<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (rybesh)</title>
    <link>https://pinboard.in/u:rybesh/public/</link>
    <description>recent bookmarks from rybesh</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="http://benschmidt.org/HDA15/?page_id=10"/>
	<rdf:li rdf:resource="https://github.com/jsonlines/guide"/>
	<rdf:li rdf:resource="https://labs.rs/en/metadata/"/>
	<rdf:li rdf:resource="http://whoo.ps/2015/02/23/futures-of-text"/>
	<rdf:li rdf:resource="http://shapeofdata.wordpress.com/2014/02/25/duality-and-coclustering/"/>
	<rdf:li rdf:resource="https://github.com/yinwang0/pysonar2"/>
	<rdf:li rdf:resource="https://github.com/bbcrd/diarize-jruby"/>
	<rdf:li rdf:resource="http://lium3.univ-lemans.fr/diarization/doku.php/"/>
	<rdf:li rdf:resource="http://clair.si.umich.edu/~radev/papers/factoids.pdf"/>
	<rdf:li rdf:resource="http://research.microsoft.com/en-us/um/people/hoifung/papers/pfi13.pdf"/>
	<rdf:li rdf:resource="http://nlp.stanford.edu/pubs/discourse-referent-lifespans.pdf"/>
	<rdf:li rdf:resource="http://www.exp-platform.com/Documents/puzzlingOutcomesInControlledExperiments.pdf"/>
	<rdf:li rdf:resource="http://home.uchicago.edu/~aabbott/Papers/dhq.pdf"/>
	<rdf:li rdf:resource="http://blog.orange11.nl/2012/09/25/whats-so-cool-about-elasticsearch/"/>
	<rdf:li rdf:resource="http://mcmc-jags.sourceforge.net/"/>
	<rdf:li rdf:resource="http://ampcamp.berkeley.edu/"/>
	<rdf:li rdf:resource="https://github.com/square/crossfilter"/>
	<rdf:li rdf:resource="http://maxogden.com/replicating-large-datasets-into-html5"/>
	<rdf:li rdf:resource="http://www.umiacs.umd.edu/~jbg/docs/acl_2012_sits.pdf"/>
	<rdf:li rdf:resource="http://www.lrec-conf.org/proceedings/lrec2010/pdf/682_Paper.pdf"/>
	<rdf:li rdf:resource="http://www.amazon.com/Thinking-Systems-Donella-H-Meadows/dp/1603580557/ref=pd_bxgy_b_text_c"/>
	<rdf:li rdf:resource="http://www.itsbeenreal.co.uk/index.php?/on-going/about/"/>
	<rdf:li rdf:resource="http://bergie.iki.fi/blog/business_analytics_with_couchdb_and_noflo/"/>
	<rdf:li rdf:resource="http://pandas.sourceforge.net/"/>
	<rdf:li rdf:resource="http://www.elasticsearch.org/guide/reference/api/search/facets/"/>
	<rdf:li rdf:resource="http://www.stat.columbia.edu/~gelman/book/"/>
	<rdf:li rdf:resource="http://casci.umd.edu/images/4/46/NodeXL_tutorial_draft.pdf"/>
	<rdf:li rdf:resource="https://wiki.digitalmethods.net/Dmi/ToolLippmannianDevice"/>
	<rdf:li rdf:resource="http://www.slideshare.net/sociomantic/facebook-network-analysis-using-gephi-3996673"/>
	<rdf:li rdf:resource="http://irgupf.com/2009/04/09/retrievability/"/>
	<rdf:li rdf:resource="http://interactionculture.wordpress.com/2009/03/24/discourse-analysis-vs-close-reading/"/>
	<rdf:li rdf:resource="http://web.fumsi.com/go/article/manage/3126"/>
	<rdf:li rdf:resource="http://www.nytimes.com/2008/08/31/business/31view.html"/>
	<rdf:li rdf:resource="http://www.creen.org/"/>
	<rdf:li rdf:resource="http://www.jstor.org/stable/2629118"/>
	<rdf:li rdf:resource="http://www.iath.virginia.edu/~jmu2m/Kings.5-00/primitives.html"/>
	<rdf:li rdf:resource="http://data.un.org/"/>
	<rdf:li rdf:resource="http://analyze.echonest.com/AudioAnalysis.html"/>
	<rdf:li rdf:resource="http://www.ifm.eng.cam.ac.uk/dstools/control/softsm.html"/>
	<rdf:li rdf:resource="http://www.harzing.com/resources.htm#/pop.htm"/>
	<rdf:li rdf:resource="http://www.cs.technion.ac.il/~gabr/resources/code/wikiprep/"/>
	<rdf:li rdf:resource="http://wiki.apache.org/lucene-hadoop/Hbase"/>
	<rdf:li rdf:resource="http://research.yahoo.com/project/pig"/>
	<rdf:li rdf:resource="http://developer.amazonwebservices.com/connect/entry.jspa?externalID=873&amp;categoryID=112"/>
	<rdf:li rdf:resource="http://code.google.com/p/ocropus/"/>
	<rdf:li rdf:resource="http://www.istockphoto.com/copyspace_guide.php"/>
	<rdf:li rdf:resource="http://www.semanticmetadata.net/wiki/doku.php?id=lire:lire"/>
	<rdf:li rdf:resource="http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm"/>
	<rdf:li rdf:resource="http://ws2006.wikisym.org/space/Paper%3E%3EFoucault+at+Wiki"/>
	<rdf:li rdf:resource="http://www.hitech-projects.com/hera/people/nesvadba/"/>
	<rdf:li rdf:resource="http://domino.research.ibm.com/comm/research_people.nsf/pages/milind.index.html"/>
	<rdf:li rdf:resource="http://www.informatik.uni-bremen.de/~herzog/"/>
	<rdf:li rdf:resource="http://mkg.iti.gr/ssms2006/"/>
	<rdf:li rdf:resource="http://bayosphere.com/blog/dan_gillmor/20060124/from_dan_a_letter_to_the_bayosphere_community"/>
	<rdf:li rdf:resource="http://erzuli.ss.uci.edu/R.stuff/"/>
	<rdf:li rdf:resource="http://www.csde.washington.edu/statnet/"/>
	<rdf:li rdf:resource="http://www.octave.org/"/>
	<rdf:li rdf:resource="http://www.style.org/stateoftheunion/"/>
	<rdf:li rdf:resource="http://www.iq.harvard.edu/blog/netgov/2005/11/google_bombs_and_flash_mobs_ef.html"/>
	<rdf:li rdf:resource="http://tamsys.sourceforge.net/osxtams/"/>
	<rdf:li rdf:resource="http://www.qsrinternational.com/products/productoverview/NVivo_7.htm"/>
	<rdf:li rdf:resource="http://maxqda.com/maxqda-eng/index.htm"/>
	<rdf:li rdf:resource="http://www.kino-eye.com/archives/2005/08/transcribing_in.html"/>
	<rdf:li rdf:resource="http://www2.wcer.wisc.edu/Transana/Transana"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="http://benschmidt.org/HDA15/?page_id=10">
    <title>Humanities Data Analysis</title>
    <dc:date>2018-11-04T15:05:17+00:00</dc:date>
    <link>http://benschmidt.org/HDA15/?page_id=10</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Data analysis in the humanities presents challenges of scale, interpretation, and communication distinct from the social sciences or sciences. It also, some argue, opens up new opportunities for creative storytelling and narrativity. This seminar will explore the emerging pratices of data analysis in the digital humanities from both a critical and a practical perspective.

What light can algorithmic approaches shed on live questions in humanistic scholarship? What new forms of research are enabled by the use of data? What sort of data do practicing humanists want museums and libraries to make available?

Our goal in this class will be to explore the new emerging forms of data analysis taking place in humanities scholarship, both in terms of applying algorithms and in terms of better investigating the presuppositions and biases of the digital object. We’ll aim to come out much more sophisticated in the use of computational techniques and much more informed about how others might use them.

A wide variety of types of data will be used but we will focus particularly on methods for analyzing texts, particularly messy data from the Chronicling America Newspapers collection and clean TEI.

Working with these texts will allow us to ask more sophisticated questions on large documents of scholarly importance.]]></description>
<dc:subject>syllabus data analysis datascience digitalhumanities</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4c3f05ad3b3b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:syllabus"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datascience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:digitalhumanities"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/jsonlines/guide">
    <title>jsonlines/guide: Tutorial on streaming JSON data analysis on the command line</title>
    <dc:date>2017-02-04T18:10:27+00:00</dc:date>
    <link>https://github.com/jsonlines/guide</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Tutorial on streaming JSON data analysis on the command line.]]></description>
<dc:subject>json data analysis tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f328912df653/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:json"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://labs.rs/en/metadata/">
    <title>Metadata Investigation : Inside Hacking Team | Share Lab</title>
    <dc:date>2016-05-10T17:02:28+00:00</dc:date>
    <link>https://labs.rs/en/metadata/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[To make a point of just how intrusive metadata analysis can be, we used this substantial amount of metadata we were able to extract from the HT’s published email database, along with publicly available knowledge and a number of free or trial versions of tools available online, to conduct our own investigation.]]></description>
<dc:subject>data analysis surveillance metadata</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:59169e6c0a39/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:surveillance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://whoo.ps/2015/02/23/futures-of-text">
    <title>Futures of text | Whoops by Jonathan Libov</title>
    <dc:date>2015-03-01T17:05:44+00:00</dc:date>
    <link>http://whoo.ps/2015/02/23/futures-of-text</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Text is an incredibly comfortable medium. Text-based interaction is fast, fun, funny, flexible, intimate, descriptive and even consistent in ways that voice and user interface often are not. Always bet on text:

Text is the most socially useful communication technology. It works well in 1:1, 1:N, and M:N modes. It can be indexed and searched efficiently, even by hand. It can be translated. It can be produced and consumed at variable speeds. It is asynchronous. It can be compared, diffed, clustered, corrected, summarized and filtered algorithmically. It permits multiparty editing. It permits branching conversations, lurking, annotation, quoting, reviewing, summarizing, structured responses, exegesis, even fan fic. The breadth, scale and depth of ways people use text is unmatched by anything.]]></description>
<dc:subject>text ui messaging nlp analysis design mobile AI</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c46daf81681d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ui"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:messaging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:mobile"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:AI"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://shapeofdata.wordpress.com/2014/02/25/duality-and-coclustering/">
    <title>Duality and Coclustering | The Shape of Data</title>
    <dc:date>2014-02-25T18:38:54+00:00</dc:date>
    <link>http://shapeofdata.wordpress.com/2014/02/25/duality-and-coclustering/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In general, an algorithm that picks out clusters of both data points and features is called a co-clustering or biclustering algorithm, for hopefully obvious reasons. Forming a bipartite graph and running a standard clustering algorithm, like we did here, is a common approach to co-clustering, though by no means the only approach. While we could get similar information by running a standard clustering algorithm and then carefully analyzing the feature values in each of the resulting clusters, co-clustering can in many cases find better clusters. Moreover, co-clustering gives you direct evidence of which features were most important in determining the clusters, rather than having to infer this after the fact. So, while co-clustering won’t necessarily make sense in all situations, it can be a powerful tool, particularly for things like market-basket data, where there is a strong sense of data point/feature duality.]]></description>
<dc:subject>data analysis graph clustering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e5232294a2a0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:graph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clustering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/yinwang0/pysonar2">
    <title>yinwang0/pysonar2</title>
    <dc:date>2013-11-19T02:05:35+00:00</dc:date>
    <link>https://github.com/yinwang0/pysonar2</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[PySonar2 is a static analyzer for Python, which does sophisticated interprocedural analysis to infer types.]]></description>
<dc:subject>python analysis development tools debugging</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d2bac7c11638/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:debugging"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/bbcrd/diarize-jruby">
    <title>bbcrd/diarize-jruby</title>
    <dc:date>2013-11-01T13:49:08+00:00</dc:date>
    <link>https://github.com/bbcrd/diarize-jruby</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This library provides an easy-to-use toolkit for speaker segmentation (diarization) and identification from audio.

This library is being used within the BBC R&D World Service archive prototype.

See http://worldservice.prototyping.bbc.co.uk/programmes/X0403940 for an example.]]></description>
<dc:subject>audio analysis segmentation tools oralhistory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:1d885416903e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:audio"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:oralhistory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lium3.univ-lemans.fr/diarization/doku.php/">
    <title>LIUM Speaker Diarization Wiki [Welcome]</title>
    <dc:date>2013-11-01T13:45:14+00:00</dc:date>
    <link>http://lium3.univ-lemans.fr/diarization/doku.php/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This wiki presents the LIUM_SpkDiarization tools. LIUM_SpkDiarization is a software dedicated to speaker diarization (ie speaker segmentation and clustering). It is written in Java, and includes the most recent developments in the domain.

LIUM_SpkDiarization comprises a full set of tools to create a complete system for speaker diarization, going from the audio signal to speaker clustering based on the CLR/NCLR metrics. These tools include MFCC computation, speech/non-speech detection, and speaker diarization methods.

This toolkit was developed for the French ESTER2 evaluation campaign, where it obtained the best results for the task of speaker diarization of broadcast news in 2008[1]. Please note that the toolbox is optimized for radio or tv shows. You should not expect the same level of performances on phone conversation and meetings.]]></description>
<dc:subject>audio analysis segmentation oralhistory tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e7adb6a87aaa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:audio"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:oralhistory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://clair.si.umich.edu/~radev/papers/factoids.pdf">
    <title>Random Walk Factoid Annotation for Collective Discourse</title>
    <dc:date>2013-07-07T21:59:39+00:00</dc:date>
    <link>http://clair.si.umich.edu/~radev/papers/factoids.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In this paper, we study the problem of automatically annotating the factoids present in collective discourse. Factoids are information units that are shared between instances of collective discourse and may have many different ways of being realized in words. Our approach divides this problem into two steps, using a graph-based approach for each step: (1) factoid discovery, finding groups of words that correspond to the same factoid, and (2) factoid assignment, using these groups of words to mark collective discourse units that contain the respective factoids. We study this on two novel data sets: the New Yorker caption contest data set, and the crossword clues data set.]]></description>
<dc:subject>facts factoids annotation discourse analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:48d72ae67fcd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:facts"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:factoids"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:annotation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:discourse"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://research.microsoft.com/en-us/um/people/hoifung/papers/pfi13.pdf">
    <title>Probabilistic Frame Induction</title>
    <dc:date>2013-05-16T13:18:35+00:00</dc:date>
    <link>http://research.microsoft.com/en-us/um/people/hoifung/papers/pfi13.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formalized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for information extraction and natural language generation, and is usually done manually. Methods for inducing frames have been proposed recently, but they typically use ad hoc procedures and are difficult to diagnose or extend. In this paper, we propose the first probabilistic approach to frame induction, which incorporates frames, events, and participants as latent topics and learns those frame and event transitions that best explain the text. The number of frame components is inferred by a novel application of a split-merge method from syntactic parsing. In end-to-end evaluations from text to induced frames and extracted facts, our method produces state-of-the-art results while substantially reducing engineering effort.]]></description>
<dc:subject>frames semantics parsing discourse analysis nlp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b733df6495c6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:frames"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semantics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:parsing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:discourse"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nlp.stanford.edu/pubs/discourse-referent-lifespans.pdf">
    <title>The Life and Death of Discourse Entities: Identifying Singleton Mentions</title>
    <dc:date>2013-03-28T13:05:31+00:00</dc:date>
    <link>http://nlp.stanford.edu/pubs/discourse-referent-lifespans.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A discourse typically involves numerous entities, but few are mentioned more than once. Distinguishing discourse entities that die out after just one mention (singletons) from those that lead longer lives (coreferent) would benefit NLP applications such as coreference resolution, protagonist identification, topic modeling, and discourse coherence. We build a logistic regression model for predicting the singleton/coreferent distinction, drawing on linguistic insights about how discourse entity lifespans are affected by syntactic and semantic features. The model is effective in its own right (78% accuracy), and incorporating it into a state-of-the-art coreference resolution system yields a significant improvement.]]></description>
<dc:subject>discourse analysis coreference nlp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:44386d4fc741/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:discourse"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:coreference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.exp-platform.com/Documents/puzzlingOutcomesInControlledExperiments.pdf">
    <title>Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained</title>
    <dc:date>2013-02-13T17:56:56+00:00</dc:date>
    <link>http://www.exp-platform.com/Documents/puzzlingOutcomesInControlledExperiments.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Online controlled experiments are often utilized to make data-driven decisions at Amazon, Microsoft, eBay, Facebook, Google, Yahoo, Zynga, and at many other companies.  While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and mining of online controlled experiments at scale—thousands of experiments now—has taught us many lessons.  These exemplify the proverb that the difference between theory and practice is greater in practice than in theory. We present our learnings as they happened: puzzling outcomes of controlled experiments that we analyzed deeply to understand and explain.  Each of these took multiple-person weeks to months to properly analyze and get to the often surprising root cause. The root causes behind these puzzling results are not isolated incidents; these issues generalized to multiple experiments. The heightened awareness should help readers increase the trustworthiness of the results coming out of controlled experiments.   At Microsoft’s Bing, it is not uncommon to see experiments that impact annual revenue by millions of dollars, thus getting trustworthy results is critical and investing in understanding anomalies has tremendous payoff: reversing a single incorrect decision based on the results of an experiment can fund a whole team of analysts.   The topics we cover include: the OEC (Overall Evaluation Criterion), click tracking, effect trends, experiment length and power, and carryover effects.]]></description>
<dc:subject>statistics machinelearning data analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d1db8655ddce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://home.uchicago.edu/~aabbott/Papers/dhq.pdf">
    <title>Efficiency in Scholarship: Do Keywords Matter?</title>
    <dc:date>2012-10-16T17:06:01+00:00</dc:date>
    <link>http://home.uchicago.edu/~aabbott/Papers/dhq.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA["Andrew Abbott’s hilarious essay on the non-impact of concordances on pre-WWII literary scholarship. It’s a shaft aimed straight at the heart of DH (or at least at the fantasies about the transformative power of search as such)."]]></description>
<dc:subject>digitalhumanities quantitative analysis methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:1691e7b623c9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:digitalhumanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:quantitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.orange11.nl/2012/09/25/whats-so-cool-about-elasticsearch/">
    <title>What’s so cool about elasticsearch? « Orange11 Blog / Orange11: Enterprise Java, Open Source, software solutions, Amsterdam</title>
    <dc:date>2012-10-05T23:52:36+00:00</dc:date>
    <link>http://blog.orange11.nl/2012/09/25/whats-so-cool-about-elasticsearch/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[You usually end up needing to search on different fields with different weights, by applying some filters or conditions to boost document rating based on the value of some pre-defined fields, facebook likes, recent documents, facets,highlighting and so on. All of this can be done through a single query and there is a need to express this complexity: the elasticsearch query DSL is the answer.]]></description>
<dc:subject>elasticsearch search IR inls520 faceted facets analysis classification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f02522710f57/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:faceted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:facets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mcmc-jags.sourceforge.net/">
    <title>JAGS - Just Another Gibbs Sampler</title>
    <dc:date>2012-10-03T22:14:44+00:00</dc:date>
    <link>http://mcmc-jags.sourceforge.net/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[JAGS is Just Another Gibbs Sampler.  It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation  not wholly unlike BUGS.]]></description>
<dc:subject>machinelearning bayesian data analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f2dca9013d7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ampcamp.berkeley.edu/">
    <title>UC Berkeley AMP Camp | The UC Berkeley Big Data AMP Camp, featuring tutorials on popular open-source software including Spark, Shark, Hive, and Mesos; overviews of the Berkeley Data Analytics System (BDAS); and Machine Learning tutorials.</title>
    <dc:date>2012-08-20T18:18:35+00:00</dc:date>
    <link>http://ampcamp.berkeley.edu/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The first UC Berkeley AMP Camp will be hosted in Berkeley (and online) August 21-22, 2012, brought to you by the AMPLab, featuring hands-on tutorials teaching Big Data analysis using the AMPLab software stack, including Spark, Shark, and Mesos. These tools help accelerate Hadoop and other popular data management platforms.

The AMPLab works at the intersection of machine learning, cloud computing, and crowdsourcing; integrating Algorithms, Machines, and People (AMP) to make sense of Big Data, and we want to share our expertise with you! Attendees will learn to solve Big Data problems using components of the Berkeley Data Analytics System (BDAS) and cutting edge machine learning algorithms.]]></description>
<dc:subject>bigdata analysis education hadoop</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:15f13ffabfa9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bigdata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:hadoop"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/square/crossfilter">
    <title>square/crossfilter</title>
    <dc:date>2012-08-06T00:18:34+00:00</dc:date>
    <link>https://github.com/square/crossfilter</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Crossfilter is a JavaScript library for exploring large multivariate datasets in the browser. Crossfilter supports extremely fast (<30ms) interaction with coordinated views, even with datasets containing a million or more records.]]></description>
<dc:subject>javascript infoviz data analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0399ec25ca87/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://maxogden.com/replicating-large-datasets-into-html5">
    <title>Max Ogden Blogotronz</title>
    <dc:date>2012-08-05T21:14:59+00:00</dc:date>
    <link>http://maxogden.com/replicating-large-datasets-into-html5</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The time for fat clients is now! There are some key projects that were all released over the last month or two that allow for some really exciting (relatively) large data manipulation and storage in the browser. If you Voltron them together you can open up an AJAX request to arbitrarily large JSON response that you can stream in realtime into a persistent client-side data store. This workflow opens up a lot of new web application possibilities.]]></description>
<dc:subject>javascript data analysis architecture html</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:542d3c35b0f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:html"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.umiacs.umd.edu/~jbg/docs/acl_2012_sits.pdf">
    <title>SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations</title>
    <dc:date>2012-07-24T21:38:16+00:00</dc:date>
    <link>http://www.umiacs.umd.edu/~jbg/docs/acl_2012_sits.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[One of the key tasks for analyzing conversational data is segmenting it into coherent topic segments. However, most models of topic segmentation ignore the social aspect of conversations, focusing only on the words used. We introduce a hierarchical Bayesian nonparametric model, Speaker Identity for Topic Segmentation (SITS), that discovers (1) the topics used in a conversation, (2) how these topics are shared across conversations, (3) when these topics shift, and (4) a person-specific tendency to introduce new topics. We evaluate against current unsupervised segmentation models to show that including person-specific information improves segmentation performance on meeting corpora and on political debates. Moreover, we provide evidence that SITS captures an individual’s tendency to introduce new topics in political contexts, via analysis of the 2008 US presidential debates and the television program Crossfire.]]></description>
<dc:subject>nlp topicmodels oralhistory discourse analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e4f1db4b96fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:topicmodels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:oralhistory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:discourse"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.lrec-conf.org/proceedings/lrec2010/pdf/682_Paper.pdf">
    <title>Annotating Event Mentions in Text with Modality, Focus, and Source Information</title>
    <dc:date>2012-07-01T12:56:29+00:00</dc:date>
    <link>http://www.lrec-conf.org/proceedings/lrec2010/pdf/682_Paper.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Many natural language processing tasks, including information extraction, question answering and recognizing textual entailment, require analysis of the polarity, focus of polarity, tense, aspect, mood and source of the event mentions in a text in addition to its predicateargument structure analysis. We refer to modality, polarity and other associated information as extended modality. In this paper, we propose a new annotation scheme for representing the extended modality of event mentions in a sentence. Our extended modality consists of the following seven components: Source, Time, Conditional, Primary modality type, Actuality, Evaluation and Focus. We reviewed the literature about extended modality in Linguistics and Natural Language Processing (NLP) and deﬁned appropriate labels of each component. In the proposed annotation scheme, information of extended modality of an event mention is summarized at the core predicate of the event mention for immediate use in NLP applications. We also report on the current progress of our manual annotation of a Japanese corpus of about 50,000 event mentions, showing a reasonably high ratio of inter-annotator agreement.]]></description>
<dc:subject>nlp events annotation discourse analysis japan</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e29b896c6644/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:events"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:annotation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:discourse"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:japan"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.amazon.com/Thinking-Systems-Donella-H-Meadows/dp/1603580557/ref=pd_bxgy_b_text_c">
    <title>Amazon.com: Thinking in Systems: A Primer (9781603580557): Donella H. Meadows: Books</title>
    <dc:date>2012-04-30T19:04:12+00:00</dc:date>
    <link>http://www.amazon.com/Thinking-Systems-Donella-H-Meadows/dp/1603580557/ref=pd_bxgy_b_text_c</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[I first learned and practiced systems analysis back in the 1970s, and it's a skill that seems neglected in the training of many young professionals I come in contact with.

"Thinking in Systems: A Primer" is a book I hoped would be informative and accessible for people who need to develop the skill or just refresh their own talents. It does present its subject systematically and without confusing jargon.

While I found the writing clear and well-organized in its development and presentation of the subject, I found many of the illustrations less than helpful. I would have liked a less holistic and more concrete development of the analysis of the examples in the book.

For use as a textbook, an appendix with a glossary of terms of art and sybols would be very helpful. Nonetheless, reading this will give the novice an appreciation of what systems analysis is, and why it is critical to problem solving. Its informal approach may be more suited for young people today than a more formal and rigidly structured treatment.]]></description>
<dc:subject>systems analysis teaching</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:03d4a08e57f4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:teaching"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.itsbeenreal.co.uk/index.php?/on-going/about/">
    <title>(En)tangled Word Bank</title>
    <dc:date>2011-11-29T13:53:28+00:00</dc:date>
    <link>http://www.itsbeenreal.co.uk/index.php?/on-going/about/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Visualizing the insertions/deletions of text through the six editions of The Origin of Species.]]></description>
<dc:subject>art collation editing text analysis infoviz</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:a0a388f7cdd4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:collation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:editing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bergie.iki.fi/blog/business_analytics_with_couchdb_and_noflo/">
    <title>Henri Bergius: Weblog: Business analytics with CouchDB and NoFlo</title>
    <dc:date>2011-10-10T17:16:55+00:00</dc:date>
    <link>http://bergie.iki.fi/blog/business_analytics_with_couchdb_and_noflo/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Any business analytics system dealing with moderate amounts of data can be built following this approach.

Apache CouchDB is the central data store
All data is stored as JSON-LD entities
NoFlo handles all data imports
Analytics based on the data are done with CouchDB map/reduce
Visualization happens with a CouchApp using JavaScript InfoVis Toolkit]]></description>
<dc:subject>couchdb nodejs flowbased programming data analysis infoviz</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2633b55506f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:couchdb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nodejs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:flowbased"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pandas.sourceforge.net/">
    <title>pandas: a python data analysis library — pandas v0.4.0dev documentation</title>
    <dc:date>2011-08-12T19:56:05+00:00</dc:date>
    <link>http://pandas.sourceforge.net/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[pandas is a python package providing convenient data structures for time series, cross-sectional, or any other form of “labeled” data, with tools for building statistical and econometric models.]]></description>
<dc:subject>python statistics dataprocessing analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d907f5573f09/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:dataprocessing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.elasticsearch.org/guide/reference/api/search/facets/">
    <title>elasticsearch - guide - Search API - Facets</title>
    <dc:date>2011-07-20T14:49:25+00:00</dc:date>
    <link>http://www.elasticsearch.org/guide/reference/api/search/facets/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Facets provide aggregated data based on a search query. In the simple case, a facet can return facet counts for various facet values for a specific field. ElasticSearch supports more advanced facet implementations, such as statistical or date histogram facets.]]></description>
<dc:subject>faceted search api howto facets analysis elasticsearch inls520 IR</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e6f705102128/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:faceted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:api"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:facets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~gelman/book/">
    <title>Home page for the book, &quot;Bayesian Data Analysis&quot;</title>
    <dc:date>2011-06-23T17:31:04+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~gelman/book/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This book is intended to have three roles and to serve three associated audi- ences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and com- putation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics.]]></description>
<dc:subject>bayes statistics data analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:458f7f2dfaf6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bayes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://casci.umd.edu/images/4/46/NodeXL_tutorial_draft.pdf">
    <title>Analyzing Social Media Networks: Learning by Doing with NodeXL</title>
    <dc:date>2011-04-11T21:16:19+00:00</dc:date>
    <link>http://casci.umd.edu/images/4/46/NodeXL_tutorial_draft.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The NodeXL Template for Microsoft Excel 2007 is a free and open source extension to the widely used spreadsheet application that provides a range of basic network analysis and visualization features. NodeXL uses a highly structured workbook template that includes multiple worksheets to store all the information needed to represent a network graph. Network relationships (i.e., graph edges) are represented as an “edge list”, which contains all pairs of vertices that are connected in the network. Other worksheets contain information about each vertex (i.e., node) and cluster. Visualization features allow users to display a range of network graph representations and map data attributes to visual properties including shape, color, size, transparency, and location.]]></description>
<dc:subject>social networks analysis tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:cd3cd1c1c712/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://wiki.digitalmethods.net/Dmi/ToolLippmannianDevice">
    <title>Lippmannian Device</title>
    <dc:date>2011-03-13T13:27:54+00:00</dc:date>
    <link>https://wiki.digitalmethods.net/Dmi/ToolLippmannianDevice</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Lippmannian device is named after Lippmann, and provides a coarse means of showing actor partisanship.]]></description>
<dc:subject>research tools analysis nlp rhetoric</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:40e94c899566/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rhetoric"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.slideshare.net/sociomantic/facebook-network-analysis-using-gephi-3996673">
    <title>Facebook network analysis using Gephi</title>
    <dc:date>2010-11-26T04:39:46+00:00</dc:date>
    <link>http://www.slideshare.net/sociomantic/facebook-network-analysis-using-gephi-3996673</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA["The good people from sociomatic have prepared a nice little slideshow on how to use gephi to analyze social network data extracted from Facebook (using netvizz).  This is a great way to start playing around with network analysis and the slides should really help with the first couple of steps…"]]></description>
<dc:subject>social networking analysis howto</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0c3f59bfd311/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:networking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://irgupf.com/2009/04/09/retrievability/">
    <title>Information Retrieval Gupf » Retrievability</title>
    <dc:date>2009-04-09T17:17:22+00:00</dc:date>
    <link>http://irgupf.com/2009/04/09/retrievability/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Popularity bias (”the inherent democratic nature of the web”) might actually prevent more information from ever being seen, because it never appears at the top of anyone’s query list.
]]></description>
<dc:subject>IR critique search analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:d7854e7e5416/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:critique"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://interactionculture.wordpress.com/2009/03/24/discourse-analysis-vs-close-reading/">
    <title>Discourse Analysis vs. Close Reading « Interaction Culture</title>
    <dc:date>2009-03-24T20:01:40+00:00</dc:date>
    <link>http://interactionculture.wordpress.com/2009/03/24/discourse-analysis-vs-close-reading/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Drop the scientism, HCI! It’s not going to meet our needs and it’s lousy science anyway (all dogmatism is). Good science and good critique should complement and reinforce each other. But as long as we categorically dismiss non-scientific strategies, we’re only fake-interdisciplinary and we’re going to botch our work.
]]></description>
<dc:subject>hci research epistemology methods analysis critique discourse interdisciplinarity interpretation</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:f5c29a767291/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:hci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:critique"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:discourse"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interdisciplinarity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interpretation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://web.fumsi.com/go/article/manage/3126">
    <title>FUMSI -- Helping you Find, Use, Manage and Share Information</title>
    <dc:date>2008-10-15T16:00:05+00:00</dc:date>
    <link>http://web.fumsi.com/go/article/manage/3126</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This two-part article is a step-by-step guide for those wishing to create new taxonomies for their business unit, or client. It will outline the many different elements that make up a quality taxonomy and the pitfalls you should be aware of when starting a new project.
]]></description>
<dc:subject>classification taxonomy information architecture methods design analysis howto</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:bb6df58af131/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:taxonomy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nytimes.com/2008/08/31/business/31view.html">
    <title>History Is Siding With Obama’s Economic Plan</title>
    <dc:date>2008-08-31T16:45:24+00:00</dc:date>
    <link>http://www.nytimes.com/2008/08/31/business/31view.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The United States economy has grown faster, on average, under Democratic presidents than under Republicans. If history is a guide, an Obama victory in November would lead to faster economic growth with less inequality, while a McCain victory would lead to slower economic growth with more inequality.
]]></description>
<dc:subject>economics policy research analysis inequality election 2008 obama mccain</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:7c1c55a5c44d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:election"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:2008"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:obama"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:mccain"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.creen.org/">
    <title>CREEN</title>
    <dc:date>2008-08-25T16:40:54+00:00</dc:date>
    <link>http://www.creen.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The aim of the CREEN project is to develop new methods to recognize emerging critical events in evolving complex networks, coupled networks and active agent networks.
]]></description>
<dc:subject>social networking analysis bibliometrics sts eu research</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:fe190f9ec563/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:networking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bibliometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:sts"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:eu"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/stable/2629118">
    <title>Decentralization by Function and Location</title>
    <dc:date>2008-08-14T17:13:37+00:00</dc:date>
    <link>http://www.jstor.org/stable/2629118</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Under what conditions is decentralization of facilities rational for a client-centered system of service or administration, and when is great centralization more cost-effective?
]]></description>
<dc:subject>decentralization systems analysis design networking economics architecture planning</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:8f8c923de79c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:decentralization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:networking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:planning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.iath.virginia.edu/~jmu2m/Kings.5-00/primitives.html">
    <title>Scholarly Primitives</title>
    <dc:date>2008-06-02T10:51:38+00:00</dc:date>
    <link>http://www.iath.virginia.edu/~jmu2m/Kings.5-00/primitives.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[My immediate intention in presenting these is to suggest a list of functions (recursive functions) that could be the basis for a manageable but also useful tool-building enterprise in humanities computing.
]]></description>
<dc:subject>digitalhumanities humanities research methods analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:eac2374e8419/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:digitalhumanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:humanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://data.un.org/">
    <title>UNdata</title>
    <dc:date>2008-03-06T05:58:15+00:00</dc:date>
    <link>http://data.un.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[An easy to use data access system was developed that meets UNSD’s vision of providing an integrated information resource with current, relevant and reliable statistics free of charge to the global community.
]]></description>
<dc:subject>statistics database opendata demographics development economics analysis archives government</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:ddc472eeef4c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:opendata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:demographics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:archives"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:government"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://analyze.echonest.com/AudioAnalysis.html">
    <title>The Echo Nest API</title>
    <dc:date>2008-01-29T19:39:52+00:00</dc:date>
    <link>http://analyze.echonest.com/AudioAnalysis.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The music analyzer API can help power music visualization, music games, artistic installations, and DJ applications with a much deeper level of music structure understanding.
]]></description>
<dc:subject>audio analysis api music metadata tools webservices</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:01fc8c36a9a0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:audio"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:api"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:music"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:webservices"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ifm.eng.cam.ac.uk/dstools/control/softsm.html">
    <title>Soft Systems Methodology</title>
    <dc:date>2007-08-29T20:53:10+00:00</dc:date>
    <link>http://www.ifm.eng.cam.ac.uk/dstools/control/softsm.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The complexity of many organisational/social problem situations defeats attempts at defining a problem: in many such situations the problem is 'what is the problem?'. SSM provides a framework for tackling such situations.
]]></description>
<dc:subject>systems analysis methods problem-solving design creativity</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:a6501e81174d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:problem-solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:creativity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.harzing.com/resources.htm#/pop.htm">
    <title>Harzing.com - Research in International and Cross-cultural Management</title>
    <dc:date>2007-08-28T18:19:46+00:00</dc:date>
    <link>http://www.harzing.com/resources.htm#/pop.htm</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Publish or Perish is a software program that retrieves and analyzes academic citations. It uses Google Scholar to obtain the raw citations
]]></description>
<dc:subject>academia citation analysis research tools</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:05e31cde329d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:citation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.technion.ac.il/~gabr/resources/code/wikiprep/">
    <title>Wikipedia Preprocessor (WikiPrep)</title>
    <dc:date>2007-08-14T17:41:53+00:00</dc:date>
    <link>http://www.cs.technion.ac.il/~gabr/resources/code/wikiprep/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[WikiPrep is a preprocessing script written in Perl that takes an XML dump of Wikipedia, and infers some information that was implicitly present there.
]]></description>
<dc:subject>wiki xml perl analysis tools research</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:0c3e6d3fd21b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:wiki"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:xml"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:perl"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://wiki.apache.org/lucene-hadoop/Hbase">
    <title>Hbase - Lucene-hadoop Wiki</title>
    <dc:date>2007-08-04T02:51:22+00:00</dc:date>
    <link>http://wiki.apache.org/lucene-hadoop/Hbase</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Data is organized into tables, rows and columns, but a query language like SQL is not supported. An Iterator-like interface is available for scanning through a row range (and of course there is an ability to retrieve a column value for a specific key).
]]></description>
<dc:subject>distributed grid database quantitative research analysis tools opensource</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:2596ad699059/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:grid"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:quantitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:opensource"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://research.yahoo.com/project/pig">
    <title>Pig | Yahoo! Research</title>
    <dc:date>2007-08-04T02:49:37+00:00</dc:date>
    <link>http://research.yahoo.com/project/pig</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The highest abstraction layer in Pig is a query language interface, whereby users express data analysis tasks as queries, in the style of SQL or Relational Algebra.
]]></description>
<dc:subject>distributed grid database quantitative research analysis tools opensource</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:943c4f792b24/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:grid"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:quantitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:opensource"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://developer.amazonwebservices.com/connect/entry.jspa?externalID=873&amp;categoryID=112">
    <title>Running Hadoop MapReduce on Amazon EC2 and Amazon S3</title>
    <dc:date>2007-08-04T02:32:20+00:00</dc:date>
    <link>http://developer.amazonwebservices.com/connect/entry.jspa?externalID=873&amp;categoryID=112</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This paper describes how to use Amazon Web Services and Hadoop to run an ad hoc analysis on a large collection of web access logs that otherwise would have cost a prohibitive amount in either time or money.
]]></description>
<dc:subject>distributed grid quantitative research nlp analysis howto</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:68d0d44999ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:grid"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:quantitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://code.google.com/p/ocropus/">
    <title>ocropus</title>
    <dc:date>2007-05-19T22:47:02+00:00</dc:date>
    <link>http://code.google.com/p/ocropus/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[OCRopus is a state-of-the-art document analysis and OCR system, featuring pluggable layout analysis, pluggable character recognition, statistical natural language modeling, and multi-lingual capabilities.
]]></description>
<dc:subject>documents analysis c++ python nlp</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:3b76b99c2eda/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:documents"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:c++"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.istockphoto.com/copyspace_guide.php">
    <title>CopySpace at iStockphoto.com</title>
    <dc:date>2007-02-15T17:44:11+00:00</dc:date>
    <link>http://www.istockphoto.com/copyspace_guide.php</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[iStockphoto.com has a search engine that can sort images based on where you could place text or a logo.
]]></description>
<dc:subject>design search image analysis advertising</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:2e0b095be67b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:image"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:advertising"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.semanticmetadata.net/wiki/doku.php?id=lire:lire">
    <title>LIRE</title>
    <dc:date>2006-08-04T15:37:16+00:00</dc:date>
    <link>http://www.semanticmetadata.net/wiki/doku.php?id=lire:lire</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The LIRE (Lucene Image REtrieval) library is a simple way to create a Lucene index of image features for content based image retrieval (CBIR).
]]></description>
<dc:subject>image analysis search mpeg-7</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:99485f790f36/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:image"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:mpeg-7"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm">
    <title>Topic Modeling Toolbox</title>
    <dc:date>2006-07-31T17:12:19+00:00</dc:date>
    <link>http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Tools for entity recognition, extraction and linking.
]]></description>
<dc:subject>nlp tools research statistics datamining analysis matlab</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:4c85f1a86d0d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:matlab"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ws2006.wikisym.org/space/Paper%3E%3EFoucault+at+Wiki">
    <title>WikiSym 2006 :: Paper&gt;&gt;Foucault at Wiki</title>
    <dc:date>2006-07-11T05:27:12+00:00</dc:date>
    <link>http://ws2006.wikisym.org/space/Paper%3E%3EFoucault+at+Wiki</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The paper argues, that Wikipedia can be understood as a discursive formation that regulates and structures the production of statements.
]]></description>
<dc:subject>wiki research discourse analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:50b157c33424/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:wiki"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:discourse"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.hitech-projects.com/hera/people/nesvadba/">
    <title>Jan Nesvadba</title>
    <dc:date>2006-07-06T03:26:27+00:00</dc:date>
    <link>http://www.hitech-projects.com/hera/people/nesvadba/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Developing content analysis algorithms and systems to generate meta-data locally on mobile/ubicomp platforms.
]]></description>
<dc:subject>people research philips SSMS2006 multimedia analysis mobile metadata ubicomp</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:ea8a6d7ae32e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:philips"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:SSMS2006"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:multimedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:mobile"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ubicomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://domino.research.ibm.com/comm/research_people.nsf/pages/milind.index.html">
    <title>Milind Naphade</title>
    <dc:date>2006-07-06T03:23:55+00:00</dc:date>
    <link>http://domino.research.ibm.com/comm/research_people.nsf/pages/milind.index.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Research interests in content analysis, information extraction, statistical machine learning and graphical modeling and detection and representation of semantic information.
]]></description>
<dc:subject>multimedia analysis machinelearning semweb people IBM SSMS2006</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:7cb67a4e6816/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:multimedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IBM"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:SSMS2006"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.informatik.uni-bremen.de/~herzog/">
    <title>Otthein Herzog</title>
    <dc:date>2006-07-06T03:20:19+00:00</dc:date>
    <link>http://www.informatik.uni-bremen.de/~herzog/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Research interests include automatic content analysis and annotation of still images, videos and sound for content-driven multimedia archiving and retrieval.
]]></description>
<dc:subject>multimedia analysis annotation archives search people academia SSMS2006</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:cb25f7b7b63b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:multimedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:annotation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:archives"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:SSMS2006"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mkg.iti.gr/ssms2006/">
    <title>SSMS 2006</title>
    <dc:date>2006-07-06T03:18:45+00:00</dc:date>
    <link>http://mkg.iti.gr/ssms2006/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Understanding and thereby manipulating, multimedia content at the semantic level is the only way towards realizing the full potential of emerging digital media technologies.
]]></description>
<dc:subject>greece thessaloniki multimedia conference annotation analysis search semweb education SSMS2006</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:df6de17fc176/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:greece"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:thessaloniki"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:multimedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:conference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:annotation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:SSMS2006"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bayosphere.com/blog/dan_gillmor/20060124/from_dan_a_letter_to_the_bayosphere_community">
    <title>From Dan: A Letter to the Bayosphere Community | Bayosphere</title>
    <dc:date>2006-02-03T04:20:08+00:00</dc:date>
    <link>http://bayosphere.com/blog/dan_gillmor/20060124/from_dan_a_letter_to_the_bayosphere_community</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Although the participants -- citizen journalists and commenters -- are essential, it's even more important to remember that publishing is about the audience in the end. Most people who come to the site are not participants.
]]></description>
<dc:subject>analysis participatory journalism media community unmediated</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:4c48b2b2deaa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:participatory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:journalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:community"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:unmediated"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://erzuli.ss.uci.edu/R.stuff/">
    <title>S Routines for Social Network Analysis in the R Environment</title>
    <dc:date>2005-12-21T17:39:28+00:00</dc:date>
    <link>http://erzuli.ss.uci.edu/R.stuff/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This is a fully documented collection of R routines for social network analysis; utilities included range from hierarchical Bayesian modeling of informant accuracy to logistic network regression.
]]></description>
<dc:subject>social networking analysis tools R statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:23f953e88631/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:networking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.csde.washington.edu/statnet/">
    <title>Statnet</title>
    <dc:date>2005-12-09T01:42:53+00:00</dc:date>
    <link>http://www.csde.washington.edu/statnet/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Statnet is a software package for social network analysis based on recent advances in the statistical modeling of random graphs. Runs in R.
]]></description>
<dc:subject>statistics social networking analysis tools</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:ce4af6f4de6c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:networking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.octave.org/">
    <title>Octave</title>
    <dc:date>2005-12-09T01:33:06+00:00</dc:date>
    <link>http://www.octave.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[GNU Octave is a high-level language, primarily intended for numerical computations.
]]></description>
<dc:subject>analysis math unix osx tools statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:f3e6a8b4d672/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:unix"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:osx"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.style.org/stateoftheunion/">
    <title>Parsing the State of the Union</title>
    <dc:date>2005-12-07T06:39:43+00:00</dc:date>
    <link>http://www.style.org/stateoftheunion/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[To search for your own words or phrases, or to compare the occurrence of two words in Bush’s State of the Union Addresses, please try the State of the Union Parsing Tool.
]]></description>
<dc:subject>politics political media analysis language infoviz speech statistics search</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:549a50576d1a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:political"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:speech"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.iq.harvard.edu/blog/netgov/2005/11/google_bombs_and_flash_mobs_ef.html">
    <title>Google bombs - Voice option and collective action</title>
    <dc:date>2005-11-21T17:22:02+00:00</dc:date>
    <link>http://www.iq.harvard.edu/blog/netgov/2005/11/google_bombs_and_flash_mobs_ef.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A "google bomb" is indirectly an example of global network building and collective action of website owners and bloggers based on a common idea or opinion.
]]></description>
<dc:subject>search social metadata collectiveaction blog web analysis EIND</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:55ede49dc6a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:collectiveaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:EIND"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://tamsys.sourceforge.net/osxtams/">
    <title>TAMS Analyzer for OS X</title>
    <dc:date>2005-11-18T19:05:05+00:00</dc:date>
    <link>http://tamsys.sourceforge.net/osxtams/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Open source qualitative transcription/analysis tool for OSX.
]]></description>
<dc:subject>qualitative analysis tools opensource osx</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:787fc654d647/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:qualitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:opensource"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:osx"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.qsrinternational.com/products/productoverview/NVivo_7.htm">
    <title>QSR International - NVivo</title>
    <dc:date>2005-11-18T18:39:36+00:00</dc:date>
    <link>http://www.qsrinternational.com/products/productoverview/NVivo_7.htm</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[NVivo 7 is ideal for team projects and research involving multiple methods.
]]></description>
<dc:subject>qualitative analysis tools</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:3017a77480e1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:qualitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://maxqda.com/maxqda-eng/index.htm">
    <title>MAXqda2</title>
    <dc:date>2005-11-18T18:28:54+00:00</dc:date>
    <link>http://maxqda.com/maxqda-eng/index.htm</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Supports qualitative data analysis and helps you systematically evaluate and interpret your texts. It is also a powerful tool for developing theories as well as testing theoretical conclusions of your analysis.
]]></description>
<dc:subject>qualitative analysis tools</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:37edd7997e13/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:qualitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.kino-eye.com/archives/2005/08/transcribing_in.html">
    <title>Transcriva makes transcribing (almost) fun</title>
    <dc:date>2005-09-01T02:01:27+00:00</dc:date>
    <link>http://www.kino-eye.com/archives/2005/08/transcribing_in.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Transcriva transform the process of transcribing interviews from a tedious chore into a graceful process with an efficient chat-like interface using keyboard shortcuts.
]]></description>
<dc:subject>audio subtitle tools qualitative analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:24bbfd7c4eb5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:audio"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:subtitle"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:qualitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www2.wcer.wisc.edu/Transana/Transana">
    <title>Transana</title>
    <dc:date>2005-04-14T19:57:01+00:00</dc:date>
    <link>http://www2.wcer.wisc.edu/Transana/Transana</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Transana is designed to facilitate the transcription and qualitative analysis of video and audio data. It provides a way to view video or play audio recordings, create a transcript, and link places in the transcript to frames in the video.
]]></description>
<dc:subject>video metadata subtitle tools python qualitative analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:b1ea4f7f6b74/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:subtitle"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:qualitative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>