Data Matters: Interview with Ben Lehner
Ben Lehner talks about his experiences accessing and using human genome data, and argues that a change in culture is needed. Read more
Ben Lehner talks about his experiences accessing and using human genome data, and argues that a change in culture is needed. Read more
Michael Milham discusses the origins of the brain-imaging data-sharing initiatives he has helped found, and what he has learned in terms of motivating scientists to share. Read more
Marco Tripodi says that while there is no standard process for sharing data in his area of neurobiology, he is optimistic that code and raw data could be reused across labs if shared in the right way. He already uses GitHub to make his own scripts available to others, and feels that scientists in his community would not hesitate to share data if it was required by journals.
Tony Hey’s job at Microsoft is to connect external scientific researchers with researchers at Microsoft to solve big data scientific problems that people care about. He observes that some scientific areas are more open to this than others, depending on the commercial value attached to the data. He believes that funding agencies and libraries need to assist researchers by providing tools and infrastructure that support open science.
Isaac Kohane combines large clinical and genomic datasets to help unravel the genetics of diseases like depression, autism, arthritis and diabetes. He describes how data sharing can enable researchers to make better diagnoses of patients with life-threatening conditions.
“Data are a bit closer to people’s hearts” says Gavin Simpson, who believes that open data sharing is not yet part of the culture in ecology. He sees a role for data repositories that better support collaboration, so that scientists can see that sharing their data is contributing to the “greater good.”
The first person representing a research funder in the ‘Data Matters’ series tells us that data sharing practices in biomedical research are a ‘hodgepodge’. He believes funders, scientists and publishers must work together to shift the scientific incentive from the traditional concept-centric paper to a data-centric publication.