An Open Question: A comparison of proprietary and open-access teaching materials for researchers

Mr Aidan Wilson1, Dr Anastatios Papaioannou1

1Intersect Australia, Sydney, Australia


Intersect Australia has been a significant eResearch training provider for several years. Since the first courses in eResearch tools like HPC and Microsoft Excel, the Intersect repertoire has expanded to over 25 distinct courses, delivered at our 12 member universities, hundreds of times per year to thousands of researchers.

Intersect began utilising open access training materials in 2015: teaching Software Carpentry’s Creative Commons licensed courseware in Python, Matlab, R, Unix, and Git. Shortly thereafter, two Intersect eResearch Analysts were accredited as Software Carpentry instructors. The following year this was expanded with four more accredited instructors, and in 2017, a further six instructors were accredited and Intersect joined the Software Carpentry Foundation as a silver member, a status we recently reaffirmed.

Throughout this period, Intersect has continued to maintain a proprietary catalogue of Intersect-developed courses taught alongside the Software Carpentry materials.

In this presentation, we will explore the differences, if any, in the reception of Intersect developed course material and openly available Software Carpentry material by course attendees. The differences in cost to maintain proprietary courseware or utilise openly available materials is explored. We will also analyse differences between the delivery of the two sets of courses based on other variables, such as the experience level and teaching style of the trainer.

This presentation will be valuable to similar organisations who are grappling with the logistics of running eResearch training courses, and deciding on strategies regarding developing their own material or using material that already exists in the public domain.

As one of Australia’s most recognised eResearch training organisations, Intersect hopes that other, similar organisations may be able to benefit from our experiences, so that the research community can ultimately benefit from high-quality training from a diverse range of providers.


Aidan Wilson is Intersect’s eResearch Analyst for the Australian Catholic University, and coordinator of Intersect’s training platform. Aidan’s research background is in documentary linguistics, concentrating on the syntax and morphology of Australia’s Aboriginal languages. He has also been actively involved in research support, and worked as a data manager for PARADISEC, an archive of Pacific and regional digital enthographical data, including linguistic and ethnomusicological recordings. In his time at Intersect, Aidan has been involved in a number of engineering and data science projects, including secure data movement for health and medical, and imaging datasets, and genome sequencing as-a-service.

Anastasios Papaioannou is Intersect’s Research Data Scientist, and one of the coordinators of Intersect’s training platform. Anastasios holds a BSc in Physics and MSc in Computational Physics, with his research focus mainly being on computational physics applied in medicine and biology. He also holds a Ph.D. in Computational Biophysics/Medical Physics from the University of Sydney. He has over 4 years of experience as an academic tutor and over 6 years in research. His role at Intersect involves working collaboratively with relevant stakeholders to develop and implement activities to ensure Intersect’s success in the Data Science field for research. He is involved in various national and state level health and medical (and other) eResearch data, while possessing a deep technical understanding of data and a combination of expertises such as programming, data and business analysis and analytics.

Roles for eResearch

Nicholas May1, Sheila Mukerjee2, Samara Neilson3

1RMIT University, Melbourne, Australia,

2La Trobe University, Melbourne, Australia,

3Swinburne University, Hawthorn, Australia,


The position descriptions of roles within the eResearch industry are not consistent and are not standardized. As an example, AeRO [1] has collected over a hundred different role titles for position descriptions within the industry. This makes the recruitment of staff and career progression within the industry much harder. However, efforts are underway, as discussed at a recent AeRO Forum [2], to determine the scope of these positions and to describe the skills associated with common roles. The first step in any movement towards standardizing position descriptions, is to set some boundaries and identify some common eResearch roles.

In this ‘Birds of a Feather’ session, participants will collaborate to perform a simple role modelling process, in which they will classify and transform existing position titles into a more manageable collection. An appropriate framework, which will be presented at the start of the session, will provide a basis for participants to classify the roles. This may be based on the overlapping domains that eResearch spans (such as: Research, Information Technology, and Innovation) or the skill categories of SFIA [3]. The role modelling process, shown in Table 1., has been adapted from an existing ‘user role modelling’ process, as described by Cohn [4]. The steps of the process that will be performed in the session include: Discovery, Organization, and Consolidation.

Participants can submit their role titles, in advance of this session, via the following URL:

The resulting set of titles will subsequently be assigned appropriate skills and levels of responsibility using an appropriate framework, such as SFIA, as has already been done for various ICT roles [5].

Step Time (Mins) Goals
Introduction 15 Present the modelling process and classification framework.
Discovery 15 A visual representation of the framework is outlined on a whiteboard or wall.

Starting list of titles is shared amongst the participants.

Everyone writes role titles on sticky notes.

Notes are posted on the framework.

No discussion of the role names is allowed in this step.

Organization 15 Move the notes around the board to represent their relationships.

If roles overlap then overlap the notes, the degree of overlap represents the degree to which the roles overlap.

Consolidation 15 If notes overlap entirely,

·         remove a note, or

·         replace both with a consolidated name.

If notes overlap partially,

·         remove a note if the difference is not significant, or

·         replace one with a title that corresponds to the difference.

Remove any notes for roles that are not significant.

Rearrange the notes to show the important relationships and hierarchies between roles.

    Inputs: List of Role Titles, Classification Framework.

Outputs: Transformed and condensed set of Role Titles.

Table 1. Session Format.


  1. Australian eResearch Organisations (AeRO),, accessed 6 June 2018.
  2. C3DIS, AeRO Forum – eResearch Workshop,, accessed 6 June 2018.
  3. SFIA Foundation, The Skills Framework for the Information Age – SFIA, Available at:, accessed 6 June 2018.
  4. Cohn, M., User Stories Applied, Addison-Wesley, 2004, ISBN: 0-321-20568-5.
  5. ACS, Common ICT Job Profiles and Indicators of Skills Mobility, ICT Skills White Paper, 30 December, 2013,
    Available at:, accessed 6 June 2018.


Nicholas May is a software developer in the Research Capability unit at RMIT University. He has over twenty-nine years of varied experience within the software engineering profession, across industries and domains, and holds the Certified Professional status with the Australian Computer Society. His current role includes the responsibility for promoting research data management across the research lifecycle at RMIT University.

Samara is a computer scientist and technologist working in the Research Analytics Services team at Swinburne University. In addition to being a representative of FAVeR, she is also on the Melbourne Committee for Random Hacks of Kindness (RHoK), Australia’s longest running hackathon for social good, and a member of Girl Geek Academy, supporting women in STEMM.

Caroline Gauld is the Deputy Director, Research Information Management (RIM) at Defence Science and Technology (DST) Group. The Research Information Management team supports research data management, knowledge management and records management across DST Group and works in collaboration with other technology specialists to support DST Group researchers to manage and preserve their research outputs and data.

What researchers really want: and what it means for researcher-centric services

Steven Chang1, Eva Fisch2, Michele Hosking3

1La Trobe University, Melbourne, Australia,

2La Trobe University, Melbourne, Australia,

3La Trobe University, Melbourne, Australia



In a scholarly environment undergoing rapid cultural changes to researcher norms and expectations, a crucial factor in the development and adoption of eResearch infrastructure is an appreciation of researchers’ attitudes, habits, and needs [1]. Moreover, researchers may face varying challenges according to discipline and their career stage [2]. The increasing emphasis on designing and reconfiguring researcher-centric services requires tailoring them to the research community’s preferences. This approach leads to wider adoption of research infrastructure, as there is a closer alignment between everyday research practice, disciplinary norms, and the research toolset.


In a 2011 literature study on researcher needs, Feijen’s environmental scan identified that non-technical, soft, or social factors influencing research data management such as control and incentives were most significant for researchers. The report identified that successful researcher support services must be of immediate benefit, local, available at the point of need, easy to use, and optional rather than forced. A “cafeteria” model is favoured, where researchers can pick and choose services most relevant to them.

A study on data management practices in Australian universities found that researchers recognise the need for formal research data management plans, but do not usually have one that is more than rudimentary. Furthermore, the evidence suggested researchers are often willing to share their data, but only when there is an easy means of doing so without bureaucratic or time-consuming constraints [3].


In order to develop and transform support structures in a researcher-centric way, up-to-date data is needed to understand the user. Accordingly, this paper investigates behaviours towards eResearch at La Trobe University based on 2017 interviews with over 130 researchers at various career stages. This data was used to develop the business needs and requirements for the University’s Enterprise Research Data Management System (ERDMS) project.  A high-level summary of the business needs and requirements from the interviews was presented at eResearch Conference 2017 by Williams, Fisch, and Huggard [4].

La Trobe’s ERDMS project is now completed and the research data management systems implemented by it (figshare, LabArchives, etc.) have now transitioned into “business as usual”. In order to effectively design services and training to maximise the value of these systems, the Library Research Data support team is revisiting the original stakeholder interviews to mine them for evidence that illuminates user preferences, behaviours, knowledge, and disciplinary needs.


This presentation discusses how these factors have significant implications for the ways in which research support staff design training and, crucially, engage with researchers from the inception of the service design process to co-create effective programs. Our qualitative evidence provides insight on what researchers really want from support services. This knowledge will inform a pilot project working with researchers to co-design customised research infrastructure and support. This approach is in line with La Trobe’s orientation towards centring technology around the research community’s needs by delivering nuanced and effective services to facilitate high-quality research outcomes.

In addition, the project personnel are reflecting and acting on current literature about the benefits of co-designing services [5]. The library and information sciences scholarship on this topic tends to focus on co-designing services in the context of public libraries, academic learning and teaching for undergraduate students, developing physical library spaces, and website redesigns [6, 7, 8, 9]. It is less common for these participatory design methods to be associated with research infrastructure development. This pilot project will contribute to broadening the deployment of co-design to the research services world in order to enhance stakeholder engagement and harmonise researcher workflows with enterprise level systems.


  1. Hickson, S., Poulton, K. A., Connor, M., Richardson, J., & Wolski, M. (2016). Modifying researchers’ data management practices: A behavioural framework for library practitioners. IFLA journal, 42(4), 253-265.
  1. Yoon, A., & Kim, Y. (2017). Social scientists’ data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories. Library & Information Science Research, 39(3), 224-233.
  1. Henty, M., Weaver, B., Bradbury, S., & Porter, S. (2008). Investigating data management practices in Australian universities. Accessible at:
  1. Williams, A., Fisch, E., & Huggard, S. (2017). Leveraging projects for institution-wide benefit – expect the best, plan for the worst, and prepare to be surprised. eResearch Australasia Conference 2017. Accessible at:
  1. Steen, M., Manschot, M., & De Koning, N. (2011). Benefits of co-design in service design projects. International Journal of Design, 5(2).
  1. Wood, T. M., & Kompare, C. (2017). Participatory Design Methods for Collaboration and Communication. Code {4} Lib Journal, 35.
  1. Bech-Petersen, S. (2016). Dokk1: co-creation as a new way of working in libraries. AIB STUDI, 56(3), 441-450.
  1. Foster (2014), Participatory Design in Academic Libraries New Reports and Findings
  1. Somerville, M. M., & Brar, N. (2006). Collaborative co-design: the Cal Poly digital teaching library user centric approach. Library Scholarship, 24.



Steven Chang is Research Data Outreach Officer at La Trobe University Library. He is interested in open scholarship, systematic review methodology, research data management, and health librarianship. Steven comes from a medical librarian background, and is the former editor of the publication Health Inform.

Eva Fisch is manager of the Library Research Team, who provide services relating to research information expertise, publication management, open access publishing, copyright, research impact, and research data management.

“It’s all about the researcher, stupid!”

Michelle Krahe1, Julie Toohey2, Malcolm Wolski3, Paul Scuffham4, Sheena Reilly5

1Menzies Health Institute Queensland, Griffith University, Gold Coast. Australia,

2Library and Learning Services, Griffith University, Gold Coast, Australia, 3eResearch Services, Griffith University, Nathan, Australia,

4Menzies Health Institute Queensland, Griffith University, Nathan, Australia

5Health Group, Griffith University, Gold Coast, Australia,



The adage coined in the 1992 presidential campaign “It’s all about the economy, stupid”, was to remind everyone that they should be focused on the plight of the working people and not get side tracked on other issues. The same could be said for research data management (RDM), it’s the researcher who should be the main focus!

Building or acquiring RDM capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that different RDM practices can have direct influences on the integrity and longevity of data, optimising institutional services and support in recognition of RDM needs is especially valuable within the context of the broader open science movement.

The national research agenda, funding requirements, institutional research strategies and the open science movement including the F.A.I.R principles, are stimulating change in academic institutions, researchers and centres to develop sound RDM practices throughout the entire research life cycle.

So do researchers understand RDM, and do they really care? In an attempt to instill sound RDM practices among the research community, Griffith University’s approach is to work with the researchers (bottom up approach) to better understand their current practices and needs. Figure 1 illustrates a typical day for a researcher, filled with competing priorities and tasks and begs to question whether RDM is a high priority.

Figure 1: Researchers to do list .


A collaborative project conducted with the Health Executive, eResearch Services and Library and Learning Services at Griffith University, evaluated factors relating to current RDM and data sharing practices among health and medical researchers within the Menzies Health Institute Queensland.

A cohort of 81 researchers were surveyed about their RDM practices including:  data storage and retention, data sharing practices and RDM tasks aligned to the research lifecycle.


This project highlights characteristics indicative of the broader academic researcher population. Current strengths and needs of the cohort were also identified that will inform priorities for future development of the eResearch and Data Management support services, training and networks.

Project findings indicate a large number of academics are conducting research on their non-secure desktops and external hard drives which many lead to potential risk of data loss.  Why is this the case?  And why don’t researchers use secured desktops, enterprise systems and computational services throughout the entire research lifecycle?  Perhaps, it is simply a lack of knowledge.

We are starting to realise that researchers don’t know what they don’t know, so in terms of developing capabilities and sound RDM practices at Griffith, what should be our next step?


At what stage of the research life cycle do we engage with researchers to potentially change their data gathering, analysing and storage behaviours?  The one size fits all approach does not work and data management plans may not be the answer.  What are the skills and knowledge gaps as well as the motivators and opportunities we can leverage to bring about necessary change?  Which university elements are responsible for delivering that change?

So what is our solution? Let’s say it is a work in progress.

For example the recent establishment of the RDM Steering Group lead by Office for Research, comprises of four working parties: Services, Infrastructure, Policy, and Skills. Working parties membership include stakeholders representatives from across the community and will steer Griffith’s efforts collaboratively providing further support services, strengthening researcher capability and good RDM practices.

This presentation will focus on an evaluation of researchers’ current RDM practices from the survey and strategies used towards building researcher capacity around good RDM practices.  This presentation will be of interest to institutions embarking on University wide collaborative approaches in working towards development and delivery of training programs supporting researchers.


Julie has worked in academic libraries for 23 years and is currently the Health Discipline Librarian at Griffith University, Gold Coast campus.  Julie is passionate about research data management practices and is in the process of publishing her first co-authored journal article. Throughout 2016, Julie co-facilitated the Australian National Data Services 23 Things (research data) Health and Medical Data Community Group webinar series and is a member of the Queensland University Libraries Office of Cooperation (QULOC) Research Support Working Party.

Understanding and governing data ecosystems using a social architecture approach: a CSIRO research infrastructure case study

Paul Box1, Cynthia Love2, Jonathan Yu3

1CSIRO Land and Water, Sydney Australia,

2CSIRO Information Services, Melbourne Australia,

3CSIRO Land and Water, Melbourne, Australia,


Today, efforts to change how we work, requires the navigation of and negotiating change in, complex social, institutional and technical environments. Knowledge workers and especially those in data intensive environments such as science, directly use and are enabled by numerous information systems. Collectively, these information and technology resources (data, information systems, and technologies), together with social and institutional contexts in which they are embedded, (work routines, standards, culture, relationships, governance and norms) comprise information infrastructure (or data ecosystem).

Successful introduction into an existing installed base of systems, practices, and institutions, of new systems or approaches to improve the way we manage and use data, requires an understanding of data ecosystem. This includes and understanding of the system(s) which it replaces or interacts with; how users interact with these existing systems, and why; and the design of institutions (such as incentives, policies, standards and governance) that shape how users will interact with the new system. An inter-disciplinary approach to designing environments that are conducive to change, called ‘social architecture’,  will be presented in this paper.

Current approaches

Tradition systems development approaches take a technical perspective and apply limited social and institutional analysis to system design, typically undertaking rudimentary stakeholder analysis to identify uses cases and requirements, and putting in place ‘boilerplate’ change management, communication and governance mechanisms. This may be sufficient for smaller largely standalone systems. However, in many cases the systems we are trying to change are much more interconnected and complex and therefore require different approaches.

Although ‘users’ are brought increasingly into the systems design processes through user centered design (UCD) approaches, these tend to focus on the end users that interact with the systems and the ‘experience’ of using the system. UCD approaches have raised the profile and provide useful tool for exploring, understanding and designing information systems with cognizance of the social and institutional context within which they operate. However these approaches tend to be rather ad hoc, and limited in scope, often neglecting to engage with and understand users’ attitudes and practices as well as the institutional context that needs to be factored into the design process.

When attempting to implement larger, more complex, interconnected systems, the primary challenge lies in working with a wide range of stakeholders to influence behaviours and enable collective action across organisational boundaries. This requires a much deeper understanding of institutional arrangements, attitudes and behaviours and the often invisible hand of data economics – the costs, value generation and benefit flows inherent in data supply chains and data platforms.


Social architecture[1] is multi-disciplinary approach to the analysis, design and implementation of complex socio-technical systems. It takes a data ecosystem (information infrastructure) perspective viewing (social and technical) systems as being interconnected in networks. It draws on a range of inter-related disciplines organised in three inter-related themes of work:

  • Social – attitudes, behaviours and practices together with the social structures and mechanism through which people formally and informally collaborate, influence each other and affect change;
  • Institutions – the rules of the road (legislation, regulation, policy, standards and licensing) and how they are created (authority structures, roles and responsibilities and decision rights);
  • Economics – the costs, value generation, benefit flows in data ecosystems , together with an understanding of data market mechanisms

Using these three lens, the aim is to explore and understand current system dynamics and design a future state together with a plan for incremental change to achieve it. A critical element of the approach is the ability to measure progress and change in behavior and attitudes necessary to secure sustainable long term outcomes.

CSIRO Data governance program

Within CSIRO, a number of efforts are underway to improve the management and re-use of data at enterprise levels and within individual business units. There is a recognition of the federated nature of the CSIRO operating model and the need for solutions that meet the differing science business needs in various parts of the organization.

The CSRO data governance program aims to develop an integrated interoperable data ecosystem with established accountability for data at all levels, a default assumption of openness, whist ensuring that licensing, ethical and contractual obligations are honoured and supported by data management and data governance tools and infrastructure. The project is being led by CSIRO Information Management & Technology in partnership with CSIRO business units and other corporate functions.

In parallel, to this enterprise wide activity, within the CSIRO Land and Water business unit a Digital Asset Management improvement program (DAMbusters) is underway which aims to support improved data management and reuse practices through a range of technical and institutional interventions.

To maximize the value of CSIRO data assets through a range of interventions at CSIRO enterprise-wide and business unit scale, there is a need to understand prevailing attitudes and practices of a range of stakeholders within the organisation and the complex external (legislative, policy, economic, legal) and internal organisational (policy, contractual and IP) environment within which they work. All of these factors shape the systems and practices that we currently use and constrain and inform our ability to change.

A social architecture approach is being used to inform how both the enterprise wide and DAMbusters projects tackle what is a complex multi-dimensions challenge.

This paper will briefly describe the theoretical underpinnings of social architecture focusing on the value, practices and institutional aspects of data. It will describe how social architecture is being used in practice to inform CSIRO efforts at enterprise and business unit level, to understand the current installed base of practices, technology and institutions as well as to design a comprehensive and instrumented change program to guide and monitor the CSIRO data ecosystem improvement. The paper will conclude by offering some lessons learned and pointers for others engaged in similar data oriented improvement programs.


  1. Box, P. and Lemon, D. (2016). Social architecture – cultivating conditions for data sharing. SciDataCon2016, Denver Collorado USA.


Paul works in the Environmental Informatics Group in CSIRO Land and Water, building and leading a program of research into the social, institutional and economic aspects large scale distributed information platforms for government. The  ‘social architecture’ practice that he is currently developing, brings together social science, economics and institutional analysis to guide and inform the development of information infrastructures (or systems of systems) that underpin effective policy and decision making.

Understanding organisational, sector-specific, disciplinary and individual factors influencing research data sharing

Claire Mason1, Paul J. Box2, Shanae Burns3

1Data61, CSIRO, Brisbane, Australia,

2Land and Water, CSIRO, Sydney, Australia,

3Data61, CSIRO, Brisbane, Australia,



CSIRO’s data governance initiative aims to improve the discoverability, management, accessibility and re-use of research data. One of the first activities carried out under the initiative was an organisation-wide survey whose purpose was to (a) provide a baseline view of data attitudes and practices and (b) reveal how organisational, sector, disciplinary and individual factors were shaping data sharing practices, responses. We report on the findings of the survey and explain how they can be used to inform CSIRO’s institutional and technical responses to promote data sharing and open data outcomes.


The survey was sent to CSIRO staff and affiliates who worked in research units and roles where they were likely to be dealing with research data. Of the 5,704 individuals who received the invitation to participate in the survey, 806 agreed to participate in the survey, representing a 22% response rate for staff and a 1% response rate for affiliates. Apart from the low representation of affiliates in the sample, the survey achieved input from across the range of age-groups, roles, tenure and education levels. Use of data management plans and channels to share research data amongst these respondents was relatively high – 87% of respondents reported sharing or depositing data through one or more channels over the past five years. However, although the response rate was high for a survey of this kind, we did not achieve input from the majority of CSIRO research staff, so these figures may not be representative of data practices organization-wide.


Organisational factors that were assessed in the survey included organizational data culture, peer support for data sharing and organisational support (funding, processes, tools and training) for data sharing. The organisational culture was open, indicating that most respondents believed that data should be made publically available when possible, although respondents also agreed that data was viewed as a source of competitive advantage (which is shared when there is a benefit to the organisation, rather than simply to benefit others). Peer support for data sharing was perceived to be high and the great majority of respondents reported a desire for organisational processes and systems to support data management and sharing, both during and beyond the life of the project.

The survey also revealed that the industry sector or domain area that researchers work in has an impact on their data attitudes and practices. Furthermore, these external influences appear to do more to inhibit than to foster data sharing. Only 39% of survey respondents reported that their funders “encourage” or “mandate” data sharing, whereas most researchers (especially those working with industry rather than government) reported that contractual arrangements, privacy concerns and ownership and licensing arrangements were important inhibitors of their ability to share data.

There was significant variability in the extent to which scientists from different research disciplines experienced support or barriers to data sharing. The conditions for data sharing appeared to be most conducive for environmental scientists (since they were most likely to report that their journal publishers encouraged them to publish their data and that their peers supported data sharing) and least conducive for researchers who worked in the field of studies in human society.

Finally, individual attitudes towards data sharing were generally positive. On average, the career benefits associated with data sharing were seen to outweigh the risks and respondents said that they would be willing to share their data to help another researcher. However, they also believed that they did not always have control over the decision about whether to share their data or not.

To understand how these organisational, sector-specific, disciplinary and individual factors influence data sharing, we asked survey respondents whether they would be likely to share their data externally (beyond the project team and client) over the next twelve months. An ordinary least squares regression model was used to test the relationship between researchers’ perceptions of organizational, sector-specific, disciplinary and individual factors and their views on the likelihood of sharing data externally.

The analysis revealed that social factors had the strongest relationship with external data sharing. The regression model revealed that open data culture, peer support for data sharing, type of science, social influence and willingness to share data all explained significant variance in external data sharing. The overall fit of the model (R2adj = .41, p < .001) was significant and indicates that the significant predictor variables explained 41% of the variance in external data sharing behavior.


These findings should be interpreted with caution because common method variance effects and non-independence in the data may have inflated these relationships. There are also some indications that the strength of these relationships varies, depending on which area of the organization respondents work in, which means that tailored strategies and a federated approach to data governance will be needed.  Nevertheless, the survey provides important insight into data attitudes and practices in CSIRO. First, it reveals that staff understand the value of data re-use but that they are also aware of important factors (e.g., ethics, privacy concerns, contractual arrangements) which constrain data sharing. Staff in some research units and industry sectors (e.g., researchers working for government rather than industry) have more freedom to operate than others when it comes to data sharing.  Finally, the results of our modelling suggest that organisational culture and norms (both within the organisation and within research disciplines) represent important levers for influencing organisational data practices.


Senior social researcher in the CSIRO’s Data61, Claire’s research focuses on understanding the opportunities and challenges associated with our increased reliance on digital technology across a range of contexts – in our homes and businesses, in our jobs, in vocational education and training, in regions and in later life. She also explores how social, organisational and institutional factors influence data practices and thus our opportunities for data driven innovation.

The case for using citizen science in research design

Mr Peter Brenton1, Mr Kheeran Dharmawardena1

1Atlas of Living Australia, CSIRO, Canberra, Australia, 


As a way of “doing science”, citizen science has existed since well before science was first recognized as a discipline. The rise of professional science through the 19th and 20th centuries saw citizen science diminished to a pursuit for amateurs that was frequently discredited and prejudiced by professionals as producing unreliable and untrustworthy data and results.

The term “citizen science” is frequently misunderstood and misused, but in essence it refers to the participation in scientific endeavor by members of the public, specifically people who are not “qualified scientists”. Endeavor in this context refers to the pursuit of answers to questions using scientific methods and participation can be in any or all parts of the scientific process.

In the 21st century, citizen science is most frequently associated with crowd-sourced data collection in biodiversity and environmental projects, but it has also been successfully applied in any many other fields of research including social, cultural, economic, technological, medical, astronomical, and others. It has also been applied in different aspects of the scientific process, other than just data collection, such as data quality assurance (validation, verification, etc.), analysis and even publication.

Designing citizen science components into research projects can be rewarding for both researchers and citizen scientists. Reasons for doing so include:

  • Research can be costly, in particular when it involves extensive data gathering and processing. Incorporating public participation into some of the more time consuming and costly aspects of projects can significantly reduce overall costs;
  • Research teams can’t be everywhere all of the time, but involving the public in projects significantly increases the number of human sensors on the ground, thus providing much greater spatial and temporal coverage than would be possible using conventional research approaches;
  • Some data generation processes are computationally intensive and require human interpretation in ways that machine processing cannot currently perform. Breaking tasks down for crowd-sourced processing has proven very successful in these cases;
  • Involving the public in science projects increases social understanding and engagement, as well as ownership of issues. This is particularly important when changes to social paradigms and behaviours are required, such as in the debates around climate change, plastics in the environment, impacts on the Great Barrier Reef, etc.;
  • Involving the public in scientific processes makes the science real for people, allowing them to connect with projects and their outcomes more fully; and
  • In cases where social and/or political institutions fail to protect or deliberately undermine the interests of citizens, citizen science can produce sound data and analyses to counter vested interests working against truth or the public good. Citizen science–based activism has some well documented cases in the USA, such as The Public Lab [1] and the domestic water supply contamination saga in Flint Michigan [2]).

A cautionary note however, is that citizen scientists are not just free resources to be exploited for the benefit of projects. They should be treated respectfully as stakeholders and contributors who have an interest in what projects are aiming to achieve. Therefore projects incorporating aspects of citizen science should thoughtfully include science communications, science engagement and science literacy into the project design.


Data generated by citizen science projects is unfortunately often discounted by many professional scientists as biased, unreliable, untrustworthy, or of inconsistent quality. These views are also often held by many researchers about data from other researchers too, unless those suppliers are known and trusted as individuals. Are such criticisms valid or appropriate? Maybe, it depends on purpose for which the data was collected and the purpose for which it is being used. Before casting criticism at citizen science data simply because it is sourced through citizen science, it is important and appropriate to assess it on it’s merits, methods and fitness for the purpose for which it is to be used, just as one would for any dataset.

Older tools and field methods relied heavily on the skills and documentary discipline of individual data collectors/recorders to accurately and comprehensively document their observations. Tools such as GPS, mobile and field-based data collection systems incorporating configured database validation and quality assurance measures were not available then as they are now. Such technologies are now ubiquitous in platforms which are readily and freely available to citizen science practitioners, significantly reducing, and often eliminating many data quality related issues at the heart of most criticisms. Therefore many citizen science generated data are at least as good if not better quality than professionally generated data of the past and probably on-par with many of today’s professionally generated datasets.

Considerations around methodology in respect to fitness for use are therefore arguably more significant nowadays than data quality per-se and it is fair to say that currently many citizen science projects do not document methodology well, though this too is improving. In addition, the lack of a (or poorly) documented methodology should not necessarily discount a citizen science dataset from use, as often both quality and method can be readily determined by a superficial assessment of the data itself, eg. Collecting bias along access routes. Where collecting bias is an issue, even those data may still be usable after application of statistical methods to remove/minimize the bias.


The presentation will illustrate points raised above using examples from a selection of successful citizen science projects including: EchidnaCSI [3], Upper Murrumbidgee [4], DigiVol [5], Fold-it [6], EyeWire [7], and GalaxyZoo [8].


  1. The Public Lab,
  2. Stefaan Verhulst Citizen Science and the Flint Water Crisis, Posted on March 2, 2016, in GovLab Digest. Available from:, accessed 9th June 2018.
  3. EchidnaCSI,
  4. Upper Murrumbidgee Waterwatch,
  5. DigiVol,
  6. Fold-it,
  7. EyeWire,
  8. GalaxyZoo,


Mr. Kheeran Dharmawardena, MBA, BComp, is the Program manager at the Atlas of Living Australia. Kheeran has over 2 decades of experience in delivery of many ICT services within the higher education and research sector, including infrastructure delivery, service delivery, data management, IT & enterprise architecture and eResearch. He has a special interest in the socio-technical challenges involved in the delivery of effective services.

Embedding the Intersect training program at La Trobe University: Tackling the Issue of “No-Shows”

Ghulam Murtaza1, Emma Curtis-Bramwell2

1Intersect Autralia Ltd., Sydney, Australia,

2La Trobe University, Bundoora, Australia,


Intersect Australia is a not-for-profit, member based organisation with 12 university members. It has a very strong focus on delivering training to researchers as part of its eResearch services. Since inception in 2008, Intersect Australia has delivered over 700 training courses to more than 7,000 researchers and graduate students. Our training is very highly rated, with 96% of attendees willing to recommend our courses.

Given that this service is funded through university membership, training does not cost anything to researchers directly. Even though the feedback suggests that researchers highly appreciate these training opportunities, no-cost also introduces the challenge that it sits low in the priority list of activities. As a result, La Trobe University (LTU) were seeing a large number of “No-Shows” in these training courses i.e., individuals who register for a course but do not attend or cancel their registration. The proportion of no-shows was around around 20% when training first commenced in at LTU in 2016, however by the end of 2016, this had increased to around 45% no-shows. This introduces huge logistical challenges around offering training in a sustainable manner.

After some investigation, it turned out that other training courses offered through the library and the Graduate Research School were also experiencing similar problems. The following three techniques were explored over the last 18 months, in partnership with the Graduate Research School’s Research Education and Development team, to find a solution.

  1. Calendar Invites; Entering the training sessions directly into the researcher’s calendar.
  2. Confirmation of Attendance Process; Asking researchers to confirm their attendance prior to the training.
  3. Expression of Interest; Asking researchers to apply for the training and make a case for themselves as to why they should be awarded a place in a particular training session

The Expression of Interest (EoI) based course offering was selected as the preferred approach at the start of 2018. The rationale behind this process is to ask researchers a couple of open-ended questions to make their case for a place in the training course. This results in an investment of time in order to secure a place. The results to date have been positive, with the percentage of no-shows reduced to around 12.4% for the 9 courses delivered at the time of writing in 2018.

In this presentation, we will go through the process of moving from an EventBrite system to an Expression of Interest system. We will discuss the stages undertaken and the impact of each of these stages. We will also go through the automations that have been performed to reduce the workload introduced by an EoI-based process. Finally, we will discuss the results and how this can be replicated by others.


Dr Ghulam Murtaza is currently Intersect Digital Research Analyst for La Trobe University. During his time at Intersect, Ghulam has worked with Australian Catholic University and La Trobe University where he has lead multiple eResearch initiatives including the efforts to imbed Intersect

services within local eResearch offerings. Ghulam is a published researcher and has previously held research and academic positions at many different reputable universities including UNSW, MAARCS institute of WSU, NEWT and Microsoft Research. Ghulam holds a Bachelor of Science (Honours) and Masters of Science in Computer Science from LUMS, Pakistan. He further completed his PhD in Computer Science from University of New South Wales (UNSW).

Emma Curtis-Bramwell graduated from the University of Warwick with a BA (Hons) degree in Classics. Emma moved to Australia and worked in event management and executive recruitment in Sydney before relocating to Melbourne in 2005. She has worked in various roles at La Trobe University for the past 13 years whilst studying for a diploma in University administration. In her position as Project/Communications Officer, she helped establish the eResearch Office at Latrobe, which later became the Office of Research Infrastructure. In this role, she was responsible for budgets, project management, website content and supporting the development of a Digital Research training program at La Trobe. She also assisted with the establishment of Research Platforms, bringing together research capabilities, expertise and equipment. Since last year, Emmahas worked for the Graduate Research School as Research Education and Development Coordinator. She is responsible for promoting the training program, managing relationships with external consultants, managing the booking database, and reporting on attendance and evaluation following program delivery.

ecocloud: an ecosystem of data, tools and people working towards confidently predicting future environmental outcomes

Sarah Richmond1, Kheeran Dharmawardena2, Jonathan Yu3

1Griffith University, Gold Coast, Australia,

2Atlas of Living Australia, Melbourne, Australia,

3CSIRO, Melbourne, Australia,


Access to good quality ecological and biodiversity data alongside analysis tools is critical to synthesising our understanding of the natural world and making forward projections into novel conditions. Recent technologies have enabled consistent and continuous collection of ecological data at high resolutions across large spatial scales, and there are a number of initiatives and institutions collecting this data. The challenge remains, however, to bring these data together and expose them to methods and tools to analyse the interaction between biodiversity and the environment. These challenges are mostly associated with the accessibility, visibility and interoperability of data hosted in disparate places, and the technical capacity, computation and analysis needs of those interpreting the data. This is where ecocloud comes in.

ecocloud is an online environment that works the way ecologists do. That is, it provides unprecedented access to datasets from hundreds of publishers across Australia in a single interface, and it connects this data with common analysis tools like RStudio & Jupyter Notebooks using Australia’s national cloud computing infrastructure. It also includes an innovative training and skills development program to help drive a skilled workforce of students, researchers, government practitioners and industry professionals working across the domain.

In line with the vision of the Science Clouds initiative[1] that established the ecocloud, it is emerging as more than another digital platform. ecocloud is beginning to provide an important collaboration vehicle across key partners within the ecosciences domain, and also across other domains such as biosciences, humanities and social sciences, and marine sciences. By leveraging the expertise of each project partner we’ve been better able to strategically align with national research priorities and a collective long-term vision of creating an ecosystem of infrastructure that provides capability to enable reliable prediction of future environmental outcomes.

In this presentation, we will showcase the ecocloud platform and the outcomes from the Ecoscience DEVL/RDC project as supported by the Australian Research Data Commons (ARDC). We will also touch on our strategic vision, how we’re planning for a sustainable future for the platform and what success looks like to us. We expect this talk will be of interest not only to people working in the Ecoscience domain, but to anyone aspiring to build and maintain digital solutions for research and decision making in the long-term.

[1] The Australian Science Clouds Project. Available from, accessed 21 June 2018.


Sarah Richmond, BSc(Hons I), is a Project Manager in eResearch Services at Griffith University. Sarah currently coordinates the development and delivery of the Ecoscience DEVL/RDC Project (ecocloud), as well as the Biodiversity and Climate Change Virtual Laboratory (BCCVL). With a research background in ecology, she has a special interest in enhancing environmental research through digital solutions by building integrated, user-friendly and supported cloud platforms for accessing data and analysis workflows. Sarah has both a professional and personal passion for tackling complex technical challenges to better allow researchers and decision-makers to efficiently discover and guide practical solutions to significant environmental problems.

Reflecting on NeSI’s Training Efforts: Past and Future

Fabiana Kubke1, Georgina Rae2, Nick Jones3

1University of Auckland, Auckland, New Zealand,

2New Zealand eScience Infrastructure, Auckland, New Zealand,

3New Zealand eScience Infrastructure, Auckland, New Zealand,


Training is integral to how NeSI supports New Zealand researchers with the work being driven by a clearly defined Training Strategy.

In late 2017, NeSI worked with Fabiana Kubke to review our achievements in training with a view to developing our future training strategy.

Our approach has been to quantify the outputs and estimate the impact of NeSI’s training activities in proportion to overall sector capacity, and to make some inferences on the possible extent of need. We have done this through the following steps:

  • Estimate workforce capacity for Higher Education Institutes and Crown Research Institutes
  1. Map the use of NeSI infrastructure against these workforce estimates
  2. Identify the reach and gaps in the current NeSI training initiatives

Having reviewed our efforts so far, we are now scoping the training that will be required to truly make a difference to the levels of digital literacy in New Zealand.

This talk will present our findings from these pieces of work.


Georgina works as Engagement Manager at the New Zealand eScience Infrastructure (NeSI) looking after NeSI’s various stakeholders through outreach, training and relationship management. She has a background in molecular biology and intellectual property.

Recent Comments

    About the conference

    eResearch Australasia provides opportunities for delegates to engage, connect, and share their ideas and exemplars concerning new information centric research capabilities, and how information and communication technologies help researchers to collaborate, collect, manage, share, process, analyse, store, find, understand and re-use information.

    Conference Managers

    Please contact the team at Conference Design with any questions regarding the conference.

    © 2018 - 2019 Conference Design Pty Ltd