Opening up climate research: A linked data approach to publishing data provenance

Traditionally, the formal scientific output in most fields of natural science has been limited to peer- reviewed academic journal publications, with less attention paid to the chain of intermediate data results and their associated metadata, including provenance. In effect, this has constrained the representation and verification of the data provenance to the confines of the related publications. Detailed knowledge of a dataset’s provenance is essential to establish the pedigree of the data for its effective re-use, and to avoid redundant re-enactment of the experiment or computation involved. It is increasingly important for open-access data to determine their authenticity and quality, especially considering the growing volumes of datasets appearing in the public domain. To address these issues, we present an approach that combines the Digital Object Identifier (DOI) – a widely adopted citation technique – with existing, widely adopted climate science data standards to formally publish detailed provenance of a climate research dataset as an associated scientific workflow. This is integrated with linked-data compliant data re-use standards (e.g. OAI-ORE) to enable a seamless link between a publication and the complete trail of lineage of the corresponding dataset, including the dataset itself.

Research data management and openness: the role of data sharing in developing institutional policies and practices

Purpose: To investigate the relationship between research data management (RDM) and data sharing in the formulation of RDM policies and development of practices in higher education institutions (HEIs). Design/methodology/approach: Two strands of work were undertaken sequentially: firstly, content analysis of 37 RDM policies from UK HEIs; secondly, two detailed case studies of institutions with different approaches to RDM based on semi-structured interviews with staff involved in the development of RDM policy and services. The data are interpreted using insights from Actor Network Theory. Findings: RDM policy formation and service development has created a complex set of networks within and beyond institutions involving different professional groups with widely varying priorities shaping activities. Data sharing is considered an important activity in the policies and services of HEIs studied, but its prominence can in most cases be attributed to the positions adopted by large research funders. Research limitations/implications: The case studies, as research based on qualitative data, cannot be assumed to be universally applicable but do illustrate a variety of issues and challenges experienced more generally, particularly in the UK. Practical implications: The research may help to inform development of policy and practice in RDM in HEIs and funder organisations. Originality/value: This paper makes an early contribution to the RDM literature on the specific topic of the relationship between RDM policy and services, and openness – a topic which to date has received limited attention.

A general-method for tracking analysis and its application to meteorological data

Recent interest in the validation of general circulation models (GCMs) has been devoted to objective methods. A small number of authors have used the direct synoptic identification of phenomena together with a statistical analysis to perform the objective comparison between various datasets. This paper describes a general method for performing the synoptic identification of phenomena that can be used for an objective analysis of atmospheric, or oceanographic, datasets obtained from numerical models and remote sensing. Methods usually associated with image processing have been used to segment the scene and to identify suitable feature points to represent the phenomena of interest. This is performed for each time level. A technique from dynamic scene analysis is then used to link the feature points to form trajectories. The method is fully automatic and should be applicable to a wide range of geophysical fields. An example will be shown of results obtained from this method using data obtained from a run of the Universities Global Atmospheric Modelling Project GCM.

Dynamic causal modeling applied to fMRI data shows high reliability

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan–rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.

Does genotype and equol-production status affect response to isoflavones? Data from a pan-European study on the effects of isoflavones on cardiovascular risk markers in post-menopausal women

The increase in CVD incidence following the menopause is associated with oestrogen loss. Dietary isoflavones are thought to be cardioprotective via their oestrogenic and oestrogen receptor-independent effects, but evidence to support this role is scarce. Individual variation in response to diet may be considerable and can obscure potential beneficial effects in a sample population; in particular, the response to isoflavone treatment may vary according to genotype and equol-production status. The effects of isoflavone supplementation (50hairspmg/d) on a range of established and novel biomarkers of CVD, including markers of lipid and glucose metabolism and inflammatory biomarkers, have been investigated in a placebo-controlled 2x8-week randomised cross-over study in 117 healthy post-menopausal women. Responsiveness to isoflavone supplementation according to (1) single nucleotide polymorphisms in a range of key CVD genes, including oestrogen receptor (ER) alpha and beta and (2) equol-production status has been examined. Isoflavones supplementation was found to have no effect on markers of lipids and glucose metabolism. Isoflavones improve C-reactive protein concentrations but do not affect other plasma inflammatory markers. There are no differences in response to isoflavones according to equol-production status. However, differences in HDL-cholesterol and vascular cell adhesion molecule 1 response to isoflavones v. placebo are evident with specific ER beta genotypes. In conclusion, isoflavones have beneficial effects on C-reactive protein, but not other cardiovascular risk markers. However, specific ER beta gene polymorphic subgroups may benefit from isoflavone supplementation.

Pragmatic oriented data interoperability for smart healthcare information systems

Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems collaborations for effective communications among departments, and radiology is one of the areas highly relies on intelligent information integration and communication. Therefore, it faces many challenges regarding integration and its interoperability such as information collision, heterogeneous data sources, policy obstacles, and procedure mismanagement. The purpose of this study is to conduct an analysis of data, semantic, and pragmatic interoperability of systems integration in radiology department, and to develop a pragmatic interoperability framework for guiding the integration. We select an on-going project at a local hospital for undertaking our case study. The project is to achieve data sharing and interoperability among Radiology Information Systems (RIS), Electronic Patient Record (EPR), and Picture Archiving and Communication Systems (PACS). Qualitative data collection and analysis methods are used. The data sources consisted of documentation including publications and internal working papers, one year of non-participant observations and 37 interviews with radiologists, clinicians, directors of IT services, referring clinicians, radiographers, receptionists and secretary. We identified four primary phases of data analysis process for the case study: requirements and barriers identification, integration approach, interoperability measurements, and knowledge foundations. Each phase is discussed and supported by qualitative data. Through the analysis we also develop a pragmatic interoperability framework that summaries the empirical findings and proposes recommendations for guiding the integration in the radiology context.

Survival analysis statistics applied to threshold data obtained from the ascending forced-choice method of limits

Sensory thresholds are often collected through ascending forced-choice methods. Group thresholds are important for comparing stimuli or populations; yet, the method has two problems. An individual may correctly guess the correct answer at any concentration step and might detect correctly at low concentrations but become adapted or fatigued at higher concentrations. The survival analysis method deals with both issues. Individual sequences of incorrect and correct answers are adjusted, taking into account the group performance at each concentration. The technique reduces the chance probability where there are consecutive correct answers. Adjusted sequences are submitted to survival analysis to determine group thresholds. The technique was applied to an aroma threshold and a taste threshold study. It resulted in group thresholds similar to ASTM or logarithmic regression procedures. Significant differences in taste thresholds between younger and older adults were determined. The approach provides a more robust technique over previous estimation methods.

The low-latitude boundary layer: Application of ISTP advances to past data

The destruction of the four Cluster craft was a major loss to the planned ISTP effort, of which studies of the magnetopause and low-latitude boundary layer (LLBL) were an important part. While awaiting the re-flight mission, Cluster-II, we have been applying advances in our understanding made using other ISTP craft (like Polar and Wind) and using ground-based facilities (in particular the EISCAT incoherent scatter radars and the SuperDARN HF coherent radars) to measurements of the LLBL made in 1984 and 1985 by the AMPTE-UKS and -IRM spacecraft pair. In particular, one unexplained result of the AMPTE mission was that the electron characteristics could, in nearly all cases, order independent measurements near the magnetopause, such as the magnetic field, ion temperatures and the plasma flow. Studies of the cusp have shown that the precipitation is ordered by the time-elapsed since the field line was opened by reconnection. This insight has allowed us to reanalyse the AMPTE data and show that the ordering by the transition parameter is also due to the variation of time elapsed since reconnection, with the important implication that reconnection usually coats most of the dayside magnetopause with at least some newly-opened field lines. In addition, we can use the electron characteristics to isolate features like RDs, slow-mode shocks and slow-mode expansion fans. The ion characteristics can be used to compute the reconnection rate. We here retrospectively apply these new techniques, developed in the ISTP era, to a much-studied flux transfer event observed by the AMPTE satellites. As a result, we gain new understanding of its cause and structure.

Assessing the sensitivities of a distributed snow model to forcing data resolution

Highly heterogeneous mountain snow distributions strongly affect soil moisture patterns; local ecology; and, ultimately, the timing, magnitude, and chemistry of stream runoff. Capturing these vital heterogeneities in a physically based distributed snow model requires appropriately scaled model structures. This work looks at how model scale—particularly the resolutions at which the forcing processes are represented—affects simulated snow distributions and melt. The research area is in the Reynolds Creek Experimental Watershed in southwestern Idaho. In this region, where there is a negative correlation between snow accumulation and melt rates, overall scale degradation pushed simulated melt to earlier in the season. The processes mainly responsible for snow distribution heterogeneity in this region—wind speed, wind-affected snow accumulations, thermal radiation, and solar radiation—were also independently rescaled to test process-specific spatiotemporal sensitivities. It was found that in order to accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100 m. Wind and wind-affected precipitation—the primary influence on snow distribution—required similar resolution. Thermal radiation scaled with the vegetation structure (~100 m), while solar radiation was adequately modeled with 100–250-m resolution. Spatiotemporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale breaks could be associated with physiographic and vegetation structures to aid a priori modeling decisions.

Massive Open Online Courses and sustainability

Massive Open Online Courses (MOOCs) have become very popular among learners millions of users from around the world registered with leading platforms. There are hundreds of universities (and other organizations) offering MOOCs. However, sustainability of MOOCs is a pressing concern as MOOCs incur up front creation costs, maintenance costs to keep content relevant and on-going support costs to provide facilitation while a course is being run. At present, charging a fee for certification (for example Coursera Signature Track and FutureLearn Statement of Completion) seems a popular business model. In this paper, the authors discuss other possible business models and their pros and cons. Some business models discussed here are: Freemium model – providing content freely but charging for premium services such as course support, tutoring and proctored exams. Sponsorships – courses can be created in collaboration with industry where industry sponsorships are used to cover the costs of course production and offering. For example Teaching Computing course was offered by the University of East Anglia on the FutureLearn platform with the sponsorship from British Telecom while the UK Government sponsored the course Introduction to Cyber Security offered by the Open University on FutureLearn. Initiatives and Grants – The government, EU commission or corporations could commission the creation of courses through grants and initiatives according to the skills gap identified for the economy. For example, the UK Government’s National Cyber Security Programme has supported a course on Cyber Security. Similar initiatives could also provide funding to support relevant course development and offering. Donations – Free software, Wikipedia and early OER initiatives such as the MIT OpenCourseware accept donations from the public and this could well be used as a business model where learners could contribute (if they wish) to the maintenance and facilitation of a course. Merchandise – selling merchandise could also bring revenue to MOOCs. As many participants do not seek formal recognition (European Commission, 2014) for their completion of a MOOC, merchandise that presents their achievement in a playful way could well be attractive for them. Sale of supplementary material –supplementary course material in the form of an online or physical book or similar could be sold with the revenue being reinvested in the course delivery. Selective advertising – courses could have advertisements relevant to learners Data sharing – though a controversial topic, sharing learner data with relevant employers or similar could be another revenue model for MOOCs. Follow on events – the courses could lead to follow on summer schools, courses or other real-life or online events that are paid-for in which case a percentage of the revenue could be passed on to the MOOC for its upkeep. Though these models are all possible ways of generating revenue for MOOCs, some are more controversial and sensitive than others. Nevertheless unless appropriate business models are identified the sustainability of MOOCs would be problematic.