DATA SCIENTIST TRAINING FOR LIBRARIANS #DST4L. C. Erdmann Designing Libraries
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1 DATA SCIENTIST TRAINING FOR LIBRARIANS #DST4L C. Erdmann Designing Libraries
2 On the Same Page We started speaking the same language. A side conversation with a Harvard faculty member has developed into a partnership involving interactive data analysis in the classroom. In the faculty s own words, the library moved from being moribund to a must have service. Another Example:
3 Breaking Down Barriers I was afraid of lost connections or opportunities, that our patrons saw us as old fashioned, that we weren t seen as being tech or data savvy. Breaking down the library fortress - Christian Laursen
4 Experience the Research Data Lifecycle Just by having some idea, background of where our researchers are coming from, we can make a connection when the opportunity presents itself and build on it, develop better services. Jeffrey Heer
5 What are they saying? > Guidance on Tools & Workflows Sharing Scholarly Output Linking to Data Programming Skills (Training) Librarians Working w/ Scientists
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7 Core Goals 1. Allow librarians to experience and understand the research data lifecycle. 2. Train librarians to have skills and practical knowledge to build and use data tools (OpenRefine). 3. Forward a culture within libraries that enables datacentric approaches (change our mindset). 4. Grow a community of data savvy librarians. <empower librarians>
8 Winning Recipe: Tools, Concepts & Context
9 DTU
10 Take away message To build better services, librarians can help by engaging in communities, understanding their infrastructural needs, and to experiment with new scholarly objects from these communities.
11 The library perspective Introduce core goals of the course, provide additional context from the librarian perspective and reassure everyone (that this stuff is really hard to learn).
12 Free your metadata Much of the work in data science is spent on data wrangling. Using OpenRefine, you can extract data, clean, reconcile, link to data and explore the data.
13 Academia must learn from open source Importance of reproducibility (explain, repeat, learn). CERN & the EU s commitment to open access. Big data at CERN. Advance from 17th century publishing model. The benefits of GitHub w/ examples from CERN (by reducing friction). Using it in services such as Zenodo but changing how we do curation.
14 GitHub Fundamentals Getting started (w/ GitHub for Desktop and Atom), introduction to Git(Hub), creating repositories, Markdown 101, using issues, demonstrating workflows, making commits, pushing changes, branching and merging. How GitHub can be used in libraries and academic settings.
15 Visualization Demystifying visualization. Why visualization, basic guidelines, visualization in libraries, overview of techniques & methods, and tools to start with.
16 Evolving Scholarly Communication Familiarity with interactive notebooks (Jupyter/IPython), baby Python, text analysis, Naives Bayes and building a classifier (for automated curation). Demonstrate the use of online algorithm services (such as Algorithmia).
17 Feedback, Experiences & Next Steps
18 Blog/Student Perspective Projects & Presentations Hackathons
19 DST4L Student Comment: v=u5zym085bno&t=1m21s...very helpful, preparing for long career, going to see it more and more, will keep using skills set, like doing it, fun problem solving Library Connect Article: Data Scientist Training for Librarians: A course and a community
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22 Find your data scientist training librarian & weave the training into your organizational fabric.
23 Training Astronomers
24 Thank you! Now let s get our hands dirty with data!
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