Registering and uploading datasets in the generation CP central registry

Poster presented at Generation Challenge Programme 2009 Annual Research Meeting. Bamako (Mali), 20-23 Sep 200

Building networks to strengthen research data management advocacy and training

University College London (UCL) is a research-intensive university with 380 research departments, units, institutes and centres that are home to 12,000 research staff and research students. The university has been at the forefront of delivering open access to research publications through Discovery, the institutional publications repository. In August 2013 the Research Data Executive Services Group published a Research Data Policy outlining the responsibilities of research staff and students and describing the variety of institutional services that are available to support Research Data Management (RDM). UCL’s Research Data Policy is supported by two Research Data Support Offi cers (RDSOs) who work as part of the Liaison and Support Services within UCL Library Services and work on a regular basis with the Research Data Service based in Research IT Services and a number of other central services. This article will briefl y describe how the RDSOs have developed links with other services in order to improve awareness of RDM services

Revealing dynamics, communities and criticality from data

Complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in the dynamics of these networks, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behaviour for parameter ranges for which no data on the system is available. We address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an {}, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behaviour of such networks can be decomposed in terms of an emergent deterministic component and a {} term. Traditionally, such fluctuations are filtered out. However, as we show, they are key to accessing the interaction structure. { We illustrate this approach on synthetic time-series of realistic neuronal interaction networks of the cat cerebral cortex and on experimental multivariate data of optoelectronic oscillators. } We reconstruct the community structure by analysing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range

Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 1)

This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of training a computer vision model to perform image classification

Assessing the impact of OCR quality on downstream NLP tasks

A growing volume of heritage data is being digitized and made available as text via optical character recognition (OCR). Scholars and libraries are increasingly using OCR-generated text for retrieval and analysis. However, the process of creating text through OCR introduces varying degrees of error to the text. The impact of these errors on natural language processing (NLP) tasks has only been partially studied. We perform a series of extrinsic assessment tasks — sentence segmentation, named entity recognition, dependency parsing, information retrieval, topic modelling and neural language model fine-tuning — using popular, out-of-the-box tools in order to quantify the impact of OCR quality on these tasks. We find a consistent impact resulting from OCR errors on our downstream tasks with some tasks more irredeemably harmed by OCR errors. Based on these results, we offer some preliminary guidelines for working with text produced through OCR

Multicomponent dynamical systems: SRB measures and phase transitions

We discuss a notion of phase transitions in multicomponent systems and clarify relations between deterministic chaotic and stochastic models of this type of systems. Connections between various definitions of SRB measures are considered as well.Comment: 13 pages, LaTeX 2

The 2006 national Red list of mammals of the Netherlands and a IUCN Regional Red List.

Conservation Biology - ol

Abundant kif21b is associated with accelerated progression in neurodegenerative diseases

Kinesin family member 21b (kif21b) is one of the few multiple sclerosis (MS) risk genes with a presumed central nervous system function. Kif21b belongs to the kinesin family, proteins involved in intracellular transport of proteins and organelles. We hypothesised that kif21b is involved in the neurodegenerative component of MS and Alzheimer¿s (AD) disease. Post-mortem kinesin expression was assessed in 50 MS, 58 age and gender matched non-demented controls (NDC) and 50 AD. Kif21b expression was five-fold increased in AD compared to MS and NDC aged below 62 years (p¿=¿8*10¿5), three-fold between 62¿72 years (p¿=¿0.005) and not different above 72 years. No significant differences were observed between MS and NDC. In AD, kif21b expression was two-fold increased in Braak stage 6 (scoring for density of neurofibrillary tangles) compared with stage 5 (p¿=¿0.003). In MS patients, kif21b correlated with the extent of grey matter demyelination (Spearman¿s rho¿=¿0.31, p¿=¿0.03). Abundant kif21b, defined as expression above the median, was associated with a two-fold accelerated development of the Kurtzke Expanded Disability Status Scale (EDSS) 6.0 (median time in low kif21b group 16 years vs. high kif21b 7.5 years, log-rank test p¿=¿0.04) in MS. Given the genetic association of kif21b with MS, the results were stratified according to rs12122721[A] single nucleotide polymorphism (SNP). No association was found between kif21b expression or the time to EDSS 6 in kif21b risk SNP carriers compared to non-risk carriers. Kif21b was expressed in astrocytes in addition to neurons. Upon astrocyte activation, kif21b increased nine-fold. Abundant kif21b expression is associated with severe MS and AD pathology and with accelerated neurodegeneration independent of the kif21b risk SNP

Why do people fail to turn good intentions into action? : The role of executive control processes in the translation of healthy eating intentions into action in young Scottish adults

Non peer reviewedPublisher PD