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Ands ttt2 perth_accelerate your data skills training_ top tips for topics and content

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

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Ands ttt2 perth_accelerate your data skills training_ top tips for topics and content

  1. 1. Curtin University, Perth Accelerate your data skills training: top tips for topics and content 17 May 2018 Curtin University, Room 300.219
  2. 2. Introductions Dr. Kathryn Barker - Data Technologist, Local ANDS Contact Natasha Simons - Program Leader, Skills Policy and Resources Dr. Frankie Stevens - eResearch Consultant
  3. 3. 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
  4. 4. 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
  5. 5. Activity Ice Breaker
  6. 6. 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
  7. 7. Research Data Management Data handling Discipline Specifics ResearcherRelevance Content Problem Specifics
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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?
  12. 12. 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….
  13. 13. 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
  14. 14. 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
  15. 15. What are data sharing models?
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. RDM#101 Cherry pick your content! Reuse, repurpose, adapt, share... Photo by Alex Block on Unsplash
  26. 26. Case Study Sue Cook
  27. 27. Activity Research Data Management Knowledge Base https://goo.gl/VfRprW
  28. 28. 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
  29. 29. Data Ingest Researcher C Researcher A Researcher B Data Access/ingest Data Handling Computation
  30. 30. Data Handling Alex Reid Australian Research and Education Network (AARNet)
  31. 31. Case Study Mark Gray Pawsey Supercomputing Centre
  32. 32. Break
  33. 33. 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
  34. 34. 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
  35. 35. 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:
  36. 36. 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
  37. 37. 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
  38. 38. ● 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
  39. 39. 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
  40. 40. 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
  41. 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 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
  42. 42. 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
  43. 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 9. Characterisation ● National network of: ○ microscopy and microanalysis ○ biomedical imaging ● Neutron scattering, deuteration, beam instrumentation, imaging and isotope production ● Synchrotron capability ● Accelerators for imaging
  44. 44. 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
  45. 45. 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
  46. 46. NCRIS Case Study Felicity Flack The Population Health Research Network
  47. 47. RDS Case Study Tim Langlois The Global Archive / Marine RDC
  48. 48. NeCTAR VL Case Study Adam Brown The VGL
  49. 49. 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
  50. 50. Activity Explore a Virtual Lab and Identify Potential Training Materials https://goo.gl/VfRprW
  51. 51. Lunch
  52. 52. 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
  53. 53. Self Help Material Online Training Workshops ResearcherReach Delivery Mechanisms
  54. 54. 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?
  55. 55. 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
  56. 56. 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!
  57. 57. 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
  58. 58. Face to face Example: 23 RD Things - online vs crash courses
  59. 59. 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
  60. 60. Online Approaches ● Self-paced learning / Instructor-led learning ● Synchronous / Asynchronous
  61. 61. 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].
  62. 62. Online training The Drawbacks… ● Social Interaction ● Technology ● Trainers ● Self Discipline
  63. 63. 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
  64. 64. 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
  65. 65. Online Tools Zoom (zoom.us)
  66. 66. Online Training - Further Reading E-learning methodologies: A guide for designing & developing e-learning courses http://www.fao.org/docrep/015/i2516e/i2516e.pdf
  67. 67. 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
  68. 68. 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
  69. 69. Self help formats ● Infographic - visual, introductory ● How-to / manual ● Pamphlets, brochures, posters, leaflets, handout ● FAQs ● Digital/printed (or both)
  70. 70. Self help findability and accessibility Source: https://www.purdue.edu/research/publications- data/infographics/ Source: https://www.ands.org.au/guides
  71. 71. Self service
  72. 72. 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
  73. 73. Case Study Rebecca Lange Hacky Hour & Software Carpentry
  74. 74. Case Study Alex Reid Zoom Webinars
  75. 75. Case Study Janice Chan Curtin Library Collateral
  76. 76. Blended Learning F2F Online Self Help
  77. 77. Activity Delivery Mechanisms Experience https://goo.gl/VfRprW
  78. 78. 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
  79. 79. 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].
  80. 80. 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?
  81. 81. 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).

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