Despite the growth of open data sets that are available to the public, it can still
be difficult to discover data sets that are both high quality and have clearly defined
license and usage terms.
To help solve this challenge, IBM created the Data Asset eXchange, or "DAX,”, which
we’ll introduce in this video.
DAX provides a trusted source for finding open data sets that are ready for to use in
enterprise applications.
These data sets and which cover a wide variety of domains, including images, video, text,
and audio.
Because DAX provides a high level of curation for data set quality, as well as licensing
and usage terms, DAX data sets are typically easier to adopt, whether in research or commercial
projects.
Wherever possible, DAX aims to make data sets available under one of the variants of the
CDLACommunity Data License Agreement, in order to foster data sharing and collaboration.
DAX also provides a single place to access unique data sets, in particular from IBM Research
projects.
To make it easier for developers to get started with using the data sets, DAX also provides
tutorials in the form of notebooks that walk through the basics of data cleaning, pre-processing,
and exploratory analysis.
For some data sets, there are also notebooks illustrating how to perform more complex analysis,
such as creating charts, statistical analysis, time-series analysis, training machine learning
models, and integrating deep learning via using the Model Asset eXchange, (a project
closely related to DAX and also available on the IBM Developer website).
In this way, DAX helps developers to create end-to-end analytic and machine learning workflows
and to consume open data and models with confidence under clearly defined license terms.
Let’s say you’ve found a data set that might be of interest to you.
On the data set page you can download the compressed data set archive from cloud storage,
explore the data set using Jupyter Notebooks, review the data set metadata, such as format,
licensing terms and size, and preview some parts of the data set.
Most data sets on DAX are complemented by one or more Jupyter Notebooks that you can
use to perform data cleaning, pre-processing, and exploratory analysis.
These notebooks run "as is"as is in Watson Studio, IBM’s Data Sciencedata science platform.
Jupyter Notebooks and Watson Studio are covered later during in this course.
In this video, you’ve learned about IBM’s open data repository, the Data Asset eXchange.
In the hands-on lab you’ll have a chance to explore the repository.