Top tips for topics and content - presented at the Accelerate your Data training event in Perth 17 May 2018.

1. Curtin University, Perth
Accelerate your data skills training: top
tips for topics and content
17 May 2018
Curtin University, Room 300.219

2. Introductions
Dr. Kathryn Barker - Data Technologist, Local ANDS Contact
Natasha Simons - Program Leader, Skills Policy and Resources
Dr. Frankie Stevens - eResearch Consultant

4. Train the Trainer… Round 1 Recap:
“Powering up your 2018 (data skills) training”
● Adult learning motivations and styles
● Designing end to end programs
● Marketing strategies
● Evaluating training programs
● Refining training
● Creating engaging training
● Theory and practice of good course design

5. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

6. Activity
Ice Breaker

7. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

8. Research Data Management
Data handling
Discipline Specifics
ResearcherRelevance
Content
Problem Specifics

9. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

10. RDM #101
Consider #WhoWhatWhyWhenHow
TIP: Consider a survey of participants beforehand to find out more about their
needs and expectations
Photo by Ian Stauffer on Unsplash

11. RDM#101
Be informed!
Read surveys of researchers’ attitudes,
behaviours, beliefs, needs in RDM
What RDM courses are already out there
and what do they cover?
You might also search for: RDM training
workshop outlines, videos, slide decks etc.
Photo by Annie Spratt on Unsplash

12. RDM#101 - What content to cover?
Possible topics could cover:
● What is research data?
● Why manage your data?
● What are data sharing models?
● Why should data be FAIR?
● What is a Data Management Plan?
● Organising data
● Describing data
● How to manage sensitive data
● How to publish data
● How to cite data
● Why preserve data?

13. What is research data?
Key message: Any definition of research data is likely to depend
on the context in which the question is asked.
http://www.ands.org.au/guides/what-is-research-data
Photo by Ricky Kharawala on Unsplash
Research data means: data in
the form of facts, observations, images,
computer program results,
recordings,measurements or
experiences on which an argument,
theory, test or hypothesis, or another
research output is based. Data may be
numerical, descriptive, visual or tactile.
It may be raw, cleaned or processed,
and may be held in any format or
media.
But this is only one definition of many….

14. Why manage your data?
Research Data Management
covers the planning, collecting,
organising, managing, storage,
security, backing up, preserving, and
sharing your data. It
ensures that research data are managed
according to legal, statutory, ethical
and funding
body requirements.
Key messages: Any research will require some level of data
management. Good data management can increase the
efficiency of your research and enable the exposure of your
research

15. Why manage your data?
Key messages: More and more research funders, publishers, governments and
institutions are either requiring or encouraging the sharing of data to support
research findings. More researchers care about data sharing and are sharing their
own data.
http://www.ands.org.au/working-with-data/skills/23-research-data-
things/all23/thing-16

16. What are data sharing models?

17. Why should data be FAIR?
What is FAIR data?
The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were
drafted in 2015 and have have since received worldwide recognition by various
organisations including FORCE11, NIH and the European Commission. The
Principles are a useful framework for thinking about sharing data in a way that will
enable maximum use and reuse.
Why make your data FAIR?
How to make your data FAIR?
https://www.ands.org.au/working-with-data/fairdata

18. What is a Data Management Plan?
A data management plan is a document that outlines how
data will be handled during and after a research project is
completed
There are a range of DMP creation
tools available for use
Key message: DMPs are a tool to help you plan how you will
manage your data
http://www.ands.org.au/guides/data-management-plans

19. Organising data
What do we mean by that?
● File types
● File naming
● Versions
● Structures
Key message: Careful thought about files at the beginning of a
research project can save a lot of time, money and heartache
later in a project.
http://www.ands.org.au/working-with-data/data-
management/file-wrangling

20. Describing data
Key message: metadata is critical because it a) allows other
researchers to find, evaluate and potentially cite your
research, and b) helps you to better organise your data
http://www.ands.org.au/working-with-data/metadata

21. How to manage sensitive data
Sensitive data are: data that
can be used to identify an
individual, species, object,
or location that introduces
a risk of discrimination, harm,
or unwanted attention.
Key message: Sensitive data can be published! Data can be
de-identified prior to publishing. Publishing your data, or just
a description of your data, means that others can discover it,
reuse it and cite it. http://www.ands.org.au/working-with-
data/sensitive-data
Photo by Andrew Worley on Unsplash

22. How to publish data
Researchers have many options when publishing data and each has different impacts
on reuse, attribution, reach and discoverability of research. Some examples include
repositories, data journals and websites.
Consider:
● Copyright and licensing
● Persistent identifiers for your data (DOI), your paper (DOI) and for yourself
(ORCID)
● Data is more than just datasets: consider publishing associated grey literature,
software, algorithms, workflows, description of samples.
Key messages: Consider your data publishing options. Include a
license so that others know how they can use or reuse your data.
Ensure persistent identifiers are assigned so that your data can
be confidently cited and attributed to you.
http://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing

23. How to cite data
Data citation refers to the practice of providing a reference to
data in the same way as researchers routinely provide a
bibliographic reference to other scholarly resources.
Include examples of data citation formats
Key messages:
● Many journal publishers now encourage or require citation of research data
● There is a global network of discipline and institutional data repositories
where research data collections are described with a preformatted citation
statement provided
● Only cited data can be counted and tracked (in a similar manner to journal
articles) to measure impact
● Some bibliographic management systems now include a template for
research data citations
http://www.ands.org.au/guides/data-citation-awareness

24. Why preserve data?
Digital preservation can be defined as a "series of managed
activities necessary to ensure continued access to digital
materials for as long as necessary"
● Some research data are unique and cannot be replaced.
● Data are needed to verify results.
● It’s good practice (and often policy) to retain data
for many years after a project concludes.
Key message: Data preservation should be a
key part of all research projects
http://www.ands.org.au/working-with-data/data-
management/data-preservation
Photo by Roberta Sorge on Unsplash

25. Align what you are presenting with what your institution
says. Know and reference your:
● Institutional data policy, procedures, guides
● Institutional library resources e.g. LibGuides
● Institutional website, which may have a data management
page(s)
● Institutional repository guidelines and procedures
● Institutional research office policies and procedures
including ethics
● ..and anything else relevant from your institution...
RDM#101 - Tips

26. RDM#101
Cherry pick your content! Reuse, repurpose, adapt, share...
Photo by Alex Block on Unsplash

27. Case Study
Sue Cook

28. Activity
Research Data Management
Knowledge Base
https://goo.gl/VfRprW

29. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

30. Data
Ingest
Researcher C
Researcher A
Researcher B
Data
Access/ingest
Data Handling
Computation

31. Data Handling
Alex Reid
Australian Research and Education
Network (AARNet)

32. Case Study
Mark Gray
Pawsey Supercomputing Centre

33. Break

34. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

35. NCRIS
National Collaborative
Research Infrastructure
Strategy
A national network of world-class research infrastructure
projects that support high-quality research that will drive
greater innovation in the Australian research sector and the
economy more broadly
NCRIS supports approximately 40,000 users each year
2016 Roadmap identified 9 focus areas.
NCRIS: https://www.education.gov.au/national-collaborative-research-infrastructure-strategy-ncris
Roadmap: https://www.education.gov.au/2016-national-research-infrastructure-roadmap

36. NCRIS - Beyond the basics
1. Advanced Physics and Astronomy
Heavy Ion Accelerator Facility
● International accelerator programs & instruments
● Precision measurement
● National nuclear facilities
● Astronomy infrastructure
27 facilities supporting research across 9 focus areas:

37. NCRIS - Beyond the basics
1. Advanced Physics and Astronomy
2. Complex Biology
● Network to drive translation of all omics data
● Plant phenomics
● Networked biobanks
● Software engineering, bioinformatics &
automation

38. NCRIS - Beyond the basics
1. Advanced Physics and Astronomy
2. Complex Biology
3. Digital Data and eResearch Platforms
● High Performance Computing
● Create national research data cloud
● Research networks
● Access & authentication

39. ● Earth monitoring & exploration
● Earth observations
● Agricultural integrated networks
● Marine systems
● Environmental prediction system
NCRIS - Beyond the basics
1. Advanced Physics and Astronomy
2. Complex Biology
3. Digital Data and eResearch Platforms
4. Earth and Environmental Systems

40. 1. Advanced Physics and Astronomy
2. Complex Biology
3. Digital Data and eResearch Platforms
4. Earth and Environmental Systems
5. Biosecurity
NCRIS - Beyond the basics
● National network for:
○ containment & prevention of endemic & exotic human &
animal diseases
○ containment & prevention of endemic & exotic aquaculture
diseases
○ containment & prevention of endemic & exotic plant
diseases
● National, state & territory biosecurity testing facilities

41. NCRIS - Beyond the basics
1. Advanced Physics and Astronomy
2. Complex Biology
3. Digital Data and eResearch Platforms
4. Earth and Environmental Systems
5. Biosecurity
6. Therapeutic Development
● Bioengineering solutions for next-generation products &
devices
● Advanced health research translation
● Integration of existing & emerging large-scale population,
tissue, microbial & genomics data sets
● High-throughput methods for candidate discovery,
manufacturing & testing

42. NCRIS - Beyond the basics
1. Advanced Physics and Astronomy
2. Complex Biology
3. Digital Data and eResearch Platforms
4. Earth and Environmental Systems
5. Biosecurity
6. Therapeutic Development
7. Platforms for Humanities, Arts and Social
Science (HASS)
● Integrated and coordinated HASS platform
● Harmonised platforms for Indigenous research
● Harmonised platforms for social sciences research

43. NCRIS - Beyond the basics
1. Advanced Physics and Astronomy
2. Complex Biology
3. Digital Data & eResearch Platforms
4. Earth and Environmental Systems
5. Biosecurity
6. Therapeutic Development
7. Platforms for HASS
8. Advanced Fabrication & Manufacturing
● Bioengineering and bio fabrication
● Engineering capability for new classes of fabricated devices
● Fabrication of materials and devices on a micro or nanoscale

44. NCRIS - Beyond the basics
1. Advanced Physics and Astronomy
2. Complex Biology
3. Digital Data & eResearch Platforms
4. Earth and Environmental Systems
5. Biosecurity
6. Therapeutic Development
7. Platforms for HASS
8. Advanced Fabrication & Manufacturing
9. Characterisation
● National network of:
○ microscopy and microanalysis
○ biomedical imaging
● Neutron scattering, deuteration, beam instrumentation,
imaging and isotope production
● Synchrotron capability
● Accelerators for imaging

45. NCRIS - Summary
27 facilities supporting research across 9 focus areas:
1. Advanced Physics and Astronomy
2. Complex Biology
3. Digital Data and eResearch Platforms
4. Earth and Environmental Systems
5. Biosecurity
6. Therapeutic Development
7. Platforms for HASS
8. Advanced Fabrication & Manufacturing
9. Characterisation
https://www.education.gov.au/funded-research-infrastructure-projects

46. Beyond the basics - Virtual Labs
Domain-oriented online environments
that draw together research data,
models, analysis tools and workflows to
support collaborative research across
institutional and discipline boundaries.
What domains do they support?
● Astronomy
● Climate
● Ecology
● Economics
● Geosciences
● Humanities
● Life Sciences
● Marine
● Social Sciences
Explore the VLs

47. NCRIS Case Study
Felicity Flack
The Population Health Research
Network

48. RDS Case Study
Tim Langlois
The Global Archive / Marine RDC

49. NeCTAR VL Case Study
Adam Brown
The VGL

50. Domain training resources
http://www.ands.org.au/working-with-data/skills &
http://www.ands.org.au/working-with-data/skills/23-
research-data-things/toolkit

51. Activity
Explore a Virtual Lab and Identify
Potential Training Materials
https://goo.gl/VfRprW

52. Lunch

53. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

54. Self Help Material
Online Training
Workshops
ResearcherReach
Delivery Mechanisms

55. How to Choose?
● What is the relative cost of each type of training?
● Is training best delivered in one unit or spread out over time?
● Does it address a short-term or a long-term training need?
● Do participants have access to needed computer and communications
equipment?
● Are participants sufficiently self-motivated for online training or self-help
modes of training?
● Do target participants’ time schedules and geographic locations enable
classroom-based training?
● Is the training for a discrete group, or general training for the masses
● Is your training subject regularly revised, updated, upgraded?
● What tools/resources are available you?

56. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

57. Face to face
Some benefits…
● Focus
● Practice
● Tailored
● Individual attention
● Instant feedback
● Learning from
others
● Facilitates dialogue,
questions,
conversation,
networking
In other words...the human touch!

58. Face to face
Some drawbacks…
● Cost
● Instructor/participant
time
● Participants who have
been told to be there
(and don’t want to be)
● Marketing/expectations
don’t match
content/delivery
● Scalability

59. Face to face
Example: 23 RD Things - online vs crash courses

60. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

61. Online Approaches
● Self-paced learning / Instructor-led learning
● Synchronous / Asynchronous

62. Why online training?
● Content to be delivered to a large number of learners
● Learners come from geographically dispersed locations / limited mobility
● Learners have limited time to devote to learning
● Learners have basic computer and Internet skills
● Learners are required to develop homogeneous background knowledge on the
topic
● Learners appreciate proceeding at their own pace
● Content must be reused for different learners’ in the future
● There is a need to collect and track data
Edusmartskills.com. (2018). [online] Available at: https://www.edusmartskills.com/webAssets/images/wso_img.jpg [Accessed 14 May 2018].

63. Online training
The Drawbacks…
● Social Interaction
● Technology
● Trainers
● Self Discipline

64. Online Tools
Smart Sparrow (smartsparrow.com)
● Enables rich, interactive and adaptive elearning courseware
● Deploy directly to learners or through your LMS
● Analyze your students’ learning and make improvements
$
helena.lynn@sydney.edu.au

65. Online Tools
Moodle (Moodle.com)
● Allows trainers to create an online space, with tools to create courses and activities
● Optimised for collaborative learning
● Open Source

66. Online Tools
Zoom (zoom.us)

67. Online Training - Further Reading
E-learning methodologies: A guide for designing &
developing e-learning courses
http://www.fao.org/docrep/015/i2516e/i2516e.pdf

68. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

69. Self help materials
Self-guided resources; digital/printed
Considerations:
● Audience; pitch
● Level of detail required
● Presentation design
● Findability and accessibility
PROS CONS
● Self explanatory
● Easy to follow
● Time saving
● Distribute in
different ways
● Linked to further
resources
● Missing
information
● Information
overload
● May not be
search engine
optimised
● Hard to find

70. Self help formats
● Infographic - visual, introductory
● How-to / manual
● Pamphlets, brochures, posters, leaflets,
handout
● FAQs
● Digital/printed (or both)

71. Self help findability and accessibility
Source: https://www.purdue.edu/research/publications-
data/infographics/
Source: https://www.ands.org.au/guides

72. Self service

73. Self help resources
How to create effective:
● Brochures
● Link 1
● Link 2
● Pamphlets
● Infographics
● Link 1
● Link 2
Course:
● https://www.udemy.com/infographics/
Websites:
● Visual.ly: https://visual.ly/
● Piktochart: https://piktochart.com/
● Infogram: https://infogram.com/
● easel.ly: https://www.easel.ly/
Source: https://visual.ly/community/infographic

74. Case Study
Rebecca Lange
Hacky Hour & Software Carpentry

75. Case Study
Alex Reid
Zoom Webinars

76. Case Study
Janice Chan
Curtin Library Collateral

77. Blended Learning
F2F
Online
Self Help

78. Activity
Delivery Mechanisms
Experience
https://goo.gl/VfRprW

79. Train the Trainer… Round 2:
“Accelerate your data skills training”
● Content:
○ Research Data Management
○ Data handling
○ Discipline specific tools
● Delivery Mechanisms
○ Face to Face
○ Online
○ Self help materials
● Begin to build your training program
● Building the community of trainers

80. Some Steps to Build a Training Program...
Analysis Design Development Implementation Evaluation
Needs
Tasks &
Topics
Target
Audience
Learning
Objectives
Delivery &
Evaluation
Strategies
Content
Development
Distribution
Managing
Learner’s
Activities
Behaviour
Learnings
Adapted from: Fao.org. (2018). [online] Available at: http://www.fao.org/docrep/015/i2516e/i2516e.pdf [Accessed 1 May 2018].

81. F2F workshop
Online Training
Self Help Materials
● Half day, Full day?
● Program Timings?
● Exercises/Activities?
● Content?
● Modules?
● Learning Outcomes?
● Technology?
● Learning assessment?
● How to?
● Software? ppt, piktochart?
● Process or Info sharing?
● A4, Brochure, Web?

82. Frankie Stevens
Kathryn Barker
Natasha Simons
Thank you and evaluations please
With the exception of third party images or where otherwise indicated, this work is licensed under the Creative
Commons 4.0 International Attribution Licence.
ANDS, Nectar and RDS are supported by the Australian Government through the National Collaborative Research
Infrastructure Strategy Program (NCRIS).