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  </channel><item rdf:about="https://mediablog.prnewswire.com/2025/03/26/verification-tools-for-journalists/">
    <title>20 Helpful Verification Tools for Journalists | Beyond Bylines</title>
    <dc:date>2025-04-20T13:28:30+00:00</dc:date>
    <link>https://mediablog.prnewswire.com/2025/03/26/verification-tools-for-journalists/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[As we near April Fool’s Day and its potential wave of misleading content, we're rounding up a few verification tools that journalists should bookmark.]]></description>
<dc:subject>journalism data</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:a6617a66b912/</dc:identifier>
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<item rdf:about="https://zenodo.org/">
    <title>Zenodo</title>
    <dc:date>2025-04-11T16:50:06+00:00</dc:date>
    <link>https://zenodo.org/</link>
    <dc:creator>amy</dc:creator><dc:subject>opendata database data research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:bf11e99cb9a0/</dc:identifier>
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<item rdf:about="https://www.datarescueproject.org/data-rescue-tracker/">
    <title>The Data Rescue Tracker</title>
    <dc:date>2025-02-16T02:10:26+00:00</dc:date>
    <link>https://www.datarescueproject.org/data-rescue-tracker/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[We are excited to introduce the Data Rescue Tracker, a collaborative tool built to catalog existing public data rescue efforts so that we can coordinate better across initiatives. At this stage, you can use the tool to help reduce duplication of rescue efforts. The Data Rescue Tracker aims to provide]]></description>
<dc:subject>data</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:20a547627612/</dc:identifier>
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<item rdf:about="https://lil.law.harvard.edu/century-scale-storage/">
    <title>Century-Scale Storage</title>
    <dc:date>2024-12-25T02:36:54+00:00</dc:date>
    <link>https://lil.law.harvard.edu/century-scale-storage/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[If you had to store something for 100 years, how would you do it?]]></description>
<dc:subject>tech data storage</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:9a88ef9cd49d/</dc:identifier>
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<item rdf:about="https://www.functionhealth.com/">
    <title>Function Health • 100 Healthy Years</title>
    <dc:date>2024-08-20T15:12:17+00:00</dc:date>
    <link>https://www.functionhealth.com/</link>
    <dc:creator>amy</dc:creator><dc:subject>health medicine data</dc:subject>
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<item rdf:about="https://www.nytimes.com/2024/03/11/technology/carmakers-driver-tracking-insurance.html">
    <title>Automakers Are Sharing Consumers’ Driving Behavior With Insurance Companies - The New York Times</title>
    <dc:date>2024-03-13T03:29:02+00:00</dc:date>
    <link>https://www.nytimes.com/2024/03/11/technology/carmakers-driver-tracking-insurance.html</link>
    <dc:creator>amy</dc:creator><dc:subject>privacy data cars surveillance</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:dcb9576defc5/</dc:identifier>
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    <title>You May Have Forgotten Foursquare, but It Didn’t Forget You | WIRED</title>
    <dc:date>2024-01-28T19:13:53+00:00</dc:date>
    <link>https://www.wired.com/story/you-may-have-forgotten-foursquare-it-didnt-forget-you/</link>
    <dc:creator>amy</dc:creator><dc:subject>data privacy</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:d6d371b30bc0/</dc:identifier>
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<item rdf:about="https://blacksmithgu.github.io/obsidian-dataview/">
    <title>Dataview obsidian plugin</title>
    <dc:date>2023-03-24T02:47:48+00:00</dc:date>
    <link>https://blacksmithgu.github.io/obsidian-dataview/</link>
    <dc:creator>amy</dc:creator><dc:subject>obsidian plugin data</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:4eab68357515/</dc:identifier>
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<item rdf:about="https://datasociety.net/library/a-primer-on-powerful-numbers-selected-readings-in-the-social-study-of-public-data-and-official-numbers/">
    <title>Data &amp; Society — A Primer on Powerful Numbers: Selected Readings in the Social Study of Public Data and Official Numbers</title>
    <dc:date>2023-01-11T15:46:05+00:00</dc:date>
    <link>https://datasociety.net/library/a-primer-on-powerful-numbers-selected-readings-in-the-social-study-of-public-data-and-official-numbers/</link>
    <dc:creator>amy</dc:creator><dc:subject>data government data_journalism</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:39ff17298160/</dc:identifier>
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<item rdf:about="https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf">
    <title>U.S. Department of Health &amp; Human Services - Office for Civil Rights</title>
    <dc:date>2022-11-15T23:34:16+00:00</dc:date>
    <link>https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf</link>
    <dc:creator>amy</dc:creator><description><![CDATA[As required by section 13402(e)(4) of the HITECH Act, the Secretary must post a list of breaches of unsecured protected health information affecting 500 or more individuals. The following breaches have been reported to the Secretary:

Cases Currently Under Investigation
This page lists all breaches reported within the last 24 months that are currently under investigation by the Office for Civil Rights.]]></description>
<dc:subject>data health healthcare privacy security</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:1c239064c308/</dc:identifier>
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<item rdf:about="https://www.gov.uk/government/publications/better-broader-safer-using-health-data-for-research-and-analysis">
    <title>Better, broader, safer: using health data for research and analysis - GOV.UK</title>
    <dc:date>2022-06-15T17:48:23+00:00</dc:date>
    <link>https://www.gov.uk/government/publications/better-broader-safer-using-health-data-for-research-and-analysis</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Better, broader, safer: using health data for research and analysis
Professor Ben Goldacre’s review into how the efficient and safe use of health data for research and analysis can benefit patients and the healthcare sector.]]></description>
<dc:subject>data health policy terra verily</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:8ddb952ac524/</dc:identifier>
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<item rdf:about="https://alphafold.ebi.ac.uk/">
    <title>AlphaFold Protein Structure Database</title>
    <dc:date>2022-03-09T18:16:49+00:00</dc:date>
    <link>https://alphafold.ebi.ac.uk/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[AlphaFold DB provides open access to over 200 million protein structure predictions to accelerate scientific research.]]></description>
<dc:subject>biology data alphafold science proteomics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:9507bf146566/</dc:identifier>
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<item rdf:about="https://restor.eco/">
    <title>Restor</title>
    <dc:date>2021-10-13T00:09:14+00:00</dc:date>
    <link>https://restor.eco/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Restor is a science-based open data platform to support and connect the global restoration movement]]></description>
<dc:subject>sustainability environment ecology data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:51a2dca0c6b7/</dc:identifier>
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<item rdf:about="https://github.com/dynamicwebpaige/kaggle-survey-spelunking">
    <title>dynamicwebpaige/kaggle-survey-spelunking</title>
    <dc:date>2021-09-17T18:41:34+00:00</dc:date>
    <link>https://github.com/dynamicwebpaige/kaggle-survey-spelunking</link>
    <dc:creator>amy</dc:creator><description><![CDATA[“Data Scientist”, “Machine Learning Developer”, “Deep Learning Engineer”, “Data Engineer”, “ML Ops Engineer”, and “Data Analyst” are often overloaded role titles -- and not necessarily indicative of a user’s day-to-day work, or the tools they are using to accomplish that work.

To better understand and characterize these diverse user segments, we can use tools, libraries, and frameworks referenced in the Kaggle: State of Machine Learning and Data Science 2020 Survey to cluster engineers into cohort groups. We can also loosely tie these cohorts to their anticipated cloud spend; identify typical tasks each user cohort is responsible for completing; assess compute and storage requirements for each user cohort; and estimate cohort size, based on survey responses.

TL;DR
Survey respondents are overwhelmingly performing exploratory analysis using small- to medium-sized data sets stored as flat files, on local machines. Machine learning projects – if ML is being attempted at all – are in early stages, using traditional methods that are best-suited for high-RAM CPU rather than GPU SKUs (ex: scikit-learn and clustering approaches).

Based on responses, data science teams trend small (0-5 engineers), with light rigor on SDLC best practices (ex: version control); and most data scientists come from non-CS backgrounds, with minimal programming experience. Preferred tools are overwhelmingly open-source and non-proprietary. If Visual Studio Code is being used by survey respondents, it is most often being used for non-interactive, production machine learning and data science work.]]></description>
<dc:subject>data_science machine_learning data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:cff87493fbee/</dc:identifier>
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</item>
<item rdf:about="https://fire.airnow.gov/">
    <title>Fire and Smoke Map</title>
    <dc:date>2021-07-22T16:53:33+00:00</dc:date>
    <link>https://fire.airnow.gov/</link>
    <dc:creator>amy</dc:creator><dc:subject>data environment maps weather</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:d3705e026121/</dc:identifier>
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<item rdf:about="https://www.unidata.ucar.edu/software/netcdf/">
    <title>Unidata | NetCDF</title>
    <dc:date>2021-07-20T17:32:50+00:00</dc:date>
    <link>https://www.unidata.ucar.edu/software/netcdf/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Network Common Data Form (NetCDF)
NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for C, C++, Java, and Fortran. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl.

Data in netCDF format is:

Self-Describing. A netCDF file includes information about the data it contains.
Portable. A netCDF file can be accessed by computers with different ways of storing integers, characters, and floating-point numbers.
Scalable. Small subsets of large datasets in various formats may be accessed efficiently through netCDF interfaces, even from remote servers.
Appendable. Data may be appended to a properly structured netCDF file without copying the dataset or redefining its structure.
Sharable. One writer and multiple readers may simultaneously access the same netCDF file.
Archivable. Access to all earlier forms of netCDF data will be supported by current and future versions of the software.
]]></description>
<dc:subject>data tools terra verily</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:6b6220d0b93e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:terra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:verily"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://earth.nullschool.net/">
    <title>world wind map</title>
    <dc:date>2021-05-30T15:24:27+00:00</dc:date>
    <link>https://earth.nullschool.net/</link>
    <dc:creator>amy</dc:creator><dc:subject>weather visualization map earth data wind</dc:subject>
<dc:source>https://apple.com/iphone/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:b29783260802/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:weather"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:map"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:earth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:wind"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/60119934/how-to-read-from-a-high-io-dataset-in-pytorch-which-grows-from-epoch-to-epoch">
    <title>python - How to read from a high IO dataset in pytorch which grows from epoch to epoch - Stack Overflow</title>
    <dc:date>2020-11-30T22:44:56+00:00</dc:date>
    <link>https://stackoverflow.com/questions/60119934/how-to-read-from-a-high-io-dataset-in-pytorch-which-grows-from-epoch-to-epoch</link>
    <dc:creator>amy</dc:creator><dc:subject>PyTorch machine_learning data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:9216b5d93985/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:PyTorch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/great-expectations/great_expectations">
    <title>GitHub - great-expectations/great_expectations: Always know what to expect from your data.</title>
    <dc:date>2020-05-09T13:48:02+00:00</dc:date>
    <link>https://github.com/great-expectations/great_expectations</link>
    <dc:creator>amy</dc:creator><dc:subject>testing data</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:68c36ee870d0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://hedonometer.org/about.html">
    <title>Hedonometer</title>
    <dc:date>2020-04-16T02:40:10+00:00</dc:date>
    <link>http://hedonometer.org/about.html</link>
    <dc:creator>amy</dc:creator><dc:subject>data journalism science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:5a5006c17b65/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:journalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.google.com/covid19/mobility/">
    <title>COVID-19 Community Mobility Reports</title>
    <dc:date>2020-04-04T03:23:19+00:00</dc:date>
    <link>https://www.google.com/covid19/mobility/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[See how your community is moving around differently due to COVID-19
As global communities respond to COVID-19, we've heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps could be helpful as they make critical decisions to combat COVID-19.

These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.]]></description>
<dc:subject>google data coronavirus covid19</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:2c000a6674e2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:coronavirus"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:covid19"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://informationisbeautiful.net/beautifulnews/">
    <title>Beautiful News</title>
    <dc:date>2019-12-01T19:22:13+00:00</dc:date>
    <link>https://informationisbeautiful.net/beautifulnews/</link>
    <dc:creator>amy</dc:creator><dc:subject>news data design visualization</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:90a5fe4b974d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:news"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://logicmag.io/nature/oil-is-the-new-data/">
    <title>Oil is the New Data</title>
    <dc:date>2019-11-27T19:43:26+00:00</dc:date>
    <link>https://logicmag.io/nature/oil-is-the-new-data/</link>
    <dc:creator>amy</dc:creator><dc:subject>environment computing data energy oil tech</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:8077c0ac3b6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:environment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:energy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:oil"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:tech"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.excavating.ai/">
    <title>Excavating AI</title>
    <dc:date>2019-09-20T16:05:27+00:00</dc:date>
    <link>https://www.excavating.ai/</link>
    <dc:creator>amy</dc:creator><dc:subject>machine_learning data bias</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:8ebf09673b2e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bias"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.fastcompany.com/90310803/here-are-the-data-brokers-quietly-buying-and-selling-your-personal-information">
    <title>The data brokers quietly buying and selling your personal information</title>
    <dc:date>2019-03-07T17:02:58+00:00</dc:date>
    <link>https://www.fastcompany.com/90310803/here-are-the-data-brokers-quietly-buying-and-selling-your-personal-information</link>
    <dc:creator>amy</dc:creator><dc:subject>data privacy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:4642821116f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:privacy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://data.fivethirtyeight.com/?twitter=@bigdata">
    <title>Our Data | FiveThirtyEight</title>
    <dc:date>2019-01-06T20:54:20+00:00</dc:date>
    <link>https://data.fivethirtyeight.com/?twitter=@bigdata</link>
    <dc:creator>amy</dc:creator><dc:subject>machine_learning data</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:ce07b5d82e5b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://quiltdata.com/">
    <title>Quilt | Manage data like code</title>
    <dc:date>2018-09-17T21:10:23+00:00</dc:date>
    <link>https://quiltdata.com/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Create a library of data
Quilt versions and deploys data]]></description>
<dc:subject>data bigdata python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:c31f9f467381/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bigdata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.blog.google/products/search/making-it-easier-discover-datasets/">
    <title>Making it easier to discover datasets</title>
    <dc:date>2018-09-06T01:21:10+00:00</dc:date>
    <link>https://www.blog.google/products/search/making-it-easier-discover-datasets/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Dataset search]]></description>
<dc:subject>google data research datasets</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:7fe871f13456/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:datasets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/fivethirtyeight/russian-troll-tweets/">
    <title>fivethirtyeight/russian-troll-tweets</title>
    <dc:date>2018-08-23T21:33:19+00:00</dc:date>
    <link>https://github.com/fivethirtyeight/russian-troll-tweets/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[This directory contains data on nearly 3 million tweets sent from Twitter handles connected to the Internet Research Agency, a Russian "troll factory" and a defendant in an indictment filed by the Justice Department in February 2018, as part of special counsel Robert Mueller's Russia investigation. The tweets in this database were sent between February 2012 and May 2018, with the vast majority posted from 2015 through 2017.]]></description>
<dc:subject>data russia twitter</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:0478fcee8e12/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:russia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:twitter"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/google/data-transfer-project">
    <title>google/data-transfer-project: The Data Transfer Project makes it easy for people to transfer their data between online service providers. We are establishing a common framework, including data models and protocols, to enable direct transfer of data both i</title>
    <dc:date>2018-07-21T19:13:25+00:00</dc:date>
    <link>https://github.com/google/data-transfer-project</link>
    <dc:creator>amy</dc:creator><description><![CDATA[The Data Transfer Project makes it easy for people to transfer their data between online service providers. We are establishing a common framework, including data models and protocols, to enable direct transfer of data both into and out of participating online service providers. http://datatransferproject.dev]]></description>
<dc:subject>data google open_source opendata</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:6d5b98306e92/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:open_source"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:opendata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://data.fivethirtyeight.com/">
    <title>Our Data | FiveThirtyEight</title>
    <dc:date>2018-02-09T23:40:55+00:00</dc:date>
    <link>https://data.fivethirtyeight.com/</link>
    <dc:creator>amy</dc:creator><dc:subject>data_science analysis data big_data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:0a3231e7ad01/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:big_data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.amazon.com/Data-Driven-DJ-Patil-ebook/dp/B00SXHFTAS">
    <title>Data Driven 1, DJ Patil, Hilary Mason, eBook - Amazon.com</title>
    <dc:date>2018-02-05T15:29:54+00:00</dc:date>
    <link>https://www.amazon.com/Data-Driven-DJ-Patil-ebook/dp/B00SXHFTAS</link>
    <dc:creator>amy</dc:creator><dc:subject>books data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:2740aed79cfc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.youtube.com/watch?v=59bMh59JQDo">
    <title>Machine Learning and Human Bias - YouTube</title>
    <dc:date>2018-02-04T17:22:55+00:00</dc:date>
    <link>https://www.youtube.com/watch?v=59bMh59JQDo</link>
    <dc:creator>amy</dc:creator><dc:subject>machine_learning discrimination data bias</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:0f4fda1ee500/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bias"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://data.seattle.gov/Permitting/Building-Permits-Current/mags-97de">
    <title>Building Permits : Current | City of Seattle Open Data portal</title>
    <dc:date>2018-02-02T15:46:01+00:00</dc:date>
    <link>https://data.seattle.gov/Permitting/Building-Permits-Current/mags-97de</link>
    <dc:creator>amy</dc:creator><dc:subject>seattle data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:a4dad3e7fa1b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:seattle"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/i/web/status/951294630355615745">
    <title>Twitter</title>
    <dc:date>2018-01-12T16:30:31+00:00</dc:date>
    <link>https://twitter.com/i/web/status/951294630355615745</link>
    <dc:creator>amy</dc:creator><description><![CDATA[RT @lak_gcp: Always wanted to learn how to handle #data the Google way? Now free to audit: @Googlecloud #bigdata and… ]]></description>
<dc:subject>data bigdata</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:6dae276bab0c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bigdata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nhc.noaa.gov/gis/">
    <title>NHC Data in GIS Formats</title>
    <dc:date>2017-10-01T19:18:41+00:00</dc:date>
    <link>http://www.nhc.noaa.gov/gis/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[NHC Data in GIS Formats

]]></description>
<dc:subject>data weather</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:ff029fe4ad6f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:weather"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/i/web/status/897579043574366208">
    <title>Twitter</title>
    <dc:date>2017-08-15T23:29:17+00:00</dc:date>
    <link>https://twitter.com/i/web/status/897579043574366208</link>
    <dc:creator>amy</dc:creator><description><![CDATA[RT @mmlee: Join @amygdala, our awesome Category Chair @google, and #data #tech innovators from local & fed govt to discuss… ]]></description>
<dc:subject>data tech</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:694d9afaab24/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:tech"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/pair-code/facets">
    <title>PAIR-code/facets: Visualizations for machine learning datasets</title>
    <dc:date>2017-08-11T20:20:50+00:00</dc:date>
    <link>https://github.com/pair-code/facets</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Visualizations for machine learning datasets https://pair-code.github.io/facets/
]]></description>
<dc:subject>data visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:7bb783a86dfc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770">
    <title>Democratizing Data at Airbnb – Airbnb Engineering &amp; Data Science – Medium</title>
    <dc:date>2017-06-08T00:59:42+00:00</dc:date>
    <link>https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Like many startups, the number of employees at Airbnb has grown significantly over the past several years. In parallel we have seen explosive growth in both the amount of data and the number of internal data resources: data tables, dashboards, reports, metrics definitions, etc. On one hand, the growth in data resources is healthy and reflects our heavy investment in data tooling to promote data-informed decision making. However it also creates a new challenge: effectively navigating a sea of data resources of varying quality, complexity, relevance, and trustworthiness. In this post we describe our observation of this problem and the Dataportal, a novel data resource search and discovery tool that addresses this issue.
The overarching goal of the Dataportal is to democratize data and empower Airbnb employees to be data informed by aiding with data exploration, discovery, and trust.]]></description>
<dc:subject>data discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:19f8a9981f18/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://usafacts.org/">
    <title>USAFacts</title>
    <dc:date>2017-04-18T21:43:24+00:00</dc:date>
    <link>http://usafacts.org/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Ballmer]]></description>
<dc:subject>government politics data</dc:subject>
<dc:identifier>https://pinboard.in/u:amy/b:8f07d77d37a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:government"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.crowdflower.com/">
    <title>AI for your business | CrowdFlower</title>
    <dc:date>2017-04-18T21:13:35+00:00</dc:date>
    <link>https://www.crowdflower.com/</link>
    <dc:creator>amy</dc:creator><description><![CDATA['training data, machine learning, and human-in-the-loop in a single platform']]></description>
<dc:subject>crowdsourcing data machine_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:0b90c21f0d97/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:crowdsourcing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:machine_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/kevpluck/status/836673010400542721/video/1">
    <title>(429) https://twitter.com/kevpluck/status/836673010400542721/video/1</title>
    <dc:date>2017-03-23T20:02:03+00:00</dc:date>
    <link>https://twitter.com/kevpluck/status/836673010400542721/video/1</link>
    <dc:creator>amy</dc:creator><description><![CDATA[RT @simongerman600: Elegantly presented #data on #climatechange. via @kevpluck ]]></description>
<dc:subject>climatechange data</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:149b690497a9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:climatechange"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://carto.com/">
    <title>CARTO — Predict through location</title>
    <dc:date>2017-02-21T22:37:13+00:00</dc:date>
    <link>https://carto.com/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[CARTO is an open, powerful, and intuitive platform for discovering and predicting the key insights underlying the location data in our world.
]]></description>
<dc:subject>analytics data mapping maps</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:2a499925005d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:analytics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:mapping"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:maps"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://howto.informationactivism.org/">
    <title>the info-activism how-to guide</title>
    <dc:date>2017-02-04T18:07:19+00:00</dc:date>
    <link>https://howto.informationactivism.org/</link>
    <dc:creator>amy</dc:creator><dc:subject>activism data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:076eb988ce35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:activism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://human-pose.mpi-inf.mpg.de/">
    <title>MPII Human Pose Database</title>
    <dc:date>2016-07-01T20:06:55+00:00</dc:date>
    <link>http://human-pose.mpi-inf.mpg.de/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation. The dataset includes around 25K images containing over 40K people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video and provided with preceding and following un-annotated frames. In addition, for the test set we obtained richer annotations including body part occlusions and 3D torso and head orientations.

Following the best practices for the performance evaluation benchmarks in the literature we withhold the test annotations to prevent overfitting and tuning on the test set. We are working on an automatic evaluation server and performance analysis tools based on rich test set annotations.]]></description>
<dc:subject>machine_learning data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:c4f022d7ddfe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.cornell.edu/people/pabo/movie-review-data/">
    <title>Data</title>
    <dc:date>2016-05-12T17:46:55+00:00</dc:date>
    <link>http://www.cs.cornell.edu/people/pabo/movie-review-data/</link>
    <dc:creator>amy</dc:creator><dc:subject>data nlp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:73eb0b6081d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:nlp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark">
    <title>ciprian-chelba/1-billion-word-language-modeling-benchmark: Formerly known as code.google.com/p/1-billion-word-language-modeling-benchmark</title>
    <dc:date>2016-02-07T02:27:15+00:00</dc:date>
    <link>https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark</link>
    <dc:creator>amy</dc:creator><description><![CDATA[The project makes available a standard corpus of reasonable size (0.8 billion words) 
to train and evaluate language models.

A few sample results we obtained at Google on this data are detailed at:
papers/naaclhlt2013.pdf

Besides the scripts needed to rebuild the training/held-out data, it also makes 
available log-probability values for each word in each of ten feld-out data sets, 
for each of the following baseline models:
. unpruned Katz (1.1B n-grams),
. pruned Katz (~15M n-grams), 
. unpruned Interpolated Kneser-Ney (1.1B n-grams), 
. pruned Interpolated Kneser-Ney (~15M n-grams)

The corpus is derived from the training-monolingual.tokenized/news.20??.en.shuffled.tokenized data distributed at http://statmt.org/wmt11/translation-task.html, Monolingual language model training data (Download it all in one file, 11 GB, at http://statmt.org/wmt11/training-monolingual.tgz). 
]]></description>
<dc:subject>data nlp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:611223c0f24b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:nlp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.unofficialgoogledatascience.com/">
    <title>The Unofficial Google Data Science Blog</title>
    <dc:date>2015-09-09T03:23:51+00:00</dc:date>
    <link>http://www.unofficialgoogledatascience.com/</link>
    <dc:creator>amy</dc:creator><dc:subject>google data blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:2ef490b1fe37/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://research.neustar.biz/2014/09/15/riding-with-the-stars-passenger-privacy-in-the-nyc-taxicab-dataset/">
    <title>Riding with the Stars: Passenger Privacy in the NYC Taxicab Dataset | Research</title>
    <dc:date>2014-10-17T03:39:55+00:00</dc:date>
    <link>http://research.neustar.biz/2014/09/15/riding-with-the-stars-passenger-privacy-in-the-nyc-taxicab-dataset/</link>
    <dc:creator>amy</dc:creator><dc:subject>data privacy</dc:subject>
<dc:source>https://apple.com/iphone/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:1b08b8eaa148/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:privacy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://google.github.io/CausalImpact/">
    <title>CausalImpact</title>
    <dc:date>2014-09-12T18:11:31+00:00</dc:date>
    <link>http://google.github.io/CausalImpact/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[What does this package do?
The CausalImpact R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package overcomes this difficulty using a structural Bayesian time-series model to estimate how the response metric would have evolved after the intervention if the intervention had not occurred.
]]></description>
<dc:subject>analysis analytics data statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:50ed05da7f5f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:analytics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blog.twitter.com/2014/introducing-twitter-data-grants">
    <title>Introducing Twitter Data Grants | Twitter Blogs</title>
    <dc:date>2014-02-08T23:50:42+00:00</dc:date>
    <link>https://blog.twitter.com/2014/introducing-twitter-data-grants</link>
    <dc:creator>amy</dc:creator><dc:subject>data research twitter</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:41843088446c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:twitter"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/alignedleft/scattered-scatterplot">
    <title>alignedleft/scattered-scatterplot</title>
    <dc:date>2014-01-11T21:34:40+00:00</dc:date>
    <link>https://github.com/alignedleft/scattered-scatterplot</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Code examples for the introductory d3.js workshop, "From Scattered to Scatterplot"
]]></description>
<dc:subject>javascript visualization data d3</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:e09f627190d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:d3"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jvns.ca/blog/2013/12/22/cooking-with-pandas/">
    <title>A pandas cookbook - Julia Evans</title>
    <dc:date>2014-01-06T00:50:10+00:00</dc:date>
    <link>http://jvns.ca/blog/2013/12/22/cooking-with-pandas/</link>
    <dc:creator>amy</dc:creator><dc:subject>python analysis data pandas</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:33b4f2d52822/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:pandas"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://prepaidwithdata.wikia.com/wiki/Pay_as_you_go_sim_with_data_Wiki">
    <title>Pay as you go sim with data Wiki</title>
    <dc:date>2013-11-03T23:26:57+00:00</dc:date>
    <link>http://prepaidwithdata.wikia.com/wiki/Pay_as_you_go_sim_with_data_Wiki</link>
    <dc:creator>amy</dc:creator><dc:subject>travel mobile europe data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:be255bef54f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:travel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:mobile"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:europe"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://worldmap.harvard.edu/tweetmap/">
    <title>TweetMap ALPHA</title>
    <dc:date>2013-09-07T23:16:43+00:00</dc:date>
    <link>http://worldmap.harvard.edu/tweetmap/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[TweetMap is an instance of MapD, a massively parallel database platform being developed through a collaboration between Todd Mostak, (currently a researcher at MIT), and the Harvard Center for Geographic Analysis (CGA). 

Learn more about MapD here - https://www.facebook.com/datarefined

The number of tweets could theoretically be increased to billions and we are working on real time streaming.

MapD is a general purpose SQL database that can be used to provide real-time visualization and analysis of just about any very large data set.  MapD makes use of commodity Graphic Processing Units (GPUs) to parallelize hard compute jobs such as that of querying and rendering very large data sets on-the-fly.]]></description>
<dc:subject>analysis data twitter visualization visualizations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:a74a99949adf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:twitter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:visualizations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://datakind.org/">
    <title>Home | DataKind</title>
    <dc:date>2013-08-20T07:07:38+00:00</dc:date>
    <link>http://datakind.org/</link>
    <dc:creator>amy</dc:creator><dc:subject>data big_data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:68092d02671e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:big_data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.palantir.com/">
    <title>Home | Palantir</title>
    <dc:date>2013-01-21T18:49:07+00:00</dc:date>
    <link>http://www.palantir.com/</link>
    <dc:creator>amy</dc:creator><dc:subject>analysis business data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:84f2c922766f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:business"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pewinternet.org/Static-Pages/Data-Tools/Download-Data/Data-Sets.aspx">
    <title>Data Sets | Pew Research Center's Internet &amp; American Life Project</title>
    <dc:date>2013-01-18T02:09:04+00:00</dc:date>
    <link>http://www.pewinternet.org/Static-Pages/Data-Tools/Download-Data/Data-Sets.aspx</link>
    <dc:creator>amy</dc:creator><dc:subject>data opendata research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:daf8df74f5ba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:opendata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://137.189.35.203/WebUI/CatDatabase/catData.html">
    <title>Cat Database</title>
    <dc:date>2013-01-17T09:30:52+00:00</dc:date>
    <link>http://137.189.35.203/WebUI/CatDatabase/catData.html</link>
    <dc:creator>amy</dc:creator><dc:subject>cats data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:5198ce0f39b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:cats"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://shop.oreilly.com/product/0636920023784.do">
    <title>Python for Data Analysis - O'Reilly Media</title>
    <dc:date>2012-11-04T19:02:10+00:00</dc:date>
    <link>http://shop.oreilly.com/product/0636920023784.do</link>
    <dc:creator>amy</dc:creator><dc:subject>books data python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:366be7fcd720/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.guardian.co.uk/news/datablog/2012/apr/20/data-journalism-handbook">
    <title>Introducing the data journalism handbook | News | guardian.co.uk</title>
    <dc:date>2012-04-21T01:59:06+00:00</dc:date>
    <link>http://www.guardian.co.uk/news/datablog/2012/apr/20/data-journalism-handbook</link>
    <dc:creator>amy</dc:creator><dc:subject>data visualizations journalism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:35c3cd2298bd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:visualizations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:journalism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html">
    <title>High Scalability - High Scalability - Big Data Counting: How to count a billion distinct objects using only 1.5KB of Memory</title>
    <dc:date>2012-04-07T07:29:05+00:00</dc:date>
    <link>http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html</link>
    <dc:creator>amy</dc:creator><dc:subject>algorithms programming data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:01c38c674625/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://data.nytimes.com/">
    <title>New York Times - Linked Open Data</title>
    <dc:date>2012-02-23T09:41:35+00:00</dc:date>
    <link>http://data.nytimes.com/</link>
    <dc:creator>amy</dc:creator><dc:subject>data nytimes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:227ef3ddbc56/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:nytimes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://radar.oreilly.com/2011/09/building-data-science-teams.html">
    <title>Building data science teams - O'Reilly Radar</title>
    <dc:date>2011-09-17T01:24:17+00:00</dc:date>
    <link>http://radar.oreilly.com/2011/09/building-data-science-teams.html</link>
    <dc:creator>amy</dc:creator><description><![CDATA[RT @OReillyMedia: Free Download: DJ Patil (@dpatil) explains the world's best new profession - #data scientist  /gg]]></description>
<dc:subject>data</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:c18f75407db0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://datashirts.spreadshirt.com/">
    <title>Datashirts</title>
    <dc:date>2011-08-18T00:34:01+00:00</dc:date>
    <link>http://datashirts.spreadshirt.com/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Data Shirts: shirts for data scientists by data scientists, proceeds go to Data without Borders.  ]]></description>
<dc:subject>t-shirts data</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:d3b190b58923/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:t-shirts"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://radar.oreilly.com/2011/08/chicago-data-apps-open-government.html">
    <title>Opening government, the Chicago way - O'Reilly Radar</title>
    <dc:date>2011-08-17T23:05:24+00:00</dc:date>
    <link>http://radar.oreilly.com/2011/08/chicago-data-apps-open-government.html</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Good piece by @digiphile: Opening government, the Chicago way ]]></description>
<dc:subject>data opengov opendata</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:54243170d216/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:opengov"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:opendata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://influenceexplorer.com/">
    <title>Influence Explorer</title>
    <dc:date>2011-06-17T15:28:27+00:00</dc:date>
    <link>http://influenceexplorer.com/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Type in the name of a COMPANY, your LAWMAKER or a prominent INDIVIDUAL, and see how they're influencing the political system.]]></description>
<dc:subject>data government journalism politics transparency</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:9719af528a28/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:government"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:journalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:transparency"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.google.com/publicdata/home">
    <title>Google Public Data Explorer</title>
    <dc:date>2011-05-27T15:02:12+00:00</dc:date>
    <link>http://www.google.com/publicdata/home</link>
    <dc:creator>amy</dc:creator><dc:subject>#gov20 gov20 analytics data google statistics visualization datamining</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:744869a5afe4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:#gov20"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:gov20"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:analytics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:datamining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://code.google.com/apis/predict/docs/getting-started.html">
    <title>Getting Started - Google Prediction API - Google Code</title>
    <dc:date>2011-05-19T22:03:54+00:00</dc:date>
    <link>http://code.google.com/apis/predict/docs/getting-started.html</link>
    <dc:creator>amy</dc:creator><dc:subject>APIs data google machine_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:e5e9f31c19ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:APIs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:machine_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://codeforamerica.org/2011/04/26/opendataphilly-launches-in-philly/">
    <title>OpenDataPhilly.org Launches in Philadelphia | Code for America</title>
    <dc:date>2011-04-28T14:20:13+00:00</dc:date>
    <link>http://codeforamerica.org/2011/04/26/opendataphilly-launches-in-philly/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[RT @codeforamerica OpenDataPhilly.org Launches in Philadelphia by @atogle  #opengov #data]]></description>
<dc:subject>data opengov</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:8775a2d8486c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:opengov"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>