Technology and data collaborations
Collaborative projects with teams at Elsevier are using cutting-edge techniques like natural language processing, machine learning, predictive modelling and data visualization to build the tools (and skills) that will transform the way researchers and institutions approach academic research.
With our collaboration partners, we’re working to improve how data is found, how meaningful information is extracted, how to predict experimental outcomes and how to bring information and people together.
The Elsevier Labs team is our advanced technology group within Elsevier. The team participates in research, consortia and grant projects to help advance technology, sharing and open data.
Improving search and reading
Amsterdam Data Science
Dr. Maarten de Rijke, Professor of Computer Science at Universiteit van Amsterdam (UvA) and Scientific Director of the Amsterdam Data Science (2017)
Finding information in a sea of data is an increasing challenge. Amsterdam Data Science (ADS) is a multi-institutional network of local higher education institutions who partner with researchers from academia, government and industry, including Elsevier, in the areas of data science and artificial intelligence. ADS specializes in deep learning, (adaptive, interactive) search engines, and semantic technologies that will help ensure that researchers get the most out of the research information that already exists.
- Read more about some of our projects with Amsterdam Data Science in our article: How smart data can lead to serendipitous discovery.
Harvard Data Science Initiative
Prof. Francesca Dominici and Prof. David Parkes, Co-Directors of HDSI, summarize their groups' insights and recommendations at the networking reception in Cabot Science Library. (Photo by Alison Bert)
By combining diverse, interdisciplinary expertise and data sets, we’re better positioned to solve tough data challenges. The Harvard Data Science Initiative was launched in 2017 to unite (data science) efforts across the university. Elsevier is supplying funding, expertise, datasets and ontologies to help develop projects around scientific impact on policy, precision medi-cine, and determinants of healthcare.
- Read more about our work with Harvard Data Science Initiative in the article: Harvard and Elsevier explore collaborations in data science.
- Live from Harvard – Join us at the FASDPA postdoctoral symposium.
Humboldt-Elsevier Advanced Data and Text (HEADT) Center
Test and data mining can be used to find more relevant data, but it can also be used to assess where plagiarism may have taken place. Launched in 2016, the Humboldt-Elsevier Data and Text Center was formed to support research into methods of assessing research integrity and reproducibility. There is a strong education program too!
The HEADT team is developing text mining to look at similarity between publications and digital approaches to images using a master “training” set of Elsevier images. Read more about the Humboldt-Elsevier Advanced Data and Text Center: Plugging the data science talent gap in academic and industry.
Semantic coloring of academic references
Initial results on the University of Bologna project with Professor Di Ioril
Critical reading of scientific literature requires delving into source material. In this project, the Elsevier Natural Language Processing team is working with Professor Angelo Di Iorio at University of Bologna to automatically annotate article references. By using colors coding and notations, each bibliographic entry will be more meaningful and easily navigated.
UCL Big Data Institute
Besides being used to find information, data science can be used to assess researchers’ behavior. Running since 2013, the UCL Big Data Institute is a long-term institute based at University College London, founded with support from Elsevier, as well as supported by other sponsors. Researchers work on multiple projects in the areas of adaptive user modelling, modelling citations and download behavior and on knowledge graphs.
Read more about early career researcher Dr Isabelle Augenstein and her work in the UCL Big Data Institute Machine Reading project in our article: The new faces of data science.
- Read more about how researchers in UCL Professor David Jones’s lab are predicting the cellular function of proteins in our article: These Elsevier collaborations use machine learning to turn data into knowledge.
Center of Excellence
Elsevier partnered with Prof. Pushpak Bhattacharyya, Director of Computer Science at the Indian Institute of Technology, Bombay, to create the Center of Excellence at IIT Patna, a facility dedicated to machine learning and NLP.
What if we could use technology to decrease the workload of peer review? The Center of Excellence at the Indian Institute of Technology is a specialist machine learning and NLP facility formed in partnership with Elsevier. The team are using machine learning and NLP to help journal editors manage submissions by looking at an article’s match to reviewer expertise and to journal scope, as well as the article’s novelty.
- Read more about how the Center of Excellence is assessing novelty in articles in our article: How researchers are using NLP and machine learning to ease your information overload.
Automated literature reviews
Prof. Yi-Ke Guo and his team at Imperial College London are using machine learning and NLP to create meaningful summaries of articles via neural networks.
The information workload can be reduced when machine reading can provide literature summaries and overviews of new ideas. Professor Yi-Ke Guo, at Imperial College London, is using Elsevier data to understand the interaction between world knowledge and language via deep learning and natural language processing techniques. To accomplish this, a large set of documents is fed to a framework based on deep neural networks, which is then trained to make inferences about knowledge and create new documents based on vocabularies and ontologies provided by Professor Guo's Team.
- Read more about the project in our article: How researchers are using NLP and machine learning to ease your information overload.
Extracting knowledge from information in specific disciplines
Finding data in published articles is one thing, but what about finding data in biological data sets? We are working with Seven Bridges Genomics, Repositive and the US Department of Veterans Affairs to support global unique identifiers for biological data sets. By participating in the Data Commons Pilot Phase at NIH, we hope to contribute a key component towards making science more interoperable. It will also help create a better understanding of the needs of biomedical researchers, as well as to forge collaborative relationships with key innovators building biomedical infrastructure today.
- Read more about this project in our press release.
Biochemical text mining
Text mining is challenging enough, but biochemical interactions information is even richer. Through a large grant awarded by the Australian Research Council, Professor Karin Verspoor at University of Melbourne is leading a project to extract chemical reaction information from scientific literature. Along with other partners in the project, such as the University of Cambridge, our data science team is providing expertise and annotated data sets.
- Read more about the project in the article: These Elsevier collaborations use machine learning to turn data into knowledge.
Big environmental data initiative
The UC Davis Pure research profile
Big environmental issues need big data solutions. By bringing diverse data and expertise together, the John Muir Institute of the Environment (JMIE), hopes to do just that. The joint Elsevier-UC Davis Data Science Program will utilize Elsevier’s support, data and tools to advance the campus’ initiative, including funding a data science scholar and online hub for environmental research. By combining data and tools, we hope to provide insights into global research trends.
- Read more about this collaboration in our article: Partnering in the US for the advancement and promotion of research.
Simulations of complex diseases
Dr. Gordon Broderick (right) with team members Hooman Sedghamiz and Dr. Matt Morris at the Center for Clinical Systems Biology at Rochester General Hospital.
Besides finding information, data science can be used to predict medical outcomes. In our collaboration with the Broderick lab at the Center for Clinical Systems Biology and Rochester General Biology, we’re supporting the team as they look to model biological circuits. These models give insights that could lead to better diagnoses and guide the design of effective treatments for complex medical conditions that defy conventional approaches. In this virtual biology environment, they collaborate with researchers around the world to tackle some of the most elusive and complex illnesses that affect the function of the endocrine, immune and nervous systems.
- Read more about the collaboration in our article How text mining is changing the way we tackle chronic disease.
Other initiatives we support
Data mining data/modelling in specific disciplines at laboratories with
- Carnegie Mellon University
- University of Muenster
- University of Glasgow
- University of Bern
- University of Leipzig
- University of Pittsburgh
- University of Cambridge