2017-09-27 Meeting notes - JGI data science progress check in

• Everyone agree

• Disparate data sources
• Good data sources are those that are well documented, the survey results bear this out
• People have a lot of interesting questions they'd like to answer

• Bill mentions what will be the best way to achieve success with the least effort? Possibly document a way to find data.
• A group at the JGI that is responsible for data stewardship, because data gets out of date very quickly. Individuals responsible for this would manage this.
• This would be where people would begin by asking questions.

• Most people are interested visualisation tools. How to visualise data, and how to make nice visualisations.
• Perhaps more emphasis on data, so people can consistently make nice visualisations
• Too many different data sources and so people get data from different places.
• There isn't a single data source for all data.
• 71% want to visualise and display data
• Only 51% of people want to visualise data
• Many people are complaining about JAMO that has missing data
• People cannot get their work done reproducibly and they use hacks that worked for someone else. They don't have a way to repeat what they did last time.

Summary

• Most people are more interested in visualisaton
• Too many disparate data sources, and don't know how they are integrated
• Many times data is missing from JAMO
• Many times cannot reproduce how they found or accessed the data they use for their analysis.

• Simona has seen that people access ITS data from secondary sources and data gets stale and the data gets stale and out of date.
• Probably many cases that happen at the JGI
• Possible data access through web services
• There are commercial tools that do data source integration
• Who is the owner of what data, and finding out where the data comes from is a bigger problem. No one knows where to go to get the data they want. E.g. getting GOLD data from JAMO etc.

• In future collect numerical answers for the questions. Everyone could choose "most interested" so there is no ranking. About a third of the people for learning styles choose most interested. Perhaps could choose ranking in future.
• No method of learning preferred, everyone choose all the same methods.
• Have to provide the data in different ways.
• How we pursue the training may depend on how which topic is being given.
• We can wait to see what methods and in-house resources we have for this.

• The decisions of what to start with is going to drive what we choose or what.
• Michael gave the wrong goal to Kecia.
• Methods for teaching people the most useful skills in the style that will fit most people
• Countering this, according to the survey results most styles will fit most people.
• Have to have a target for what are we trying to learn
• Need to structure a syllabus for people, other methods can be auxillary support for this.
• Knowing who people can talk to will be very helpful too.
• Some bioinformatics that give an overview of the skills people need. People wouldn't need to did into big statistics
• Could we generate a curated set of resources and backgroud for people to use.
• Focused target

• Bill suggested problem based learning
• Have to focus problems on what is specific to solving problems related to the JGI
• Kjiersten will find it articulate that this should be a priority if we can't related this to what people are doing at the JGI.
• Some people are already taking classes themselves.
• Workshops may useful, but we need to justify and follow up and that people are using these skills in their daily skills.
• Staff who aren't connected to directly sequence data would still want to learn data science skills.

• Have a presentation in first draft for next meeting

Key points for presentation

• There are a lot of people who are motivated improve their data analysis skills (visualisation)
• People want to know where to get data at the JGI
• People don't feel they have have time for this or that it is a priority. It will be going forward, this will become part of people's jobs.