This blog is part of the 2020 Call for Code Global Challenge.
Introduction to open data sets and the importance of metadata
More data is becoming freely available through initiatives such as institutions and research publications requiring that data sets be freely available along with the publications that refer to them. For example, Nature magazine instituted a policy for authors to declare how the data behind their published research can be accessed by interested readers.
To make it easier for tools to find out what’s in a data set, authors, researchers, and suppliers of data sets are being encouraged to add metadata to their data sets. There are various forms for metadata that data sets use. For example, the US Government data.gov site uses the standard DCAT-US Schema v1.1 whereas the Google Dataset Search tool relies mostly on schema.org tagging. However, many data sets have no metadata at all. That’s why you won’t find all open data sets through search, and you need to go to known portals and explore if portals exist in the region, city, or topic of your interest. If you are deeply curious about metadata, you can see the alignment between DCAT and schema.org in the DCAT specification dated February 2020. The data sets themselves come in various forms for download, such as CSV, JSON, GeoJSON, and .zip. Sometimes data sets can be accessed through APIs.
Another way that data sets are becoming available is through government initiatives to make data available. In the US, data.gov has more than 250,000 data sets available for developers to use. A similar initiative in India, data.gov.in, has more than 350,000 resources available.
Companies like IBM sometimes provide access to data, like weather data, or give tips on how to process freely available data. For example, an introduction to NOAA weather data for JFK Airport is used to train the open source Model Asset eXchange Weather Forecaster (you can see the model artifacts on GitHub). You may also be interested in the IBM Data Asset eXchange (DAX) where you can explore useful data sets for enterprise data science. You can also register to access IBM's PAIRS (Physical Analytics Integrated Data Repository and Services) data sets at https://ibmpairs.mybluemix.net/. These data sets are normalized and easy to use.
When developing a prototype or training a model during a hackathon, it’s great to have access to relevant data to make your solution more convincing. There are many public data sets available to get you started. I’ll go over some of the ways to find them and provide access considerations. Note that some of the data sets might require some pre-processing before they can be used, for example, to handle missing data, but for a hackathon, they are often good enough.
Ways to find data sets: Dataset Search
You can use Google Dataset Search. With the Dataset Search tool, you can locate data sets through keywords such as a country or city, or a category such as medical or agriculture. There are additional filters you can apply such as how recently the data set was updated, the download format (for example, JSON or image), usage rights (commercial or non-commercial), and whether the data set is free. Dataset Search is a great tool for data sets where metadata (such as https://schema.org/ tags) have been supplied with the data set. However, there are data sets that do not yet have metadata in the form that Google Dataset Search uses so that’s when you go to locations where there are many data sets. Of course, some data sets can be found using both methods.
Ways to find data sets: Go to locations where there are many data sets
Many governments and institutions such as the United Nations and the World Economic Bank provide data sets. Following are some examples:
data.gov: Look for the data.gov site for the country you are interested in. This is typically where you’ll find data supplied by government sources. For example, for Ireland go to https://data.gov.ie/ and you’ll find more than 10,000 data sets on topics like energy, the environment, and transport. For Australia, go to https://data.gov.au/ and you’ll find more than 80,000 data sets. Usually, these sites have their own search tool as well as a data set catalog. And you can find data sets particularly pertinent to the country, such as coral reefs for Australia.
weather: Look for the weather data in the US at weather.gov. From there, you can find your way to the NOAA (National Oceanic and Atmospheric Administration) data sets https://www.ncdc.noaa.gov/cdo-web/datasets and model data sets https://www.ncdc.noaa.gov/data-access/model-data/model-data, aviation data metars https://www.aviationweather.gov/metar, and many more. Some countries offer tools to explore climate data, like the Netherlands at https://climexp.knmi.nl/start.cgi.
nasa.data.gov: The NASA (National Aeronautics and Space Administration) open data portal offers tens of thousands of data sets that are often used in the annual NASA Space Apps Challenge.
UNdata: You can find data about agriculture, crime, education, energy, industry, labor, national accounts, population, and tourism at UNdata. The statistics available through UNdata are produced by United Nations Statistics and Population Divisions as well as other UN agencies.
Data set aggregator sites and miscellaneous catalogs
Some sites collate data sets into categories sourced from other locations including data sets from the data.gov sites. It’s worth taking a look at these sites, noting that some do charge for specialized access. However, these aggregator sites do give you an idea of what’s available. Examples of sites that aggregate collections of data sets or provide introductions to open data sets include:
- Open Knowledge Foundation: Lists over 550 data portals from around the world http://datacatalogs.org/.
- Open data sets in the cloud: You can find data sets like the IoT-based earthquake early-warning (EEW) from Grillo.
- Kaggle: You can find many data sets often used in contests (https://www.kaggle.com/datasets). For example, 24 years of data on wildfires in the US is available at https://www.kaggle.com/rtatman/188-million-us-wildfires.
- Wikipedia list of machine learning data sets: You can find lists of labeled data sets for machine learning at https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research.
License and privacy considerations
It is easier to use factual data sets such as measurements, tabular data, land mass, reservoirs, and weather, and avoid personal data such as names and pictures of people that might have privacy concerns, which vary from country to country.
Occasionally, you will find data sets that will state that they are for academic use only. The owners are usually fine with the data set being used in a hackathon setting, but it is best to check. An example of such a data set is a multimodal (image and text) Deep Learning For Disaster Response data set (https://gitlab.com/awadailab/crisis_multimodal), which states that it is available for download only for academic purposes. In this case, we have confirmed with the author that she is agreeable that the data set may be used in hackathons, particularly those for social good. You can take a similar approach. And please note if you move on and start selling the software you created in the hackathon or make it part of a product, then you should not use data sets that are marked for academic use.
Many data sets, where there is a license specified, will have a Creative Commons (CC) license. An example of such a data set is the earthquake data EEW. Be aware that the CC by NC variant means that the data set cannot be used for commercial purposes.