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The Center for Open and REproducible Science is excited to share the Open Science Innovator Prize. The Open Science Innovator Prize is awarded to research trainees who have demonstrated a commitment to furthering transparent and reproducible science practices. This could include, for example: sharing research objects, nurturing an open and inclusive research community, or developing open science software. These contributions should be further described in the applicant’s Open Science narrative (below).
Eligible recipients include graduate students, post doctoral scholars, research fellows, and research associates. Applicants may either self-nominate or nominate a colleague. Researchers from all fields of study are welcome to apply! Applications by trainees from underrepresented groups are encouraged.
The recipients will be awarded at the Center for Open and REproducible Science annual meeting.
The Stanford Data Science Institute’s Center for Open and REproducible Science (DSI-CORES) aims to develop and nurture transparency and reproducibility in the collection, analysis, and dissemination of data across all domains of scientific activity. The Center focuses on two primary objectives: 1) developing resources and supporting activities that promote the adoption of open science practices at Stanford and beyond and 2) fostering methodological innovations that can enhance the adoption and effectiveness of open science practices.
Program priorities
Research transparency
Reproducibility
Open Science
Eligibility:
This Prize is intended for Stanford trainees - graduate students, post doctoral scholars, research fellows, research associates - who have demonstrated a commitment to open science and reproducibility. There is no constraint on the discipline the recipient is associated with. All applicants must be enrolled at or employed by Stanford.
Requirements:
Current CV
Open Science narrative
Please describe your open science contributions and how they have helped move your field toward being more transparent and reproducible. Including links to, for example, open data, code, or other open materials, is encouraged when applicable.