On why I love the data community

Her + Data International Women's Day 2018

Presented By
Tania Allard, PhD
https://trallard.github.io/Talks/her+data

Well what I just said is not very comprehensive

I wear a lot of hats:

  • OSS & reproducibility advocate
  • RSE UK committee elected member
  • SSI Fellow
  • RForwards core member
  • Diversity chair JuliaCon
  • Mozilla OpenLeaders mentor
  • NASA Datanauts
  • Software carpentry/data carpentry instructor/trainer
  • Women in tech advocate/mentor/teacher (?)
Jack of all trades master of none
Jack of all trades master of none some.
True at least in the (data) science community

Let me tell you a secret...

No one can be amazing at everything



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The truth most of the time you'll only have some pieces of the puzzle

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Being part of a community has made me a better scientist and developer.

A deviation

Back in my PhD I really did not belong to a community... or rather I was not encouraged to belong to a community

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Now some of the most interesting things I work on are community focused / driven

Mozilla Open Leaders: round 4

Coding foundation:
Project aimed to increase the fair representation in Computer science and related areas. Using a proactive approach rather than a reactive one.

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Mozilla Open Leaders: round 5

Mentoring Phinch:
An open-source framework for visualizing biological data

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Data visualization networks

Building a scientific data visualization network at Sheffield and also liaising with the University of Oxford

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[Data] Scientists are also storytellers so it's only fair they use the best tools in their narratives

  • Workshops around data visualization
  • Consult on data viz... and data curation
  • Bring together data viz ninjas and beginners
  • Will be soon expanding to other institutions

NASA Datanauts

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Interlinking of NASA data dictionaries

Efficiently summarize and search for data using concepts familiar to whoever is doing the search. OCIO (Office of the Chief Information Officer) has been collecting 'data dictionaries' (may include taxonomies, ontologies, etc.).

  1. Machine learning to discover possible connections between dictionaries and identify how these are lexically related
  2. Developing an API (or various microservices) that allow the access and aggregation of the dictionaries for data search.
  3. Effective visualization of multiple data dictionaries -> help understand the terms in the dictionaries and the connection to one another

Hackathons using open NASA data

  • Open NASA hackathon
  • NASA datavizathon (?)

Out of Our Minds

Leverhulme trust

Using machine learning to understand how is that humans learn second languages and improve current teaching techniques.

Disclose: NASA datanauts prepared me for this!

Need more reasons to love the community?

  • You have a wider variety of puzzle pieces
  • Your next collaborator/boss might be sitting next to you
  • You are constantly learning and broadening your perspective
  • Loads of interesting opportunities