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.
Are you looking for support or training for text and data mining? Then you’re at the right place! OpenMinTeD recently released a Knowledge Base, that will host open access support and training material. At the moment we are still in the process of uploading content, but you can already have a look.
Text and data mining is important to different scientific communities, but what do these different user communities need to mine succesfully? One of the aims of workpackage 4 of the OpenMinTeD project is to collect these requirements. This was done using a combination of methods, including online surveys and focus groups. The results are summarized in the ‘White paper on OpenMinTed Community Requirements’ that was finished last week.