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Roger Chen

Why the cloud cannot obscure the scientific method - 0 views

arstechnica.com/...ure-the-scientific-method.html

data mining thinking

shared by Roger Chen on 26 Jun 08 - Cached
  • Overall, the foundation of the argument for a replacement for science is correct: the data cloud is changing science, and leaving us in many cases with a Google-level understanding of the connections between things. Where Anderson stumbles is in his conclusions about what this means for science. The fact is that we couldn't have even reached this Google-level understanding without the models and mechanisms that he suggests are doomed to irrelevance.
  • Anderson appears to take the position that the new research part of the equation has become superfluous; simply having a good algorithm that recognizes the correlation is enough.
  • Correlations are a way of catching a scientist's attention, but the models and mechanisms that explain them are how we make the predictions that not only advance science, but generate practical applications.
  • ...1 more annotation...
  • without the testable predictions made by the theory, we'll never be able to tell how precisely it is wrong
  • Roger Chen on 26 Jun 08
     
    This article is a response to Chris Anerson's article "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete" - http://www.wired.com/science/discoveries/magazine/16-07/pb_theory
Roger Chen

The End of Theory: The Data Deluge Makes the Scientific Method Obsolete - 0 views

www.wired.com/...pb_theory

data mining google statistics thinking

shared by Roger Chen on 29 Jun 08 - Cached
  • Sixty years ago, digital computers made information readable. Twenty years ago, the Internet made it reachable. Ten years ago, the first search engine crawlers made it a single database.
  • Google's founding philosophy is that we don't know why this page is better than that one: If the statistics of incoming links say it is, that's good enough.
  • The scientific method is built around testable hypotheses. These models, for the most part, are systems visualized in the minds of scientists. The models are then tested, and experiments confirm or falsify theoretical models of how the world works. This is the way science has worked for hundreds of years.
  • ...6 more annotations...
  • Peter Norvig, Google's research director, offered an update to George Box's maxim: "All models are wrong, and increasingly you can succeed without them."
  • Once you have a model, you can connect the data sets with confidence. Data without a model is just noise.
    • Roger Chen
      Roger Chen on 29 Jun 08
       
      That's what Chris Anderson thought is old-school.
  • But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete.
    • Roger Chen
      Roger Chen on 29 Jun 08
       
      Come to conclusion? I don't think so.
  • There is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.
  • What can science learn from Google?
  • This kind of thinking is poised to go mainstream.
    • Roger Chen
      Roger Chen on 29 Jun 08
       
      ???
  • Roger Chen on 29 Jun 08
     
    "All models are wrong, and increasing you can succeed without them."
Roger Chen

Evolving Thoughts - Basic Concepts in Science: A list - 0 views

scienceblogs.com/...c_concepts_in_science_a_li.php

reference science

shared by Roger Chen on 01 Jul 08 - Cached
  • Roger Chen on 01 Jul 08
     
    This is a list of the Basic Concepts posts being put up by Science Bloggers and others. It will be updated and put to the top when new entries are published.
Roger Chen

Chris Harrison's Homepage - 0 views

www.chrisharrison.net/index.html

academia people visualization

shared by Roger Chen on 28 Jul 08 - Cached
  • Roger Chen on 28 Jul 08
     
    Chris Harrison is a Ph.D. student in the Human-Computer Interaction Institute at Carnegie Mellon University. This site is used as a repository for some of his many projects. These hail from a variety of fields, including computer science, information visualization, engineering, history and HCI.
Roger Chen

2collab Survey Reveals that Scientists and Researchers are "All Business" with Social A... - 0 views

www.elsevier.com/...companynews05_00947

academia social network web2.0

shared by Roger Chen on 27 Jun 08 - Cached
  • scientists are using blogs, wikis, and social networking and bookmarking applications primarily for professional reasons. Results show that these social media applications have provided scientists and researchers with additional resources to help them collaborate, connect, share and discover information.
  • Over 50% of respondents see web-based social applications playing a key role in shaping the future of research. The largest influence will be on critical analysis and evaluation of research data, professional networking and collaboration, dissemination of research output, career development, as well as grant application and funding.
  • Comments from survey respondents identified several issues need to be addressed before mass acceptance by the research community is possible – namely the need for specialist tools, higher security, and validation of users. However, these concerns were not seen as insurmountable obstacles, and many anticipated tremendous potential for social media.
  • Roger Chen on 27 Jun 08
     
    2collab, the research collaboration platform from Elsevier, the world's leading publisher of science, technology and medical (STM) information, announced today the results of a survey, asking researchers about the role of social media in their professional lives. The survey, which yielded over 1,800 responses, revealed that scientists are using blogs, wikis, and social networking and bookmarking applications primarily for professional reasons. Results show that these social media applications have provided scientists and researchers with additional resources to help them collaborate, connect, share and discover information.
Roger Chen

The End Of The Scientific Method… Wha….? « Life as a Physicist - 0 views

gordonwatts.wordpress.com/...d-of-the-scientific-method-wha

data mining thinking

shared by Roger Chen on 27 Jun 08 - Cached
  • His basic thesis is that when you have so much data you can map out every connection, every correlation, then the  data becomes the model. No need to derive or understand what is actually happening — you have so much data that you can already make all the predictions that a model would let you do in the first place. In short — you no longer need to develop a theory or hypothesis - just map the data!
  • First, in order for this to work you need to have millions and millions and millions of data points. You need, basically, ever single outcome possible, with all possible other factors. Huge amounts of data. That does not apply to all branches of science.
  • The second problem with this approach is you will never discover anything new. The problem with new things is there is no data on them!
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  • Correlations are a way of catching a scientist’s attention, but the models and mechanisms that explain them are how we make the predictions that not only advance science, but generate practical applications. One only needs to look at a promising field that lacks a strong theoretical foundation—high-temperature superconductivity springs to mind—to see how badly the lack of a theory can impact progress
  • Anderson is right — we are entering a new age where the ability to mine these large amounts of data are going to open up whole new levels of understanding
  • This is a new tool, and it will open up all sorts of doors for us. But the end of the scientific method? No — because that implies an end of discovery. And end of new things.
Roger Chen

UBC Academic Search - Another Impact Factor Metric - <i>W-index</i> - 0 views

weblogs.elearning.ubc.ca/...046689.html

academia papers

shared by Roger Chen on 19 Jun 08 - Cached
  • Roger Chen on 19 Jun 08
     
    A new index measuring a scientist's impact in his/her field has been developed called the Wu index or w-index. Developed by Qiang Wu from the University of Science and Technology of China in Hefei, it was published as The w-index: A significant improvement of the h-index in this week's Physics arxiv.
Roger Chen

Attribute-Relation File Format (ARFF) - 0 views

www.cs.waikato.ac.nz/...arff.html

data mining python reference

shared by Roger Chen on 30 Jul 08 - Cached
  • Roger Chen on 30 Jul 08
     
    An ARFF (Attribute-Relation File Format) file is an ASCII text file that describes a list of instances sharing a set of attributes. ARFF files were developed by the Machine Learning Project at the Department of Computer Science of The University of Waikato for use with the Weka machine learning software.
Roger Chen

GroupLens Research - 0 views

www.grouplens.org

academia people data mining recommender

shared by Roger Chen on 26 Jun 08 - Cached
  • Roger Chen on 26 Jun 08
     
    GroupLens is a research lab in the Department of Computer Science and Engineering at the University of Minnesota. We conduct research in several areas, including: * recommender systems * online communities * mobile and ubiquitious technologies * digital libraries * local geographic information systems
Roger Chen

Michael Nielsen » Open science - 0 views

michaelnielsen.org/blog

academia thinking

shared by Roger Chen on 14 May 08 - Cached
  • Scientific papers represent only a tiny fraction of the useful knowledge that scientists have to share with the world:
Roger Chen

Data mining is not just a data recovery tool | Styx online - 0 views

www.styx-online.net/...-not-just-a-data-recovery-tool

shared by Roger Chen on 01 Aug 08 - Cached
  • Data Mining is a process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using statistical, data analysis and mathematical techniques
  • Data mining is the crucial process that helps companies better comprehend their customers. Data mining can be defined as ‘the nontrivial extraction of implicit, previously unknown, and potentially useful information from data’ and also as ‘the science of extracting useful information from large sets or databases’.
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