With the help of visualization, one can convey the message in an efficient way and can increase the research output

1. RESEARCH VISUALIZATION TOOLS
Dr Mayank Trivedi
University Librarian & Senate Member
Smt. Hansa Mehta Library
The Maharaja Sayajirao University of
Baroda
Date : 28th Jan, 2021
Email : librarian-hml@msubaroda.ac.in
1

2. INTRODUCTION
 Society is undergoing profound and rapid
changes resulting from the development of the
information superhighway
 According to Cosby (2001) the revolution in
information and communication technologies
(ICT) has created a platform for the free flow of
information, ideas and knowledge across
the globe
 The metamorphosis of the library professional to
information profession largely reflects the
shifting in the emphasis and activities aimed at
realizing the basic goal of profession that is to
participate and facilitate the creation,
transmission and use of data
2

3. CURRENT SCENARIO
 What is Research/Research Data?
 Research & Research Data Life Cycles
 In the past, more emphasis was given to
publications, this is changing
 Global increase in Publications
 The status quo is for most research data to
(eventually) disappear: except for large well
organized projects, historically most research
data collected has already disappeared.
 Not through malice, just through
mismanagement or more accurately a lack of
management
3

4. RESEARCH LIFE CYCLE
4

5. RESEARCH PROCESS
5

6. THE VALUE OF VISUALIZATION
6

7. VISUALIZATION
• Visualization is a kind of narrative, providing a clear answer to a
question without extraneous Details - Ben Fry, 2008, p. 4
• Visualization is a graphical representation of some data or
concepts - Colin Ware, 2008, p. 20
• The purpose of visualization is insight, not pictures. The main
goals of this insight are discovery, decision
making, and explanation
• The use of computer-supported, interactive, visual
representations of abstract elements to amplify cognition
• The science of analytical reasoning facilitated by interactive
visual interfaces
7

8. VISUALIZATION
 The role of visualization systems is to provide
visual representations of datasets that help
people carry out tasks more effectively.
 Visualization is suitable when there is a need to
augment human capabilities rather than replace
people with computational decision-making
methods.
 A Visualization should
 Save time
 Have a clear purpose*
 Include only the relevant content*
 Encodes data/information appropriately
8

9. VISUALIZATION
How to turn raw data into an appealing and user-friendly information?
 The data visualization tool is an efficient tool that represents any
specific information through visual elements like charts,
graphs, and maps. It is a simple method to see and understand the
trends and patterns through graphical representation.
 These tools play an important role in making any specific
information visually appealing. This way a large number of
visitors to a website can easily understand the information that is
published on a web page.
 Since images are faster than texts in conveying messages, the
data visualization tools plays a major role in simplifying the
information.
 There is a popular phrase in English “A picture is worth a
thousand words” that means sometimes thousands of ideas can be
conveyed by a single image.
 Also, the images conveys the meaning of a contextual
information more effectively than any verbal description.
 Representing any information through visual elements like
charts, graphs, and maps are one of the important aspects of
web design and development.
9

10. WHY VISUALIZATION
 Danger of getting lost in data, which may be:
 Irrelevant to the current task in hand
 Processed in an inappropriate way
 Presented in an inappropriate way
 Good graphics….
 Point relationships, trends or patterns
 Explore data to infer new things
 To make something easy to understand
 To observe a reality from different viewpoints
 To achieve an idea to be memorized
10

11. OLD VS. NEW DATA VISUALIZATION
 Dynamic data = Dynamic Visualizations
 Visual querying. Drill downs. Drop downs.
 Animated visualization.
 If a particular dimension, such as time, has hundreds
or thousands of values (i.e. daily values over
multiple years), manually clicking through every day
is not practical.
 An animated scroll up/down is more practical
11

12. RESEARCH SUPPORT
 Universities often have three pillars: teaching, research,
and service
 So if you work with faculty, addressing research is a great
relationship builder.
 Provides more routes to engage with faculty and keep
the conversation going
 Research is an alternate way to reach faculty who don’t
use library instructional services
 Great for liaisons, scholarly communications librarians…
and anyone interested in building relationships and creating
a broader view of libraries!
 There are many types of assistance that librarians can offer:
 Grant database search skills training
 Researcher profiles
 Dissemination support
 Data management planning
 Citation management training and consultation
 The Track Record -metrics for publishing trajectory 12

13. OPPORTUNITIES FOR LIBRARIES
 Availability :
 Lower barriers to researchers to make their data available
 Integrate data sets into retrieval services
 Findability :
 Support of persistent identifiers
 Engage in developing common meta-description schemas and common citation practices
 Promote use of common standards and tools among researchers, Support crosslinks between
publications and datasets
 Interpretability :
 Provide and help researchers understand meta-descriptions of datasets,
 Establish and maintain a knowledge base about data and their context,
 Curate and preserve datasets, archive software needed for re-analysis of data
 Re-usability :
 Be transparent about conditions under which data sets can be re-used (expert
knowledge needed, software needed)
 Engage in establishing uniform data citation standards
 Citability :
 Support and promote persistent identifiers
 Transparency about Curation of submitted data
 Promote good data management practice
 Curation/Preservation :
 Collaborate with data creators
 Instruct researchers on discipline specific best practices in data creation,
 Preservation formats, documentation of experiment
13

14. NEW ROLE OF LIBRARIAN
 Managing data/licensed
data (Open Data/Big Data)
 Build infrastructure
 Data advisory services
 Training and support
 Advice on intellectual
property rights
 Coordinate research data
support –
 Build Services to
contribute to
Institutional Research
 Support for Collaboration
and Research funding
 Analysis and
enhancement of user
experiences
 Support for social media
 Support for systematic
reviews
 Clinical informationist
 Help for faculty or staff
with authorship issues
 Implementation of
researcher profiling
and collaboration tools
 Data management
 Translational research
14

15. DATA
'Data is the new oil‘
 The digital play sweeping the world as the fourth
industrial revolution and said data is the "new oil”.
 "The foundation of the fourth industrial revolution is
connectivity and data. Data is the new natural
resource”
 Salient feature of this revolution is "convergence of the
physical biological and digital sciences“--Mukesh
Ambani
 “What gets measured, gets managed” –Peter Drucker
 You can have data without information, but you
cannot have information without data -Daniel
Keys Moran
 “Perfection is achieved not when there is nothing more
to add, but when there is nothing left to take away”
- Antoine de Saint-Exupery
15

16. DATA/METADATA
 No single agreed upon definition
 One person‘s data is another person‘s
information
 ……data about data
 …….information about data
 Metadata or data about data‘ describes the content,
quality, condition, and other characteristics of
data.
 Structured information about an object (data)
that facilitates functions associated with the
object.
 Data often implies the
―Raw stuff lacking context
– Scholarly context, written assessment
―Essence of science (Greenberg, et al, 2009)
16

17. METADATA
 instructionalMethod
 FormatOf
 PartOf
 ReferencedBy
 ReplacedBy
 RequiredBy
 issued
 VersionOf
 language
 license
 mediator
 medium
 modified
 Provenance
17
 dateAccepted
 dateCopyrighted
 dateSubmitted
 description
 educationLevel
 extent
 format
 hasFormat
 hasPart
 hasVersion
 identifier
 abstract
 accessRights
 accrualMethod
 accrualPeriodicity
 accrualPolicy
 alternative
 audience
 available
 Bibliographic
Citation
 conformsTo
 contributor
 coverage
 created
 creator
 date
 references
 relation
 replaces
 requires
 rights
 rightsHolder
 source
 spatial
 subject
 tableOfConte
nts
 temporal
 title
 type
 valid

18. RDM
 “Research data management concerns the organisation of data,
from its entry to the research cycle through to the
dissemination and archiving of valuable results
 Reliable verification of results, and permits new and innovative
research built on existing information
 Good Data Management helps you work more efficiently and
effectively
 Save time and reduce frustration
 Highlight patterns or connections that might otherwise be missed
 Enable data re-use and sharing
 Allow you to meet funders’ and institutional requirements
 Research data must be managed to the highest standards
throughout their life-cycle in order to support excellence in
research practice.
 Data types, formats, standards and capture methods
 Ethics and Intellectual Property
 Access, Data Sharing and Re-use
 Short-term storage/Archival and data management
 Deposit and long-term preservation 18

19. WHY DATA SHARING
 Encourages scientific enquiry
 Collaborations between data users and data creators reduces the cost
of duplicating data collection
 Provides important resources for education and training
 Encourages the improvement and validation of research methods
 Promotes the research that created the data and its outcomes
 Provide a direct credit to the researcher as a research output
 Within this new technological context, more widespread and efficient
access to research data will have substantial benefits for public
scientific research.
 Open access to, and sharing of, data reinforces open scientific inquiry,
encourages diversity of analysis and opinion, promotes new research,
makes possible the testing of new or alternative hypotheses and methods
of analysis, permits the creation of new data sets when data from
multiple sources are combined.
 Sharing and open access to publicly funded research data not only helps to
maximize the research potential of new digital technologies and
networks, but provides greater returns from the public investment in
research.
 Re-use of Data
19

20. VISUALIZATION
 Nowadays large number of data visualization
tools offering different possibilities.
 These tools can be classified based on three
factors
 data type,
 visualization technique type,
 and by the interoperability
20

21. DATA TYPE
 Univariate data One dimensional arrays, time
series, etc.
 Two-dimensional data Point two-
dimensional graphs, etc.
 Multidimensional data Financial indicators,
results of experiments, etc.
 Texts and hypertexts Newspaper articles,
web documents, etc.
 Hierarchical and links The structure
subordination in the organization, e-mails,
documents and hyperlinks, etc.
21

22. VISUALIZATION TECHNIQUES
 both elementary (line graphs,
charts, bar charts) and complex
(based on the mathematical
apparatus)
22

23. INTEROPERABILITY
 The application used for the visualization should
present visual forms that capture the essence
of data itself.
 However, it is not always enough for a complete
analysis.
 Data representation should be constructed in
order to allow a user to have different
visual points of view.
23

24. INTEGRATION WITH AUGMENTED AND
VIRTUAL REALITY(AR AND VR)
 The vision perception capabilities of the
human brain are limited.
 Furthermore, handling a visualization process
on currently used screens requires high
costs in both time and health.
 The use of AR and VR in the visualization area
might solve many issues from narrow visual
angle, navigation, scaling, etc.
24

25. TOOLS
For mostly or all numeric (e.g., gross domestic product over time, species counts, coded
survey data, etc.)
 Excel : Excel remains a frequently used platform for exploratory (and explanatory) data
visualization, especially for those in business, marketing, economics, and finance.
 Tableau : Tableau works with numeric and categorical data to produce advanced
graphics. Browse the Tableau public gallery to see examples of visuals and dashboards.
 RAW Graphs : RAW Graphs is an online platform to make data visualizations. The
interface allows users to select graph type (i.e., scatterplot, bar chart, dendrogram, etc.)
based on type of input data (i.e., numeric, categorical).
 Datawrapper : Datawrapper is a free online platform to create PNG charts and maps
with no coding required. The available customization makes professional-quality
visualizations.
 Plotly : Plotly is an entirely web-based interface for making graphics. It does not require
any coding knowledge, but can interface with both R and Python. The community
version of plotly is free to use.
 Gephi : Gephi is a free software for visualizing networks, comprised of "nodes" and
"edges". The main website hosts official tutorials and also links to popular community-
developed tutorials.
 Platform-specific tools : Some websites/organizations that host data available for
analysis also include visualization tools specifically for that data.
 PowerBI : is a business analytics service by Microsoft. 25

26. TOOLS
 Qlik : produces software such as QlikView and Qlik Sense used for data visualization and
business intelligence.
 AnyChart : provides JavaScript libraries and other tools for data visualization in charts
and dashboards.
 Google Chart : is a JavaScript-based web service made and supported by Google for
creating graphical charts.
 Sisense : provides a front-end for building data visualizations including dashboards and
reports.
 Webix : is a UI toolkit that includes dedicated tools for information visualization.
If your data is: raw text (e.g., newspaper articles, journal articles, any literature)
 Voyant : Voyant is an online point-and-click tool for text analysis. While the default
graphics are impressive, It allows limited customizing of analysis and graphs and may be
most useful for exploratory visualization.
 Corpus-specific tools : Certain corpora have built-in visualization tools, such as Google
Books ngram viewer, HathiTrust Bookworm, or JSTOR for Research.
 LaTex : Typing Tool
If you want general purpose templates:
 Canva : Canva is a an online graphic design platform. Users can start from a variety of
templates, including for infographics.
If you are working with a scripting language :
 R : R is a standard statistical analysis tool, but also a powerful visualization platform
 Python : Like R, Python has libraries to make impressive visualizations.
While matplotlib is the main graphics library, there are additional Python libraries
focused on visualization, including making interactive plots/charts, 3D images, maps, and
more.
26

27. TOOLS FOR DATA VISUALIZATION
 ArcGIS
 AVS
 Express
 Ferret
 Ggobi
 Google
Visualization API
 Matlab
 OpenDX
 Prefuse
 R
0 Mathematica
0 VisTrails
0 VisIt
0 VTK
0 SPSS
0 Grads
0 S-Plus
0 Integrated Data
0 Viewer
0 UV-CDAT
0 D3
27

28. LINE CHARTS
Likely your
best choice to
show a trend,
especially over
time.
28

29. BAR CHART
Bar charts are a
classic for a
reason—they’re
often (usually?)
the best way to
communicate
data that isn’t
right for a line
chart. 29

30. PIE CHART
 There is virtually
nothing a pie chart
can do that a bar
chart can’t do
better.
 It’s reasonable as a
graphic way to
show two or maybe
three percentages
of a whole. 30

31. TABLES
 There’s nothing
wrong with a
simple table of
numbers,
especially when
communicating
with a more
sophisticated
audience.
31

32. PLOTS
Scatter plots or
bubble charts
can effectively
show the trend
of a lot of
different data
points.
32

33. MAPS
Maps can be a
powerful way to
represent data
geographically
33

34. MICROSOFT EXCEL
Part of the
Microsoft Office
Suite.
Installed on
Windows, Mac,
or online.
34

35. INFOGR.AM (HTTPS://INFOGRAM.COM/)
 A reasonable
possibility for creating
good looking charts
based on 30+ chart
templates. Free to
publish publicly
online.
35

36. TABLEAU
 Tableau Public is a free
platform to publicly share and
explore data visualizations
online.
 Anyone can create
visualizations using either
Tableau Desktop Professional
Edition or the free Public
Edition.
 Explore data, create good
looking charts, and share charts
and dashboards online.
 Free for one data source (which
must be made public)..
 Installed on Windows or Mac.
 Tableau has a lot of
functionality to allow you to
create robust shared
dashboards.
36

37. 37
 It allows easy transfer of data to or from
popular file formats such as XLS, CSV,
XML etc. and the user can draw up charts
and histograms of varying complexities as
and when needed.

38. 38

39. MICROSOFT POWER BI
 Microsoft’s Power BI, an
online cloud tool, is
quite comparable to
Tableau.
 Free for data
visualization type use.
 Power BI also provides
robust shared
dashboards.
 There are a number of
Comparable tools:
 Plot.ly,
 Periscope,
 Qlikview, and many
more
39

40. ILLUSTRATION SOFTWARE
Serious creative license requires serious design software.
Illustrator and Photoshop from Adobe’s Creative Suite are
available for a discount at TechSoup.
40

41. STATISTICAL CODING LANGAUAGES
 Coding Languages—
 Python,
 R,
 Stata
 SPSS—are often what
data scientists use.
R Studio
41

42. INFOGRAPHICS
 The science of analytical
reasoning facilitated
by interactive visual
interfaces
 The graphic visual
representations of data,
information or
knowledge intended to
present complex
information quickly
and clearly
42

43. GEPHI
 The Open Graph Viz Platform
 Gephi is the leading visualization
and exploration software for all
kinds of graphs and networks.
Gephi is open-source and free.
 Exploratory Data Analysis:
intuition-oriented analysis by
networks manipulations in real
time.
 Link Analysis: revealing the
underlying structures of
associations between objects.
 Social Network Analysis: easy
creation of social data connectors to
map community organizations and
small-world networks.
 Biological Network analysis:
representing patterns of biological
data.
 Poster creation: scientific work
promotion with hi-quality printable
maps. 43

44. 44
it is a freely available statistics specific search cum
calculation engine which is more than capable of
producing cutomizable, informative representations such
as pie chards and histograms.

45. 45

46. IBM’s Free Online Data Visualization tool
46

47. 47
CartoDB is one such tool which allows easy integration of tabular
data wiht maps.A csv file containing a string of adresses
can be uploaded and CartDB will work its magic by convering them
to latitudes and logitudes andplotting them on
Location Intelligence & Data Visualization tool
carto.com

48. Online Chart builder, No coding required, https://www.chartblocks.com/en/
48

49. A charting tool that produces automatic, shareable charts from any data file
charted.co , Github
49

50. D3.js (also known as D3, short for Data-Driven
Documents) is a JavaScript library for producing dynamic,
interactive data visualizations in web browsers. Bring data to
life with SVG, Canvas and HTML. d3js.org
50

51. Dygraphs is a fast, flexible open source JavaScript charting library.
The chart is interactive: you can mouse over to highlight individual
values. You can click and drag to zoom. Double-clicking will zoom you
back out. https://dygraphs.com/
51

52. R is a free software environment for statistical computing and graphics.
R provides a wide variety of statistical (linear and nonlinear modelling,
classical statistical tests, time-series analysis, classification, clustering,
…) and graphical techniques, and is highly extensible.
https://www.r-project.org/
52

53. https://visual.ly/
53

54. BIG DATA WITH AR & VR
[OUTER VIEW]
Offering a way to have a complete 360-degrees view
with a helmet can solve an angle problem.
54

55. LATEX
 Representing Experimental Results
in EPS Figures
 Producing General EPS Figures for
Concepts, Illustration, etc
 LaTeX is a typesetting system (a
word processor)
 It is most suited to produce
scientific and mathematical
documents of high typographical
quality.
 LaTeX uses TeX as its
formatting engine.
 This short introduction describes
LaTeX2e and should be sufficient
for most applications of LaTeX.
 LaTeX is a macro package which
enables authors to typeset their
work at the highest typographical
quality, using a predefined,
professional layout.
 https://www.latex-project.org/
55

56. ADOBE ILLUSTRATOR
Open pdf document in Adobe Illustrator
1. If you don’t see the Tools window, go to
the Window menu and click Tools to
turn it on.
2. The black arrow is called the Selection
tool. Select it, and your mouse pointer
becomes a black arrow.
3. Click and drag it over the border. The
border appears highlighted. This is
know as a clipping mask.
4. Press delete on your keyboard to get rid
of it.
5. If this deletes the graphic, undo the edit,
and use the Direct Selection tool, which
is represented by a white arrow, to
highlight the clipping mask instead.
6. Use the Selection tool to change fonts,
change colors, add text, etc.
7. Trial is free
8. https://www.adobe.com/in/products/illus
trator/free-trial-download.html
56

57. DRYAD
 Dryad ―enables scientists
to validate published
findings, explore new
analysis methodologies,
repurpose data for research
questions unanticipated by
the original authors, and
perform synthetic studies.
 The Dryad Digital
Repository is a curated
resource that makes
research
data discoverable, freely
reusable, and citable.
Dryad provides a general-
purpose home for a wide
diversity of data types.
 (http://datadryad.org/)
57

58. CONCLUSION AND RECOMMENDATION
 It is evident that information professionals have a key role to play in the
era of open data.
 A new generation of Librarians have to combine the skills of statistics and
IT Skills along with the visualization expertise of a graphic designer
and a story teller.
 Information professionals and librarians need to know their community
research practices in regards to information use, production, and
sharing, and the platforms, tools and services
 Advocating and raising awareness: promotion of the benefits of Open
Science
 RDM should be offered as an elective course in LIS curriculum and
Res Visualization Tools should be a part of it.
 Libraries can advocate within institutions to develop open access policies
and roadmaps.
 This will benefit not only researchers, but also other stakeholders at
institutional level and international level, and even the whole
society, promoting Open Science and engaging with citizens
 There is need for them to cultivate skills as “data scientists” as well.
 Librarians can take lead in visualizing Research
58

59. REFERENCES
 http://link.springer.com/article/10.1186/s40537-
015-0031-2
 https://www.ge.com/digital/blog/when-virtual-
reality-meets-big-data
 http://en.holographica.space/news/platform-big-
data-analysis-ar-vrvirtualitics-received-3-million-
investment-7211
 http://pubs.sciepub.com/dt/1/1/7/
 http://www.ngrain.com/3-reasons-why-
visualization-is-the-biggest-vfor-big-data/
 http://www.forbes.com/sites/bernardmarr/2016/05/0
4/how-vr-willrevolutionize-
 big-data-visualizations/ 59

60. THANKS
 Stay Safe and Take Care…
 PPTs will be available on :
https://www.slideshare.net/DrTrivedi1
https://www.slideshare.net/mayanktrivedi21/presentations
60