August 12, 2017

Data Retriever with Henry Senyondo

17 minutes

Summary

Analyzing and interpreting data is a large portion of the work involved in scientific research. Getting to that point can be a lot of work on its own because of all of the steps required to download, clean, and organize the data prior to analysis. This week Henry Senyondo talks about the work he is doing with Data Retriever to make data preparation as easy as retriever install.

Preface

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Your host as usual is Tobias Macey and today I’m interviewing Henry Senyondo about Data Retriever, the package manager for public data sets.

Interview

Introductions

How did you get introduced to Python?

Can you explain what data retriever is and the problem that it was built to solve?

Are there limitations as to the types of data that can be managed by data retriever?

What kinds of data sets are currently available and who are the target users?

What is involved in preparing a new dataset to be available for installation?

How much of the logic for installing the data is shared between the R and Python implementations of Data Retriever and how do you ensure that the two packages evolve in parallel?

How is the project designed and what are some of the most difficult technical aspects of building it?

What is in store for the future of data retriever?

Keep In Touch

Github

@henrykironde on Twitter

Picks

Tobias

Otium Bluetooth Receiver

Panasonic Ergofit Headphones

Nitize Adhesive Pocket Clip

Henry

The Three Idiots

Links

Weecology Lab

University of Florida

Data Retriever

Julia

Open Knowledge Foundation

Frictionless Data Format

Data Weaver

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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By Tobias Macey

4.4

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