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17 MARCH 2014

The Pandora Music Genome Project

"We believe that each individual has a unique relationship with music–no one else has tastes exactly like yours. So delivering a great radio experience to each and every listener requires an incredibly broad and deep understanding of music. That's why Pandora is based on the Music Genome Project, the most sophisticated taxonomy of musical information ever collected. It represents over ten years of analysis by our trained team of musicologists, and spans everything from this past Tuesday's new releases all the way back to the Renaissance and Classical music.

Each song in the Music Genome Project is analyzed using up to 450 distinct musical characteristics by a trained music analyst. These attributes capture not only the musical identity of a song, but also the many significant qualities that are relevant to understanding the musical preferences of listeners. The typical music analyst working on the Music Genome Project has a four–year degree in music theory, composition or performance, has passed through a selective screening process and has completed intensive training in the Music Genome's rigorous and precise methodology. To qualify for the work, analysts must have a firm grounding in music theory, including familiarity with a wide range of styles and sounds.

The Music Genome Project's database is built using a methodology that includes the use of precisely defined terminology, a consistent frame of reference, redundant analysis, and ongoing quality control to ensure that data integrity remains reliably high. Pandora does not use machine–listening or other forms of automated data extraction.

The Music Genome Project is updated on a continual basis with the latest releases, emerging artists, and an ever–deepening collection of catalogue titles.

By utilizing the wealth of musicological information stored in the Music Genome Project, Pandora recognizes and responds to each individual's tastes. The result is a much more personalized radio experience – stations that play music you'll love – and nothing else."

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TAGS

analysing dataappeal • attributes • automated data extraction • characteristicsdata analysisdata gathering instruments • data integrity • databasedescriptive labels • ersonalised radio experience • frame of reference • individual preference • individual taste • internet radio • listener preference • machine-listening • metricsmusic • music analyst • Music Genome Project • music taste • music theory • musical characteristicsmusical identitymusical information • musical preferences • musicological information • musicologist • Pandora Radiopersonal taste • precisely terminology • qualities • quality control • radio • radio experience • redundant analysis • relatednesssegmentationsongtaste (sociology)taxonomy • unique taste • user behavioursuser segmentation

CONTRIBUTOR

Simon Perkins
07 OCTOBER 2013

Data Journalism Handbook 1.0

"This website is dedicated to providing anyone interested in getting started with data driven journalism with a collection of learning resources, including relevant events, tools, tutorials, interviews and case studies. The data journalism community and mailing list are dedicated to strengthening the community of journalists, designers, data providers and others, and encouraging collaboration and exchange of expertise."

(European Journalism Centre)

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TAGS

2013analysing dataanalysis data • analysis model • analysis of quantitative informationdatadata analysisdata collection and analysis • data driven journalism • data gathering instrumentsdata infrastructuredata into informationdata journalism • Data Journalism Handbook • data miningdata-drivendigital humanitiesdigital journalism • Dutch Ministry of Education Culture and Science • European Journalism Centre (EJC) • handbookhistorical datajournalismOpen Knowledge Foundationquantitative dataquantitative informationstatisticstrend analysis

CONTRIBUTOR

Simon Perkins
12 MAY 2013

With Enough Data, the Numbers Speak for Themselves...

"Not a chance. The promoters of big data would like us to believe that behind the lines of code and vast databases lie objective and universal insights into patterns of human behavior, be it consumer spending, criminal or terrorist acts, healthy habits, or employee productivity. But many big–data evangelists avoid taking a hard look at the weaknesses. Numbers can't speak for themselves, and data sets –– no matter their scale –– are still objects of human design. The tools of big–data science, such as the Apache Hadoop software framework, do not immunize us from skews, gaps, and faulty assumptions. Those factors are particularly significant when big data tries to reflect the social world we live in, yet we can often be fooled into thinking that the results are somehow more objective than human opinions. Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation."

(Kate Crawford, 12 May 2013, Foreign Policy)

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TAGS

Apache Hadoop • biasbig data • big-data science • blind spot • causal relationshipscausationcodecomputer utopianism • consumer spending • criminal actscyberspacedata abstractiondata analysisdata collection and analysisdataset • Foreign Policy (magazine) • globalisationhealthy habitsimplicit informationimplicit meaningInternetinternet utopianism • looking at the numbers • network ecologynetworked society • objects of human design • patterns of human behaviourpatterns of meaningquantified measurementreliability and validityscientific ideas • security intelligence • social world • terrorist acts • Twitterunderlying order • universal insights • universal methoduniversal rationality

CONTRIBUTOR

Simon Perkins
31 MARCH 2013

Qualitative research primarily is inductive in its procedures

"qualitative inquiry is inductive and often iterative in that the evaluator may go through repeated cycles of data collection and analysis to generate hypotheses inductively from the data. These hypotheses, in turn, need to be tested by further data collection and analysis. The researcher starts with a broad research question, such as 'What effects will information systems engendered by reforms in the UK's National Health Service have on relative power and status among clinical and administrative staff in a teaching hospital?' [48].The researcher narrows the study by continually posing increasingly specific questions and attempting to answer them through data already collected and through new data collected for that purpose. These questions cannot all be anticipated in advance. As the evaluator starts to see patterns, or discovers behavior that seems difficult to understand, new questions arise. The process is one of generating hypotheses and explanations from the data, testing them, and modifying them accordingly. New hypotheses may require new data, and, consequently, potential changes in the research design."

(Bonnie Kaplan and Joseph A. Maxwell, p.38, 2005)

Kaplan, B. and J. Maxwell (2005). Qualitative Research Methods for Evaluating Computer Information Systems. Evaluating the Organizational Impact of Healthcare Information Systems. J. Anderson and C. Aydin. New York, Springer: 30–55.

TAGS

Bonnie Kaplandata analysisdata collectiondata collection and analysis • generating explanations • generating hypotheses • hypothesishypothesis testinginductive enquiryinductive proceduresinductive reasoningiterative cycleJoseph Maxwellpatterns of meaning • qualitative enquiry • qualitative researchresearch designresearch questionresearcher • specific questions

CONTRIBUTOR

Simon Perkins
09 JANUARY 2013

Big Data: Text Mining in the Digital Humanities

"Not surprisingly the focus on research methodology in the presentations was also explicitly articulated as an important aspect of drawing out a scholarly practice for the Digital Humanities. It was emphasized that the disclosure of the philosophical and technological rational behind a research methodology is important to develop a sort of academic accountability. These methodological choices are deliberate and meaningfully affect the results of a study.

The rigorous process of explaining and justifying the methodological process is in effect a safe guard against spurious use of computational and statistical tools. 'Big Data' will not allow for humanistic arguments to be proved statistically. Instead it is about producing a dialectic between analytic and anecdotal, such that the computational tools of computers can be assimilated into the process of humanistic scholarship. An important aspect of this is to develop meaningful visualizations to render data readable."

(Mark Turcato, 18 May 2012, Digital Humanities McGill)

TAGS

academic accountability • affect the results of a study • analytic • anecdotal • big data • computational tools • computational tools of computers • data analysis • deliberate and meaningfully • dialectic between analytic and anecdotal • digital humanitiesdisclosure • explaining and justifying • humanistic • humanistic arguments • humanistic scholarship • McGill University • meaningful visualisations • methodological choices • methodological process • philosophical rational • process of humanistic scholarship • proved statistically • render data readable • research methodology • rigorous process • rigourrobustness • safeguard • scholarly practice • spurious use • statistical analysis • statistical tools • technological rationa

CONTRIBUTOR

Simon Perkins
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