Einstein Foundation Early Career Award for Christopher Schmied and Helena Jambor

The Einstein Early Career Award for Promoting Quality in Research honors early career researchers whose work is advancing the quality, transparency, and reproducibility of science and research. Five finalists were selected from around 100 global applications and had the opportunity to present their groundbreaking projects on the 5th of November at the Berlin Science Week. An international jury selected the winning project.

The jury:
•    Marcia McNutt, President of the National Academy of Sciences of the United States
•    Gowri Gopalakrishna, Assistant Professor, Faculty Health, Medicine & Life Sciences, Maastricht University
•    Anna Dreber Almenberg, Professor of Economics, Stockholm School of Economics
•    Helen Nader, President Brazilian Academy of Sciences
•    Alastair Buchan, Professor of Stroke Medicine, Oxford University
•    Yuval Shany, Professor of Public International Law, Hebrew University, Director of the Cyber Law Center
•    Batool Almarzouq, Research Project Manager, Alan Turing Institute
•    Suzy Styles, Professor of Psycholinguistics, Nanyang Technological University
•    Julie Maxton, Executive Director of the Royal Society

The prize of 100.0000 EUR will be presented at a ceremony in Berlin in March 2025.

Making Science Pictures More Honest and Reliable: PixelQuality’s Mission

Researchers use powerful microscopy methods to capture stunning biological processes. The generated images document and communicate scientific findings in publications. Images need to be explained and processed correctly to preserve and represent the result properly. Missing explanations or incorrect processing can degrade the scientific result.

Christopher Schmied and Helena Jambor led a global initiative with currently 149 members composed of imaging experts from academia, industry, and scientific publishers to improve the quality of published images. Over the last 3 years, they formulated clear and easy-to-use guidelines for the publication of images and image analysis. These guidelines were published in Nature Methods in 2024 and marked a significant step toward improving the reliability of visual data in scientific literature https://doi.org/10.1038/s41592-023-01987-9.

The next goals of PixelQuality are as follows:
1. Create open-source training material based on the published guidelines. 2. To widely disseminate the training material and the guidelines to make them common scientific practice.
3. Update the guidelines to the emerging impact of the usage of artificial intelligence (AI) in the processing, analysis, and generation of images.

“Transparency is crucial when publishing scientific results including results based on image data,” said Dr. Schmied, co-founder of the PixelQuality project. “Our guidelines and the training this project will generate, give researchers clear instructions on how to achieve such transparency when publishing images and the results of image analysis including the use of AI.”

Ultimately, PixelQuality aspires to foster cross-disciplinary consensus on image quality standards, reinforcing trust in scientific publications by ensuring that the images used in research are as accurate as possible, and in the long term also benefiting other fields that reply in images like materials science, geology, and medical imaging.