The Data Journalism Handbook How Journalists Can Use Data to Improve the News
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| Médium: | E-kniha |
| Jazyk: | English |
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Sebastopol
O'Reilly Media, Incorporated
2012
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| Vydanie: | 1 |
| ISBN: | 1449330061, 9781449330064 |
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| Author | Chambers, Lucy Gray, Jonathan Bounegru, Liliana |
|---|---|
| Author_xml | – sequence: 1 fullname: Gray, Jonathan – sequence: 2 fullname: Chambers, Lucy – sequence: 3 fullname: Bounegru, Liliana |
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| ContentType | eBook |
| DEWEY | 070.4/30285 |
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| Discipline | Journalism & Communications |
| EISBN | 1449330045 9781449330040 |
| Edition | 1 |
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| PublicationDate | 2012 |
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| PublicationPlace | Sebastopol |
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| PublicationYear | 2012 |
| Publisher | O'Reilly Media, Incorporated |
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| Subtitle | How Journalists Can Use Data to Improve the News |
| TableOfContents | Following the Money: Data Journalism and Cross-Border Collaboration -- Our Stories Come As Code -- Kaas & -- Mulvad: Semi-Finished Content for Stakeholder Groups -- Processes: Innovative IT Plus Analysis -- Value Created: Personal and Firm Brands and Revenue -- Key Insights of This Example -- Business Models for Data Journalism -- Chapter 3. Case Studies -- The Opportunity Gap -- A Nine Month Investigation into European Structural Funds -- 1. Identify who keeps the data and how it is kept -- 2. Download and prepare the data -- 3. Create a database -- 4. Double-checking and analysis -- The Eurozone Meltdown -- Covering the Public Purse with OpenSpending.org -- Finnish Parliamentary Elections and Campaign Funding -- 1. Find data and developers -- 2. Brainstorm for ideas -- 3. Implement the idea on paper and on the Web -- 4. Publish the data -- Electoral Hack in Realtime (Hacks/Hackers Buenos Aires) -- What Data Did We Use? -- How Was It Developed? -- Pros -- Cons -- Implications -- Data in the News: WikiLeaks -- Mapa76 Hackathon -- The Guardian Datablog's Coverage of the UK Riots -- Phase One: The Riots As They Happened -- Phase Two: Reading the Riots -- Illinois School Report Cards -- Hospital Billing -- Care Home Crisis -- The Tell-All Telephone -- Which Car Model? MOT Failure Rates -- Bus Subsidies in Argentina -- Who Worked on the Project? -- What Tools Did We Use? -- Citizen Data Reporters -- The Big Board for Election Results -- Crowdsourcing the Price of Water -- Chapter 4. Getting Data -- A Five Minute Field Guide -- Streamlining Your Search -- Browse Data Sites and Services -- Ask a Forum -- Ask a Mailing List -- Join Hacks/Hackers -- Ask an Expert -- Learn About Government IT -- Search Again -- Write an FOI Request -- Your Right to Data -- Wobbing Works. Use It! -- Case Study 1: Farm Subsidy -- Case Study 2: Side Effects Intro -- Table of Contents -- Preface -- For the Great Unnamed -- Contributors -- What This Book Is (And What It Isn't) -- Conventions Used in This Book -- Safari® Books Online -- How to Contact Us -- Chapter 1. Introduction -- What Is Data Journalism? -- Why Journalists Should Use Data -- Why Is Data Journalism Important? -- Filtering the Flow of Data -- New Approaches to Storytelling -- Like Photo Journalism with a Laptop -- Data Journalism Is the Future -- Number-Crunching Meets Word-Smithing -- Updating Your Skills Set -- A Remedy for Information Asymmetry -- An Answer to Data-Driven PR -- Providing Independent Interpretations of Official Information -- Dealing with the Data Deluge -- Our Lives Are Data -- A Way to Save Time -- An Essential Part of the Journalists' Toolkit -- Adapting to Changes in Our Information Environment -- A Way to See Things You Might Not Otherwise See -- A Way To Tell Richer Stories -- Some Favorite Examples -- Do No Harm in the Las Vegas Sun -- Government Employee Salary Database -- Full-Text Visualization of the Iraqi War Logs, Associated Press -- Murder Mysteries -- Message Machine -- Chartball -- Data Journalism in Perspective -- Computer-Assisted Reporting and Precision Journalism -- Data Journalism and Computer-Assisted Reporting -- Data Journalism Is About Mass Data Literacy -- Chapter 2. In The Newsroom -- The ABC's Data Journalism Play -- Our Team -- Where Did We Get the Data From? -- What Did We Learn? -- The Big Picture: Some Ideas -- Data Journalism at the BBC -- Make It Personal -- Simple Tools -- Mining The Data -- Understanding An Issue -- Team Overview -- How the News Apps Team at the Chicago Tribune Works -- Behind the Scenes at the Guardian Datablog -- Data Journalism at the Zeit Online -- How to Hire a Hacker -- Harnessing External Expertise Through Hackathons Case Study 3: Smuggling Death -- Getting Data from the Web -- What Is Machine-Readable Data? -- Scraping Websites: What For? -- What You Can and Cannot Scrape -- Tools That Help You Scrape -- How Does a Web Scraper Work? -- The Anatomy of a Web Page -- An Example: Scraping Nuclear Incidents with Python -- The Web as a Data Source -- Web Tools -- Web Pages, Images, and Videos -- Emails -- Trends -- Crowdsourcing Data at the Guardian Datablog -- How the Datablog Used Crowdsourcing to Cover Olympic Ticketing -- Using and Sharing Data: the Black Letter, the Fine Print, and Reality -- Chapter 5. Understanding Data -- Become Data Literate in Three Simple Steps -- 1. How was the data collected? -- Amazing GDP growth -- Crime is always on the rise -- What you can do -- 2. What's in there to learn? -- Risk of Multiple Sclerosis doubles when working at night -- On average, 1 in every 15 Europeans totally illiterate -- What you can do -- 3. How reliable is the information? -- The sample size problem -- Drinking tea lowers the risk of stroke -- What you can do -- Tips for Working with Numbers in the News -- Basic Steps in Working with Data -- Know the Questions You Want to Answer -- Cleaning Messy Data -- Data May Have Undocumented Features -- The £32 Loaf of Bread -- Start With the Data, Finish With a Story -- Data Stories -- Data Journalists Discuss Their Tools of Choice -- Using Data Visualization to Find Insights in Data -- Using Visualization to Discover Insights -- Learn how to visualize data -- Analyze and interpret what you see -- Document your insights and steps -- Transform data -- Which Tools to Use -- An Example: Making Sense of US Election Contribution Data -- What To Learn From This -- Get the Source Code -- Chapter 6. Delivering Data -- Presenting Data to the Public -- To Visualize or Not to Visualize? -- Using Motion Graphics -- Telling the World Publishing the Data -- Opening Up Your Data -- Starting an Open Data Platform -- Making Data Human -- Open Data, Open Source, Open News -- Add A Download Link -- Know Your Scope -- How to Build a News App -- Who Is My Audience and What Are Their Needs? -- How Much Time Should I Spend on This? -- How Can I Take Things to the Next Level? -- Wrapping Up -- News Apps at ProPublica -- Visualization as the Workhorse of Data Journalism -- Tip 1: Use small multiples to quickly orient yourself in a large dataset -- Tip 2: Look at your data upside down and sideways -- Tip 3: Don't assume -- Tip 4: Avoid obsessing over precision -- Tip 5: Create chronologies of cases and events -- Tip 6: Meet with your graphics department early and often -- Tips For Publication -- Using Visualizations to Tell Stories -- Seeing the Familiar in a New Way -- Showing Change Over Time -- Comparing Values -- Showing Connections and Flows -- Designing With Data -- Showing Hierarchy -- Browsing Large Databases -- Envisioning Alternate Outcomes -- When Not To Use Data Visualization -- Different Charts Tell Different Tales -- Data Visualization DIY: Our Top Tools -- Google Fusion Tables -- Tableau Public -- Google Spreadsheet Charts -- Datamarket -- Many Eyes -- Color Brewer -- And Some More -- How We Serve Data at Verdens Gang -- Numbers -- Networks -- Maps -- Text Mining -- Concluding Notes -- Public Data Goes Social -- Engaging People Around Your Data |
| Title | The Data Journalism Handbook |
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