Open Sources of Data
In this article, I am going to discuss Open Sources of Data. Please read our previous article, where we discussed the Lifecycle of a Data Science Project.
Open Sources of Data
In layman’s terms, Open Data refers to data that is available for anyone to access, modify, reuse, and share. Open Data is founded on various “open movements” such as open-source, open hardware, open government, open science, and so on.
Governments, independent organizations, and agencies have stepped forward to open the floodgates of data, resulting in an increasing amount of open data available for free and easy access. Because the world has become increasingly data-driven, open data is critical. However, if there are restrictions on data access and use, the concept of data-driven business and governance will not be realized.
As a result, open data has carved out its own niche. It can provide a more complete understanding of global problems and universal issues. It has the potential to provide a significant boost to businesses. It has the potential to be a powerful impetus for machine learning. It can aid in the fight against global issues such as disease, crime, and famine. Open data has the potential to empower citizens and thus strengthen democracy. It has the potential to streamline the processes and systems that society and governments have established.
1. World Bank Data
World Bank Open Data is an important source of Open Data because it is a repository of the world’s most comprehensive data on what is happening in various countries around the world. It also gives you access to other datasets that are mentioned in the data catalog. The World Bank Open Data collection is massive, with 3000 datasets and 14000 indicators covering microdata, time-series statistics, and geospatial data.
It’s also simple to find and access the data you’re looking for. All you have to do is enter the names of the indicators, countries, or topics, and it will open the treasure trove of Open Data for you. It also allows you to download data in various formats such as CSV, Excel, and XML.
If you are a journalist or an academic, you will be enthralled by the array of tools available to you. You can gain access to analysis and visualization tools to help you with your research. It can promote a deeper and more comprehensive understanding of global issues.
2. World Health Organisation (WHO) Data
WHO’s Open Data repository is how the organization keeps track of health-related statistics from its 194 member countries.
The repository organizes the data in a systematic manner. It can be accessed based on the needs of the user. For example, whether it is mortality or disease burden, data can be accessed under 100 or more categories such as the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water, and sanitation), non-communicable diseases and risk factors, epidemic-prone diseases, health system, and so on.
You can sort the datasets by themes, category, indicator, and country to find what you’re looking for. The good news is that you can download any data you require in Excel format. You can also use its data portal to monitor and analyze data. There is also an API for the World Health Organization’s data and statistics content.
3. Google Public Data Explorer
Google Public Data Explorer, which was launched in 2010, allows you to explore massive amounts of public-interest datasets. You can visualize and communicate the data for your specific applications. It makes data from various agencies and sources available. You can, for example, access data from the World Bank, the U.S. Bureau of Labor Statistics, the OECD, the IMF, and others.
This data is accessed by various stakeholders for a variety of purposes. You can use this tool to create visualizations of public data whether you are a student or a journalist, a policymaker, or an academic.
Kaggle is fantastic because it encourages the use of various dataset publication formats. The better part is that it strongly advises dataset publishers to share their data in an accessible, non-proprietary format. The platform is compatible with open and accessible data formats. It is critical not only for access but also for whatever you intend to do with the data. As a result, Kaggle Dataset clearly defines the file formats that are recommended for data sharing.
The distinct feature of Kaggle datasets is that they are more than just a data repository. Each dataset represents a community that allows you to discuss data, find public codes and techniques, and conceptualize your own Kernels projects.
Kaggle supports file formats such as CSV, JSON, SQLite, Archive, Big Query, and others. You can find a variety of resources to help you get started on your open data project. The best part is that you can publish and share datasets privately or publicly on Kaggle.
5. UCI Machine Learning Repository
It is a comprehensive repository of databases, domain theories, and data generators that the machine learning community uses for empirical analysis of machine learning algorithms. There are currently 463 datasets available in this repository as a service to the machine learning community.
It is hosted and maintained by the University of California, Irvine’s Center for Machine Learning and Intelligent Systems. It was created by David Aha as a graduate student at UC Irvine. Since then, it has served as a reliable source of machine learning datasets for students, educators, and researchers all over the world.
Each dataset has its own webpage that contains all of the known details, including any relevant publications that investigate it. These datasets are available for download as ASCII files, which are frequently in the useful CSV format.
The details of datasets can be sorted and searched by attributes such as attribute types, number of instances, number of attributes, and year published.
In the next article, I am going to discuss Data Format, Data Quantity, and Data Quality. Here, in this article, I try to explain the Open Sources of Data and I hope you enjoy this Open Sources of Data article.