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About the DataONE Personas

DataONE personas were developed to guide development of the DataONE cyberinfrastructure. “A persona, first introduced by Alan Cooper, defines an archetypical user of a system, an example of the kind of person who would interact with it. The idea is that if you want to design effective software, then it needs to be designed for a specific person.” [1] Personas are similar to use cases and scenarios, but with additional richness. Use cases treat all interactions as equally important; scenarios focus on tasks, rather than users [2, p. 59]. Personas add detail about user interests, emotions, settings and needs that drive system usage. “Personas are incredibly useful when you don’t have easy access to real users because they act as ‘user stand-ins’, helping to guide your decisions about functionality and design.” [1]. Having a shared set of personas help developers maintain a common vision of the user and promote agreement between different stakeholders.

There are several kinds of personas: primary (the main user or users of the system); secondary (those who will be served as long as doing so doesn't affect the primary users); negative (those who will explicitly not be served because to do so would move the project in an undesired direction); and buyer (those who make decisions about the project and whose opinions need to be understood) [3]. For DataONE, there are many primary personas, some secondary and no negative or buyer personas.

Most of the primary personas are research scientists. Research scientist personas were developed to span multiple dimensions that might affect the individual’s use of DataONE:

  • Work setting: Academic (tenure and non-tenure track), government/tribal, private
  • Career stage (early, mature, late)
  • Subject/discipline
  • Single discipline vs. use of multi-disciplinary data
  • Research setting: Field, lab, modeller
  • Data: Human vs. machine-collected
  • Data management skills: novice to expert

DataONE personas were developed drawing from the Data Conservancy scenarios (from Anne Thessen ), usage Scenarios developed by the DataONE Sustainability and Governance Working Group, Data Conservancy profiles from Illinois and Purdue (http://datacurationprofiles.org), the researcher survey done by the DataONE Usability and Assessment Working, interviews and the life experiences of the authors. Sources for each persona are given in the persona description.

The description of a persona for DataONE include [3]:

  • Background
  • Name, age, and education
  • Socioeconomic class and socioeconomic desires
  • Life or career goals, fears, hopes, and attitudes
  • Reasons for using DataONE to share and to reuse data
  • Needs and expectations of DataONE tools
  • Intellectual and physical skills that can be applied
  • Technical support available
  • Personal biases about data sharing and reuse (and data management more generally)
  • DataONE usage scenarios (see the Appendix for a generic list of functionality)

Related work
Purdue has guidelines for developing “Data Curation Profiles”; a copy is on the DataONE document site: see http://bit.ly/DCCprofiles. Cornell library developed a set of persona for library users: see http://hdl.handle.net/1813/8302. The Data Conservancy also developed personas.

Personas and the data lifecycle

For each primary persona, we show the data lifecycle (Figure 1), depicting which of the stages of the lifecycle the individual performs currently (in blue) and which might be performed using tools provided by DataONE (in mauve). See Figure 2 for an example data lifecycle figure for a persona. Stages shown shaded out are not performed by the persona; those shown in smaller or italicized font are performed but at a lesser level (i.e., less than what would be considered best practice). Solid lines from stage to stage represent workflows performed by the persona. Note that the lifecycle is only a cycle from the perspective of the data; from the perspective of a persona, there is a generally a break between preserve and discover, as the individuals preserve data for others to (potentially) use and similarly discover other people’s data to use themselves. Curved 3D lines in the figure represent flows of data from one user to another (as shown in Figure 3).


NEW generic data life cycle

Figure 1. The DataONE data lifecycle (from https://www.dataone.org/best-practices).



Sun Desert Tortoise Researcher

Figure 2. Example representation of workflow around data for a particular persona (Sun). Blue lines and stages represent current practice (i.e., without DataONE); mauve lines and stages represent practice enabled by use of DataONE tools; and red wavy lines represent data flows from the focus individual to other researchers.



NEW generic data life cycle

Figure 3. Red wavy lines represent data flows from the focus individual to others.



References
[1] Ambler, Scott W. (2009). Introduction to Persona. Available from: http://www.agilemodeling.com/artifacts/personas.htm. Accessed 28 April 2015.
[2] Madsen, Sabine and Nielsen, Lene. (2009). Exploring persona-scenarios: Using storytelling to create design ideas. In Dinesh Katre, Rikke Orngreen, Pradeep Yammiyavar, Torkil Clemmensen (Eds). Human Work Interaction Design: Usability in Social, Cultural and Organizational Contexts: Proceedings of the Second IFIP WG 13.6 Conference, Pune, India, 7-8 October.
[3] Rind, Bonnie. (2007). The Power of the Persona. The Pragmatic Marketer Magazine, 5(4), 18-22. Available from: http://www.pragmaticmarketing.com/publications/magazine/5/4/the-power-of.... Accessed 28 April 2015.

Download the full methods and personas here.