9 research outputs found
Research Software Engineers: State of the Nation Report 2017
Most research would be impossible without software, and this reliance is forcing a rethink of the skills needed in a traditional research group. With the emergence of software as the pre-eminent research tool used across all disciplines, comes the realisation that a significant majority of results are based, ultimately, on the skill of the experts who design and build that software.
The UK has led the world in supporting a new role in academia: the Research Software Engineer (RSE). This report describes the new expert community that has flourished in UK research, details the successes that have been achieved, and the barriers that prevent further progress
The challenges of data in future pandemics
This work was supported by Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/R014604/1. M.C. was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/V054236/1. G.M. is supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS). J.P-G’s work is supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care. L.P. is funded by the Wellcome Trust, UK and the Royal Society, UK (grant no. 202562/Z/16/Z). L.P. is supported by the UK Research and Innovation (UKRI) through the JUNIPER modelling consortium (grant no. MR/V038613/1). L.P. is also supported by The Alan Turing Institute for Data Science and Artificial Intelligence, UK. R.R was supported by the Natural Environment Research Council (NERC) grant no. NE/T004193/1 and NE/T010355/1, the Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/V054236/1 and the Science and Technology Facilities Council (STFC) grant no. ST/V006126/1.The use of data has been essential throughout the unfolding COVID-19 pandemic. We have needed it to populate our models, inform our understanding, and shape our responses to the disease. However, data has not always been easy to find and access, it has varied in quality and coverage, been difficult to reuse or repurpose. This paper reviews these and other challenges and recommends steps to develop a data ecosystem better able to deal with future pandemics by better supporting preparedness, prevention, detection and response.Publisher PDFPeer reviewe
FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows
Modern epidemiological analyses to understand and combat the spread of
disease depend critically on access to, and use of, data. Rapidly evolving
data, such as data streams changing during a disease outbreak, are particularly
challenging. Data management is further complicated by data being imprecisely
identified when used. Public trust in policy decisions resulting from such
analyses is easily damaged and is often low, with cynicism arising where claims
of "following the science" are made without accompanying evidence. Tracing the
provenance of such decisions back through open software to primary data would
clarify this evidence, enhancing the transparency of the decision-making
process. Here, we demonstrate a Findable, Accessible, Interoperable and
Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that
allows easy annotation of data as they are consumed by analyses, while tracing
the provenance of scientific outputs back through the analytical source code to
data sources. Such a tool provides a mechanism for the public, and fellow
scientists, to better assess the trust that should be placed in scientific
evidence, while allowing scientists to support policy-makers in openly
justifying their decisions. We believe that tools such as this should be
promoted for use across all areas of policy-facing research
Mammal responses to global changes in human activity vary by trophic group and landscape
Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe
Biological Earth observation with animal sensors
Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change
The challenges of data in future pandemics
The use of data has been essential throughout the unfolding COVID-19 pandemic. We have needed it to populate our models, inform our understanding, and shape our responses to the disease. However, data has not always been easy to find and access, it has varied in quality and coverage, been difficult to reuse or repurpose. This paper reviews these and other challenges and recommends steps to develop a data ecosystem better able to deal with future pandemics by better supporting preparedness, prevention, detection and response.</p
International Harmonization of Nomenclature and Diagnostic Criteria (INHAND): Nonproliferative and Proliferative Lesions of the Rabbit.
The INHAND (International Harmonization of Nomenclature and Diagnostic Criteria for Lesions Project (www.toxpath.org/inhand.asp) is a joint initiative of the Societies of Toxicologic Pathology from Europe (ESTP), Great Britain (BSTP), Japan (JSTP) and North America (STP) to develop an internationally accepted nomenclature for proliferative and non-proliferative lesions in laboratory animals. The purpose of this publication is to provide a standardized nomenclature for classifying microscopic lesions observed in most tissues and organs from the laboratory rabbit used in nonclinical safety studies. Some of the lesions are illustrated by color photomicrographs. The standardized nomenclature presented in this document is also available electronically on the internet (http://www.goreni.org/). Sources of material included histopathology databases from government, academia, and industrial laboratories throughout the world. Content includes spontaneous lesions as well as lesions induced by exposure to test materials. Relevant infectious and parasitic lesions are included as well. A widely accepted and utilized international harmonization of nomenclature for lesions in laboratory animals will provide a common language among regulatory and scientific research organizations in different countries and increase and enrich international exchanges of information among toxicologists and pathologists
FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows
RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses
The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.</p