Here ! Check the videos prepared by the ICOPS, the International Committee on Open Phytolith Science for their launching event. Here is one, but more are available on YouTube:
Read MoreIn 2022, the International Committee on Phytolith Morphometry (ICPM) launched a new project to study inter- and intra-observer variation in phytolith morphometry. Phytolith researchers from different labs worldwide measured the same set of images of bilobate, bulliform, and dendritic phytolith morphotypes to statistically examine the range of variation and extent of observer bias inherent […]
Read MoreWe are happy to announce that the FAIR Phytoliths project has been successful in gaining funding through the European Open Science Cloud Digital Life Science Call. FAIR stands for Findable, Accessible, Interoperable and Reusable (Wilkinson et al. 2016) and it is the aim of this project to improve the FAIRness of phytolith data. We will […]
Read MoreThe Phytoliths in the Flora of Ecuador project was undertaken by Deborah M. Pearsall and colleagues from 1997 to 2008, with periodic work through 2012, to establish diagnostic phytolith types and phytolith vegetation signatures to enhance archaeological and paleoenvironmental phytolith applications in coastal and Amazonian Ecuador. Research was conducted in Ecuador and at the University […]
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Phytoliths and Soil Classification have many things in common!!!!! The use of a multiproxy approach to understand vegetation changes and the genesis of soils in Neotropical regions constitutes an area of investigation of great interest, which has recently attracted much attention in Brazil. In this context, the analysis of phytoliths is used as a complementary […]
Read MoreCaroline Strömberg and Tim Gallaher developed a machine learning and computer vision (ML&CV) tool for automatically identifying grass silica short cell phytoliths (GSSCP). To make this tool better than a human at identifying GSSCP, they asked phytolith researchers to take a test. You can still take this ~10-minute anonymous survey to try to identify 14 […]
Read MoreCaroline Strömberg and Tim Gallaher are developing a machine learning and computer vision (ML&CV) tool for automatically identifying grass silica short cell phytoliths (GSSCP). To make this tool better than a human at identifying GSSCP, they have to know just how good us phytolith researchers are. They need your help! Please take this ~10-minute anonymous […]
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