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User Experience in Science: the new kid on the block
 

User Experience in Science: the new kid on the block

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EcoViz (Ecology visualisation ) workshop where I was asked to introduce User Experience and how it could be applied in science. I used an example of a previous bioinformatics project I had done.

EcoViz (Ecology visualisation ) workshop where I was asked to introduce User Experience and how it could be applied in science. I used an example of a previous bioinformatics project I had done.

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  • ISO 9241-210[1] defines user experience as "a person's perceptions and responses that result from the use or anticipated use of a product, system or service". According to the ISO definition, user experience includes all the users' emotions, beliefs, preferences, perceptions, physical and psychological responses, behaviors and accomplishments that occur before, during and after use.
  • User Experience is a blend of a variety of disciplines to achieve the ultimate user experience.It is based on a large chunk of the behavioural sciences such as Human computer interaction, human factors and psychology. It blends architecture, information architecture and visual design into the mix.It has developed it’s own best practices to achieve the ultimate user experience
  • The IPOD when it was released was not novel in its idea of a music player. There were many music players around. Why was it enormously successful? Because it delivered the whole user experience package. It wasn’t just a music player but it allowed you to manage and download music. It revolutionalised the music industry and changed the fortunes of a company.
  • You get that Kodak feeling….Kodak was the forerunner of photography for many decades even coining the term “Kodak moment”.It developed a digital camera as early as 1975 but product was dropped for fear of it inflicting damage to photographic sales.Later in the 1990’s when computers were emerging as part of everyday life and users were chomping at the bit for an alternative photography form Kodak did not understand its users and their goals. Their management team thought that they knew best but in the end other companies developed the technology and gave users a way to manage photographs digitally.Instead they clung to the belief that photographic film would be the future and that had to be protected at all costs.
  • Ok lets take a step back.First lets define why Science can be difficult. I like to refer to it as a complex environment because it has parallels with other universes.So what is a complex environment?
  • highly inter-connectedhas depth (big picture and high level of granularity important)high volume, big scaleunfamiliar since it is a ‘niche’/’expert’ fieldneeding security/ privacy/ authenticationreal time-critical
  • Specialists think they know what they want/need
  • EBI library picture
  • New technologies have led to an explosion in biological dataWhilst DNA sequencing from genomics covers a large amount of this, other areas of biology have seen large increasing in the capacity to produce data200 Gigabytes – 40 DVD movies; 238 Terabytes – content of the US Library of Congress in 2011; 16 Petabytes - Large Hadron Collider at CERN in a single year of operation!Biology is rapidly becoming a data heavy science, but is still far behind physics
  • The best way to illustrate UCD is by example.At the EBI we had a massive problem with disparate resources around enzymes. We made it so difficult for people to find information on enzymes with a number of resources in different places, disparate and mismatched data and not to mention a different styling for each resource that dealt with enzymes.So a few years ago it was decided to attempt to fix the problem. At around the same time UCD was starting out at the EBI and we decided to follow a UCD approach.That means we placed the user at the forefront of our thinking.This work has been published so you can get the full article if you want more details. As this was a very complex UX problem I thought it would make a good example to illustrate how UX is applied in science.
  • Understand as much about the stakeholders needs, wants and pain points up front as possible.Championing the case of UX. Find out which stakeholders you can rely on to work with UCD and those who will need some convincing.Find a strategy to bring them on board – we used personas during a stakeholder meeting.Incentivising Example of publishing.Keep them keen – we created biweekly meetings which were interactive and gave them a say in the
  • Not called user-centreddesign for nothing, User-driven not technology drivenI used to do cartoon users – later found out this is a documented approach!Just because javascript can support configurable widgets does it mean you should have them?
  • Ask your buddy (domain expert) what sort of questions they would like to know about the userFind out in advance a little about the user so that you can speak the same lingo….Don’t be afraid to say when you don’t understand and explain upfront that you are very much a novice.The niche vocabularly becomes an issue.Record the interview so that you can come back to it and ask your buddy later.Example: sildenafil as a drug for jet lag which we could use later for doing wireframes with real data.
  • How will the website fit into someone’s day?Key user stories on a single mapSpot the ‘hubs’ which will be key pages to designUse the map to validate at testing stage & to design scenarios and tasks Run this by your buddy… see what gaps are missing and questions they would ask?
  • We quickly realised that our users were specialists and they liked to have everything. So if we asked them then they would just give us everything, hence the complex user stories we modeled.
  • We needed a way to enable them to prioritise and establish what is important to them and inadvertently establish a kind of domain information architecture. We wanted to understand how they see the world of enzymes. To do this we designed a game called Canvas Sort for users to “play”. It was designed in a workshop setting with a 4 participants at a table all tackling the same canvas.
  • Real data illustrates the problem in terms that they understand and can get into it.Scientific debate rather than a managementy/ux debate. You can see these two gentleman reaNot only is this good to get the consensus on the day. Word gets out and its interesting activity and gives more momentum for future activities.
  • This example tally is for data items only can do it for actions/ functionalitiesTallying up and comparing what each group chose and did per canvas is interesting.Some times overlap others don’t. Here are two different canvas done for the EP project. The second one has lots of overlaps and the first one doesn’t.
  • Building the user mental model in the language they understand.
  • We could do speed sketching where involved the stakeholders and developers based on the canvas sort data output.It was a quick way to get ideas up and running and people talking.
  • This is where your domain knowledge really becomes important because without some basic knowledge you won’t be able to draw credible examples.The developers found it easier to create what users wanted Implementation was fastConfident their work meets needs of usersTechnical solutions could be investigated by the developers early on – because user needs were known about in advance
  • We used paper prototypes to test the usability of the interface mainly because they are cheap to make and fix.In complex data environments and with such intricate data it is sometimes difficult to make high fidelity prototypes without investing huge resources. By using paper prototypes we could keep the ideas flowing and have a quick turnaround with ideas.Paper prototyping is a technique in which you use paper wireframes as your “screen” and pencil as your “mouse”. The transitions between wireframes are taken care of by a human operator instead of a computer.Why did work well?We used a credible example e.g. viagra which is also being tested as a drug to reduce the effects of jetlag. Users found it engaging and soon forgot it is paper. Note as specialists expect them to nit pick over the tiniest details of the data but not the colours and buttons and layout.Advantages:Leaves participant free to concentrate on the interactions, not distracted by colours, fonts, etc.5-8 users is usually enoughFinding new issues plateausMake sure you get consentHighlights issues doesn’t give you the solutionNB: Because it was a credible, believable example – users found it engaging and soon forgot it is paper. Note as specialists expect them to nit pick over the tiniest details of the data but not the colours and buttons and layout.
  • Invariably things go wrong. BE FLEXIBLE is the keywords here.Here are some of the issues we often deal with:Users want data that does not exist or is really difficult to source. Bring awareness of this problem to the stakeholders as they are likely to solve this problem if they are aware of it. In our case we combined different data and clever coding to retrieve our results.Things go wrong on release dayHave a strategy of how to deal with this i.e. plan B.In the EP on release day we uncovered a major bug that occurred on a specific version of a browser and OS after a sequence of steps. It happens.What did we do?Delayed the release and the press releaseGot in touch of the stakeholders and let them know.3. Our stakeholders would occassionally feel like they were not being listened to. This was quite rare but you almost need somebody full time to look after them and ensure they are being listened to and that they are communicated to.
  • Diverse user groupsYou may think all scientists are a bit wet – but we mean informaticiansvs bench bodsdiverse needs and skillsNovice user – easy to learn, less efficient to useExpert – hard to learn but efficient for expert userscurves cross = sweet spot
  • Measuring impact of UCD is hardscientific ‘discovery’ is hard to measure/ intangibleThis seems a bit odd… its too specific to us I think?
  • Domain expert and interested in ux?Lack of individuals with mix of skills/ desire to be UX practitioners in our field
  • The funding cycle in academic science makes it sometimes difficult to get UX upfront.Project leaders need to propose projects and then they are awarded funding. When this funding is in place then they can start with UX but really UX should be done upfront at the project proposal phase so that the UX can guide the deliverables and ultimately the direction of the project.
  • With the more data available and the use of multiple “sciences” to solve a problem data is often noisy, unstructured and dynamic.Data is often in a mismatched formats (sometimes just in Excel or free text) and they all need to be linked up.
  • Chilana paper too
  • What experience do we have to share?First ever examples of UX practitioners writing how to do UX for bioinformatics – target readership is scientists working in biology or biomedical research

User Experience in Science: the new kid on the block User Experience in Science: the new kid on the block Presentation Transcript

  • The new kid on the block User Experience in Science Flickr: Lars Ploughmann Paula de Matos
  • My name is Paula de Matos I live in Cambridge, UK I am an Freelance User Experience Analyst I tweet @Paula_deMatos I am South African & Portuguese I specialise in the application of UX in complex scientific domains
  • What is User Experience (UX) Peter Morville: UX Honecomb Flickr: Stefson
  • Where does UX come from? Dan Schaffer
  • A good UX example: The whole package
  • What happens when you don‟t consider UX? Flickr: Adam Frame
  • UX in complex environments
  • Complex environments have data that is/ may be… • highly inter-connected • has depth (big picture and high level of granularity important) • high volume, big scale • unfamiliar since it is a „niche‟/‟expert‟ field (niche vocab.) • needing security/ privacy/ authentication • real time-critical
  • People in complex environments… • May have geographically separated team • People (always complex but added complications may be…) • Not aware of UX (“fluffy stuff”) • Do not know who users are/ not interested in the user • Think they know their user but are making assumptions
  • Bioinformatics research/services is a complex environment
  • EMBL-EBI EuroHub for Bioinformatics in Hinxton • Part of the European Molecular Biology Laboratory • International, non-profit research institute • 540 people work at EMBL-EBI, 48 nations represented • Average age: 37 yrs
  • What is bioinformatics? • At the heart of modern biology research • Science of storing, retrieving and analysing biological information • An interdisciplinary science involving biologists, biochemists, computer scientists and mathematicians
  • 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 TB of data L-O-A-D-S of data 18000 16000 14000 12000 10000 8000 6000 4000 2000 0
  • UX and Bioinformatics: Enzyme Portal We have over 10 databases dealing with enzymes I can‟t see anything? I can‟t find anything? Data is loosely linked and mismatched
  • Enzyme Portal: the workflow
  • Love thy stakeholders • Understand stakeholders and requirements • UCD buy-in strategy • Incentives?
  • Personaes used to mitigate „self-as-user‟ outlook
  • We interviewed users • Preferably in their own lab • Observe and understand context • Ask questions, learn and validate
  • We modeled their user stories Created using yWorks yEd Graph Editor
  • What happens if you ask? What is important to you? What do you want?
  • Establish priorities and Information Architecture
  • Stimulates discussion esp. dot vote to get consensus
  • Canvas Sort: Engaging IA „head scratcher‟ for target users (experts)
  • Canvas Sort Result #1: Relative priorities of data items and actions
  • Result #2: Model of the information architecture for the portal
  • Sketching
  • Wireframes provided a visual specification Created using Balsamiq Mockups (http://www.balsamiq.com/products/mockups)
  • Quick and easy prototyping
  • Be flexible – it‟s complex, things will go wrong! • Things going wrong on release day • Data that does not exist • Stakeholder posturing
  • Challenges with UX in Science
  • Diverse users Sweet spot? „Dry‟ and „wet‟ scientists use the same software Jakob Nielsen, Usability Engineering 1993
  • We don‟t sell stuff… Flickr: Kristian Niemi
  • Finding the people can be difficult
  • Complexities of policy and funding Flickr: 401 (K) 2013
  • 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 TB of data Managing data 18000 16000 14000 12000 10000 8000 6000 4000 2000 0
  • Get in touch Paula de Matos Email: paula.dematos@gmail.com LinkedIn: pauladematos Twitter: @Paula_deMatos
  • Useful references Complex UX • Chilana, P.K. et al (2010) Understanding usability practices in complex domains. CHI 2010 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2337-2346 Personae • Baron-Cohen, S. et al (2003) The systemizing quotient: an investigation of adults with Asperger syndrome or high-functioning autism, and normal sex differences. Philosophical transactions of the Royal Society of London 358: 361-74 • William Hudson (2009) Reduced Empathizing Skills Increase Challenges for User-Centered Design CHI 2009 April 3–9, Boston, MA, USA Gamestorming • Gray D, Brown S, Macanufo J (2010) Game storming: A Playbook for Innovators, Rulebreakers and Changemakers. California: O‟Reilly Media.
  • Recent papers: UX and Bioinformatics (open access)
  • Thanks for listening! EMBL-EBI staff, Genome Campus, Cambridge
  • Questions?