When scientists need information about the structure, name or properties of small molecules, they often turn to a high quality database called ChEBI. This database is largely curated manually and this process takes a lot of time. OpenMinTeD is working on a textmining application that can help to speed up the process, while maintaining the quality of the database.
Joris van Eijnatten is professor of cultural history at Utrecht University, The Netherlands. He has a fascination for numbers that not many historians have. Last year he was the research fellow for digital humanities at the National Library of The Netherlands, where he applied text and data mining to study the image people have of Europe based on newspapers. I interviewed him about text and data mining in humanities, his work and his personal romance with numbers.
What is the real novelty of a research paper? How do different researchers contribute to innovation? And does this change throughout their career? Shubhanshu Mishra of the University of Illionois uses textmining techniques to study the novelty of biomedical articles.
Systematic review of medical research papers can lead to new knowledge and treatments of diseases. The existing software tools however, are very limited and often a lot of manual work is involved. Stephen Gilbert of Iowa State University uses artificial intelligence and machine learning to automate the process of systematic review.
While discussions at the EU on copyright reform and an exception for text and data mining (TDM) are very much live, FutureTDM, a Commission funded project of TDM experts has, for the past year, already been gathering information, mapping the TDM landscape and listening to the wide variety of individuals and organisations involved in data analytics. The project has just produced the first in a series of reports, providing a range of stakeholders with recommendations to improve TDM uptake in the EU. This FutureTDM policy framework document sets out high-level principles and recommendations.
Frederico Nanni was not always a text miner. He actually started out as a historian and then switched to digital humanities. During his PhD, he developed a method to detect interdisciplinary research, based on scientific abstracts. Now, he finds text mining fascinating and thinks more historians should learn how to do it.