation of network architecture. It maps out 
the structure of a network with a variety of different symbols 
and line connections. It is the ideal way to share the layout 
of a network because the visual presentation makes it easier 
for users to understand how items are connected.

Flow map

A mix of maps and flow charts that show the movement of 
objects from one location to another, such as the number of 
people in a migration.

Subway/ tube map

This represents connected data based on the visual 
metaphor of a tube map. Each tube line represents a target 
group and each station a milestone. The tube map is a 
powerful metaphor to communicate a complex connected 
data when different groups of persons are involved. The 
creators of the graphic considered the tube map useful 
because it provides an overview and can display detailed 
information in one image and because it helps create 
a mental model for the abstract concept of a quality 
development process. [22]

18

Alluvial diagram

Network diagram

Flow map

Subway/ tube map

Reusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

Table 5 

Types of data visualisations — Lines

Types of data visualisations — Lines below lists the different types of data visualisations related 
to lines.

Types of lines

Description

Example

Line graph (bump 
chart (3D))

This is a diagram made up by connected data points, which 
represent successive changes in the value of a variable 
quantity or quantities. [23]

It is often used to visualise a trend in data over intervals 
of time (i.e. a time series), thus the line is often drawn 
chronologically.

Line graphs are the most versatile and most extensively 
used family of graphs. They are also called line charts.

Step chart

It is a chart similar to a line graph, but with the line forming 
a series of steps between data points. [24] Step charts are 
useful for representing significant changes that occur within 
a very short period.

Connected dot plot

Also known as a dumbbell dot plot, it is a chart where there 
are no gridlines but there is a line that connects each pair of 
points.

Line graph

Step chart

Connected scatter 
plot

It is a special case of visualisation type for showing how two 
variables change together over time. It is a scatterplot with 
the dots connected by lines. [25]

Connected dot plot

Connected scatter plot

19

Types of lines

Description

Example

Area chart

It displays graphically quantitative data. It is based on the 
line chart. The area between axis and line are commonly 
emphasised with colours, textures and hatchings. 
Commonly one compares with an area chart two or more 
quantities.

Stream graph

It is a type of stacked area graph which is displaced around a 
central axis, resulting in a flowing, organic shape.

Area chart

Horizon chart

Horizon charts are used to examine how a large number 
of items changed through time, and to do so in a way 
that allows them to spot extraordinary behaviours 
and predominant patterns, view each of the items 
independently from the others when they wish, make 
comparisons between the items and view changes that 
occurred with enough precision to determine if further 
examination is required. [26] Horizon charts can be thought 
of as area charts split into layers using cut-offs, stacked on 
top of each other. Horizon charts are usually derived from 
time series of a single variable (e.g. unemployment rate) for 
several observations (e.g. states, countries). In the depicted 
example, what is represented for each observation, is the 
difference from average unemployment rate across all 
states.

Stream graph

Horizon chart

20

Reusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

Table 6 

Types of data visualisations — Dots and bubbles

Types  of  data  visualisations  —  Dots  and  bubbles  below  lists  the  different  types  of  data 
visualisations related to dots and bubbles.

Types of dots/ 
bubbles

Scatter plot

Description

Example

Scatter plots are similar to line graphs in that they use 
horizontal and vertical axes to plot data points. However, 
they have a very specific purpose. Scatter plots show how 
much one variable is affected by another. The relationship 
between two variables is called their correlation. Scatter 
plots usually consist of a large body of data. The closer the 
data points come when plotted to making a straight line, 
the higher the correlation between the two variables, or the 
stronger the relationship. If the data points make a straight 
line going from the origin out to high x- and y-values, then 
the variables are said to have a positive correlation. If the line 
goes from a high-value on the y-axis down to a high-value 
on the x-axis, the variables have a negative correlation. [27]

Bubble cloud chart A bubble chart is a type of chart that displays three 

Scatter plot

dimensions of data. Each entity with its triplet (v1, v2, v3) 
of associated data is plotted as a disk that expresses two 
of its associated values through the disk’s xy location and 
its third value — through its size. Bubble charts can be 
considered a variation of the scatter plot, in which the data 
points are replaced with bubbles. A bubble chart can be 
used instead of a scatter chart if the data has three data series 
that each contain a set of values. The sizes of the bubbles are 
determined by the values in the third data series. [28]

Venn diagram

A Venn diagram (also known as a set diagram or logic 
diagram) is a diagram that shows possible logical relations 
and overlaps between a finite collections of different sets.

Bubble cloud chart

Circle packing 
diagram

A circle packing diagram is an arrangement of circles inside a 
given boundary such that no two overlap and some (or all) of 
them are mutually tangent. [29]

Venn diagram

Circle packing diagram

21

05101520949698100102Scatterplot for quality characteristic XXXProcess inputQuality characteristic XXXTable 7 

Types of data visualisations — Bars

Types  of  data  visualisations  —  Bars  below  list  the  different  types  of  data 
visualisation related to bar graphs.

Types of bars

Description

Example

Bar chart

A bar chart represents grouped data with rectangular bars 
with lengths proportional to the values that they represent. 
The bars can be plotted vertically or horizontally.

Stacked bar chart

A stacked bar chart is a graph that is used to compare the 
parts to the whole. The bars in a stacked bar graph are 
divided into categories. Each bar represents a total. [30]

Histogram

A histogram is a graphical representation of the distribution 
of the values of a continuous variable.

Bullet chart

This is a variation of a bar chart. It features a single, primary 
measure (for example, current year-to-date revenue), 
compares that measure to one or more other measures 
to enrich its meaning (for example, compared to a target), 
and displays it in the context of qualitative ranges of 
performance, such as poor, satisfactory, and good. The 
qualitative ranges are displayed as varying intensities 
of a single hue to make them discernible by those who 
are colour blind and to restrict the use of colours on the 
dashboard to a minimum. Bullet graphs may be horizontal 
or vertical, and may be stacked to allow comparisons of 
several measures at once.

22

Bar chart

Stacked bar chart

Histogram

Bullet chart

ABCDE01020304050607080901001101201301401501601st W2nd WReusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

Types of bars

Description

Example

Range chart

It is a variable-control chart in which range of a subgroup is 
used to track instantaneous variations, and to evaluate the 
stability of the variability, within a process. It shows how the 
range of the subgroups changes over time. [31]

Waterfall chart

It is a form of data visualisation that helps in understanding 
the cumulative effect of sequentially introduced positive or 
negative values. The waterfall chart is also known as a flying 
bricks chart.

Gantt chart

A Gantt chart is a type of bar chart that illustrates a project 
schedule. Gantt charts illustrate the start and finish dates of 
the terminal elements and summary elements of a project. 
Terminal elements and summary elements comprise the 
work breakdown structure of the project. Gantt charts 
also show the dependency (i.e. precedence network) 
relationships between activities.

Dot plot

It is a statistical chart consisting of data points plotted on 
a fairly simple scale, typically using filled-in circles. It is 
an alternative to the bar chart, in which dots are used to 
depict the quantitative values (e.g. counts) associated with 
categorical variables. Dot plots are used for continuous, 
quantitative and univariate data.

Marimekko chart

A marimekko chart is similar to a 2D stacked chart. However, 
in addition to depicting data through varying heights (as 
in a regular stacked chart), it depicts an added dimension 
of data through varying column widths. Marimekko charts 
are generally used for analysing marketing and sales data. 
However, they can be used to visualise other forms of data 
as well. [32]

Range chart

Waterfall chart

Gantt chart

Dot plot

Marimekko chart

23

Types of bars

Description

Example

Pyramid:  
back-to-back chart

Back-to-back charts, also known as deviation bar graphs, are 
simply two bar charts aligned, where one of the charts runs 
right to left rather than left to right. The two charts report 
on the same categories but differ in terms of respondent 
group or some other variable. [33]

Candlestick chart

This is a style of financial chart used to describe price 
movements of a security, derivative, or currency. Each 
‘candlestick’ typically shows one day. It is like a combination 
of line chart and bar chart. Each bar represents all four 
important pieces of information for that day: the open, the 
close, the high and the low. Candlestick charts are most 
often used in technical analysis of equity and currency price 
patterns. They appear superficially similar to box plots, but 
are unrelated. 

Box-and-whisker 
plot

It is a graphic way to display the median, quartiles, and 
extremes of a dataset on a number line to show the 
distribution of the data. The ‘box’ in the box-and-whisker 
plot contains, and thereby highlights, the middle half of 
these data points. [34]

Pyramid/back-to-back chart

Candlestick chart

Box-and-whisker plot

24

Reusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

Table 8 

Types of data visualisations — Grids

Types of data visualisations — Grids below lists the different types of data visualisation related 
to grids.

Types of grids

Description

Heat map

Tree map

A heat map is a graphical representation of data where the 
individual values contained in a matrix are represented as 
colours.

Each data item is represented on a heat map by a shape. 
The size and colour of each box are determined by data for 
that item. One variable determines the size while another 
determines the colour. Also heat maps are a great way to 
compare categorical data using colour.

This is a method for displaying hierarchical data by using 
nested rectangles. Tree maps display hierarchical (tree-
structured) data as a set of nested rectangles. Each branch 
of the tree is given a rectangle, which is then tiled with 
smaller rectangles representing sub-branches. A leaf node’s 
rectangle has an area proportional to a specified dimension 
of the data. Often the leaf nodes are coloured to show 
a separate dimension of the data. When the colour and 
size dimensions are correlated in some way with the tree 
structure, one can often easily see patterns that would be 
difficult to spot in other ways, such as if a certain colour is 
particularly relevant.

Deformation grid

It is based on statistical computing, examining topography/
shape change in a sample, and representing the change 
from one axis extreme (x = – 1) to the other axis extreme 
(x = + 1) (can be several axes too) using vectors at landmarks 
(the displacement of each point from the grid is defined by a 
vector at that point, which ‘deforms’ the grid).

Example

Final version in Illustrator

Heat map

Tree map

Deformation grid

25

Types of grids

Description

Example

Matrix chart

A matrix chart shows relationships between two or more 
variables in a dataset in grid format. Essentially, the matrix 
chart is a table made up of rows and columns that present 
data visually and can be seen as the visual equivalent of a 
cross-tabulation that divides data between the variables. 
The matrix chart is formed through at least two variables, for 
the X- and Y-categories. If there is a third or fourth variable, 
colour or another dimension can be added to the matrix to 
represent it. [35]

Waffle chart/
square pie

A waffle chart helps visualising progress towards goals. A 
waffle chart is a square divided into a 10 × 10 grid. Each grid 
box represents 1 % toward a goal of 100 %. [36]

Matrix chart

Radar/spider chart

A radar chart is a graphical method of displaying 
multivariate data in the form of a two-dimensional chart of 
three or more quantitative variables represented on axes 
starting from the same point. [37]

Waffle chart/square pie

Polar chart

A polar chart displays multivariate data in the form of a two-
dimensional chart of more than three variables represented 
on axes starting from the same point. [38]

Radar/spider chart

π/ 2

3π/ 4

π/ 4

r = 2 − 2 sinθ

π

0

1

2

3

0

5π/ 4

7π/ 4

3π/ 2

Polar chart

26

Reusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

Table 9 

Types of data visualisations — Shapes and proportions

Types of data visualisations — Shapes and proportions below lists the different types of data 
visualisation related to shapes and proportions.

Types of shapes/ 
proportions

Description

Example

Pie chart

A pie chart (or a circle chart) is a circular statistical graphic, 
which is divided into slices to illustrate numerical proportion. 
In a pie chart, the arc length of each slice (and consequently 
its central angle and area), is proportional to the quantity it 
represents.

Proportional shape 
chart

A proportional shape comparison diagram compares two 
(or more) values by using the area of shapes, usually squares, 
rectangles, or circles. It is important that the shapes be exact 
to the proportions of the numbers being compared. [39]

Pie chart

Word cloud

Word clouds or tag clouds are graphical representations 
of word frequency that give greater prominence to words 
that appear more frequently in a source text. The larger the 
word in the visual the more common the word is in the 
document. [40]

Proportional shape chart

Donut chart

Donut or doughnut chart is the same as a simple pie chart 
but with a hole inside, in the centre.

Word cloud

Donut chart

27

27%5%9%4%38%9%2%6%PESEULOtherUENEPPELDREDDEFAEuropean Parliament election, 200420.3%40.4%11.8%6.6%19.8%HydroWindBiomassBiogasSolarTypes of shapes/ 
proportions

Surface rendering

Description

Example

Surface rendering involves the careful collection of data on a 
given object in order to create a three-dimensional image of 
that object on a computer. [41]

Sunburst chart

The sunburst chart is used for displaying hierarchical data. 
Each level of the hierarchy is represented by one ring or 
circle with the innermost circle as the top of the hierarchy. 
A sunburst chart without any hierarchical data (one level of 
categories) looks similar to a doughnut chart. However, a 
sunburst chart with multiple levels of categories shows how 
the outer rings relate to the inner rings. The sunburst chart 
is most effective at showing how one ring is broken into its 
contributing pieces, while another type of hierarchical chart, 
the tree map chart, is better suited for comparing relative 
sizes. [42]

Wedge stack graph

The stacked wedge graph displays hierarchal data on a radial 
system, by splitting up the sectors of a simple pie chart and 
on different rings. [43]

Icicle/partition 
chart

An icicle or partition chart is simply a sunburst chart 
transformed from polar to Cartesian coordinates. [44]

Pictogram

It is a type of graph that uses pictures or symbols to show, 
communicate or compare data. [45]

Surface rendering

Sunburst chart

Wedge stack graph

Icicle/partition chart

Pictogram

28

Reusable data visualisation  tools to support data-driven  policymaking in the EU3. Types of data and types of visualisations

Table 10  Types of data visualisations — Infographics

Types of data visualisations — Infographics below explains the meaning of an infographic.

Types of shapes/ 
proportions

Description

Example

Infographic

An infographic is a visual representation containing visuals, 
in some cases data visualisation, and text, aimed to convey 
complex information to an audience in a manner that can be 
quickly consumed and easily understood [6].

Infographic

29

 
4.  User requirements for  
data visualisation tools

In  the  context  of  the  EU  institutions,  the  Publications  Office’s  report  on  data  visualisation 
tools reveals that the 74 % of the institutions using visualisation tools use them to visualise 
data  on  the  institution’s  website,  while  60  %  use  visualisation  tools  for  internal  purposes 
and  for  publications.  This  information  should  be  taken  into  account  when  specifying  the 
user  requirements  for  data  visualisation  tools.  The  user  requirements,  described  below  in 
Table  11  User  requirements  for  data  visualisation  tools,  were  extracted  from  the  report  on 
data visualisation tools, relevant literature and experience, and from the workshop (2) on ‘Data 
visualisation tools for the EU institutions’ organised on 14 April 2016 at the Publications Office 
in Luxembourg.

The  role  of  these  user  requirements  was  to  guide  the  development  of  the  documentation 
template  for  the  data  visualisation  tools  (Section  5.4.  Documentation  of  data  visualisation 
tools), but most importantly, to guide the assessment of the tools that should be within the 
scope of the next phase of the exercise (i.e. the piloting of the catalogue and the creation of 
sample visualisations for selected tools). Thus, these user requirements for data visualisation 
tools will be translated into selection criteria that will impact the content of the catalogue. 
To  improve  readability,  the  identified  user  requirements  were  grouped  into  five  high-level 
categories, each associated with a specific functional meaning, and namely ergonomics and 
usability, learning effort, knowledge and sharing, portability, and costs (Table 11). Each of these 
five high-level categories is defined below.

 ■ Ergonomics  and  usability:  a  property  describing  the  user-software  interaction, 

related to the ease of use, effectiveness and efficiency of a tool.

 ■ Learning effort: a property related to the time investment required and the type of 

learning commitment required in order to use a tool effectively.

 ■ Knowledge and sharing: a property related to the ability to disseminate work made 
with the tool to the wider audience as well as the capacity of the tool to produce 
visualisations clear enough to be understood by non-specialists.

 ■ Portability:  the  ability  of  a  tool  to  be  ported  from  one  system  or  platform  (i.e. 

desktop, mobile, tablet) to another.

 ■ Costs: the expenses associated with tool purchasing or training.

(2)  https://joinup.ec.europa.eu/community/semic/event/workshop-data-visualisation-tools-eu-Institutions

30

Reusable data visualisation  tools to support data-driven  policymaking in the EU4. User requirements for data visualisation tools

Table 11  User requirements for data visualisation tools

Requirement 
higher-level 
category

Requirement name

Requirement description

Ergonomics and 
usability

Reusability

The reusability of the tool (i.e. applicable to different datasets) was listed as an 
important or a very important requirement by 77 % of the survey participants.

Supporting interaction with 
the data

The possibility to manipulate the data (i.e. with interactive tools) was listed as an 
important or a very important requirement by 66 % of the survey respondents. 
The users might want to have the opportunity to perform different operations 
with the sample to facilitate the exploration of data such as zooming, searching, 
filtering, mathematical transformations, etc. Furthermore, interaction with 
the raw data involving the capacity of the tool to handle data processing 
such as statistical computing is often required to produce more sophisticated 
visualisations. Display of results of statistical significance tests in visualisations 
(e.g. confidence intervals, p-values) was already brought up as a means to 
increase data trustworthiness.

Supporting adaptable 
visualisations design

The ability of the tool to fulfil specific design requirements was listed as an 
important or very important requirement by 58 % of the survey respondents.

Supporting data storage

The ability of a tool to store data was listed as an important or very important 
requirement by 40 % of the survey respondents.

Supporting easy 
deployment, not requiring a 
big IT commitment

Supporting data export in 
various standard formats

Tools should require little time to deploy and not require a substantial 
infrastructure commitment.

The ability to handle the tool easily internally was listed as an important or a 
very important requirement by 77 % of the survey respondents.

The possibility to export data in various formats was listed as an important or 
a very important requirement by 73 % of the survey respondents. The export 
of both transformed data and produced visualisations should be seen as 
important. This can also eliminate the need for manual format transformations.

Supporting data format 
(or data structure) 
transformations

The ability to support data structure transformations which involves algorithms 
(e.g. macros transforming a matrix into an array) is important, because it might 
allow for a wide variety of possible visualisations.

Supporting input in 
different data formats

The ability to handle as input a number of different data formats (e.g. .pdf, .xls, 
.xml, etc.).

Supporting linking with 
other data

The possibility to ‘stitch’ subsequent imports of additional data rather than 
having to reassemble the entire sample outside the tool and then re-import it. 
This can be seen as a big advantage.

Supporting the visualisation 
of results from inferential 
statistical analyses

A valid user requirement is to have tools supporting the visualisation of results 
from statistical comparisons between samples, or the so-called significance 
testing. Such visualisations are the boxplots, confidence internals and 
confidence ellipses often used in scientific tests to check for differences in 
the performance of technologies or for differences in response to different 
factors. In the EU institutions, it is already acknowledged that visualisations 
including information from statistical comparison between groups (e.g. MSs, 
the evolution of metrics in the same region or MS over time) may increase the 
trustworthiness of the data communicated to citizens, businesses and public 
administrations.

Supporting the adding of 
one or more dimensions to 
the data

A user requirement for data visualisation tools might be the possibility to add a 
third dimension to X-Y charts by either adding colour coding, size or motion to 
charts.

Supporting the connection 
to a service to visualise real-
time data

A visualisation tool that could be connected to a service and visualise real-time 
data such as weather data and transport data would provide high added value.

31

Requirement 
higher-level 
category

Requirement name

Requirement description

Supporting the handling 
and visualisation of big data

User requirements for visualisation tools evolve with big data (e.g. real-time 
data from sensors, medical imaging, data from social media). The requirements 
for tools supporting the visualisation of big data are different, because these 
data cannot be easily turned into tables and analysed in a relational database 
engine [46]. Additional capabilities of tools allowing the visualisation of big data 
are the possibilities offered by software such as Hadoop that allow organising 
data stored in NoSQL databases (e.g. MongoDG, Cassandra, etc.) on ‘racks’ 
of servers. Data integration, data quality checks and data management are 
necessary tasks to be completed before visualising big data.

Tools that are part of or 
easy to integrate with data 
management tools

There is demand for tools that also allow managing the data and the datasets 
(e.g. versioning, content management system, keeping data up to date, data 
governance, maintaining data quality).

Support geolocation 
visualisations

Allowing users of the 
product of the visualisations 
to trace back to the source 
data

Supporting easy 
deployment, not requiring a 
big IT commitment

Usable by individuals with 
no programming skills 

Possibility to display data on maps, etc.

This is really important in order to improve trustworthiness of the produced 
visualisations.

Tools should require little time to deploy and not require a substantial 
infrastructure commitment.

As many departments seeking to benefit from visualising their data are staffed 
by people with no technical background it is important to have such tools 
available. Communication and policy officers should be able to use them with 
minimal training beforehand (user-friendly user interfaces are really important).

Learning effort

Tools for which training is 
not expensive

Preferably done by someone other than the company owning the tool or 
external contractors.

Multilingualism Multiple language support

Due to the numerous nationalities of EU workers the tools should be available in 
as many languages as possible.

Allowing the creation of 
multilingual visualisations

The possibility to produce visualisations that allow text input in multiple 
languages is perceived as an important requirement in the EU context.

Knowledge and 
sharing

Support publishing 
visualisations fit for the 
wider public

Easily publishable visualisation on the web in an interactive format or in the 
form of infographics for the wider public to explore and interact with, and help 
generate questions from journalists.

Supporting the production 
of embeddable 
visualisations

Supporting the embedding of visualisations in websites strongly facilitates 
sharing and reuse.

Portability

Independence vis-à-vis 
the platform (i.e. desktop/
tablet/mobile)

Platform-independence of the tool (i.e. accessibility via computer, tablet or 
phone) was listed as an important or a very important requirement by 67 % of 
the survey respondents.

Independence from the 
CMS

The independence of the tool from the content management system in place 
was listed as an important or very important requirement by 58 % of the survey 
respondents.

Costs 

Licences of commercial 
tools

The costs associated with purchasing of licences of a tool is a factor considered 
when choosing a tool.

Adaptation/customisation 
for open source tools

Customising open source tools incurs additional costs, thus it should be 
considered when selecting a tool for an organisation.

32

Reusable data visualisation  tools to support data-driven  policymaking in the EU5. Specification of a catalogue of reusable data visualisation tools

5.  Specification of a catalogue 

of reusable data visualisation 
tools

5.1. High-level description of the catalogue of 
reusable data visualisation tools
The catalogue of reusable data visualisation tools is a service package providing to EU staff 
support related to the use of data visualisation and data visualisation tools. It is composed of 
three elements.

 ■ The  catalogue  itself,  which  represents  a  collection  of  data  visualisation  tools  and 
visua lisations made available via an online service. In addition to tools and visualisa-
tions, the knowledge base of the catalogue will also provide links to relevant docu-
mentation, studies and reports, learning resources, user reviews of data visualisation 
tools, etc.

 ■ A community around the catalogue comprising EU staff who will exchange know-

ledge and ideas in an online environment internal to the EU institutions.

 ■ A set of capacity-building services for data visualisation, e.g. provision of training and 

creation of sample visualisations for selected datasets.

The evolution of the catalogue will be community driven. Thus, the catalogue will not have 
a  single  curator  per  se.  Users  will  be  able  to  add  tools,  comments,  visualisations  and  other 
information and material.

The catalogue will be built in a step-wise manner. The core service, and namely finding the 
right tool will be developed first, by adding functionalities (c.f. Table 12) incrementally on the 
principle of iterative prototyping and based on user needs and demand. This approach allows 
for monitoring the catalogue’s success over time and performing a cost–benefit analysis on 
the next steps.

The  surveying  of  a  number  of  catalogues  of  data  visualisation  tools  and  platforms  for 
interoperability  solutions  (e.g.  Joinup)  identified  the  following  elements  (i.e.  catalogue 
functionalities) for consideration (Table 12).

33

Table 12  Proposed functionalities of the catalogue of reusable data 

Functionality

Building blocks

Content type

Catalogue-wide functionalities

Searching of information 
— general search

Searching of information 
— faceted search

Structured and 
comparative displaying of 
the results of the search

Exporting the results of the 
search

visualisation tools

User story providing short description of functionality

The list of content types described includes: data visualisations, data types, data visualisation tools 
(based on properties of the tool description template), forum, events, library, FAQ and user profile 
properties. It comprises also all associated CRUD operations on those content types.

As a user, regardless of whether or not I am authenticated, I want to be able to perform a text 
search from any page of the catalogue. The underlying search engine, e.g. Apache Solr, should 
allow searching for terms and specifying term modifiers (e.g. wildcard searches, fuzzy searches, 
proximity searches, range searches, use of Boolean operators), specify fields, search for phrases 
and escape special characters. I want the search to be performed in the fields of content type 
(data types, data visualisations, data visualisation tools (a number of fields corresponding to the 
tools’ template), blogs and events, library, forum, and profile.

As a user, I want to be able to filter and drill down using the following top facets: data types (i.e. 
numerical data, geospatial data, network data, textual data), visualisation types (maps, relations 
and dependencies, lines, dots and bubbles, bars, grids, shapes and proportions, infographics), all 
fields related to the template describing data visualisation tools, blogs and events, library, forum 
and profiles.

As a user, I would like the results of the search to be displayed in a consistent and structured way 
for allowing comparison between data visualisation tools.

As a user, I must be able to export the results of the search or comparative analysis I have 
performed so that I can save them for later. 
Comment: an ‘export’ button should be available when the search results are displayed, allowing 
exports in Excel, PDF or Word.

Sharing on social media

As a user, I want to be able to share content items on Facebook, LinkedIn and Twitter. 
Comment: a persistent URL should be used for the sharing.

Link to tool’s social media 
activity

‘Follow us on’ widget

Language selector

As a user, I want to be able to follow the social media activity associated with tools I am interested 
in from the catalogue’s site. 
Comment: Twitter, Facebook and LinkedIn links pointing to the activity associated with a tool, on 
the tool’s page of the catalogue, would be necessary.

As a user (anonymous or not), I want to be able to be redirected to the corresponding social 
media group through the homepage of the platform.  
Comment: There will be one button for each of the following social media: Facebook, LinkedIn 
and Twitter.

As a user (anonymous or not), I must be able to select the language for the content item I want to 
read, provided that this content is available in multiple languages. 
Comment: When creating new content items, the creator will have the possibility to create 
translations.

Keeping the user logged in

As a user, I must be able, when I log in, to decide to keep my session open even when I close the 
website or close my browser.

Print option

As a user, I must be able to print any content item I am visiting. All information of the content 
must be printed in an A4 format.  
Comment: The print functionality should only print the content (metadata + text) of the content 
item and not the entire page (footer, header, menu, etc.) for which the browser print function can 
be used. The goal here is to have a clean A4 document containing only the relevant information 
regarding the content item that is printed.

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Reusable data visualisation  tools to support data-driven  policymaking in the EU5. Specification of a catalogueof reusable data visualisation tools

Functionality

User story providing short description of functionality

Persistent URLs or 
permalink

Liking a content item

Sharing content

As a user, I must be able to share a persistent link towards any content item.  
Comment: Using the persistent URL based on the node ID is one solution but this URL is not user 
friendly. On the other side, using a URL based on the title of the content is more user friendly but 
not persistent. There is a strong need to have persistent URLs that are easily accessible (without 
having to edit the pages) by the end-user and that do not degrade the ranking of the page in 
search engines. The mapping of URLs should be supported in case content is moved. Old URLs 
should be forwarded to the new ones.

As a registered and logged user, I must be able to ‘like’ any blog, event, discussion in forum, 
document/resource in library, data visualisation tool that I can access. When I like a content item, 
the counter of likes is increased and the popularity of the content item within the platform will 
increase. 
Comment: The liking action can be achieved through a button ‘Like’ on the item page.

As a registered user, I must be able to provide descriptions of the content items (e.g. comments, 
added tools) I create using the content editor. 
Comment: The content editor is a control that allows the user to provide enriched content. For 
data visualisation tools, the content editor should have the form of the tools description template 
in Section 5.4. Documentation of data visualisation tools.

Removing shared content

I must be able to remove a shared content. 
Comment: Removing a shared content item in the topic or subtopic implies that it will be no 
longer searchable as an item and it will be removed from the topic or subtopic overview page.

FAQ

A section with frequently asked questions about the catalogue.

User management and roles

Creating a new user 
account

As an anonymous user, I must be able to create an account directly in the site of the catalogue by 
specifying my username, email address, password and basic user information.

Using alternative ways of 
authenticating

As an anonymous user, I must be able to create an account directly in the site of the catalogue by 
specifying my username, email address, password and basic user information.

Editing a user profile

As a registered user, I must be able to change my profile data.

Deleting a user account 
and user profile

Anonymous user

Registered user

As a registered user, I must be able to remove my account.

A user who is not authenticated.

A registered user. Registered users can post comments, initiate discussions in the forum, add tools, 
visualisations, source code, share blogs, etc.

Moderator

A moderator has full privileges on the catalogue, and is responsible for troubleshooting. The 
moderator can validate the addition of tools to the catalogue.

5.2. Faceted search and associated user stories

This  section  sets  the  foundations  for  the  catalogue  focusing  on  user  stories,  a  high-level 
architecture, and a common way for documenting tools in it.

User story 1

Juan, a statistical assistant at Eurostat, wants to plot unemployment rate over time in each MS 
and have a coloured indication of those MSs that stand out as positive or negative outliers 
from the average unemployment rate within the EU. Juan needs a visualisation that compares 
trends over time from MS to MS, and knows that trends over time are usually represented as 
line charts. Thus, Juan selects ‘Lines’.

35

Figure 1  Proposal for the homepage of data visualisations (User story 1)

He has many observations (i.e. 28 MSs) which he wants to plot individually but as a part of the 
same chart, thus each line has to be labelled. After visually assessing the ‘Lines’ visualisations 
(i.e.  checking  the  presented  visual  examples)  for  the  number  of  observations  and  variables 
that can be represented by the different visualisations of lines, his choice is the horizon chart.

Figure 2  Proposal for a page of a data visualisation type (User story 1)

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Reusable data visualisation  tools to support data-driven  policymaking in the EU 
 
 
5. Specification of a catalogueof reusable data visualisation tools

User story 2

Maria,  a  communication  specialist  at  Eurofound  (3),  received  public  opinion  survey  results 
(e.g.  http://ec.europa.eu/public_opinion/flash/fl128_volb.pdf)  spanning  across  several 
MSs. Maria needs to identify a visualisation that can represent the link between a MS and the 
relative frequency of respondents from this MS who picked a specific answer of a multiple 
choice  question.  The  purpose  of  the  visualisation  would  be  to  compare  the  distribution 
of answers per country.  In  Maria’s  dataset,  both  answers  and  countries  are  variables  with 
discrete states. Maria wants to identify correlations between states of discrete variables (e.g. 
the link between company country and company sector, etc.). Maria clicks on visualisations, 
then on ‘Relations/dependencies’.

Figure 3  Proposal for the homepage of data visualisations (User story 2)

To compare the distribution of answers between MSs and identify frequent associations 
between answers, Maria needs each discrete variable to be plotted on an individual axis (e.g. 
one company country axis along with the possible states of this variable, one company activity 
axis,  etc.).  Thus,  after  visually  assessing  the  ‘Relations/dependencies’  and  after  reading  the 
descriptions of the visualisations, she chooses the hive plot.

(3)  Eurofound: http://www.eurofound.europa.eu

37

 
Figure 4  Proposal for a page of a data visualisation type (User story 2)

User story 3

Jean-Claude, an analyst at EFSA (4), has received quarterly data on the prevalence of four types of 
pathogens in chicken meat across the MSs. Jean-Claude’s data represent the absolute number 
of cases in which each pathogen was found in each country. He wants to find a software that 
will allow him to both compare the prevalence of each pathogen across MSs and show the 
relative prevalence (i.e. assess the proportion) of each pathogen within each MS. Jean-Claude 
wants to plot his visualisation as a single stacked bar chart. Jean-Claude searches for ‘stacked 
bar chart’ with the search function available in the data visualisations tab.

(4)  European Food Safety Authority  (http://www.efsa.europa.eu).

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5. Specification of a catalogueof reusable data visualisation tools

Figure 5  Proposal for the homepage of data visualisation types  

(User story 3)

Browsing this visualisation, he finds the list of software that can produce it.

Figure 6  Proposed wireframe for display of search results (User story 3)

39

 
 
Figure 7  Proposal for a page of a specific data visualisation (User story 3)

User story 4

Martin, a communication specialist at DG Communications Networks, Content and Technology, 
wants to produce a visualisation of keywords extracted from stakeholders’ comments posted 
on the Futurium platform (5). Martin has already mined the relevant text data to extract key 
concepts in a text analytics software, and now wants to find a software that allows to 
produce a wordle (6) visualisation of the list of keywords and their associated frequencies of 
occurrence. Martin selects data types and clicks on ‘Textual data’.

(5)  Futurium platform (https://ec.europa.eu/futurium/en).
(6)  Wordle (http://www.wordle.net).

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Reusable data visualisation  tools to support data-driven  policymaking in the EU5. Specification of a catalogueof reusable data visualisation tools

Figure 8  Proposal for the homepage for data types (User story 4)

Then, he explores the software allowing the visualisation of text by reading their descriptions.

Figure 9  Proposal for a wireframe on textual data (User story 4)

Martin selects Tableau (7), an off-the-shelf solution, which does not require programming 
skills or offer a comprehensive suite of text-mining analyses, yet it allows the production of 
high-quality text visualisations such as wordles.

(7)  Tableau (http://www.tableau.com).

41

Figure 10  Proposal for a wireframe on data visualisation tool (User story 4)

User story 5

Ellie,  a  communication  officer  at  the  Innovation  and  Networks  Executive  Agency  has  data 
containing research funding in the EU that she would like to analyse in terms of relations and 
dependencies.  Since  Ellie  does  not  have  previous  experience  with  this  kind  of  project  and 
visualisation, she decides to post an enquiry in the forum around this type of visualisation, with 
the aim of collecting interinstitutional experience and good practices on visualising relations and 
dependencies, tools used, etc. Ellie first logs into the forum.

Figure 11  Proposal for a wireframe for the Forum tab related to user login 

or registration (User story 5)

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Reusable data visualisation  tools to support data-driven  policymaking in the EU5. Specification of a catalogueof reusable data visualisation tools

Then, Ellie selects a topic corresponding to the visualisation of relations and dependencies, or 
directly selects ‘Post a new forum subtopic’. The latter option allows Ellie to select a topic in 
which her subtopic will be classified.

Figure 12  Proposal for a wireframe for the Forum tab with clickable 
selection of topics and subtopics (User story 5)

Ellie creates a subtopic by entering its title and content, and then clicks ‘Post’.

Figure 13  Proposal for a wireframe associated with posting a subtopic in a 

forum (User story 5)

43

5.3. High-level architecture options
The architecture of the catalogue comprises the following building blocks:

 ■ a user interface, which supports faceted search of data visualisation tools (e.g. by 

keywords or selection of the data types or visualisation types);

 ■ a  content  management  system  supporting  the  management  of  descriptions 
of data visualisation tools, e.g. including a new tool in the catalogue, updating the 
description of a tool or removing a tool from the catalogue, and the management 
of users;

 ■ a repository which hosts the descriptions of data visualisation tools as well as any 

other administrative data.

The metadata associated with the description of data visualisation tools are described based 
on ADMS-AP (8) version 2.0, as explained in Section 5.4. Documentation of data visualisation 
tools. The ADMS-AP is a profile of the DCAT-AP used to describe reusable solutions.

With regard to the implementation of the catalogue, four alternatives have been identified:

 ■ the catalogue implemented using the Drupal 7 multisite platform of the European 

Commission;

 ■ the catalogue as a part of Joinup;

 ■ the catalogue as a part of a standalone service; or

 ■ the catalogue as a part of the EU ODP.

The  plausible  solution  is  reusing  the  existing  Drupal  7  multisite  platform  of  the  European 
Commission for the implementation of the catalogue, combined with hosting by the DIGIT 
Data Centre. Doing this can be combined with the usage of the common URL ‘data.europa.eu’ 
for the catalogue and the EU ODP. Using a common URL for the EU ODP site and the catalogue 
of reusable visualisation tools would allow the making of an easy connection between the 
datasets published on the EU ODP and their created visualisations. It has yet to be decided 
whether the sample visualisations of EU datasets will be hosted in the catalogue of reusable 
data visualisation tools, or on the EU ODP.

The Drupal 7 multisite platform functions as ‘software as a service’ that allows webmasters to 
easily build websites including management of users, design of the site structure and editing 
of various content types [47]. It represents a handy solution for the creation and configuration 
of  dynamic,  interactive  websites  which  applies  the  standard  Commission  visual  identity. 
Statistics  on  web  technology  indicate  that  as  the  number  of  technologies  (e.g.  PHP,  Oracle 
Portal, Weblogic Java, etc.) used increases, costs of maintenance increase exponentially. Thus, 
the use of a single comprehensive solution such as a multisite platform is recommended [47].

(8) ADMS-AP (https://joinup.ec.europa.eu/asset/adms/asset_release/adms-ap-joinup-version-20).

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Reusable data visualisation  tools to support data-driven  policymaking in the EU5. Specification of a catalogueof reusable data visualisation tools

The Commission multisite platform offers a set of functionalities with ‘all-in’ functionality: i.e. 
content types, user permissions, menus, default configuration data, etc. These functionalities 
are  automatically  activated  and  ready  to  use  ‘out-of-the-box’.  The  platform  is  characterised 
by regular implementation of new extensions to existing modules (i.e. features can also be 
implemented  upon  request)  and  by  good  protection  against  security  breaches.  The  core 
functionalities of the multisite platform are:

 ■ a selection of predefined roles with basic permissions;

 ■ a workbench workflow module allowing the moderation of content with role-based 

access;

 ■ multilingual baseline options;

 ■ a tool allowing changes of website appearance;

 ■ a notification mechanism (subscribing to content);

 ■ the ability to vote on content;

 ■ the ability to create complex menu structures and side submenu blocks;

 ■ print or PDF views of the content;

 ■ several contexts allowing the display of blocks depending on conditions;

 ■ the possibility to enrich any content type or user profile with new fields (files, image, 

text fields …);

 ■ the possibility to log in to the site using the European Commission authentication 

service account;

 ■ a powerful search engine that uses the popular Open Source Solr implementation; 

and

 ■ an easy-to-use Wysiwyg HTML editor [47].

Additional features include, among others, contact form, communities, e-library, events, FAQ, 
media  galleries  supporting  taxonomies,  EU  press  releases,  suggest  ideas,  like/dislike,  links, 
multilingual  tools,  multiuser  blogs,  news,  notifications,  sitemap,  social  bookmark,  surveys, 
wikis,  search  into  document,  charts  and  maps.  As  indicated  above,  the  customer  websites 
can request the integration of additional existing standard Drupal community modules or to 
contribute to the development of new modules.

Importantly, when considering iterative prototyping, the multisite platform offers a statistics 
and reporting capability. Other service levels include availability management key performance 
indicator,  performance  and  incident  and  request  management.  Additionally,  the  multisite 
platform offers a playground environment in the form of a sandbox, where the customer can 
experiment and test new features or configurations.

Another alternative is to host the catalogue of reusable visualisation tools as a part of Joinup. 
Joinup already provides access to a collection of reusable interoperability solutions (9), and is 
in fact based on ADMS-AP. It provides user management, content management and faceted 
search.  Joinup  is  developed  based  on  Drupal  8.  The  promotion  of  solutions  published  on 
Joinup is supported by the ISA2 programme via a raise-awareness and community-building 
action. Thus, Joinup has ready solutions for several of the requirements of the catalogue of 

(9) Joinup (https://joinup.ec.europa.eu/interoperability/search).

45

reusable data visualisation tools. Some of the constraints associated with Joinup may be that 
Joinup is perceived as complex and as focusing on open source solutions, while the catalogue 
may  also  include  commercial  ones.  An  advantage  of  Joinup  compared  to  the  Drupal  7 
multisite platform of the European Commission is that Joinup is based on Drupal 8. Thus, it has 
to be evaluated whether Drupal 8 has a significant advantage over Drupal 7 in the light of the 
requirements of the catalogue of reusable data visualisation tools.

Implementing the catalogue of reusable data visualisation tools as a standalone service may 
have advantages such as flexibility and may be easier and faster to prototype. However, in the 
long term it has the disadvantage of requiring the development of a completely new service, 
featuring all three key architecture building blocks discussed earlier. Hence the development 
costs will be much higher than implementing the catalogue as part of an existing Commission 
portal.  Additionally,  implementing  the  catalogue  as  a  standalone  service  would  require 
recurring costs for hosting and promotions, which can also be minimised should one of the 
other two alternatives be followed. This option is recommended should there be a need for 
creating a quick prototype for the catalogue in order to assess the demand and the interest 
of the users.

The option of implementing the catalogue into the EU ODP contributes to the creation of a 
data ecosystem for the EU institutions. Including the data visualisation tools catalogue there will 
make it a one-stop-shop for people working with EU institutions data. However, this result can 
be achieved equally well through the first alternative. Should the catalogue be implemented 
as a part of the EU ODP, recurring costs for hosting and promotion would be minimised, as the 
existing platform and services provided by the EU ODP would be reused. The real challenge 
for hosting the catalogue on EU ODP, however, would lie in reusing and adapting the content 
management system of the EU ODP, which is based on the CKAN data management system 
(the data tab) and on Drupal 5 (all the other tabs). Naturally, development that is both time-
consuming  and  costly  would  be  required  to  ensure  that  the  CKAN  content  model  can 
accommodate the metadata necessary to describe data visualisation tools, specified in Section 
5.4. Documentation of data visualisation tools. Additionally, the user interface of the catalogue 
would also have to be developed, based on the existing faceted search functionality of the EU 
ODP, which might add unnecessary complexity to the project.

Thus, in view of the above-presented high-level analysis of alternatives, the following scenarios 
will be further analysed: (i) the potential of hosting the catalogue as a standalone service, (ii) 
reusing the Commission Drupal 7 multisite environment, and (iii) hosting the catalogue as a 
part of the Joinup platform.

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Reusable data visualisation  tools to support data-driven  policymaking in the EU5. Specification of a catalogueof reusable data visualisation tools

5.4. Documentation of data visualisation tools

Data  visualisation  tools  in  the  catalogue  can  be  described  according  to  the  following  tool 
description template. This template allows for an inventory of tools including technical aspects 
and information on reusability. Included in the template are the properties of the ADMS-AP 
v2.0.0 used to describe each field. The information collected on tools via this template will be 
used as a basis to organise the information contained in the proposed ‘Data visualisation tools’ 
tab of the catalogue.

Name of tool

Title  
(dct:title)
Type 
(dct:type)

Description 
(dct:description)

Title of the tool

Library, API, web application, desktop application, mobile application, wizard, etc. It 
will inform the user if the tool requires programming skills.
Among other things, the description should specify: 
Is the tool good for beginners? 
Does the tool have a shallow learning curve? 
Is the tool easy to share and embedded on other sites?

Name or link to publisher

Name or link to contact point

Free of charge or paid service

Website of the tool

Publisher 
(dct:publisher)
Contact point 
(dcat:contactPoint)
Pricing
Website 
(dcat:publisher)
Access to resource 
(dcat:accessURL)
Distribution of resource 
(dcat:distribution)
Documentation and tutorials  (foaf:page) Any documentation
Related resources 
(dct:relation)
Input format 
(dct:format)
Output format 
(dct:format)

Link to the resource for download. 
Link to existing github page if any.

Format of dataset, format of images

Distributions for Windows, Mac, Linux, binary, etc.

Components used to build the tool, or other tools to customise this tool.

URL, XLS, CSV, TSV, JSON, drag-and-drop JSON, XML, copy-paste, etc.

Technology 
(dct:format)

License 
(dct:licence)
How is the data accessed:

File (or file system)

Relational database

RDF store

Other

Technology associated with the architecture of the software itself and with any 
programming language that may need to be used to generate visualisations. 
Examples: JavaScript, Python, Ruby, etc.
GNU General Public License, Creative Commons Attribution, Creative Commons Non-
Commercial, Creative Commons Attribution-Non-Commercial, etc.

Yes/No

47

Name of tool

Other features:

Data cleansing 

Support for semi-structured data

Data validation 

Data transformation

Mathematical/statistical processing

Other
Data interaction  
(e.g. filter data, sort values, write notes)

Extending components 
(e.g. plug-ins, add-ons, libraries, etc.)

Supported visualisations 
(i.e. Full list of inventory 
of visualisations)
Troubleshooting support 
(e.g. community or corporate service)
Used by European Union institutions
Developed by European Union 
institutions
Use cases

Yes/No

Yes/No. Explain what can be done.

Plug-ins for R (analysis), plug-ins for GRASS (analysis), plug-ins for Orfeo Toolbox 
(analysis), plug-ins for SAGA (analysis), PostgreSQL/PostGIS (database), SpatiaLite 
(database), MSSQL (database), Oracle (database), plug-ins for OWS layers, plug-ins for 
WCS layers, plug-ins for WMS layers, plug-ins for WFS layers, etc.

Maps (list types), relations/dependencies (list types), lines (list types), dots/bubbles 
(list types), grids (list types), shapes/proportions (list types)

Yes/No, provide link if applicable.

Yes/No, indicate which EU institutions use the tool.

Yes/No, indicate by which institution if applicable.

Link to any projects which have used this tool.

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Reusable data visualisation  tools to support data-driven  policymaking in the EU6. Conclusions and next steps

6. Conclusions and next steps

The principal objective of this study, which is funded under Action 1.1 of the ISA programme 
of  the  European  Commission,  is  to  carry  out  preparatory  work  for  a  catalogue  of  reusable 
data  visualisation  tools  that  would  allow  users  to  easily  identify  the  most  appropriate  tool 
corresponding to their needs, taking into account the nature and structure of the data as well 
as the internal capacity to handle it. To do that, we did the following.

 ■ Created a list of user requirements for data visualisation tools for the EU institutions 
referring  to  the  Publications  Office’s  report  on  data  visualisation  tools,  to  feedback 
received at the workshop ‘Data visualisation tools for the EU institutions’, as well as to 
hands-on experience with data.

 ■ Defined  a  classification  of  data  types  (i.e.  numerical  data,  geospatial  data,  network 
data, textual data) that will provide some of the dimensions by which the content of 
the catalogue will be organised.

 ■ Defined  a  typology  of  data  visualisation  types  (i.e.  maps,  relations/dependencies, 
lines, dots and bubbles, bars, grids, shapes and proportions) associated with a rich 
library of data visualisations that will provide some of the dimensions by which the 
content in the catalogue will be organised.

 ■ Specified high-level information and user requirements for the catalogue of reusable 
data  visualisation  tools  by  means  of  wireframes  based  on  the  EU  ODP  site  and  by 
means of user stories.

 ■ Provided  an  analysis  of  the  high-level  architecture  of  the  catalogue  and  examined 
four alternatives regarding the implementation of the catalogue (i.e. Drupal multisite 
platform of the European Commission, EU ODP, Joinup, standalone service).

 ■ Identified and documented some of the data visualisation tools currently in use by the 
EU institutions as well as other relevant visualisation tools used by other organisations, 
specifying their reusability, their technical requirements, their requirements for data 
structure,  their  visualisations  capabilities,  their  customisability  and  their  limitations, 
among  others  characteristics.  The  initial  phase  of  this  inventory  focused  on  the 
definition of a metadata schema aligned with ADMS-AP version 2.0.0 used to describe 
reusable solutions.

This  work  is  complementary  and  was  carried  out  in  parallel  with  the  organisation  and  the 
synthesis  of  the  information  collected  at  the  workshop  ‘Data  visualisation  tools  for  the  EU 
institutions’,  which  aimed  at  distilling  requirements  and  collecting  feedback  with  regard  to 
the catalogue of reusable data visualisation tools and establishing a community around the 
catalogue, to support data-driven policymaking in the EU. Both this report and the workshop 
report  provide  valuable  foundation  for  the  building  of  the  catalogue  of  reusable  data 
visualisation tools.

49

Next steps following this work include:

 ■ agreeing on the implementation options for the catalogue of visualisation tools (e.g. 
hosting of the catalogue on the EU ODP, on Joinup, on the Drupal multisite platform 
of the European Commission, as a standalone service);

 ■ creating a working pilot of the catalogue;

 ■ selecting the best-fit tools to be included in the catalogue;

 ■ creating sample visualisations for high-value datasets published on the EU ODP;

 ■ creating comparative analysis tables for the best-fit tools to help EU staff decide which 

one best fits their needs; and

 ■ defining  support  services  for  developing  data  visualisation  capabilities  in  the  EU 
institutions (e.g. preparing training in collaboration with the EU institutions for data 
visualisation, and raising awareness of the EU institutions about the catalogue and its 
value proposition).

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Reusable data visualisation  tools to support data-driven  policymaking in the EUAnnex I. Glossary

Annex I. Glossary

Table 13  Glossary of commonly used terms

TERM

Data

Data model

Dataset

Data visualisation tool

Data type

Data structure

Data theme

Catalogue

DEFINITION

Data are facts represented as text, numbers, graphics, images, sound, or video.  
Data are the raw material used to represent information, or from which information can be 
derived [48].

A data model includes formal data names, comprehensive data definitions, proper data 
structures and precise data integrity rules. [49]

A dataset is a collection of data, published or curated by a single agent, and available for 
access or download in one or more formats [50].

Software (web, mobile, desktop) used to present, explore and communicate data 
graphically.

A type of data refers to the nature of the content of the data. Examples of data types 
include: statistical data, geospatial data, text data, etc. A data type may have data 
organised in a number of ways depending on the aimed analysis and/or visualisation. For 
example, for analysis data can be placed into rows and columns in a table format and for 
visualisation data can be placed within a ranking.

The way according to which data belonging to a data type is organised (e.g. observations 
in rows, variables in columns). Data structures are usually based on data models.

An area of work from which the data is produced. Examples from the Data Themes NAL 
of the Publications Office include: economy and finance; science and technology; justice, 
legal system and public safety; population and society, etc.

A service that facilitates the storage and maintenance of descriptions of assets (i.e. 
data visualisation tools) classified according to a number of principles, along with a 
functionality that allows users to search and access the assets.

Descriptive metadata

Descriptive metadata describes a resource for purposes such as discovery and 
identification. It can include elements such as title, abstract, author, and keywords [51].

Visualisation/visualisation type

The graphical representation used to visualise data (e.g. maps, relations/dependencies, 
lines, dots and bubbles, bars, grids, shapes and proportions). Visualisations depict 
qualitative or quantitative data, without necessarily using a well-defined reference system 
[1] (e.g. scatter plots, pie charts, word clouds, etc.).

51

     
Annex II. Sample inventory  
of data visualisation tools

Provided below is the documentation of three tools (EEA DaViz, QGIS and R) as examples of 
use of the proposed documentation template. A number of other identified tools are briefly 
described in Table 14 following the examples. Once the scope of the catalogue is clarified, it 
will be decided which tools will be documented in detail.

EEA DaViz

Title (dct:title)
Type (dct:type)
Description (dct:description)

Publisher (dct:publisher)
Contact point (dcat:contactPoint)

Pricing
Website (dcat:publisher)
Access to resource (dcat:accessURL)
Documentation and tutorials 
(foaf:page)

Related resources (dct:relation)

Input format (dct:format)

Output format (dct:format)
Technology (dct:format)
License (dct:licence)

EEA DaViz
Library
EEA DaViz is a web tool developed by the European Environment Agency (10) which 
helps create interactive data visualisations easily through the web browser, no extra 
tools are necessary. 
It allows the generation of attractive and interactive charts and combining them in a 
dashboard with facets/filters which updates the charts simultaneously. Data can be 
uploaded as CSV/TSV or one can specify SPARQL to query online linked open data 
servers (aka SPQRQL endpoints). 
DaViz is the first semantic web data visualisation tool for Plone CMS, entirely web-
based. 
At the moment Simile Exhibit and Google Charts visualisations are supported. The 
architecture allows to extend DaViz with more visualisation libraries (visualisation 
plugins).

•  The tool requires advanced IT and programming skills as it is a library.
•  Slow learning curve.
•  Embedding on websites available.

European Environmental Agency
Antonio De Marinis 
demarinis@eea.europa.eu
Free of charge
http://www.eea.europa.eu/data-and-maps/daviz
https://github.com/eea/eea.daviz
Documentation: https://github.com/eea/eea.daviz 
Video tutorials: https://www.youtube.com/watch?list=PLVPSQz7ahsByeq8nVKC7TT9a
pArEXBrV0&v=CYEAAAdyWII
https://developers.google.com/chart/ 
http://www.simile-widgets.org/exhibit/ 
https://jquery.com/ 
https://plone.org/ 
More information under: https://code.activestate.com/pypm/eea.exhibit/
(dependencies tree)

•  URL (CSV/TSV/JSON)
•  drag-and-drop (CSV/TSV)
•  through SPARQL endpoints (linked data, formats: JSON, Exhibit JSON, HTML, XML, 

XML with Schema, CSV, TSV)
•  copy-paste from data table
•  XLS
Image files
JavaScript
GNU General Public License (11)

(10) European Environment Agency (http://www.eea.europa.eu).
(11) GNU General Public License (http://www.gnu.org/licenses/gpl.html).

52

Reusable data visualisation  tools to support data-driven  policymaking in the EUAnnex II. Sample inventory of data visualisation tools

EEA DaViz

How is the data accessed:
•  File (or file system)
•  Relational database
•  RDF store
•  Other

Other features: 

Yes
No
Yes
No

•  Data cleansing 
•  Support for semi-structured data
•  Data validation 
•  Data transformation
•  Mathematical/statistical processing
•  Other

No
No
No
Yes
Yes
Yes. SPARQL search.

Data interaction  (e.g. filter data, sort 
values, write notes)
Extending components 
(e.g. plug-ins, add-ons, libraries, etc.)
Supported visualisations 
(i.e. full list of inventory of 
visualisations)

Yes

Yes, with visualisation libraries.

Google charts such as:

•  lines: line chart, area chart
•  dots and bubbles: scatter plot, bubble chart
•  bars: bar chart, candlestick chart, Gantt chart, histogram
•  relations/dependencies: Sankey diagram
•  grids: radar chart, tree map
•  shapes and proportions: donut chart, pie chart
•  Google maps.

Troubleshooting support 
(e.g. community or corporate service)
Used by European Union institutions
Developed by European Union 
institutions
Use cases

No

Yes

Yes

-

QGIS

Title (dct:title)
Type (dct:type)
Description (dct:description)

QGIS
Desktop application
Create, edit, visualise, analyse and publish geospatial information on Windows, Mac, 
Linux, BSD (Android coming soon). 
QGIS, a cross-platform free and open source software, has become one of the leading 
GIS in the market in recent years. Thanks to the work of an active group of developers, 
QGIS provides geoprocessing modules similar to standard tools found in privative 
GIS, supports most of the vector and raster file formats and provides an interface to 
databases such as PostgreSQL/PostGIS, SpatiaLite and MySQL.
QGIS Development Team
QGIS Development Team
Free of charge
http://www.qgis.org/en/site/
https://github.com/qgis

Publisher (dct:publisher)
Contact point (dcat:contactPoint)
Pricing
Website (dcat:publisher)
Access to resource (dcat:accessURL)
Documentation and tutorials (foaf:page) http://docs.qgis.org/2.0/en/docs/index.html
Related resources (dct:relation)

Analysis capabilities offered by:
SAGA (http://www.saga-gis.org/en/index.html)
GRASS (https://grass.osgeo.org/)
Orfeo Toolbox (https://www.orfeo-toolbox.org/)
R (https://www.r-project.org/)
Qt (http://www.qt.io/product/)

53

•  .qgis (project format)
•  .csv
•  .shp
•  .dbf
•  .prj
•  .shx
•  .gfs
•  .gml
•  .xsd
•  .gpx
•  .xml
•  .tif
•  .txt
•  other
•  .qgs (software-specific file)
•  .bmp
•  .ico
•  .jpeg
•  .pdf
•  .png
•  .ppm 
•  .svg 
•  .tif 
•  .tiff 
•  .xbm 
•  .xpm 

C++, Python, Qt library
GNU General Public License

Yes
Yes
No
No

No
No
No
Yes
Yes
-

Yes, adding a filtered set of records from a table as a layer is possible, by using queries.

Yes,

•  plug-ins for R (analysis)
•  plug-ins for GRASS (analysis)
•  plug-ins for Orfeo Toolbox (analysis)
•  plug-ins for SAGA (analysis)
•  PostgreSQL/PostGIS (database)
•  SpatiaLite (database)
•  MSSQL (database)
•  Oracle (database)
•  plug-ins for OWS layers 
•  plug-ins for WCS layers 
•  plug-ins for WMS layers
•  plug-ins for WFS layers
•  others can be installed.

QGIS

Input format (dct:format)

Output format (dct:format) 

Technology (dct:format)
License (dct:licence)
How is the data accessed: 
• File (or file system) 
• Relational database
• RDF store
• Other 
Other features: 
• Data cleansing 
• Support for semi-structured data
• Data validation 
• Data transformation
• Mathematical/statistical processing
• Other
Data interaction 
(e.g. filter data, sort values, write notes)
Extending components 
(e.g. plug-ins, add-ons, libraries, etc.)

54

Reusable data visualisation  tools to support data-driven  policymaking in the EUQGIS

Supported visualisations 
(i.e. full list of inventory of visualisations) 
Troubleshooting support 
(e.g. community or corporate service)
Used by European Union institutions
Developed by European Union 
institutions
Use cases

R

Title  (dct:title)
Type (dct:type)
Description (dct:description)

Annex II. Sample inventory of data visualisation tools

Maps (dot distribution map, choropleth, heat map, cartogram, contour map, 
dasymetric map).

Yes, community

Yes

No

Assessment of open source GIS software for water resources management in 
developing countries

http://publications.jrc.ec.europa.eu/repository/handle/JRC62984

R (The R project for statistical computing)
Desktop application
R is a language and environment for statistical computing and graphics. R was created 
by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and 
is currently developed by the R Development Core Team. It is a GNU project which is 
similar to the S language and environment which was developed at Bell Laboratories 
(formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be 
considered as a different implementation of S. There are some important differences, 
but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical 
statistical tests, time-series analysis, classification, clustering, etc.) and graphical 
techniques, and is highly extensible. The S language is often the vehicle of choice 
for research in statistical methodology, and R provides an open source route to 
participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots 
can be produced, including mathematical symbols and formulae where needed. 
Great care has been taken over the defaults for the minor design choices in graphics, 
but the user retains full control.

•  Programming skills are required for R. 
•  Steep learning curve.
•  Embedding to websites.

The googleVis R package provides an interface between R and Google Visualisation 
API to create interactive charts which can be embedded into web pages. More 
information is available on:

https://cran.r-project.org/web/packages/googleVis/vignettes/googleVis.pdf

https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Gesmann+de~Castillo.pdf

R allows for performing pivoting operations, through the package reshape  
(http://had.co.nz/reshape/)

Publisher (dct:publisher)
Contact point  (dcat:contactPoint)
Pricing
Website (dcat:publisher)
Access to resource (dcat:accessURL)
Distribution of resource 
(dcat:distribution)
Documentation and tutorials  (foaf:page) https://cran.r-project.org/manuals.html

There is an RStudio Integrated Development Environment.
R Core Team
-
Free of charge
https://www.r-project.org/
https://cran.r-project.org/mirrors.html

UNIX platforms, Windows and MacOS

https://www.r-project.org/mail.html

55

R

Related resources  (dct:relation)

https://github.com/ldbk/EMISR

https://cran.r-project.org/web/packages/eurostat/eurostat.pdf

https://github.com/rOpenGov/eurostat

https://www.rstudio.com/

https://rpubs.com/adam_dennett/8955
.csv, .xml, .txt, .dat, .dta, .xls, .dbf, .r

Data from SPSS, S-PLUS, SAS, Stata, Systat, EpiInfor, Mititab, Octave and SAS can be 
read.

.bmp, .jpeg, .png, .tiff, .pbm, .pgm, .ppm
.csv, .txt, .r, .pdf, other image files, etc.
C, Fortran, and R.
GNU General Public License.
File, relational database

Input format (dct:format)

Output format (dct:format)
Technology (dct:format)
License (dct:licence)
How is the data accessed:

•  File (or file system)
•  Relational database
•  RDF store
•  Other

Other features:

•  Data cleansing
•  Support for semi-structured data
•  Data validation
•  Data transformation
•  Mathematical/statistical processing
•  Other

Yes
Yes
No
Yes
Yes
-

Data interaction 
(e.g. filter data, sort values, write 
notes)
Extending components 
(e.g. plug-ins, add-ons, libraries, etc.)
Supported visualisations 
(i.e. full list of inventory of 
visualisations)
Troubleshooting support 
(e.g. community or corporate service)
Used by European Union institutions
Developed by European Union 
institutions
Use cases

Yes. Axes, titles, colours and significance measures can be included in the 
visualisations.

-

Maps (list types), relations/dependencies (list types), lines (list types), dots/bubbles 
(list types), grids (list types), shapes/proportions (list types)

Yes. https://stat.ethz.ch/mailman/listinfo/r-help

Yes (c.f., related resources).

No

•  Environmental Marine Information System: making datasets available to scientific 
and environmental managerial community through R (EMIS-R), to enable the use 
to conduct regional assessments: http://mcc.jrc.ec.europa.eu/emis/

•  Mapping Data from Eurostat using R: https://rpubs.com/adam_dennett/8955

56

Reusable data visualisation  tools to support data-driven  policymaking in the EUAnnex II. Sample inventory of data visualisation tools

Table 14  List of identified tools for the inventory

Tool

Type

Short description

StatWing

Free of charge

A statistical analysis software that allows the cleaning of data, exploring of relationships, and 
creating of charts.

CartoDB

Free of charge

Gapminder

Free of charge

Kibana

Free of charge

ElasticSearch

Free of charge

Gephi

Free of charge

A cloud computing platform enabling the use of several APIs for building advanced, 
dynamic geospatial datasets and scalable maps for applications. It is built on open source 
software such as PostGIS and PostgreSQL. JavaScript is used in front-end web application, 
while Node.js is used for the back-end.

This platform supports all kinds of maps, static and interactive, and it is split in several 
components:

• web application, where users manage data and create custom maps, and

• Maps API.

A tool for illustrating aspects of world development and understanding statistics and other 
information about social, economic and environmental development at local, national 
and global levels. This tool offers free downloads for visualisations. Raw data and links to 
visualise the raw data are available on its website. The tool also allows .xls and .csv files as 
input formats and supports different types of visualisations, such as maps, charts, tables and 
networks, both static and interactive.

A data visualisation software that allows native interaction with data in Elasticsearch. Data 
can come from many different sources and third-party technologies. Kibana supports 
visualisation types ranging from histograms to geomaps. Custom dashboards can also be 
created. The software is released as open source under the terms of the Apache license.

A server to search and analyse data in real time which provides a distributed, multitenant-
capable full-text search engine with an HTTP web interface and schema-free JSON 
documents. This software is developed in Java and it is released as open source under the 
terms of the Apache license.

Tool for exploring and understanding graphs, real-time visualisations, exploratory data 
analysis, link analysis, social network analysis, biological network analysis and poster creation. 
It is customisable via plugins. The user interacts with the representation, manipulates the 
structures, shapes and colours to reveal patterns.

This tool can read most graph file formats, including GDF, GraphML, GML, NET(Pajek), GEXF, 
GraphViz DOT, UCINET DL, Tulip TPL, Netdraw VNa, Spreadsheet and also CSV. It is developed 
in Java 1.7+ and it supports network graphs, maps, static and interactive.

Atavist

Steller

Commercial

A multimedia, publishing and storytelling platform, used to produce all kind of stories.

Free of charge

A visual storytelling mobile application.

Google Charts

Free of charge

Socrata

Commercial

IBM SPSS 
Statistics

Commercial

A library that lets people easily create a chart from some data and embed it in a web page. 
Google creates a .png image of a chart from data and formatting parameters in an HTTP 
request. Many types of charts are supported.

This solution, often referred to as platform for digital government, offers several products 
for governments that help make data more accessible and understandable, such as Open 
Data (which supports the entire lifecycle of data), Open Performance (a performance 
management solution) or Perspectives (which enables governments to connect the dots 
between policy and outcomes to better engage their constituents). Socrata Open Data is a 
cloud-based solution.

IBM SPSS Statistics is an off-the-shelf software solution that represents an integrated family 
of products that addresses the entire analytical process (including statistical significance 
tests), from planning to data collection to analysis, reporting (including the production of 
visualisation) and deployment. With more than a dozen fully integrated modules to choose 
from, the user can find the specialised capabilities needed to conduct research and make 
better decisions.

57

Tool

Type

Short description

IBM SPSS Modeler  Commercial

Matlab

Commercial

Statistica

Commercial

An extensive predictive analytics platform that is designed to bring predictive intelligence 
to decisions made by individuals, groups, systems and enterprises, by providing a range of 
advanced algorithms and techniques that include text analytics and entity analytics.

The Matlab software relies on the matrix-based Matlab language, a natural way to 
express computational mathematics. This language is good for performing data analytics, 
algorithms and data exploration. Built-in graphics make it easy to visualise and gain insights 
from data. A library of prebuilt toolboxes lets one get started with algorithms essential to 
a domain. The desktop environment allows experimentation, exploration, and discovery. 
Matlab is in automobile active safety systems, interplanetary spacecraft, health-monitoring 
devices, smart power grids, and LTE cellular networks. It is further used for machine learning, 
signal processing, image processing, computer vision, communications, computational 
finance, control design, robotics, etc.

Statistica products provide a comprehensive array of data analysis, data management, data 
visualisation, and data mining procedures. Statistica techniques include a wide selection of 
predictive modelling, clustering, classification, and exploratory techniques in one software 
platform.

Mathematica  
(link to 
visualisation part)

Commercial

Mathematica relies on the Wolfram Language, which integrates many aspects of statistical 
data analysis, from getting and exploring data to building high-quality models and 
deducing consequences.

The Wolfram Language provides multiple ways to get data, starting with built-in data 
sources, importing from a variety of file formats, or connecting to databases. Basic 
processing of data, including computing statistical quantities, smoothing, testing, and 
visualising, gives a first level of analysis. By adding models to the mix, such as distributional 
or regression models, one can answer a wider range of analysis questions or even provide 
predictive capabilities. Mathematica can produce dynamic visualisations in a wide variety of 
formats — both predefined and arbitrarily extensible.

Tableau Desktop

Commercial

A big data visualisation tool which is used for data analytics and it allows to take data offline 
or bring it in-memory.

Tableau Public

Free of charge

ArcGIS

Commercial

OpenStreetMap

Free of charge

Simile Widgets

Free of charge

Tableau Desktop also offers the possibility to blend disparate data sources, it supports 
interactive and static maps, charts, tables and the input formats can be spreadsheet, SQL 
database or Hadoop. Tableau products are Unicode-enabled, compatible with data stored in 
any language. Available in English, French, German, Spanish, Portuguese, etc.

A big data visualisation tool that allows spreadsheet, SQL databases and Hadoop. Also allows 
data analytics, and the possibility to take data offline or bring it in-memory and to blend 
disparate data sources. Tableau products are Unicode-enabled, compatible with data stored 
in any language. Available in English, French, German, Spanish, Portuguese, etc.

This software, existing in several varieties (e.g. ArcGIS Online (mapping platform), ArcGIS 
for Desktop (complete professional GIS), ArcGIS for Server (GIS for enterprise, ArcGIS for 
developers (tools to build location-aware apps), ArcGIS Solutions (free template maps 
and apps for industry), ArcGIS Marketplace (apps and data for organisations)), allows the 
visualisation of large amounts of geospatial data and conveying of information in a powerful 
way.

A collaborative project that contains free and editable maps. Maps are created using 
geographic information captured with mobile GPS devices, orthophotos and other free 
sources. Both the cartography and the images created as vector data stored in the database 
are distributed under the Open Database License (ODbL).

Registered users can upload GPS tracks, create and edit vector data using tools created by 
the OpenStreetMap community.

OpenStreetMap uses a topological data structure. Data are stored in WGS84 datum lat/lon 
(EPSG: 4326) Mercator projection format.

Free, open-source data visualisation web-based widgets that were created as part of a large 
semantic-web based project at MIT called Simile (Semantic Interoperability of Metadata and 
Information in unLike Environments.)

58

Reusable data visualisation  tools to support data-driven  policymaking in the EUAnnex II. Sample inventory of data visualisation tools

Tool

D3.js

Type

Short description

Free of charge

A JavaScript library for manipulating documents based on data. D3.js helps bring data using 
HTML, SVG, and CSS and it also allows the binding of arbitrary data to a document object 
model (DOM), and then the application of data-driven transformations to the document.

D3sparql.js

Free of charge

A JavaScript library for visualisation of SPARQL results. It plots results of SPARQL search, i.e. 
transforms SPARQL query results from JSON format into several JSON formats which most 
D3 layouts can accept. This fills a gap left by other visualisation tools implemented with the 
d3.js library and which depend on a predefined JSON data structure, different from the JSON 
structure that SPARQL endpoints return.

Japersoft BI 
suite: Business 
Intelligence 
Platform

Commercial

Big data visualisation and analysis software. The product also includes Visualise.js, a 
framework that allows programmatic visualisations.

Pentaho

Commercial

Big data visualisation software, offering interactive analysis, drill through, zooming and 
attribute highlighting.

Sigma.js

Free of charge

JavaScript library for graph drawing, developed by the MIT. It includes Canvas and WebGL 
renderers, mouse and touch support for easier network manipulation for the end user.

Recline.js

Free of charge

Library for building data applications in JavaScript and HTML that allows the viewing 
and editing of data in table interface, built-in visualisations, possibilities to load data 
from multiple sources, clean-up data and design. Built on Backbone, the library supplies 
components and structure to data-heavy applications and reuses several presentation 
libraries (SlickGrid, Leaflet, Flot, D3). This library consists of three parts: Models, Back-ends 
and Views. The Views act as web user interfaces, displaying some model object in the DOM. 
This tool can generate maps, graphs, data grids, timelines, multiviews, choropleth maps, etc.

Raw

Free of charge  Web application developed by the DensityDesign Research Lab (Politecnico di Milano), and 
built on the d3.js library. It is used to create vector-based visualisations that are customisable 
and extensible. Exporting visualisations is possible in vector (SVG) or raster (PNG) format; 
visualisations are embeddable. Data can be input using plain text files (with delimiter-
separated values like .csv or .tsv), copy-pasting it from a spreadsheet or directly typing data 
in (useful for editing). The tool does not support JSON or XML.

Timeline.js

Free of charge

Infogr.am

Commercial

Tool developed by the Knight Lab for Northwestern University, for building timeline 
visualisations, both static and interactive. Can be used by laymen and experts. It is built on 
the d3.js library and takes URLs to input data of various media types, but common format is 
JSON.

Data visualisation SaaS, updates reports automatically by connecting databases to charts. 
Infogr.am consumes JSON-format data and can produce any type of static or interactive 
charts.

Chart.js

Piwik

Free of charge

Data visualisation library developed by MIT that consumes JSON-format data.

Free of charge

Customisable, extensible analytics platform. There is a self-hosted and a cloud-hosted 
version.

Engagor

Commercial

Data analysis and visualisation SaaS, including sentiment, speech and text analysis and 
natural language processing.

Cola.js

VisPy

The Data 
Visualisation 
Catalogue

Free of charge

JavaScript library for arranging HTML5 documents and diagrams, using constraint-based 
optimisation techniques.

Free of charge

Python library for interactive scientific 2D/3D data visualisation.

Free of charge

Catalogue of different types of data visualisation, searchable by function. It can be viewed 
by symbol or in a list. The different data visualisation types include a description, link to 
a related academic paper, functions they fulfil, tools that can be used to generate the 
visualisation and variations on it.

Protovis

Free of charge

Graphics library, composes custom views on data, allows inheritance, scales and layouts.

59

Tool

Vega

Visual.ly

NodeXL

Type

Short description

Free of charge

Visualisation grammar, i.e. declarative format for creating, saving and sharing interactive 
visualisation designs. Define and load data to visualise. Data is transformed before 
visualisation.

Commercial

A tool used to create infographics, all types of charts and interactive visualisations.

Commercial

Template for Excel used to easily explore network graphs.

JavaScript InfoVis 
Toolkit

Free of charge

A library for creating interactive data visualisations that supports different types of 
visualisations, such as area, bar and pie charts, sunburst graphs, icicles, force-directed 
graphs, tree maps, space trees, RGraphs, hyper trees and other advanced visualisations.

OpenLayers3

Free of charge

QlikView

Commercial

Piktochart

Commercial

Venngage

Commercial, 
with a free 
version

Highcharts

Free of charge

FusionCharts

Commercial

JavaScript library for displaying map data in web browsers. This library provides an API for 
building web applications in the geographic domain and supports GeoRSS, KML, GML, 
GeoJSON, and also static and interactive maps from any source.

A visualisation tool that offers an integrated Business Intelligence (BI) platform and has 
a software’s dynamic model that allows you to quickly analyse your data in multiple 
dimensions. This tool is able to work off data in memory instead of off a disk, allowing for 
real-time operational BI environments and also able to work with a wide variety of data 
sources (such as Excel spreadsheets, SAP or Oracle), combining these different data sources 
into a single visualisation or dashboard.

Piktochart democratises graphic design by providing an easy-to-use infographics maker, 
through a number of available templates, and the possibility for sharing the created 
infographics on the web. This tool has built-in social media options, offers the possibility to 
take HTML code and add it to personal websites, and generates links to the infographics for 
email sharing. Piktochart also offers online presentations and downloads in JPG, PNG and 
PDF, in different resolutions.

Venngage is an infographic creator allowing to choose from hundreds of templates for 
infographics, reports, posters, promotions and social media posts. It allows telling a visual 
story with hundreds of free charts, maps, icons, and other visuals. The created infographics 
can be shared in social networks, embedded on websites or downloaded as images or as 
PDF. Venngage also offers a teaching toolbox.

Highcharts is a charting library supporting a large number of visualisations, written in 
JavaScript, offering an easy way of adding interactive charts to a website or to a web 
application.

FusionCharts is a comprehensive charting JavaScript library offering 90+ chart types and 
965 maps. The FusionCharts consists of four products, each of which has a specific purpose 
and contains different chart types. The tool allows easy integration with other libraries, 
frameworks and languages. FusionCharts has a collection of business dashboards with 
downloadable source code, and use cases in sales, finance, marketing, manufacturing, 
health care, education, government and IT. FusionCharts allows to easily download/export 
all charts in JPEG, PNG, PDF or SVG. Both client-side and server-side exporting is supported. 
The FusionCharts visualisations work seamlessly across PCs, Macs, iPhones, iPads, and 
Android devices.

60

Reusable data visualisation  tools to support data-driven  policymaking in the EUReferences

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61

[18]  

‘Golden  Software:  Prism  maps’,  [Online]  Available:  http://www.goldensoftware.com/
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62

Reusable data visualisation  tools to support data-driven  policymaking in the EUReferences

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63

List of figures

Figure 1  Proposal for the homepage of data visualisations (User story 1) 

Figure 2  Proposal for a page of a data visualisation type (User story 1) 

Figure 3  Proposal for the homepage of data visualisations (User story 2) 

Figure 4  Proposal for a page of a data visualisation type (User story 2) 

Figure 5  Proposal for the homepage of data visualisation types (User story 3) 

Figure 6  Proposed wireframe for display of search results (User story 3) 

Figure 7 

 Proposal for a page of a specific data visualisation (User story 3) 

Figure 8   Proposal for the homepage data types (User story 4) 

Figure 9   Proposal for a wireframe on textual data (User story 4) 

Figure 10   Proposal for a wireframe on data visualisation tool (User story 4) 

36

36

37

38

39

39

40

41

41

42

Figure 11   Proposal for a wireframe for the Forum tab related to user login or registration  42

Figure 12   Proposal for a wireframe for the Forum tab with clickable selection of topics  

and subtopics (User story 5) 

Figure 13  Proposal for a wireframe associated with posting a subtopic in a forum 

43

43

64

Reusable data visualisation  tools to support data-driven  policymaking in the EUList of tables

Table 1 

Types of data 

Table 2 

Types of data visualisations 

Table 3 

Types of data visualisations — Maps 

Table 4 

Types of data visualisations — Relations/dependencies 

Table 5 

Types of data visualisations — Lines 

Table 6 

Types of data visualisations — Dots and bubbles 

Table 7 

Types of data visualisations — Bars 

Table 8 

Types of data visualisations — Grids 

Table 9 

Types of data visualisations — Shapes and proportions 

Table 10  Types of data visualisations — Infographics 

Table 11  User requirements for data visualisation tools 

Table 12  Proposed functionalities of the catalogue of reusable data visualisation tools 

Table 13  Glossary of commonly used terms 

Table 14  List of identified tools for the inventory 

List of tables

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21

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27

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31

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