Monday, February 23, 2015
Open API and Public Apps as Law and Policy for Innovation and Competition
MIT City Council member Ben Kallos advocated use of an Open API (enabling many innovative, competitive and different apps) and a new "E-Hail" NYC branded public App to provide an accessible, fair, free baseline service to anchor a growing dynamic economic marketplace of mobile apps for transportation services.
This creative approach could provide the basic formula for a method of law and policy making that can work better for the information age not only in the transportation and other markets as well.
Tuesday, February 3, 2015
Public Data Time Series of US Code
Use the above code to explore the US Code draft data set provided by law.MIT.edu.
Virtualized Organizations
In order to successfully complete the transition to digital business in networked economies, key legal, financial and operational aspects of organizations remain to be brought online in a universalizable manner.
This video presents a conceptual approach to designing totally virtualized (distributed and automated) organizations:
This video discussion with leaders of the Inquiri and Loomio collaboration platforms explores opportunities and options for bringing deliberation and decision making functionality available as a common and reusable capability expressed as a REST service:
This GitHub repository explores an approach to prototyping and testing a "Distributed Autonomous Organization" (DAO) using the type of blockchain underpinning BitCoin:
This video presents a conceptual approach to designing totally virtualized (distributed and automated) organizations:
This video discussion with leaders of the Inquiri and Loomio collaboration platforms explores opportunities and options for bringing deliberation and decision making functionality available as a common and reusable capability expressed as a REST service:
This GitHub repository explores an approach to prototyping and testing a "Distributed Autonomous Organization" (DAO) using the type of blockchain underpinning BitCoin:
Toward Reproducible Computational Law
Reproducibility will be an especially critical component of Computational Law, given the underlying context of rights, responsibilities, requirements, recourse and remedies.
This series of posts is intended to raise awareness, catalyze and foster development of a common understanding about how and when to create and preserve documentation needed to achieve reproducible Computational Law methods and results.
Professor Victoria Stodden has articulated the case for reproducibility as a key requirement for computational science generally. Professor Stodden's recent book "Implementing Reproducible Research" (see below) advocates:
Data and Code Sharing in Bioinformatics: From Bermuda to Toronto to Your Laptop
Summary
Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result.
Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes:
Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research.
Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.
Open Data and Reproducibility in Research: Victoria Stodden @ OpenCon 2014 Talk delivered November 15th, 2014 at OpenCon 2014, the student and early career researcher conference on Open Access, Open Education, and Open Data.
OpenCon is organized by the Right to Research Coalition, SPARC (The Scholarly Publishing and Academic Resources Coalition), and an Organizing Committee of students and early career researchers from around the world. [This video was published by the The Right to Research Coalition]
Reproducing Statistical Results
Toward Reliable and Reproducible Inference in Big Data
This series of posts is intended to raise awareness, catalyze and foster development of a common understanding about how and when to create and preserve documentation needed to achieve reproducible Computational Law methods and results.
Professor Victoria Stodden has articulated the case for reproducibility as a key requirement for computational science generally. Professor Stodden's recent book "Implementing Reproducible Research" (see below) advocates:
"In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden."Professor Stodden has informally been kind enough to offer valuable advice about the scope and direction of law.MIT.edu prior to launch and been an important collaborator on a flagship book on Big Data Privacy. As a start toward building a coherent framework for reproducibility of Computational Law we will explore the following materials by Professor Stodden:
Data and Code Sharing in Bioinformatics: From Bermuda to Toronto to Your Laptop
Abstract
Large-scale sequencing projects paved the way for the adoption of pioneering open data policies, making bioinformatics one the leading fields for data availability and access. In this talk I will trace the history of open data in -omics based research, and discuss how open code as well are data are being addressed today. This will include discussing leading edge tools and computational infrastructure developments intended to facilitate reproducible research through workflow tracking, computational environments, and data and code sharing. [March 13, 2014 talk by Professor Victoria Stodden, Department of Statistics, Columbia University. The talk was given at UC Berkeley.]
Large-scale sequencing projects paved the way for the adoption of pioneering open data policies, making bioinformatics one the leading fields for data availability and access. In this talk I will trace the history of open data in -omics based research, and discuss how open code as well are data are being addressed today. This will include discussing leading edge tools and computational infrastructure developments intended to facilitate reproducible research through workflow tracking, computational environments, and data and code sharing. [March 13, 2014 talk by Professor Victoria Stodden, Department of Statistics, Columbia University. The talk was given at UC Berkeley.]
Implementing Reproducible Research
Summary
Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result.
Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes:
Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research.
- Chapters are fully reproducible with material available on the editors’ website.
- Covers the three principal areas of reproducible research: tools, practices, and platforms
- Contains contributions from leading figures in the field
- Explores the use of reproducible research in bioinformatics and large-scale data analyses
- Provides case studies and advice on best practices and legal issues, including recommendations of the Reproducible Research Standard
Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.
Open Data and Reproducibility in Research: Victoria Stodden @ OpenCon 2014 Talk delivered November 15th, 2014 at OpenCon 2014, the student and early career researcher conference on Open Access, Open Education, and Open Data.
OpenCon is organized by the Right to Research Coalition, SPARC (The Scholarly Publishing and Academic Resources Coalition), and an Organizing Committee of students and early career researchers from around the world. [This video was published by the The Right to Research Coalition]
Reproducing Statistical Results
Talk given October 23rd, 2014 to the Berkeley Initiative for Transparency in the Social Sciences (BITSS) on framing transparency in research in historical context and across various disciplines.
Toward Reliable and Reproducible Inference in Big Data
Abstract The 21st century is surely the century of data. New technologies now permit the collection of data by virtually all scientific, educational, governmental, societal and commercial enterprises, leading to not only an explosion in the amount of data but also in its diversity, complexity, and velocity. The opportunities for information and knowledge extraction from these data are enormous, however they present new challenges to reproducibility and verifiability. In this talk I will outline issues in reproducibility in the big data context and motivate both technical and nontechnical solutions. I will present ResearchCompendia.org, a tool I have been collaboratively developing to both persistently associate data and code with published findings, and verify those findings. I will also present recent empirical research intended to illuminate data and code sharing practices and inform policy steps to enable really reproducible research. [This talk was delivered December 1st, 2014 to the NCSA Colloquium]
We expect to add more background and resource links on this topic. You are invited to share your comments, questions, ideas or helpful background resources.
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