Upcoming workshop on text and data mining will explore methods and policy
Elsevier to participate in 3rd International Workshop on Mining Scientific Publications September 12 in London
By Kris Jack, PhD Posted on 4 September 2014
Dr. Kris Jack, Chief Data Scientist at Mendeley, served on the organizing committee for this workshop, which was organized by Mendeley in conjunction with The Open University and The European Library. In addition, he has been working closely with the Access and Policy team at Elsevier to find out more about how the updated Text and Data Mining policy impacts research on a practical level.
Here, he writes about how you can participate in the workshop and what to expect.
Following a very successful call for papers, the 3rd International Workshop on Mining Scientific Publications will feature experts from Europe and the US presenting short and long papers and demonstrations on a wide range of themes and ideas — from "Mining Facts from Images" to "Enriching Scientific Publications with Annotations and Semantics."
Those interested in attending the event, which takes place on Friday, September 12, in London, can register via the DL 2014 website.
I've been very encouraged by Elsevier's strong wish to engage directly with the research community, so we agreed that it would be good to have a representative from Elsevier's policy team at the event to give an overview on the updates and answer relevant questions from researchers.
Gemma Hersh, Policy Director at Elsevier, will be giving a lunchtime presentation and talking to attendees about Elsevier's text and Data Mining policy.
"We are really pleased to have another opportunity to speak to researchers directly, to explain how our policy has been designed to support their research, and to hear any feedback they have for us," she said. "We have always said that our policy development process for TDM is one of engagement and evolution, and our participation at this event is another example of this."
The audience for the workshop will be a mixture of cross-disciplinary researchers, practitioners from industry and government, open access enthusiasts and digital library developers.
Keynote speakers include Dr. C. Lee Giles, David Reese Professor of Information Sciences and Technology at Pennsylvania State University, and Dr. Birger Larsen, Professor of Information Analysis and Information Retrieval in the Department of Communication at Aalborg University in Copenhagen. Together, we will discuss different approaches to addressing the challenges of mining scientific publications,
The workshop will be structured around three main themes:
1. The whole ecosystem of TDM infrastructures including repositories, aggregators, text-and data-mining facilities, impact monitoring tools, datasets, services and APIs that enable analysis of large volumes of scientific publications.
2. Semantic enrichment of scientific publications by means of text-mining, crowdsourcing or other methods.
3. Analysis of large databases of scientific publications to identify research trends, high impact, cross-fertilisation between disciplines, research excellence etc.
This year, we have also put together a CORE publications dataset containing a large array of publications from various research areas. This includes full-text as well as enriched versions of metadata, with the aim of providing workshop participants with a framework for developing and testing methods and tools around the workshop topics. You can access this data through the CORE portal.
To attend the workshop
Date: September 12, 2014
Location: The event will take place in conjunction with the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2014) at the Guildhall School of Music in London.
Registration: Via the City University London DL2014 Website
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Elsevier Connect Contributor
Dr. Kris Jack (@_krisjack) is the Chief Data Scientist at Mendeley, where he manages their R&D activities and leads the Data Science team. He is passionate about creating tools for researchers with a particular focus on helping researchers to make new discoveries.