There is a desire to be leaders in shaping how data science is used for positive good. Lead the conversation and demonstrate how data science is being used to improve the world.
Increasing the number of women who know about and engagewith NASA data is valuable and requires work.
Any effort should support the work the women are already doing with complementary tasks/activities/treasure chests.
There is a desire to have data and data science more accessible -through development of more tools and and communicating the value of diverse range of skills and knowledge needed to engaged with data.
Communication and sharing of inspirational stories is important and desired for framing what is possible and encouraging trailblazing. Even better if we can be a part of these story creations and sharing.
Community is important. Being a part of a community that is doing cutting edge meaningful work in a collaborative way is critical. Building off of each other, partnering and supporting. Not competing and not replicating work that is already being done.
In person engagements are appreciated and facilitate accelerated/expanded networks, strengthened relationships and increased collaboration.
So what does this mean for how we move forward?
We are focused now on creating specific entry points (opportunities) to:
Encourage new data problem-solvers (with initial focus on women),
Advance the data science “manifesto,”
Build/enhance the data innovation community of practice,
Enable people to leapfrog into new skills, and
Create breakthrough innovations.
Based on the inputs we had in DC, and through subsequent conversations with many of you, we have developed five potential pathways (we are currently calling them ‘tracks’) to advance those particular goals. These pathways include a State of Data manifesto, Online and In-person engagements around data, a chance to shape the Datanaut selection process or help set up a Data Innovation Wiki. We’ve set up a google doc for each track so that you can jump in and engage, adding resources or giving feedback. (There is a sixth document in case you review the tracks and come up with other ideas!) We need your help - please feel free to jump into the conversation on any or all of these tracks. What’s interesting to you and the people in your network? What helps you to advance the goals you have (professionally and personally)?
Track 1: Strategy of Data Science (and Datanauts)
Objectives for the State of Data track:
#1 Encourage new data problem-solvers
#2 Advance the data science “manifesto”
#3 Build/enhance the data innovation community of practice
The State of Data track offers Datanauts the chance to contribute to a community manifesto on the State of Data Science. This effort is a collaborative conversation to define, influence, and shape the current state of Data, particularly data science for public good.
Questions to consider:
What is the initial definition to chew on -- Hilary Mason’s definition
How could a manifesto be used to signal to the public what data science is for public good?
What would make this most credible (i.e. other adopters/signatories)?
Can we put it on a cubesat?? Or have it written on a Dove?
Criteria for Datanauts - what is valuable for a future datanaut
Code of Conduct
Set the tone - responsible for making this successful
Jennifer: Key because this sets the framework for who we are, what we want to achieve. A manifesto is powerful in that we write it and make it a cornerstone
Beth: have to be very sure to make people feel special… while being clearly inclusive
Jennifer: once we get this crafted can we get a sign off on it? Beth: might not be an official sign off… but that’s not required (and sometimes can hold it back) < add to the ROI discussion
“Can we help NASA understand how they can best support us” (as the Datanaut community)
Why do this? Why is any articulation of “state of data” useful?
Leslie - I usually hear about data from a hackathon perspective - it’s brought up once someone wants to make a specific thing or application
Beth (speaking for Hilary) - “the definition of data science is degrading.” as new providers and companies come up, they are doing what they can monetize - but not what the heart is. < so enabling the community to maintain that purity of vision. “the flag on the moon.”
Leslie - I was googling right before this, and everything I saw was moneymaking
Beth - I have a struggle with that, because being paid for good analytics and good data science isn’t bad. If we define it differently, we can point back to it. “How do datanauts benefit by having a pure definition in your back pocket of what data science is?”
“The job of the data scientist is to ask the right questions,” Mason explains. “If I ask a question like ‘how many clicks did this link get?’ which is something we look at all the time, that’s not a data science question. It’s an analytics question. If I ask a question like, ‘based on the previous history of links on this publisher’s site, can I predict how many people from France will read this in the next three hours?,’ that’s more of a data science question.”
Jennifer - Ask everyone “what does Data Science mean to you” and then we can synthesize those things.
Sasha - this is less the definition of data science, but more a vision statement of what data sharing and openness can be for agencies. What’s the so-what?
Leslie - That inspires me to have an ever-changing manifesto - a word cloud that’s “the so what”
Drawing connections on the planet -- to see what the issues are
NASA becoming not just the scientists in the building - but being all the people getting value from the data - becoming something a little bit bigger than they currently are
Beth’s question - What is data science to you? What does it do for us? (Max’s book - Thinking with Data)
Helps to show how things relate
Showing connections and making sense of information
Unintended insights - learning to ask better questions - a learned skill
Multiple people looking at data enables them to connect.
Making existing things smarter. Adds on intelligence - allows you to know things you didn’t know before
Layer of granularity - look at a number of people clicking on your page, but you can’t look at individual people usually. But data science gives you improved specificity
Positive in terms of layers of data (for good)
Strategy is about getting from the current state to the desired future state. Can we flesh out some of these?
Desired future state
The practices widely accepted and competencies assumed in Silicon Valley are equally engaged in government
Extensibility - come as you are, diverse perspectives are valued (aside from degrees)
Community to be representative and inclusive
friendly to women
wide range of technical skills?
How does data science relate to coding? Are we interrogating the data?
Also need to hack data visualization - what’s the next-dimension form of that?