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World Wide Web of Plankton Image Curation (www.pic)

Grant number: 18/24167-5
Support type:Regular Research Grants
Duration: June 01, 2019 - May 31, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Cooperation agreement: Belmont Forum
Principal Investigator:Nina Sumiko Tomita Hirata
Grantee:Nina Sumiko Tomita Hirata
Principal investigator abroad: Jean-Olivier Irisson
Institution abroad: Université Paris-Sorbonne (Paris 4), France
Home Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Assoc. researchers:Roberto Hirata Junior ; Rubens Mendes Lopes


Scientific research is generating an increasing number of digital images, from micrographs of cells to pictures of galaxies. Automated instruments can capture many images, which are then processed automatically to extract data from them. For this data to be useful to the scientific community and benefit the general public, it needs to be fast to generate (even for millions of images), consistent, and easy to share. In environmental sciences, common questions are: how many organisms are present in a given environment? How diverse are they? Does that change in time? Digital imaging can help answer these questions, particularly underwater, where direct observation by humans is difficult. For example, images of billions of planktonic organisms (i.e., the organisms that drift with ocean currents) have been taken and need to be analysed. Plankton largely contributes to the regulation Earth's climate, the production of the oxygen we breathe, the feeding of the fish we eat, etc. Estimating its abundance and diversity is therefore critical. Yet, efforts to process and classify images for such ecological studies have been scattered and not interoperable. The main goal of this project is to build a World Wide Web of Plankton Image Curation applications (WWW.PIC) that collect images of plankton, allow scientists to name them consistently, store associated ecological information (such as time, location, etc.), and make all data easily accessible to the community. It will leverage cutting edge advances in database design and machine learning to process billions of images, will be hosted on public web servers to be easily accessible, and will foster an atmosphere of collaboration and sharing that the Belmont Forum values and, we think, is essential for the progress of science. Then, we will use this network of applications to tackle studies that have proved challenging without it, such as fast plankton monitoring to assess ecosystem health, or global estimations of the distribution of planktonic diversity and its contribution to carbon storage or ecosystem productivity. (AU)

Matéria(s) publicada(s) na Agência FAPESP sobre o auxílio:
Advances in machine learning enable new technologies based on image analysis