The Private and Public Benefits of Posting Data and Code

[From the blog “Why researchers should publish their data” by Karl Rubio, posted at http://www.povertyactionlab.org]
“There has been a growing research transparency movement within the social sciences to encourage broader data publication. In this blog post we share some background on this movement and recent statistics, key factors for researchers to consider before publishing data, and tools and resources to support data publication efforts.”
“There are many long-run benefits to publishing original research data. Open data can increase visibility of the research and number of citations counts. For example, there is some evidence that publishing research articles for open access, rather than behind a paywall, increases citations.”
“Similarly, a preliminary paper by J-PAL affiliate Ted Miguel, with Garret Christensen and Allan Dafoe, concluded that papers in top economics and political science journals with public data and code are cited between 30-45 percent more often than papers without public data and code.”
“Open data has the potential to generate new ideas and spark new collaborations between researchers and policymakers–but it only serves this purpose when others are actually reusing the data. For example open data becomes a public good when data are reused for:”
– “Research (reanalysis, meta-analysis, secondary analysis, replication)”
– “Teaching (curriculum use for presentations and assignments)”
– “Learning (dataset exploration)”
“The J-PAL Dataverse, a subset dataverse in the Harvard Dataverse, is an open data repository which stores data associated with studies conducted by J-PAL affiliated researchers.”
“We collected data from our database in J-PAL Dataverse users using a guestbook to better understand who was accessing this open data, and for what purpose.”
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“J-PAL and our partner organization Innovations for Poverty Action (IPA) have created resources to help researchers publish their data and improve research transparency. IPA’s best practices for data and code management illustrate good coding practices that can be used to help clean and finalize your data and code before publication. J-PAL North America’s data security procedures for researchers provide context on elements of data security and working with individual-level administrative and survey data.”
To read more, click here.

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