Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data.

NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data.

Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life

Background. The assembly of the tree of life has seen significant progress in recent years but algae and protists have been largely overlooked in this effort. Many groups of algae and protists have ancient roots and it is unclear how much data will be required to resolve their phylogenetic relationships for incorporation in the tree of life. The red algae, a group of primary photosynthetic eukaryotes of more than a billion years old, provide the earliest fossil evidence for eukaryotic multicellularity and sexual reproduction. Despite this evolutionary significance, their phylogenetic relationships are understudied. This study aims to infer a comprehensive red algal tree of life at the family level from a supermatrix containing data mined from GenBank. We aim to locate remaining regions of low support in the topology, evaluate their causes and estimate the amount of data required to resolve them. Results. Phylogenetic analysis of a supermatrix of 14 loci and 98 red algal families yielded the most complete red algal tree of life to date. Visualization of statistical support showed the presence of five poorly supported regions. Causes for low support were identified with statistics about the age of the region, data availability and node density, showing that poor support has different origins in different parts of the tree. Parametric simulation experiments yielded optimistic estimates of how much data will be needed to resolve the poorly supported regions (ca. 103 to ca. 104 nucleotides for the different regions). Nonparametric simulations gave a markedly more pessimistic image, some regions requiring more than 2.8 105 nucleotides or not achieving the desired level of support at all. The discrepancies between parametric and nonparametric simulations are discussed in light of our dataset and known attributes of both approaches. Conclusions. Our study takes the red algae one step closer to meaningful inclusion in the tree of life. In addition to the recovery of stable relationships, the recognition of five regions in need of further study is a significant outcome of this work. Based on our analyses of current availability and future requirements of data, we make clear recommendations for forthcoming research.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Comorbidity in severe asthma requiring systemic corticosteroid therapy: cross-sectional data from the Optimum Patient Care Research Database and the British Thoracic Difficult Asthma Registry

Objective: To determine the prevalence of systemic corticosteroid-induced morbidity in severe asthma.
Design: Cross-sectional observational study.Setting The primary care Optimum Patient Care Research Database and the British Thoracic Society Difficult Asthma Registry.
Participants: Optimum Patient Care Research Database (7195 subjects in three age- and gender-matched groups)—severe asthma (Global Initiative for Asthma (GINA) treatment step 5 with four or more prescriptions/year of oral corticosteroids, n=808), mild/moderate asthma (GINA treatment step 2/3, n=3975) and non-asthma controls (n=2412). 770 subjects with severe asthma from the British Thoracic Society Difficult Asthma Registry (442 receiving daily oral corticosteroids to maintain disease control).
Main outcome measures: Prevalence rates of morbidities associated with systemic steroid exposure were evaluated and reported separately for each group.
Results: 748/808 (93%) subjects with severe asthma had one or more condition linked to systemic corticosteroid exposure (mild/moderate asthma 3109/3975 (78%), non-asthma controls 1548/2412 (64%); p<0.001 for severe asthma versus non-asthma controls). Compared with mild/moderate asthma, morbidity rates for severe asthma were significantly higher for conditions associated with systemic steroid exposure (type II diabetes 10% vs 7%, OR=1.46 (95% CI 1.11 to 1.91), p<0.01; osteoporosis 16% vs 4%, OR=5.23, (95% CI 3.97 to 6.89), p<0.001; dyspeptic disorders (including gastric/duodenal ulceration) 65% vs 34%, OR=3.99, (95% CI 3.37 to 4.72), p<0.001; cataracts 9% vs 5%, OR=1.89, (95% CI 1.39 to 2.56), p<0.001). In the British Thoracic Society Difficult Asthma Registry similar prevalence rates were found, although, additionally, high rates of osteopenia (35%) and obstructive sleep apnoea (11%) were identified.

Conclusions: Oral corticosteroid-related adverse events are common in severe asthma. New treatments which reduce exposure to oral corticosteroids may reduce the prevalence of these conditions and this should be considered in cost-effectiveness analyses of these new treatments.