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Dalmeet Singh Chawla on Muck Rack

Dalmeet Singh Chawla

Verified
(He/Him)
London
As seen in: Elemental, Medium, OneZero, The New York Times, Nature, The Guardian, NPR, New York Magazine, Slate, The Sunday Times and
Science Journalist. Bylines: @NYTScience @Nature @ScienceMagazine @Cenmag @PhysicsToday @Undarkmag @RetractionWatch et al. Tips: dalmeet@protonmail.com

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Dalmeet Singh Chawla’s Biography

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Hello, I'm Dalmeet! I'm an award-winning science journalist based in London, UK. I have a Biochemistry BSc degree from University College London, a Science Communication MSc degree from Imperial College London, and a passion for neat storytelling. I was honoured and humbled to receive the 'Excellence in Statistical Reporting' Award from the American Statistical Association in 2020. In September 2015, I was also shortlisted for the 'Outstanding Young Journalist' category of the Asian Media Award…

Articles

Just over 400 U.S. patents contain citations to retracted scientific papers

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By Dalmeet Singh Chawla Verified
| Retraction Watch
Around 400 U.S. patents contain at least one citation to a retracted scientific paper, a new study has found. The analysis, posted last month to the preprint server SSRN, analyzed granted patents in the United States in PatentsView, a database developed by the United States Patent and Trademark Office (USPTO) using artificial intelligence algorithms, and the Retraction Watch Database.
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Will making ‘replication studies’ easier to find help science self-correct?

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By Dalmeet Singh Chawla Verified
| Nature Verified
Credit: bpawesome/Getty The replication of experimental results is important for building credible science, but replication studies are often difficult to find. A team of researchers wants to change that. They have started putting replication studies on the post-publication peer-review platform PubPeer, together with links to the original studies.
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Doctored data sets could trick AI agents

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By Dalmeet Singh Chawla Verified
| Nature Verified
Credit: Denis Borisov/Getty Researchers are increasingly using autonomous artificial-intelligence agents for data interpretation. The accuracy of this analysis depends on the data sets that are available to these programs, and a new study documents how easy it is for fraudsters to ‘poison’ these resources by uploading manipulated data sets that closely resemble the original ones but lead to different conclusions.
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