- Quote: "Data journalism or data-driven journalism (DDJ) is journalism based on the filtering and analysis of large data sets for the purpose of creating or elevating a news story."
The use of data analysis and visualization to report on and visualize complex issues and stories.
Data collection and research methods: This includes various techniques for gathering and refining data, including interviews, surveys, and polls.
Data analysis and visualization: Once data has been collected, it needs to be analyzed and presented in a clear, concise, and visually appealing manner. This involves using tools such as spreadsheets, databases, and data visualization software.
Statistics and data interpretation: Journalists must understand basic statistical concepts to accurately interpret data and avoid misleading conclusions. This includes concepts such as mean, median, mode, correlation, and regression.
Data ethics and privacy: Understanding ethical guidelines and legal considerations when working with data is crucial for journalists. This includes issues related to privacy, confidentiality, and data protection.
Writing for audiences: Data journalists must be able to communicate complex data in a way that is accessible and understandable to their audience. This involves creating compelling narratives and using storytelling techniques to engage readers.
Data-driven storytelling: By combining data with traditional journalistic techniques such as narrative, characters, and plot, journalists can create compelling stories that are grounded in data.
Data visualization techniques: There are several techniques for visualizing data, including infographics, maps, and charts. Understanding when to use each technique is essential when presenting data to different audiences.
Data journalism best practices: There are several best practices that journalists should follow when working with data, from verifying sources to fact-checking and providing context for data.
Data journalism tools: There are numerous tools available to journalists for collecting, analyzing, and visualizing data. These include software such as Microsoft Excel, Google Sheets, and Tableau, as well as online resources such as data.gov and open data portals.
Real-world applications of data journalism: Data journalism has the potential to inform and influence society in numerous ways. Understanding how data journalism has been used in real-world situations can provide insights into its potential impact.
Investigative Journalism: This type of journalism involves in-depth research and investigation on a particular topic or issue. It often requires obtaining data through freedom of information requests or other means.
Computer-Assisted Reporting: This type of journalism relies on data analysis tools and technologies to help journalists find patterns, trends, and insights in data sets.
Data Visualization Journalism: This type of journalism uses graphs, charts, maps, and other visuals to communicate complex information and data in a more visually appealing and accessible way to the audience.
Interactive Journalism: This type of journalism enables audiences to engage with data and interact with it in real-time. Interactive journalism may involve quizzes, polls, calculators, maps, and other digital tools.
Data-driven Feature Writing: This type of journalism uses data as the primary source to write stories that highlight the human impact of a particular issue.
Statistical Analysis: This type of journalism involves the application of statistical methods and models to draw insights and conclusions from data.
Data-Driven Audio Journalism: This type of journalism uses audio storytelling techniques to communicate complex data and analysis.
Data Journalism for Social Media: This type of journalism involves analyzing social media data to uncover trends, patterns, and insights that can inform news coverage and investigation.
Data-driven Podcasts: This type of journalism uses data and analysis to produce podcasts on specific topics, enabling audiences to gain a deeper understanding of complex issues.
Data journalism for Digital Storytelling: This type of journalism uses digital storytelling techniques to communicate data-driven storytelling.
- Quote: "Data journalism reflects the increased role of numerical data in the production and distribution of information in the digital era."
- Quote: "It involves a blending of journalism with other fields such as data visualization, computer science, and statistics, 'an overlapping set of competencies drawn from disparate fields'."
- Quote: "Many data-driven stories begin with newly available resources such as open source software, open access publishing and open data, while others are products of public records requests or leaked materials."
- Quote: "This approach to journalism builds on older practices, most notably computer-assisted reporting (CAR) a label used mainly in the US for decades."
- Quote: "Data-driven journalism strives to reach new levels of service for the public, helping the general public or specific groups or individuals to understand patterns and make decisions based on the findings."
- Quote: "The findings from data can be transformed into any form of journalistic writing."
- Quote: "Visualizations can be used to create a clear understanding of a complex situation."
- Quote: "Furthermore, elements of storytelling can be used to illustrate what the findings actually mean, from the perspective of someone who is affected by a development."
- Quote: "This connection between data and story can be viewed as a 'new arc' trying to span the gap between developments that are relevant, but poorly understood, to a story that is verifiable, trustworthy, relevant, and easy to remember."
- Quote: "The process builds on the growing availability of open data that is freely available online and analyzed with open source tools."
- Quote: "Data-driven journalism strives to reach new levels of service for the public, helping the general public or specific groups or individuals to understand patterns and make decisions based on the findings."
- Quote: "Other labels for partially similar approaches are 'precision journalism', based on a book by Philipp Meyer, published in 1972, where he advocated the use of techniques from social sciences in researching stories."
- Quote: "Telling stories based on the data is the primary goal."
- Quote: "This approach to journalism builds on older practices, most notably computer-assisted reporting (CAR) a label used mainly in the US for decades."
- Quote: "Many data-driven stories begin with newly available resources such as open source software."
- Quote: "Data-driven journalism might help to put journalists into a role relevant for society in a new way."
- Quote: "Furthermore, elements of storytelling can be used to illustrate what the findings actually mean, from the perspective of someone who is affected by a development."
- Quote: "Visualizations can be used to create a clear understanding of a complex situation."
- Quote: "Many data-driven stories begin with newly available resources such as open access publishing."