Authors & Affiliation
Philip Doganis, Georgia Tsiliki, Charalampos Chomenidis, Angelos Valsamis, Evangelia Anagnostopoulou and Haralambos Sarimveis. - School of Chemical Engineering, National Technical University of Athens
Computational assessment of product features has become an essential part of product development, from design to the manufacturing stage. However, modelling infrastructures are seldom available online. Jaqpot Quattro (JQ) is an open source web infrastructure that has been developed for modelling biological endpoints for nanoparticles, based on machine learning and data mining algorithms. Its structure allows generalization across disciplines and integration to e-infrastructures in order to fulfil modelling needs.
It includes functionalities such as creating datasets, training models for both classification and regression data, making predictions and performing external, cross-, or training set split validation, allowing users to store models, use and create algorithms (any language) and datasets, as well as view reports for their calculations.
The JQ modelling infrastructure has also been made available through application program interfaces (APIs). These are exposed via the API documentation framework Swagger at http://jaqpot.org:8080/jaqpot/swagger/. Each API supports one or more HTTP methods for the RESTful services.
Here we show ORCID peer review functionality in relation to open science workflows and demonstrate the value ORCID identifiers and other types of persistent identifier can bring to the process.