Exploring the international sharing of genetic sequence across time

On a recent rainy Sunday, I finally finished a little code project I have been working on for the last several months: creating animated time-lapse charts in ChartJS. There are better frameworks for this, but I like the simplicity of ChartJS and wanted to see how far I could push it given my rudimentary Javascript skills.

The test case I have been using is a neat chart I encountered while working in 2021 and 2022 in the science policy team at the DSMZ. It was created by some of my ex-colleagues in the WiLDSI project.

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Support the open and equitable sharing of genetic data from biodiversity


Important negotiations regarding the sharing of genetic data from biodiversity are taking place this year in the context of 15th Conference of the Parties to the Convention on Biological Diversity. The outcome of these negotiations could shape how researchers are able to access and share genetic data for decades to come.

The DSI Scientific Network and a number of major research organizations from around the world have published an Open Letter calling for a policy solution that will preserve open data sharing and promote biodiversity research, while increasing benefit-sharing for countries of origin. The letter has already been signed by over 400 researchers, with the list growing each day.

From climate change, to biodiversity loss, to the SARS-CoV-2 pandemic, researchers must to be able to collaborate across borders and to share their data. Major international organizations, including UNESCO, have aligned solidly behind the principles of open science, which stress the importance of unfettered access to scientific knowledge for all. Open science, however, must be achieved in a way that values diversity and shares the benefits of research equitably.

Sign your name now to show your support for open & equitable science

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Dispelling common data sharing myths

This blog is a distillation of a number common misunderstandings about data sharing that I have encountered during my career, especially while working as a scientific editor. There are plenty of reasons why data sharing can be complicated. Academic researchers can face conflicting incentives, a confusing legal landscape, as well as substantial technical challenges, especially when dealing with large or dynamic datasets. Pernicious misconceptions, however, create unnecessary confusion and make data sharing seem more complicated than it needs to be. Below I list five of what are, in my experience, the most common and most easily dispelled of these “data sharing myths”.

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Generating journal citation distributions from open data

The journal impact factor (JIF) and other citation-based journal metrics still have a strong hold on researchers and journal editors, despite being widely criticized. One of the main criticisms is that the underlying distributions are too skewed and overlapping for these numbers to provide any statistically meaningful information about the impact of individual pieces of research. In 2016, a group of influential researchers and editors proposed that showing journal citation distributions could “help to refocus attention on individual pieces of work and counter the inappropriate usage of JIFs” (Larivière et al). Clarivate, the provider of the JIF, as well as a number of journals, now show citation distributions on their websites (see e.g. Clarivate, Journal of Cheminformatics, Nature Portfolio, Science journals).

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Greenhouse gas emissions for common protein sources, renormalized

January 5, 2020

Back in 2011, Environmental Working Group and CleanMetrics released a guide that helps meat eaters understand the climate impact of meat consumption (The Meat Eaters Guide to Climate Change + Health). The report includes CO2 equivalent estimates for a selection of common protein sources, and a detailed report explaining their methodology.

As a guilty meat-eater, I took to the report with interest, but I wasn’t entirely convinced by the way this study had normalized the CO2 equivalent estimates. These are presented as kilograms of CO2 equivalents per kilogram of food consumed (eCO2/kg). This can lead to some seemingly strange conclusions. For example, eating cheese seems to be much worse for the environment than drinking milk (13.5 vs 1.9 eCO2/kg). But, cheese is basically concentrated milk. I might drink 300 g of milk during a normal lunch, but I wouldn’t eat 300 g of cheese. It doesn’t quite make sense to me to think in terms of kilograms consumed when actually planning a meal.

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