Data research

Software for the Future

Author: Adrian Jackson
Posted: 7 Jan 2015 | 10:18

Reconstructed neuronal connections in the brain Credit: Thomas Schultz - CC BY-SABig-Data compressive sensing: fast, parallelised and distributed algorithms

EPCC is excited to be part of a new project, funded through EPSRC's Software for the Future programme, to develop and exploit compressive-sensing algorithms for large-scale data problems.

University-led €428m bid could boost Scotland’s growing reputation for innovation in health and care

Author: Mark Parsons
Posted: 25 Nov 2014 | 16:13

The University of Edinburgh is leading an international bid to secure European funding to support the establishment of a European Institute of Innovation & Technology (EIT) “Knowledge and Innovation Community” (KIC) focused on healthy living and active ageing.

Funded by the EIT through Horizon 2020 (The EU Framework Programme for Research and Innovation), LifeKIC, as the Scotland-led KIC is called, will support the EIT and EU’s goal of delivering the triple win of: increased healthy life years; economic growth and increased competitiveness; and sustainable health and care.

Big Data training session: EPCC & EUDAT at ISC Big Data

Author: Adam Carter
Posted: 9 Sep 2014 | 09:53
ISC is Europe’s biggest supercomputing conference, and EPCC has been represented at the event for a number of years now. More recently, as the interest in Big Data has grown, ISC has launched a new conference - ISC Big Data - which is specifically focussed on this new field.

Research data infrastructure: where next?

Author: Rob Baxter
Posted: 30 Jul 2014 | 16:11

The rise of data-driven science, the increasing digitisation of the research process, has spawned its own jargon and acronyms. “Research data infrastructure” is one such term, but what does it mean? 

SPRINT v1.0.6 released! OpenMPI and R V3 compatible

Author: Terry Sloan
Posted: 30 Jun 2014 | 13:30
 
EPCC and the Division of Pathway Medicine at the University of Edinburgh have released version 1.0.6 of the SPRINT R software package. This is compatible with R Version 3 and now supports OpenMPI as well as MPICH.

Causing a Storm in MPI: easier data processing for scientists

Author: Amy Krause
Posted: 17 Jun 2014 | 15:00

After several years of working with users who are not computer scientists (seismologists and geoscientists), we have realised two main points: these communities usually have problems that should be addressed with parallel computing, but they don't often have the skills and training to do so. We set out to build a programming library, Dispel4Py, that both enables users to easily write a description of a data-processing application and takes care of running that application in different parallel environments.

Bootstrapping with R and SPRINT

Author: Terry Sloan
Posted: 24 Feb 2014 | 12:10

EPCC and the Division of Pathway Medicine at the University of Edinburgh have made public the report from their recent study into the performance of bootstrapping within their SPRINT R software package.

Data infrastructure: highlights of the EUDAT Conference 2013

Author: Rob Baxter
Posted: 13 Nov 2013 | 14:27

EUDAT - the European Data Infrastructure project - has reached the end of its second year and has, with some success, distilled the first version of a common, collaborative, horizontal data infrastructure from among the vertical stacks of its various partners.

Data interoperability is a state of mind

Author: Rob Baxter
Posted: 9 Sep 2013 | 09:58

The research data tsunami is firmly upon us. Open access to data is very much on the agenda. One of the hopes for capturing and preserving all these data is that reuse and recombination may yield new science. Improving the interoperability of data from different domains is key to making this a reality.

Now, data interoperability is not technically hard, so why are we not further on?

Securely citing datasets

Author: Guest blogger
Posted: 22 Aug 2013 | 14:50

This post was written by Adrian Mouat, a former EPCC employee who is now an independent software consultant.

Citing a paper is a reasonably straightforward and well-defined task; just give a reference to the author and the publication you found the paper in and you're pretty much there. Anyone else who wants to look up the reference just has to find the publication and they should see exactly the same text you saw.

Unfortunately, citing datasets is not as simple, at least not if you want the security of knowing that readers who follow the citation will find exactly the same data you used.

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