Difference between revisions of "RepeatabilityCentral"
(Created page with '''Site under construction'' Repeatability Central A hallmark of the scientific method has been that experiments should be described in enough detail that they can be repeated a…') |
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'''Project Leaders:''' [http://www.cs.utah.edu/~juliana Juliana Freire] and [http://www.cs.nyu.edu/shasha/ Dennis Shasha] | |||
''Site under construction'' | ''Site under construction'' | ||
'''Abstract''' | |||
A hallmark of the scientific method has been that experiments should be described in enough detail that they can be repeated and perhaps generalized. This implies the possibility of repeating results on nominally equal configurations and then generalizing the results by replaying them on new data sets, and seeing how they vary with different parameters. In principle, this should be easier for computational experiments than for natural science experiments, because not only can computational processes be automated but also computational systems do not suffer from the ``biological variation'' that plagues the life sciences. Unfortunately, the state of the art falls far short of this goal. Most computational experiments are specified only informally in papers, where experimental results are briefly described in figure captions; the code that produced the results is seldom available; and configuration parameters change results in unforeseen ways. Because important scientific discoveries are often the result of sequences of smaller, less significant steps, the ability to publish results that are fully documented and reproducible is necessary for advancing science. While concern about repeatability and generalizability cuts across virtually all natural, computational, and social science fields, no single field has identified this concern as a target of a research effort. | A hallmark of the scientific method has been that experiments should be described in enough detail that they can be repeated and perhaps generalized. This implies the possibility of repeating results on nominally equal configurations and then generalizing the results by replaying them on new data sets, and seeing how they vary with different parameters. In principle, this should be easier for computational experiments than for natural science experiments, because not only can computational processes be automated but also computational systems do not suffer from the ``biological variation'' that plagues the life sciences. Unfortunately, the state of the art falls far short of this goal. Most computational experiments are specified only informally in papers, where experimental results are briefly described in figure captions; the code that produced the results is seldom available; and configuration parameters change results in unforeseen ways. Because important scientific discoveries are often the result of sequences of smaller, less significant steps, the ability to publish results that are fully documented and reproducible is necessary for advancing science. While concern about repeatability and generalizability cuts across virtually all natural, computational, and social science fields, no single field has identified this concern as a target of a research effort. | ||
Revision as of 22:11, 8 July 2010
Project Leaders: Juliana Freire and Dennis Shasha
Site under construction
Abstract
A hallmark of the scientific method has been that experiments should be described in enough detail that they can be repeated and perhaps generalized. This implies the possibility of repeating results on nominally equal configurations and then generalizing the results by replaying them on new data sets, and seeing how they vary with different parameters. In principle, this should be easier for computational experiments than for natural science experiments, because not only can computational processes be automated but also computational systems do not suffer from the ``biological variation that plagues the life sciences. Unfortunately, the state of the art falls far short of this goal. Most computational experiments are specified only informally in papers, where experimental results are briefly described in figure captions; the code that produced the results is seldom available; and configuration parameters change results in unforeseen ways. Because important scientific discoveries are often the result of sequences of smaller, less significant steps, the ability to publish results that are fully documented and reproducible is necessary for advancing science. While concern about repeatability and generalizability cuts across virtually all natural, computational, and social science fields, no single field has identified this concern as a target of a research effort.
This collaborative project between the University of Utah and New York University consists of tools and infrastructure that supports the process of sharing, testing and re-using scientific experiments and results by leveraging and extending the infrastructure provided by provenance-enabled scientific workflow systems. The project explores three key research questions: 1) How to package and publish compendia of scientific results that are reproducible and generalizable. 2) What are appropriate algorithms and interfaces for exploring, comparing, re-using the results or potentially discovering better approaches for a given problem? 3) How to aid reviewers to generate experiments that are most informative given a time/resource limit.
An expected result of this work is a software infrastructure that allows authors to create workflows that encode the computational processes that derive the results (including data used, configuration parameters set, and underlying software), publish and connect these to publications where the results are reported. Testers (or reviewers) can repeat and validate results, ask questions anonymously, and modify experimental conditions. Researchers, who want to build upon previous works, are able to search, reproduce, compare and analyze experiments and results. The infrastructure supports scientists, in many disciplines, to derive, publish and share reproducible results.