ReaDI Program

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ReaDI Program at Columbia University

What the ReaDI Program Offers

Resources

A collection of tutorials, templates, and "best practices" guidelines to aid in the conduct of research, data management and new rigor and reproducibility requirements. 

Outreach

Workshops, lectures, and trainings for faculty researchers, postdocs and graduate students will be given on the Morningside and CUMC campuses.

Consultations

Customized research group presentations for Columbia research groups and departments are available.

We Want to Hear From You!

The ReaDI Program is dedicated to bringing the most relevant and useful resources to the Columbia research community. By providing your feedback on the resources provided, you will be continuing to strengthen the ReaDI Program's robust repository. Fill out the resource feedback form. 

 

Robust data and research integrity is vital to ensuring that research results are reproducible and verifiable.  The ReaDI Program aims to make such good practices as transparent and easy to implement as possible for researchers at all levels at Columbia. The ReaDI Program has gained national attention and has been mentioned in Science.

Many of the resources here are applicable to researchers at any institution and we encourage the dissemination of these resources. If you are planning to share the ReaDI Program content with your institution (via website, etc.), we kindly request some information to help us better understand how these resources are being utilized.

Please fill out this information form. If you have any questions about the ReaDI Program please email Michelle Benson at mb3852@columbia.edu

Research Integrity - NIH defines research integrity as the honest, verifiable methods used in the proposal, performance, and evaluation of research and the adherence to rules, regulations, guidelines, and commonly accepted professional norms.1

Data Integrity  - can be defined as the proper data selection, collection, analysis, and handling, in accordance with discipline standards. Data integrity also means that the data is consistent and accurate between what is collected and reported as well as established procedures for the archiving of data.2,3

Reproducibility - The National Academies defines reproducibility as "achieving consistent results using the same input data, computational steps, methods, code, and conditions of analysis as prior studies." 4

Replicability - The National Academies defines replicability as "obtaining consistent results across studies that are aimed at answering the same scientific question but have obtained independent data." 4


1: http://grants1.nih.gov/grants/research_integrity/whatis.htm
2,3: http://www.businessdictionary.com/definition/data-integrity.html, http://ori.hhs.gov/education/products/n_illinois_u/datamanagement/dmotopic.html
4: http://sites.nationalacademies.org/sites/reproducibility-in-science/index.htm

 

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