Launch of new features on UKPMC website
2010-01-11
On 12 January, the British Library will showcase a whole range of new search and data mining tools designed to unlock the scientific knowledge held by UK PubMed Central (UKMPC). The text mining functionalities of these new tools have been provided by NaCTeM, in collaboration with the European Bioinformatics Institute (EBI).
The latest Beta version will enable researchers to:
- Conduct a full-text search of 1.7 million articles
- Access abstracts for over 19 million articles
- Exploit the scientific literature with innovative features which enrich abstracts and full-text articles by linking scientific terms to other sources of quality assured and useful information
- Search content not included in traditional journal literature - including clinical guidelines as well as other hard to find material such as PhD theses
The full press release contains further details.
Previous item | Next item |
Back to news summary page |
Featured News
- Invited talk at the 8th Annual Women in Data Science Event at the American University of Beirut
- Invited talk at the 2nd Symposium on NLP for Social Good (NSG), University of Liverpool
- Postdoctoral research position in Athens, Greece. Application deadline: 18th March 2024
- Four-year funded PhD in collaboration with A*STAR, Singapore. Deadline 20 March 2024
- PhD opportunity in collaboration with Athens Univ. of Economics and Business. Deadline 31 Mar 2024
- iCASE EPSRC funded PhD- multimodal NLP - UoM & BAE - Application deadline 30th March 2024
- CFP: BIONLP 2024 and Shared Tasks @ ACL 2024
- Advances in Data Science and Artificial Intelligence Conference 2024
- New review article on emotion detection for misinformation
Other News & Events
- Invited talk at Annual Meeting of the Danish Society of Occupational and Environmental Medicine
- BioNLP 2024 accepted as workshop at ACL 2024
- Junichi Tsujii awarded Order of the Sacred Treasure, Gold Rays with Neck Ribbon
- Chinese Government AwardAward for PhD student Tianlin Zhang
- Keynote talk at EMBL-EBI industry club Machine Learning for Text Mining