Open Source Data

With the existence of the Internet and Big Data comes the emergence of so-called open source data. What is open source data? Can open source data be useful for your business?

7 Minutes
Table of Contents
  1. What is open source data?
  2. Why should we use open source data?
  3. What should you remember before using open source data?
  4. What are some free open data sources over the Internet?

With the existence of the Internet and Big Data comes the emergence of so-called open source data. What is open source data? Can open source data be useful for your business? Find out more by reading this short overview.

What is open source data?

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Open source data can be defined simply as data that anyone can access, use, and share. What does this mean?

Data is not free to host, so it is often government agencies and nonprofit organizations that take the initiative to host open source data. Open source data can also include licenses such as Creative Commons that do not restrict how you can use the data but specify how to properly attribute the source of the data. 

Why should we use open source data?

Besides the fact that open source data is free to access, use, and share, here are some of the other benefits:

What should you remember before using open source data?

Open Data Indicator Europe lists four things one should consider when using open datasets.

First impressions matter

The first impression you get from the page containing the dataset matters! It reflects the amount of effort put into preparing the dataset. A high-quality open source dataset also has the following qualities:

These describe what you see first before downloading the dataset. Usually, the reputation of the source is enough to assure us of the dataset quality, but websites that host open data can also secure open data certificates to declare that the data they host is of high quality.

Datasets can have problems in its content

Even the datasets from reputable sources may contain issues in its content. Some of them are the following: 

These are not insurmountable problems, but you will need to spend time cleaning up the dataset so it can be properly processed. The process is called data munging.

Datasets need to be fixed before being analyzed

As we have stated in the previous section, we have introduced the concept of data munging. Data munging can spot possible issues in the implementation of the schema of the dataset, invalid values, and missing data. 

For other errors such as incorrect values, they may require cross-checking with other sources such as master lists and standard sources to ensure their correctness. 

Good quality sometimes depends on your needs

Finally, always remember: Perfection is the enemy of the good. Sometimes, you may feel the urge to dig through the Internet for the best dataset for your needs. However, these datasets may prove to be an overkill, usually because they contain extra data that may not be needed but can slow down the subsequent data analysis. These extra data can be safely scrubbed through data mugging. 

What are some free open data sources over the Internet?

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Here are some of the free open data sources that you can access over the Internet.

Global and regional data

National data

Business and financial data

Social trends data

Open data for data science, machine learning, and app development

References

Four things you should know about open data quality

Open Data and Privacy 

What is open data

What is open data?

The Pros and Cons of Open Data

Why open data?

Open Data: What Is It and Why Should You Care?

6 Major Benefits of Open Data – ProWebScraper

These Are The Best Free Open Data Sources Anyone Can Use

50 Best Open Data Sources Ready to be Used Right Now

Open data - Wikipedia

15 free & open-source data resources for your next data science project | by Kajal Yadav | Towards Data Science

There are 14 open source datasets available on data.world.

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