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	<rdf:li rdf:resource="https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/"/>
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  </channel><item rdf:about="https://www.theverge.com/features/23764584/ai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots">
    <title>Inside the AI Factory: the humans that make tech seem human - The Verge</title>
    <dc:date>2023-06-28T15:53:18+00:00</dc:date>
    <link>https://www.theverge.com/features/23764584/ai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- This is well-written, and no doubt accurate.  I am very much inclined to assign it, the next time I teach data mining, as a look behind the curtain of where labels come from.
--- BUT: it stacks the deck rhetorically pretty strongly.
--- Instance 1, which leapt out at me as the child of a development economist: It never mentions the prevailing wages in these countries, to give any sense of whether those are good jobs _in context_.  For the record, the World Bank puts Kenya's national income in 2021 at just under $2100 [https://data.worldbank.org/country/kenya?view=chart].  At an 8 hour day * 5 days/week * 50 weeks/year that comes to $1.05/hr.  Suddenly paying $1--$3 an hour does not sound that bad!  (And the initial rates of up to $10/hr were princely --- the same ratio to national income in the US would be around $350/hr!) [*] 
--- Now, whenever there's a positive-sum productive activity, there is a zero-sum competition over how to divide the surplus.  I am always in favor of the workers getting a bigger share.  I would 100% support (e.g.) the Kenyan annotators unionizing to get more stable and better-paid jobs.  But creating a small labor aristocracy neither a development strategy nor a moral obligation.
--- Instance 2, the unfavorable comparisons to mid-20-century office work in the US and other developed countries.  Those jobs were famously alienating!  We developed whole artistic genres about how alienating they were!

*: Obviously, the average wage for wage-earners has to be higher than the national income per person.  But, for comparison, the US national income per person divided by the length of the working year comes out to $35/hr.  (Somewhat to my surprise, the World Bank puts Kenya's Gini index at 40.8, vs. 39.7 for the US [https://data.worldbank.org/indicator/SI.POV.GINI].)  Again: prevailing wages in big cities like Nairobi are probably higher than the rest of the country; I didn't find any good figures on that in five minutes of search.]]></description>
<dc:subject>data_mining data_sets machine_learning have_read via:alison_gopnik !_at_the_via in_NB</dc:subject>
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<item rdf:about="https://acleddata.com/2020/09/03/demonstrations-political-violence-in-america-new-data-for-summer-2020/">
    <title>Demonstrations and Political Violence in America: New Data for Summer 2020</title>
    <dc:date>2023-03-18T14:24:28+00:00</dc:date>
    <link>https://acleddata.com/2020/09/03/demonstrations-political-violence-in-america-new-data-for-summer-2020/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Last tag is very tentative, as this particular data set might be too inflammatory (you should pardon the expression).  Maybe if aggregated with others over a longer time period?]]></description>
<dc:subject>data_sets violence whats_gone_wrong_with_america spatial_statistics to_teach:data_over_space_and_time</dc:subject>
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    <title>Homepage: Xinjiang Data Project</title>
    <dc:date>2022-03-25T19:10:38+00:00</dc:date>
    <link>https://xjdp.aspi.org.au/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets xinjiang china:prc to_teach via:absfac</dc:subject>
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<item rdf:about="https://archive.ics.uci.edu/ml/datasets/Bank+Marketing">
    <title>UCI Machine Learning Repository: Bank Marketing Data Set</title>
    <dc:date>2022-03-12T13:20:21+00:00</dc:date>
    <link>https://archive.ics.uci.edu/ml/datasets/Bank+Marketing</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[In which I decide to have The Kids act as decision-support for telemarketers.
Notes to self (already in 2022HW7):
- European "term deposit" \approx American "certificate of deposit"
- "nr.employed` is apparently # of employed persons in Portugal, in thousands.
- `euribor3m` = Euro Inter Bank Offer Rate for 3 month loans (not sure if deposit interest rates are formally pegged to this but they should certainly co-vary)
- "variation rate" or "rate of variation" is apparently how you say "percentage growth rate" or "percentage rate of change" in Portuguese and Spanish.
- A little Googling suggests that telemarketers in Lisbon (currently) make in the range of 7--9  Euros.

--- Link to my homework assignment: http://www.stat.cmu.edu/~cshalizi/dm/22/hw/07/hw-07.pdf]]></description>
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    <title>US General Social Survey (GSS) Data for R • gssr</title>
    <dc:date>2021-11-10T18:19:56+00:00</dc:date>
    <link>https://kjhealy.github.io/gssr/</link>
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    <title>US General Social Survey (GSS) Data for R • gssr</title>
    <dc:date>2021-09-29T13:19:34+00:00</dc:date>
    <link>https://kjhealy.github.io/gssr/index.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R data_sets surveys to_teach:undergrad-ADA to_teach:data_over_space_and_time to_teach:statistics_of_inequality_and_discrimination healy.kieran</dc:subject>
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<item rdf:about="https://mobile.twitter.com/HistDem/status/1395774558096039938">
    <title>Steven Ruggles on Twitter: &quot;/1. Yesterday at the ACS Data Users Conference, the Census Bureau described its plans to replace the American Community Survey (ACS) microdata with “fully synthetic” data over the next three years. https://t.co/8btLxiA3iM&quot; </title>
    <dc:date>2021-06-11T18:31:16+00:00</dc:date>
    <link>https://mobile.twitter.com/HistDem/status/1395774558096039938</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- As described, this seems ridiculous.  (See also [https://mobile.twitter.com/HistDem/status/1402712595707084805].)  I have always been suspicious of differential privacy [http://bactra.org/weblog/1138.html], but this would indeed be catastrophic for many, many users of Census data, _if_ it's as described.  Because it's Twitter, my figuring out whether this really is accurate would be a minor project in and of itself.  (Ruggles is a respected historian and demographer and it's implausible that he's just panicking, but in this day and age, who can say?)]]></description>
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<item rdf:about="https://archive.ics.uci.edu/ml/datasets/Geographical+Original+of+Music">
    <title>UCI Machine Learning Repository: Geographical Original of Music Data Set</title>
    <dc:date>2021-04-29T16:50:02+00:00</dc:date>
    <link>https://archive.ics.uci.edu/ml/datasets/Geographical+Original+of+Music</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The dataset was built from a personal collection of 1059 tracks covering 33 countries/area. The music used is traditional, ethnic or `world' only, as classified by the publishers of the product on which it appears. Any Western music is not included because its influence is global - what we seek are the aspects of music that most influence location. Thus, being able to specify a location with strong influence on the music is central.
"The geographical location of origin was manually collected the information from the CD sleeve notes, and when this information was inadequate we searched other information sources. The location data is limited in precision to the country of origin.
"The country of origin was determined by the artist's or artists' main country/area of residence. Any track that had ambiguous origin is not included. We have taken the position of each country's capital city (or the province of the area) by latitude and longitude as the absolute point of origin.
"The program MARSYAS[1] was used to extract audio features from the wave files. We used the default MARSYAS settings in single vector format (68 features) to estimate the performance with basic timbal information covering the entire length of each track. No feature weighting or pre-filtering was applied. All features were transformed to have a mean of 0, and a standard deviation of 1. We also investigated the utility of adding chromatic attributes. These describe the notes of the scale being used. This is especially important as a distinguishing feature in geographical ethnomusicology. The chromatic features provided by MARSYAS are 12 per octave - Western tuning, but it may be possible to tell something from how similar to or different from Western tuning the music is."

--- Might make for an interesting problem set.]]></description>
<dc:subject>data_sets music spatial_statistics to_teach:data_over_space_and_time</dc:subject>
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<item rdf:about="https://www.washingtonpost.com/graphics/investigations/police-shootings-database/">
    <title>Police shootings database 2015-2021 - Washington Post</title>
    <dc:date>2021-04-19T03:00:07+00:00</dc:date>
    <link>https://www.washingtonpost.com/graphics/investigations/police-shootings-database/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>violence police data_sets</dc:subject>
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<item rdf:about="https://arxiv.org/abs/2104.05711">
    <title>[2104.05711] The world-wide waste web</title>
    <dc:date>2021-04-14T14:44:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.05711</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Globally, 7-10 billion tonnes of waste are produced annually, including 300-500 million tonnes of hazardous wastes (HW)--explosive, flammable, toxic, corrosive, and infective ones. About 10 % of these HW are traded through a world-wide waste web (W4). The volume of HW traded through the W4 in the last 30 years has grown by 500 % and will continue to grow, creating serious legal, economic, environmental and health problems at global scale. Here we investigate the tip of the iceberg of the W4 by studying networks of 108 categories of wastes traded among 163 countries in the period 2003-2009. Although, most of the HW were traded between developed nations, a disproportionate asymmetry existed in the flow of waste from developed to developing countries. Using a dynamical model we simulate how waste congestion propagates through the W4. We identify 32 countries with poor environmental performance which are at high risk of waste congestion. Therefore, they are a threat of improper handling and disposal of HW. We found contamination by heavy metals (HM), by volatile organic compounds (VOC) and/or by persistent organic pollutants (POP), which were used as chemical fingerprints (CF) of the improper handling of HW in 94 % of these countries."

--- The dynamical simulation sounds weird, but the data set sounds cool (if perhaps depressing).]]></description>
<dc:subject>to:NB economics networks data_sets garbage environmental_management to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4e485f17b3f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:garbage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:environmental_management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.12583">
    <title>[2012.12583] The structure of behavioral data</title>
    <dc:date>2020-12-24T15:44:51+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.12583</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral data is fundamental to many scientific fields, there is currently no widely adopted standard for formatting, naming, organizing, describing or sharing such data. This lack of standardization is a major bottleneck for scientific progress. Not only does it prevent the effective reuse of data, it also affects how behavioral data in general are processed, as non-standard data calls for custom-made data analysis code and prevents the development of efficient tools. To address this problem, we develop the Behaverse Data Model (BDM), a standard for structuring behavioral data. Here we focus on major concepts in behavioral data, leaving further details and developments to the project's website "]]></description>
<dc:subject>to:NB data_sets statistics social_measurement</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4023263f1eba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.washingtonpost.com/graphics/2018/national/mass-shootings-in-america/">
    <title>Mass shooting statistics in the United States - Washington Post</title>
    <dc:date>2020-12-05T19:36:59+00:00</dc:date>
    <link>https://www.washingtonpost.com/graphics/2018/national/mass-shootings-in-america/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>violence data_sets mass_shootings re:statistics_of_muckers visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f1fb448513b9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mass_shootings"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:statistics_of_muckers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://crcns.org/data-sets/motor-cortex/pmd-1">
    <title>pmd-1 (premotor cortex 1) — CRCNS.org</title>
    <dc:date>2020-11-29T20:18:21+00:00</dc:date>
    <link>http://crcns.org/data-sets/motor-cortex/pmd-1</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets neural_data_analysis neural_coding_and_decoding neural_control_of_action time_series point_processes to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:db7b31c84e6c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_coding_and_decoding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_control_of_action"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:point_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pabirdatlas.psu.edu/map/species_maps">
    <title>Second Atlas of Breeding Birds in Pennsylvania: companion website maps</title>
    <dc:date>2020-11-21T03:38:24+00:00</dc:date>
    <link>http://www.pabirdatlas.psu.edu/map/species_maps</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[But how do I download the data?!?]]></description>
<dc:subject>maps birds to_teach:data_over_space_and_time data_sets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bd316525ab30/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:birds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://earthquake.usgs.gov/fdsnws/event/1/">
    <title>API Documentation - Earthquake Catalog</title>
    <dc:date>2020-09-02T20:12:14+00:00</dc:date>
    <link>https://earthquake.usgs.gov/fdsnws/event/1/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Only supports rectangles and circles (or their intersection).  Presumably someone has R code already to only include points with coordinates within an arbitrary polygon?]]></description>
<dc:subject>data_sets earthquakes geology point_processes to_teach:data_over_space_and_time to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b1c6b5b0cca3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:earthquakes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:geology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:point_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kjhealy.github.io/gssr/articles/overview.html">
    <title>An Overview of gssr • gssr</title>
    <dc:date>2019-10-11T00:41:35+00:00</dc:date>
    <link>https://kjhealy.github.io/gssr/articles/overview.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The General Social Survey, or GSS, is one of the cornerstones of American social science and one of the most-analyzed datasets in Sociology. It is routinely used in research, in teaching, and as a reference point in discussions about changes in American society since the early 1970s. It is also a model of open, public data. The National Opinion Research Center already provides many excellent tools for working with the data, and has long made it freely available to researchers. Casual users of the GSS can examine the GSS Data Explorer, and social scientists can download complete datasets directly. At present, the GSS is provided to researchers in a choice of two commercial formats, Stata (.dta) and SPSS (.sav). It’s not too difficult to get the data into R (especially now that the Haven package is pretty reliable), but it can be a little annoying to have to do it repeatedly. After doing it one too many times, I got tired of it and I made a package instead. The gssr package provides the GSS Cumulative Data File (1972-2018) and the GSS Three Wave Panel Data File (2006-2010), together with their codebooks, in a format that makes it straightforward to get started working with them in R. The gssr package makes the GSS a little more accessible to users of R, the free software environment for statistical computing, and thus helps in a small way to make the GSS even more open than it already is"]]></description>
<dc:subject>to_explore sociology R data_sets healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1b102e610ff8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_explore"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data/">
    <title>US Mass Shootings, 1982-2019: Data From Mother Jones’ Investigation – Mother Jones</title>
    <dc:date>2019-08-08T01:02:17+00:00</dc:date>
    <link>https://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets violence whats_gone_wrong_with_america re:statistics_of_muckers mass_shootings</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b3ba69f0d2d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:whats_gone_wrong_with_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:statistics_of_muckers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mass_shootings"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.12720">
    <title>[1907.12720] Exploring large scale public medical image datasets</title>
    <dc:date>2019-08-07T17:27:58+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.12720</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Rationale and Objectives: Medical artificial intelligence systems are dependent on well characterised large scale datasets. Recently released public datasets have been of great interest to the field, but pose specific challenges due to the disconnect they cause between data generation and data usage, potentially limiting the utility of these datasets. 
"Materials and Methods: We visually explore two large public datasets, to determine how accurate the provided labels are and whether other subtle problems exist. The ChestXray14 dataset contains 112,120 frontal chest films, and the MURA dataset contains 40,561 upper limb radiographs. A subset of around 700 images from both datasets was reviewed by a board-certified radiologist, and the quality of the original labels was determined. 
"Results: The ChestXray14 labels did not accurately reflect the visual content of the images, with positive predictive values mostly between 10% and 30% lower than the values presented in the original documentation. There were other significant problems, with examples of hidden stratification and label disambiguation failure. The MURA labels were more accurate, but the original normal/abnormal labels were inaccurate for the subset of cases with degenerative joint disease, with a sensitivity of 60% and a specificity of 82%. 
"Conclusion: Visual inspection of images is a necessary component of understanding large image datasets. We recommend that teams producing public datasets should perform this important quality control procedure and include a thorough description of their findings, along with an explanation of the data generating procedures and labelling rules, in the documentation for their datasets."]]></description>
<dc:subject>to:NB data_sets data_mining to_teach:data-mining statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0df44cbf70f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.04711">
    <title>[1906.04711] ProPublica's COMPAS Data Revisited</title>
    <dc:date>2019-06-14T11:32:48+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.04711</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper I re-examine the COMPAS recidivism score and criminal history data collected by ProPublica in 2016, which has fueled intense debate and research in the nascent field of `algorithmic fairness' or `fair machine learning' over the past three years. ProPublica's COMPAS data is used in an ever-increasing number of studies to test various definitions and methodologies of algorithmic fairness. This paper takes a closer look at the actual datasets put together by ProPublica. In particular, I examine the distribution of defendants across COMPAS screening dates and find that ProPublica made an important data processing mistake when it created some of the key datasets most often used by other researchers. Specifically, the datasets built to study the likelihood of recidivism within two years of the original COMPAS screening date. As I show in this paper, ProPublica made a mistake implementing the two-year sample cutoff rule for recidivists in such datasets (whereas it implemented an appropriate two-year sample cutoff rule for non-recidivists). As a result, ProPublica incorrectly kept a disproportionate share of recidivists. This data processing mistake leads to biased two-year recidivism datasets, with artificially high recidivism rates. This also affects the positive and negative predictive values. On the other hand, this data processing mistake does not impact some of the key statistical measures highlighted by ProPublica and other researchers, such as the false positive and false negative rates, nor the overall accuracy."]]></description>
<dc:subject>data_sets crime prediction to_teach:data-mining algorithmic_fairness scores_and_classes to_teach:statistics_of_inequality_and_discrimination in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bb95f43d3110/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crime"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:scores_and_classes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.10498">
    <title>[1905.10498] Cold Case: The Lost MNIST Digits</title>
    <dc:date>2019-05-28T16:47:01+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.10498</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hanaoka, 1995], the precise processing steps for this derivation have been lost to time. We propose a reconstruction that is accurate enough to serve as a replacement for the MNIST dataset, with insignificant changes in accuracy. We trace each MNIST digit to its NIST source and its rich metadata such as writer identifier, partition identifier, etc. We also reconstruct the complete MNIST test set with 60,000 samples instead of the usual 10,000. Since the balance 50,000 were never distributed, they enable us to investigate the impact of twenty-five years of MNIST experiments on the reported testing performances. Our results unambiguously confirm the trends observed by Recht et al. [2018, 2019]: although the misclassification rates are slightly off, classifier ordering and model selection remain broadly reliable. We attribute this phenomenon to the pairing benefits of comparing classifiers on the same digits."]]></description>
<dc:subject>to:NB data_sets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b31d794870e3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://people.csail.mit.edu/ludwigs/papers/imagenet.pdf">
    <title>Do ImageNet Classifiers Generalize to ImageNet?</title>
    <dc:date>2019-02-16T19:56:06+00:00</dc:date>
    <link>http://people.csail.mit.edu/ludwigs/papers/imagenet.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been
the focus of intense research for almost a decade, raising the danger of overfitting to excessively
re-used test sets. By closely following the original dataset creation processes, we test to what
extent current classification models generalize to new data. We evaluate a broad range of models
and find accuracy drops of 3% – 15% on CIFAR-10 and 11% – 14% on ImageNet. However,
accuracy gains on the original test sets translate to larger gains on the new test sets. Our results
suggest that the accuracy drops are not caused by adaptivity, but by the models’ inability to
generalize to slightly “harder” images than those found in the original test sets."

--- The astonishing thing to me is the _linear_ relationship between accuracy on the old and new data-set versions.  It's uncannily good.  (Also: tiny changes  in data-preparation make a big difference!)]]></description>
<dc:subject>to:NB have_read classifiers neural_networks data_sets to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b6e122dee5de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4037">
    <title>Youth-Parent Socialization Panel Study, 1965-1997: Four Waves Combined</title>
    <dc:date>2019-01-06T19:50:41+00:00</dc:date>
    <link>https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4037</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Youth-Parent Socialization Panel Study is a series of surveys designed to assess political continuity and change across time for biologically-related generations and to gauge the impact of life-stage events and historical trends on the behaviors and attitudes of respondents. A national sample of high school seniors and their parents was first surveyed in 1965. Subsequent surveys of the same individuals were conducted in 1973, 1982, and 1997. This data collection combines all four waves of youth data for the study. The general objective of the data collection was to study the dynamics of political attitudes and behaviors by obtaining data on the same individuals as they aged from approximately 18 years of age in 1965 to 50 years of age in 1997. Especially when combined with other elements of the study as released in other ICPSR collections in the Youth Studies Series, this data collection facilitates the analysis of generational, life cycle, and historical effects and political influences on relationships within the family. This data collection also has several distinctive properties. First, it is a longitudinal study of a particular cohort, a national sample from the graduating high school class of 1965. Second, it captures the respondents at key points in their life stages -- at ages 18, 26, 35, and 50. Third, the dataset contains many replicated measures over time as well as some measures unique to each data point. Fourth, there is detailed information about the respondents' life histories. Background variables include age, sex, religious orientation, level of religious participation, marital status, ethnicity, educational status and background, place of residence, family income, and employment status."

--- Used in Rochon's book about value change, in a way which would make it a good case study for propensity-score matching (which Rochon did _not_ do, confounding his inferences).  Query, can I get access via CMU, or are we not part of the consortium?]]></description>
<dc:subject>data_sets us_politics public_opinion to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bfea183bf8b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_opinion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://cran.r-project.org/web/packages/rWind/rWind.pdf">
    <title>rWind package on CRAN</title>
    <dc:date>2018-10-04T06:24:22+00:00</dc:date>
    <link>https://cran.r-project.org/web/packages/rWind/rWind.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[For access to wind velocity data sets.  (Surprisingly slow access, but very glad somebody has written this so I don't have to!)

--- ETA: The server they're yanking the data from is very temperamental, and grabbing a long temporal stretch is almost sure to fail.  But grabbing about 30 days of data at a time seems OK.]]></description>
<dc:subject>R data_sets to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:42a87a67f055/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</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>2018-09-19T15:25:38+00:00</dc:date>
    <link>https://www.blog.google/products/search/making-it-easier-discover-datasets/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Data set search; not sure how well it really works yet (or how long it will live, before Google breaks it.  [Why, yes, I am still bitter about Reader.])]]></description>
<dc:subject>data_sets to_teach</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:af585e9ae47c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.airnow.gov/index.cfm?action=airnow.local_city&amp;cityid=164&amp;mapdate=20180905">
    <title>AirNow - Pittsburgh, PA Air Quality</title>
    <dc:date>2018-09-06T06:19:05+00:00</dc:date>
    <link>https://www.airnow.gov/index.cfm?action=airnow.local_city&amp;cityid=164&amp;mapdate=20180905</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Apparently one can retrieve historical data by tweaking the URL, at least a coarse, city-by-city, daily-average level.  (The animated maps suggest much more detailed data available somewhere.)]]></description>
<dc:subject>data_sets pollution pennsylvania to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a9c064891069/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pollution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pennsylvania"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.dep.pa.gov/Business/Air/BAQ/MonitoringTopics/AirQualityIndex/Pages/default.aspx">
    <title>Air Quality Index</title>
    <dc:date>2018-09-06T06:15:04+00:00</dc:date>
    <link>https://www.dep.pa.gov/Business/Air/BAQ/MonitoringTopics/AirQualityIndex/Pages/default.aspx</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Appears to only have the most recent measurement, not an archive.  Write to see if that's available?]]></description>
<dc:subject>data_sets pollution to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f092278fbeea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pollution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.dep.pa.gov/Business/Air/BAQ/MonitoringTopics/PrincipalPollutants/Pages/Discrete-Data-Reports.aspx#.VwUWCZ3D9hE">
    <title>Discrete Data Reports</title>
    <dc:date>2018-09-06T06:11:57+00:00</dc:date>
    <link>https://www.dep.pa.gov/Business/Air/BAQ/MonitoringTopics/PrincipalPollutants/Pages/Discrete-Data-Reports.aspx#.VwUWCZ3D9hE</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Particulate pollution from PA DEP --- reports are tables in PDFs, so need to either get a tool to extract from there, or to re-type.]]></description>
<dc:subject>data_sets pollution to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:982f26eefa59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pollution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://atmenv.envi.osakafu-u.ac.jp/aono/kyophenotemp4/">
    <title>Cherry blossom phenology and temperature reconstructions at Kyoto | 生態気象学研究グループ</title>
    <dc:date>2018-08-26T22:03:05+00:00</dc:date>
    <link>http://atmenv.envi.osakafu-u.ac.jp/aono/kyophenotemp4/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I have searched and collected the phenological data for full flowering date of cherry tree (Prunus jamasakura) from many diaries and chronicles written by Emperors, aristocrats, goveners and monks at Kyoto in historical time. The dates, on which cherry blossom viewing parties had been held or full flowerings had been observed, were collected from old documents. On this page, you can see the long series of phenological data of full flowering for cherry tree at Kyoto since 9th century. All of full flowering dates are expressed in Gregorian calender. The titles of old documents used as references are also shown."
This data was applied to climatic reconstruction of March mean temperature in Kyoto city. You can see the result of climatic reconstruction in our two papers. Data from the 9th to the 14th centuries was acquired and analyzed by Aono and Saito (2010; International Journal of Biometeorology, 54, 211-219). Phenology for 15th to 21st centuries was acquired and analyzed by Aono and Kazui (2008; International Journal of Climatology, 28, 905-914). If you want to use this phenological data for your research or want to quote this data in your figure, please give me the notice of that and indicate above credit for citation.]]></description>
<dc:subject>data_sets time_series climatology climate_change to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1023c73b65ff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climatology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://creatingdata.us/datasets/US-cities/">
    <title>The Alperin-Sheriff/Wikipedia Population dataset</title>
    <dc:date>2018-03-12T19:48:07+00:00</dc:date>
    <link>http://creatingdata.us/datasets/US-cities/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to_read data_sets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f0ed23a7bfa2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/">
    <title>Exploring the ChestXray14 dataset: problems – Luke Oakden-Rayner</title>
    <dc:date>2018-01-30T17:25:50+00:00</dc:date>
    <link>https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read data_analysis data_sets medicine classifiers statistics via:tslumley spatial_statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:63604ef7bed5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:medicine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:tslumley"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://konect.uni-koblenz.de/">
    <title>KONECT - The Koblenz Network Collection</title>
    <dc:date>2016-05-26T12:46:15+00:00</dc:date>
    <link>http://konect.uni-koblenz.de/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["KONECT contains over a hundred network datasets of various types, including directed, undirected, bipartite, weighted, unweighted, signed and rating networks. The networks of KONECT are collected from many diverse areas such as social networks, hyperlink networks, authorship networks, physical networks, interaction networks and communication networks. The KONECT project has developed network analysis tools which are used to compute network statistics, to draw plots and to implement various link prediction algorithms. The result of these analyses are presented on these pages. Whenever we are allowed to do so, we provide a download of the networks."]]></description>
<dc:subject>to:NB to_teach:baby-nets data_sets networks network_data_analysis social_networks via:BenjaminLind</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2e9b7ca215fa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:BenjaminLind"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/briatte/awesome-network-analysis">
    <title>GitHub - briatte/awesome-network-analysis: A curated list of awesome network analysis resources.</title>
    <dc:date>2016-04-21T13:42:58+00:00</dc:date>
    <link>https://github.com/briatte/awesome-network-analysis</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>network_data_analysis data_sets to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5fb8abfd706c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://eh.net/database/international-currencies-1890-1910/">
    <title>International Currencies 1890-1910</title>
    <dc:date>2016-04-19T13:45:47+00:00</dc:date>
    <link>http://eh.net/database/international-currencies-1890-1910/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Data set underlying the "Ties that Divide" paper, https://pinboard.in/u:cshalizi/b:8027cc78db0d]]></description>
<dc:subject>economics economic_history finance network_data_analysis data_sets to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:83c55f6b0491/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://crcns.org/">
    <title>Welcome to the CRCNS data sharing website — CRCNS.org</title>
    <dc:date>2015-03-31T22:56:22+00:00</dc:date>
    <link>http://crcns.org/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Sharing neural data; some of the data sets require an (anonymous) login.

--- See about using one of the movement data sets for a multivariate-analysis problem set (or exam?).]]></description>
<dc:subject>neuroscience data_sets to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da1c2926cb63/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nlsinfo.org/">
    <title>National Longitudinal Surveys | A Program of the U.S. Bureau of Labor Statistics</title>
    <dc:date>2015-03-10T22:52:05+00:00</dc:date>
    <link>https://www.nlsinfo.org/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets to_teach:undergrad-ADA re:g_paper time_series to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2abe28922d40/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:g_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature.com/articles/sdata201454">
    <title>A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures : Scientific Data</title>
    <dc:date>2015-01-22T00:17:53+00:00</dc:date>
    <link>http://www.nature.com/articles/sdata201454</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Here we present a test-retest dataset of functional magnetic resonance imaging (fMRI) data acquired at rest. 22 participants were scanned during two sessions spaced one week apart. Each session includes two 1.5 mm isotropic whole-brain scans and one 0.75 mm isotropic scan of the prefrontal cortex, giving a total of six time-points. Additionally, the dataset includes measures of mood, sustained attention, blood pressure, respiration, pulse, and the content of self-generated thoughts (mind wandering). This data enables the investigation of sources of both intra- and inter-session variability not only limited to physiological changes, but also including alterations in cognitive and affective states, at high spatial resolution. The dataset is accompanied by a detailed experimental protocol and source code of all stimuli used."]]></description>
<dc:subject>to:NB data_sets fmri re:functional_communities</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bda54315b8cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fmri"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:functional_communities"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ericachenoweth.com/research/wcrw/">
    <title>Why Civil Resistance Works | Erica Chenoweth</title>
    <dc:date>2015-01-16T19:10:54+00:00</dc:date>
    <link>http://www.ericachenoweth.com/research/wcrw/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Considered making the replication data the basis for the next problem set, in honor of MLK day, but it really needs binary-outcome models --- which, fortunately, we'll cover just in time to make this the mid-term exam.]]></description>
<dc:subject>books:noted data_sets non-violence political_science to_teach:undergrad-ADA have_taught</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3253da41f216/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_taught"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.unido.org/en/resources/statistics/statistical-databases.html">
    <title>Statistical Databases: UNIDO</title>
    <dc:date>2015-01-15T23:14:32+00:00</dc:date>
    <link>http://www.unido.org/en/resources/statistics/statistical-databases.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[These would make for great problem sets, but I'd have to get one of the "secondary dissemination" licenses, and those seem to start at 800 euros!  Yowza.  Maybe if I ever have unspent funds at the end of a grant...]]></description>
<dc:subject>data_sets economics to_teach</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ff7eb93c297f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm">
    <title>BEA: News Release: Personal Consumption Expenditures by State, 1997-2012 (Prototype Estimates)</title>
    <dc:date>2014-09-21T17:57:44+00:00</dc:date>
    <link>http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets economics to_teach:statcomp time_series to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:100e6de68393/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://simplystatistics.org/2014/06/30/piketty-in-r-markdown-we-need-some-help-from-the-crowd/">
    <title>Piketty in R markdown – we need some help from the crowd | Simply Statistics</title>
    <dc:date>2014-07-07T21:02:04+00:00</dc:date>
    <link>http://simplystatistics.org/2014/06/30/piketty-in-r-markdown-we-need-some-help-from-the-crowd/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[The non-proportional spacing of points on the time axis bugged me too, but I think it's more a case of spreadsheet defaults than anything else.]]></description>
<dc:subject>piketty.thomas economics data_sets to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cbc25cda71eb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:piketty.thomas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/jep.28.2.99">
    <title>JEP (28,2) p. 99 - Slicing Up Global Value Chains</title>
    <dc:date>2014-06-19T03:28:29+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/jep.28.2.99</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we "slice up the global value chain" using a decomposition technique that has recently become feasible due to the development of the World Input-Output Database. We trace the value added by all labor and capital that is directly and indirectly needed for the production of final manufacturing goods. The production systems of these goods are highly prone to international fragmentation as many stages can be undertaken in any country with little variation in quality. We seek to establish a series of facts concerning the global fragmentation of production that can serve as a starting point for future analysis. We describe four major trends. First, international fragmentation, as measured by the foreign value-added content of production, has rapidly increased since the early 1990s. Second, in most global value chains there is a strong shift towards value being added by capital and high-skilled labor, and away from less-skilled labor. Third, within global value chains, advanced nations increasingly specialize in activities carried out by high-skilled workers. Fourth, emerging economies surprisingly specialize in capital-intensive activities."]]></description>
<dc:subject>to:NB economics globalization international_trade data_sets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b8166f6a25d1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:globalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:international_trade"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.washingtonpost.com/blogs/monkey-cage/wp/2014/06/09/four-things-everyone-should-know-about-wartime-sexual-violence/">
    <title>Four things everyone should know about wartime sexual violence - The Washington Post</title>
    <dc:date>2014-06-14T19:30:51+00:00</dc:date>
    <link>http://www.washingtonpost.com/blogs/monkey-cage/wp/2014/06/09/four-things-everyone-should-know-about-wartime-sexual-violence/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>war war_crimes misogyny data_sets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cbf21913539c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:war"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:war_crimes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misogyny"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.equality-of-opportunity.org/">
    <title>The Economic Impacts of Tax Expenditures Evidence from Spatial Variation Across the U.S.</title>
    <dc:date>2013-07-22T15:05:21+00:00</dc:date>
    <link>http://www.equality-of-opportunity.org/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Looks nice, and sharing the data is great.  But allow me to be geekier than thou for a moment: _Excel_ files, gentlemen?  Do the words "Reinhart and Rogoff" mean nothing to you?]]></description>
<dc:subject>economics inequality class_struggles_in_america spatial_statistics data_sets statistics to_teach:undergrad-ADA to_teach:statcomp have_read to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d83163e4439a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:class_struggles_in_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mitpress.mit.edu/books/raw-data-oxymoron">
    <title>&quot;Raw Data&quot; Is an Oxymoron | The MIT Press</title>
    <dc:date>2013-06-26T16:22:24+00:00</dc:date>
    <link>http://mitpress.mit.edu/books/raw-data-oxymoron</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We live in the era of Big Data, with storage and transmission capacity measured not just in terabytes but in petabytes (where peta- denotes a quadrillion, or a thousand trillion). Data collection is constant and even insidious, with every click and every “like” stored somewhere for something. This book reminds us that data is anything but “raw,” that we shouldn’t think of data as a natural resource but as a cultural one that needs to be generated, protected, and interpreted. The book’s essays describe eight episodes in the history of data from the predigital to the digital. Together they address such issues as the ways that different kinds of data and different domains of inquiry are mutually defining; how data are variously “cooked” in the processes of their collection and use; and conflicts over what can—or can’t—be “reduced” to data. Contributors discuss the intellectual history of data as a concept; describe early financial modeling and some unusual sources for astronomical data; discover the prehistory of the database in newspaper clippings and index cards; and consider contemporary “dataveillance” of our online habits as well as the complexity of scientific data curation."]]></description>
<dc:subject>to:NB books:noted data_analysis data_sets history_of_science data_mining history_of_technology history_of_statistics to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:70c12e553636/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.econ.yale.edu/~shiller/data.htm">
    <title>Online Data - Robert Shiller</title>
    <dc:date>2013-01-14T15:07:38+00:00</dc:date>
    <link>http://www.econ.yale.edu/~shiller/data.htm</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I feel that the Kids are excessively interested in finance, so I'd rather not feed that, but at the same time, showing how crazy many of the models they're taught in their other classes are is a legitimate pedagogical goal...]]></description>
<dc:subject>data_sets finance to_teach:undergrad-ADA via:jbdelong have_taught</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f81e72ea96c5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:jbdelong"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_taught"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://moreno.ss.uci.edu/data.html#ckm">
    <title>Datasets: Coleman, Katz and Menzel - Diffusion of Innovation</title>
    <dc:date>2012-10-29T16:56:15+00:00</dc:date>
    <link>http://moreno.ss.uci.edu/data.html#ckm</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This data set was prepared by Ron Burt. He dug out the 1966 data collected by Coleman, Katz and Menzel on medical innovation. They had collected data from physicians in four towns in Illinois, Peoria, Bloomington, Quincy and Galesburg.
"They were concerned with the impact of network ties on the physicians' adoption of a new drug, tetracycline..."
]]></description>
<dc:subject>network_data_analysis diffusion_of_innovations data_sets to_teach:complexity-and-inference to_teach:statcomp social_networks to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a37ff945cd2f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diffusion_of_innovations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.quake.geo.berkeley.edu/anss/catalog-search.html">
    <title>ANSS Catalog Search</title>
    <dc:date>2012-10-25T18:06:07+00:00</dc:date>
    <link>http://www.quake.geo.berkeley.edu/anss/catalog-search.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Earthquake catalogs!]]></description>
<dc:subject>data_sets earthquakes time_series to_teach:undergrad-ADA to_teach:statcomp point_processes spatio-temporal_statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6481fcaa270d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:earthquakes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:point_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/jep.26.2.189">
    <title>Using Internet Data for Economic Research</title>
    <dc:date>2012-05-08T19:22:12+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/jep.26.2.189</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The data used by economists can be broadly divided into two categories. First, structured datasets arise when a government agency, trade association, or company can justify the expense of assembling records. The Internet has transformed how economists interact with these datasets by lowering the cost of storing, updating, distributing, finding, and retrieving this information. Second, some economic researchers affirmatively collect data of interest. For researcher-collected data, the Internet opens exceptional possibilities both by increasing the amount of information available for researchers to gather and by lowering researchers' costs of collecting information. In this paper, I explore the Internet's new datasets, present methods for harnessing their wealth, and survey a sampling of the research questions these data help to answer. The first section of this paper discusses "scraping" the Internet for data—that is, collecting data on prices, quantities, and key characteristics that are already available on websites but not yet organized in a form useful for economic research. A second part of the paper considers online experiments, including experiments that the economic researcher observes but does not control (for example, when Amazon or eBay alters site design or bidding rules); and experiments in which a researcher participates in design, including those conducted in partnership with a company or website, and online versions of laboratory experiments. Finally, I discuss certain limits to this type of data collection, including both "terms of use" restrictions on websites and concerns about privacy and confidentiality."]]></description>
<dc:subject>to:NB economics data_sets web re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d3301a184de7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.occupyresearch.net/2012/03/23/preliminary-findings-occupy-research-demographic-and-political-participation-survey/">
    <title>Preliminary Findings: Occupy Research Demographic and Political Participation Survey | Occupy Research</title>
    <dc:date>2012-04-28T18:04:48+00:00</dc:date>
    <link>http://www.occupyresearch.net/2012/03/23/preliminary-findings-occupy-research-demographic-and-political-participation-survey/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets occupy_wall_street to_teach:undergrad-ADA to_teach:data-mining via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0afee1dc90bd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:occupy_wall_street"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://etcsl.orinst.ox.ac.uk/">
    <title>The Electronic Text Corpus of Sumerian Literature</title>
    <dc:date>2012-04-14T13:48:58+00:00</dc:date>
    <link>http://etcsl.orinst.ox.ac.uk/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Sumerian is the first language for which we have written evidence and its literature the earliest known. The Electronic Text Corpus of Sumerian Literature (ETCSL), a project of the University of Oxford, comprises a selection of nearly 400 literary compositions recorded on sources which come from ancient Mesopotamia (modern Iraq) and date to the late third and early second millennia BCE.
"The corpus contains Sumerian texts in transliteration, English prose translations and bibliographical information for each composition. The transliterations and the translations can be searched, browsed and read online using the tools of the website."

(Re to_teach:data_mining tag: here are some bags of words for classification, principal components, topic models, maybe even manifold learning...)]]></description>
<dc:subject>mesopotamia archaeology history_of_ideas data_sets to_teach:data-mining via:? sumeria</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da2ebf0be575/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mesopotamia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:archaeology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sumeria"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://magistraetmater.blog.co.uk/2012/01/16/making-charters-useful-12468505/">
    <title>Making charters useful - Magistra et Mater</title>
    <dc:date>2012-02-15T17:07:27+00:00</dc:date>
    <link>http://magistraetmater.blog.co.uk/2012/01/16/making-charters-useful-12468505/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>medieval_european_history data_sets historiography magistra to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3a679903f3a7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:medieval_european_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:historiography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:magistra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pwt.econ.upenn.edu/php_site/pwt_index.php">
    <title>Penn World Table, index</title>
    <dc:date>2012-01-24T03:07:05+00:00</dc:date>
    <link>http://pwt.econ.upenn.edu/php_site/pwt_index.php</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets economics economic_growth to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3b14bbb01dc2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_growth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://g-mond.parisschoolofeconomics.eu/topincomes/">
    <title>The World Top Incomes Database - G-MonD, PSE-Paris School of Economics</title>
    <dc:date>2011-10-25T14:40:00+00:00</dc:date>
    <link>http://g-mond.parisschoolofeconomics.eu/topincomes/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Possible computational project: code up estimating a Pareto tail for income (all sources) from these statistics, and tracking evolution over time (and perhaps across countries).

Or, an ADA project, suggested by conversation with John B.: look for correlation between (lack of) progressive taxation and job creation, as predicted by the usual right-wing suspects.]]></description>
<dc:subject>inequality economics data_sets to_teach:undergrad-ADA to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d2df6f7adc84/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www3.norc.org/GSS+Website/">
    <title>General Social Survey</title>
    <dc:date>2011-10-17T14:20:31+00:00</dc:date>
    <link>http://www3.norc.org/GSS+Website/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to_teach:data-mining to_teach:undergrad-ADA data_sets sociology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:df4263524f35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.meta-activism.org/">
    <title>The Meta-Activism Project | A Non-Traditional Digital Activism Think Tank</title>
    <dc:date>2011-09-26T17:33:00+00:00</dc:date>
    <link>http://www.meta-activism.org/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Flagged "to_teach:data-mining" if I can think of a good project for students with this.
]]></description>
<dc:subject>networked_life politics data_sets to_teach:data-mining</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3a6edd161940/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lib.stat.cmu.edu/datasets/strikes">
    <title>Western on Strikes</title>
    <dc:date>2011-04-23T17:31:46+00:00</dc:date>
    <link>http://lib.stat.cmu.edu/datasets/strikes</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Missing the union density variable.  Wrote to ask about it.  Referenced paper is http://www.jstor.org/stable/271022, which seems to me exactly the kind of thing Andy and I should mention in "Philosophy and Practice".  --- ETA: Prof. Western wrote back within hours with the union density data, but I'm not sure I can make it public...
]]></description>
<dc:subject>to_teach:undergrad-ADA strikes data_sets</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dfb42df36c71/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:strikes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.bea.gov/regional/gdpmetro/">
    <title>BEA : Gross Domestic Product by Metropolitan Area</title>
    <dc:date>2010-12-24T17:42:46+00:00</dc:date>
    <link>http://www.bea.gov/regional/gdpmetro/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[For the "urban scaling? what urban scaling" post.  Thought: make this into a data analysis exercise in 402?
]]></description>
<dc:subject>data_sets cities economics urban_economics to_teach:undergrad-ADA re:urban_scaling_what_urban_scaling</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0cd5299a1c71/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:urban_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:urban_scaling_what_urban_scaling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstatsoft.org/v37/i06/">
    <title>US Census Spatial and Demographic Data in R: The UScensus2000 Suite of Packages</title>
    <dc:date>2010-12-13T18:58:57+00:00</dc:date>
    <link>http://www.jstatsoft.org/v37/i06/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The US Decennial Census is arguably the most important data set for social science research in the United States. The UScensus2000 suite of packages allows for convenient handling of the 2000 US Census spatial and demographic data. The goal of this article is to showcase the UScensus2000 suite of packages for R, to describe the data contained within these packages, and to demonstrate the helper functions provided for handling this data. The UScensus2000 suite is comprised of spatial and demographic data for the 50 states and Washington DC at four different geographic levels (block, block group, tract, and census designated place). The UScensus2000 suite also contains a number of functions for selecting and aggregating specific geographies or demographic information such as metropolitan statistical areas, counties, etc. ... This article will provide the necessary background for working with this data set, helper functions, and finish with an applied spatial statistics example."
]]></description>
<dc:subject>data_sets census R to_teach:undergrad-ADA</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:81e7083c1749/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:census"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ncdc.noaa.gov/paleo/recons.html">
    <title>World Data Center for Paleoclimatology - Climate Reconstructions</title>
    <dc:date>2010-09-17T14:25:49+00:00</dc:date>
    <link>http://www.ncdc.noaa.gov/paleo/recons.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>climate_change to_teach re:growing_ensemble_project data_sets climatology time_series to_teach:data_over_space_and_time</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fae80986aabd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:growing_ensemble_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climatology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ldeo.columbia.edu/~small/DayNight/">
    <title>Night &amp; Day</title>
    <dc:date>2010-08-31T15:16:04+00:00</dc:date>
    <link>http://www.ldeo.columbia.edu/~small/DayNight/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Urban remote sensing", in part to estimate urban population aggregations w/o reference to administrative districts
]]></description>
<dc:subject>cities urbanism data_sets via:aaron_clauset</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3e705a4130c1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:urbanism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:aaron_clauset"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1280841732">
    <title>Make Research Data Public?—Not Always so Simple: A Dialogue for Statisticians and Science Editors</title>
    <dc:date>2010-08-05T21:42:04+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1280841732</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Nothing very profound or surprising, sadly.
]]></description>
<dc:subject>statistics social_life_of_the_mind data_sets</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c92c31407c4b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://data.worldbank.org/">
    <title>Data | The World Bank</title>
    <dc:date>2010-04-26T23:14:54+00:00</dc:date>
    <link>http://data.worldbank.org/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets development_economics economics to_teach:data-mining world_bank via:warrenellis no_really_via:warrenellis to_teach:undergrad-ADA</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:80177ea73e9d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:development_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:world_bank"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:warrenellis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:no_really_via:warrenellis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lib.stat.cmu.edu/datasets/sleep">
    <title>http://lib.stat.cmu.edu/datasets/sleep</title>
    <dc:date>2010-02-02T19:25:42+00:00</dc:date>
    <link>http://lib.stat.cmu.edu/datasets/sleep</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Correlates of sleep in mammals" data set; to use in 490 for illustrating factor analysis.
]]></description>
<dc:subject>data_sets sleep to_teach:undergrad-research to_teach:data-mining to_teach:undergrad-ADA</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6be9337e7723/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sleep"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://files.oakland.edu/users/dow/web/personal/vitae/method/wc14-dow.pdf">
    <title>Using R for Cross-Cultural Research (Dow)</title>
    <dc:date>2009-11-21T12:50:52+00:00</dc:date>
    <link>http://files.oakland.edu/users/dow/web/personal/vitae/method/wc14-dow.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Describes working with the standard cross-cultural sample in R.  TODO: track down the actual file!  TODO: think about devising suitable examples/problems for data mining.
]]></description>
<dc:subject>anthropology R data_sets via:nikete to_teach:data-mining track_down_references to_teach:undergrad-ADA</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a911acec3529/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:anthropology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:nikete"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.soc.duke.edu/~mcphersn/index.html">
    <title>Ten Towns Dataset Resource Page</title>
    <dc:date>2009-11-14T15:26:05+00:00</dc:date>
    <link>http://www.soc.duke.edu/~mcphersn/index.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>social_networks data_sets to_teach:complexity-and-inference network_data_analysis mcpherson.miller</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3281b2c8fd36/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mcpherson.miller"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.amstat.org/publications/jse/datasets/04cars.txt">
    <title>http://www.amstat.org/publications/jse/datasets/04cars.txt</title>
    <dc:date>2009-09-16T14:15:21+00:00</dc:date>
    <link>http://www.amstat.org/publications/jse/datasets/04cars.txt</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[2004 cars and trucks data.
]]></description>
<dc:subject>data_sets to_teach:data-mining</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8793438f6bf7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008T19">
    <title>LDC Catalog: New York Times Annotated Corpus</title>
    <dc:date>2009-08-21T15:25:53+00:00</dc:date>
    <link>http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008T19</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Sounds like it would be perfect for 350.  Now how the **** do I get access?
]]></description>
<dc:subject>information_retrieval text_mining newspapers data_sets to_teach:data-mining via:myl</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:90c5e1d1e9ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_retrieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:text_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:newspapers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:myl"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.cmu.edu/~enron/">
    <title>Enron Email Dataset</title>
    <dc:date>2009-06-28T17:40:55+00:00</dc:date>
    <link>http://www.cs.cmu.edu/~enron/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>enron fraud corporations networks network_data_analysis criminal_conspiracies data_sets</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:de3757b6582e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:enron"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fraud"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:corporations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:criminal_conspiracies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ida.first.fhg.de/projects/bci/competition_iii/#datasets">
    <title>BCI Competition III</title>
    <dc:date>2009-06-28T16:56:35+00:00</dc:date>
    <link>http://ida.first.fhg.de/projects/bci/competition_iii/#datasets</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[EEG data sets.
]]></description>
<dc:subject>EEG data_sets time_series neuroscience</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:016ce3819772/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:EEG"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.audubon.org/bird/cbc/FAQ.html">
    <title>Christmas Bird Count</title>
    <dc:date>2009-04-29T16:58:59+00:00</dc:date>
    <link>http://www.audubon.org/bird/cbc/FAQ.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Use this as an example of mixture modeling?  Unfortunately there doesn't seem to be a good option to just download a set of all the counts from all years.  Perhaps write to them to see if they'd make such a thing available?
]]></description>
<dc:subject>birds to_teach:data-mining data_sets via:myl</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8b0db793e6e6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:birds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:myl"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>