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Preface | |
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Seeing Your Life in Data | |
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Personal Environmental Impact Report (PEIR) | |
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your.flowingdata (YFD) | |
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Personal Data Collection | |
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Data Storage | |
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Data Processing | |
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Data Visualization | |
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The Point | |
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How to Participate | |
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The Beautiful People: Keeping Users in Mind When Designing Data Collection Methods | |
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Introduction: User Empathy Is the New Black | |
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The Project: Surveying Customers About a New Luxury Product | |
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Specific Challenges to Data Collection | |
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Designing Our Solution | |
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Results and Reflection | |
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Embedded Image Data Processing on Mars | |
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Abstract | |
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Introduction | |
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Some Background | |
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To Pack or Not to Pack | |
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The Three Tasks | |
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Slotting the Images | |
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Passing the Image: Communication Among the Three Tasks | |
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Getting the Picture: Image Download and Processing | |
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Image Compression | |
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Downlink, or, It's All Downhill from Here | |
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Conclusion | |
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Cloud Storage Design in a Pnutshell | |
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Introduction | |
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Updating Data | |
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Complex Queries | |
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Comparison with Other Systems | |
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Conclusion | |
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Information Platforms and the Rise of the Data Scientist | |
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Libraries and Brains | |
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Facebook Becomes Self-Aware | |
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A Business Intelligence System | |
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The Death and Rebirth of a Data Warehouse | |
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Beyond the Data Warehouse | |
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The Cheetah and the Elephant | |
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The Unreasonable Effectiveness of Data | |
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New Tools and Applied Research | |
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MAD Skills and Cosmos | |
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Information Platforms As Dataspaces | |
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The Data Scientist | |
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Conclusion | |
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The Geographic Beauty of a Photographic Archive | |
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Beauty in Data: Geograph | |
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Visualization, Beauty, and Treemaps | |
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A Geographic Perspective on Geograph Term Use | |
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Beauty in Discovery | |
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Reflection and Conclusion | |
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Data Finds Data | |
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Introduction | |
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The Benefits of Just-in-Time Discovery | |
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Corruption at the Roulette Wheel | |
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Enterprise Discoverability | |
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Federated Search Ain't All That | |
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Directories: Priceless | |
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Relevance: What Matters and to Whom? | |
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Components and Special Considerations | |
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Privacy Considerations | |
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Conclusion | |
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Portable Data In Real Time | |
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Introduction | |
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The State of the Art | |
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Social Data Normalization | |
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Conclusion: Mediation via Gnip | |
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Surfacing the Deep Web | |
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What Is the Deep Web? | |
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Alternatives to Offering Deep-Web Access | |
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Conclusion and Future Work | |
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Building Radiohead's House of Cards | |
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How It All Started | |
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The Data Capture Equipment | |
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The Advantages of Two Data Capture Systems | |
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The Data | |
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Capturing the Data, aka "The Shoot" | |
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Processing the Data | |
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Post-Processing the Data | |
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Launching the Video | |
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Conclusion | |
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Visualizing Urban Data | |
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Introduction | |
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Background | |
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Cracking the Nut | |
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Making It Public | |
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Revisiting | |
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Conclusion | |
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The design of sense.us | |
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Visualization and Social Data Analysis | |
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Data | |
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Visualization | |
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Collaboration | |
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Voyagers and Voyeurs | |
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Conclusion | |
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What Data Doesn't do | |
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When Doesn't Data Drive? | |
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Conclusion | |
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Natural Language Corpus Data | |
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Word Segmentation | |
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Secret Codes | |
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Spelling Correction | |
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Other Tasks | |
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Discussion and Conclusion | |
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Life in Data: The Story of DNA | |
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DNA As a Data Store | |
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DNA As a Data Source | |
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Fighting the Data Deluge | |
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The Future of DNA | |
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Beautifying Data in the Real World | |
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The Problem with Real Data | |
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Providing the Raw Data Back to the Notebook | |
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Validating Crowdsourced Data | |
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Representing the Data Online | |
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Closing the Loop: Visualizations to Suggest New Experiments | |
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New Experiments | |
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Building a Data Web from Open Data and Free Services | |
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Superficial Data Analysis: Exploring Millions of Social Stereotypes | |
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Introduction | |
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Preprocessing the Data | |
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Exploring the Data | |
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Age, Attractiveness, and Gender | |
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Looking at Tags | |
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Which Words Are Gendered? | |
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Clustering | |
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Conclusion | |
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Bay Area Blues: The Effect of the Housing Crisis | |
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Introduction | |
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How Did We Get the Data? | |
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Geocoding | |
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Data Checking | |
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Analysis | |
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The Influence of Inflation | |
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The Rich Get Richer and the Poor Get Poorer | |
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Geographic Differences | |
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Census Information | |
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Exploring San Francisco | |
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Conclusion | |
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Beautiful Political Data | |
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Example 1: Redistricting and Partisan Bias | |
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Example 2: Time Series of Estimates | |
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Example 3: Age and Voting | |
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Example 4: Public Opinion and Senate Voting on Supreme Court Nominees | |
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Example 5: Localized Partisanship in Pennsylvania | |
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Conclusion | |
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Connecting Data | |
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What Public Data Is There, Really? | |
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The Possibilities of Connected Data | |
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Within Companies | |
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Impediments to Connecting Data | |
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Possible Solutions | |
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Conclusion | |
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Contributors | |
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Index | |