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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 | |