Interpolating fields of carbon monoxide data using a hybrid statistical-physical model

Atmospheric Carbon Monoxide (CO) provides a window on the chemistry of the atmosphere since it is one of few chemical constituents that can be remotely sensed, and it can be used to determine budgets of other greenhouse gases such as ozone and OH radicals. Remote sensing platforms in geostationary Earth orbit will soon provide regional observations of CO at several vertical layers with high spatial and temporal resolution. However, cloudy locations cannot be observed and estimates of the complete CO concentration fields have to be estimated based on the cloud-free observations. The current state-of-the-art solution of this interpolation problem is to combine cloud-free observations with prior information, computed by a deterministic physical model, which might introduce uncertainties that do not derive from data. While sharing features with the physical model, this paper suggests a Bayesian hierarchical model to estimate the complete CO concentration fields. The paper also provides a direct comparison to state-of-the-art methods. To our knowledge, such a model and comparison have not been considered before.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS168 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Towards standardisation of the longline CPUE series from the lobster fisheries around Inaccessible, Nightingale and Gough islands

Two CPUE indicies exist for the tristan lobster (Jasus tristani ) fisheries surrounding Inaccessible, Nightingale and Gough islands. Here the nominal indicies are presented, alongside descriptions of how they will be standardised to ensure they each provide a more accurate index of population density

A powerful new light source for ultraviolet detection of scorpions in the field

We describe a new portable solid-state ultraviolet (UV) light that should greatly improve the ease and effectiveness of fluorescent detection of scorpions in the field. The design is based on an array of InGaN light emitting diodes (LEDs). It is in-expensive, lightweight, simple to implement, energetically efficient and mechanically rugged. Scorpion cuticular fluorescence excited by an LED array is 13-fold brighter than excitation by previously available fluorescent tube lamps drawing the same current from the battery

Standardisation of the commercial CPUE series for abalone in Zones E and G from 1980 to 2007 using a mixed effects model

The commercial CPUE series is considered during modeling of resource dynamics as an index of population abundance. However, a number of factors other than abundance may influence recorded values. Standardisation is able to take into account some of these effects, thereby producing a more reliable index

Scholarly insight Autumn 2017:a Data wrangler perspective

As the OU is going through several fundamental changes, it is important that strategic decisions made by Faculties and senior management are informed by evidence-based research and insights. One way how Data Wranglers provide insights of longitudinal development and performance of OU modules is the Key Metric Report 2017. A particular new element is that data can now also be unpacked and visualised on a Nation-level. As evidenced by the Nation-level reporting, there are substantial variations of success across the four Nations, and we hope that our interactive dashboards allow OU staff to unpack the underlying data. The second way Data Wranglers provide insight to Faculties and Units is through the Scholarly insight report series. Building on the previous two reports whereby we reported on substantial variation and inconsistencies in learning designs and assessment practices within qualifications across the OU, in this Scholarly insight Autumn 2017 report we address four big pedagogical questions that were framed and co-constructed together with the Faculties and LTI units. Many Faculties and colleagues have reacted positively on our Scholarly insight Spring 2017 report, whereby for the first time we were able to show empirically that students experienced substantial variations in success within 12 large OU qualifications. As evidenced in our previous report, 55% of variation in students’ success over time was explained by OU institutional factors (i.e., how students were assessed within their respective module; how students were able to effectively transition from one learning design of one module to the next one), rather than students’ characteristics, engagement and behaviour. We have received several queries and questions from Faculties and Units about how to better understand these students’ journeys, and how qualifications and module designs could be better aligned within their respective qualification(s). As these are complex conceptual and Big Pedagogy questions, in Chapter 1 we continued these complex analyses by looking at the transitional processes of the first two modules that OU students take, and how well aligned these modules and qualification paths are. In Chapter 2, we explored the more fine-grained, qualitative, and lived experiences of 19 students across a range of qualifications to understand how OU grading practices and (in)consistencies of assessment and feedback influenced their affect, behaviour, and cognition. In addition to building on previous topics, we introduced two new Scholarly insights in Chapter 3 and Chapter 4. As the OU is increasingly using learning analytics to support our staff and students, in Chapter 3 we analysed the impact of giving Predictive Learning Analytics to over 500 Associate Lecturers across 31 modules on student retention. Finally, in Chapter 4 we explored the impact of first presentations of new modules on pass rates and satisfaction, whereby we were able to bust another myth that may have profound implications for Student First Transformation. Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties , we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight

Standardised longline lobster CPUE from Inaccessible Island for the 1996-2005 period

The commercial CPUE series of a resource is often used as an index of population density and consequently population abundance when modelling the dynamics of the underlying population. It is known, however, that a number of other factors besides density may influence the recorded values of CPUE. Where sufficient data exist, General Linear Mixed Model (GLMM) standardisation is able to take some of these further effects into account, thereby producing a more reliable index of abundance. This document reports the application of a GLMM standardisation to Jasus tristiani lobster catch and effort data from around Inaccessible Island for the period 1996- 2005

Parent-Child Sexual Communication Among Middle School Youth

Middle school youth (N=1472) in Central Indiana completed a survey about parent-adolescent sexual communi-cation. Being older, female, mixed race, ever had sex, ever arrested, and higher HIV knowledge were associatedwith more frequent sexual communication

Development of a boundary setting algorithm based on migration rates estimated using BayesAss and its preliminary application to TOSSM datasets

A Boundary Setting Algorithm (BSA) is developed that utilises migration rates estimated by the program BayesAss. It is subjected to preliminary testing through application of the BSA to a subset of TOSSM datasets from Archetypes I and II. Two Fully Internally Mixed Areas (FIMAs) are considered, connected by varying rates of migration. The objective of the BSA, implemented through the run.tossm control program, is to decide whether to manage FIMAs as one or two units. Performance is assessed using the depletion and catch statistics produced by run.tossm. Although BayesAss has the potential to provide information relevant to management, performance of the BSA was found to be poor, apparently due to inconsistent and unreliable estimation of the migration rate

Scholarly insight Spring 2018: a Data wrangler perspective

In the movie classic Back to the Future a young Michael J. Fox is able to explore the past by a time machine developed by the slightly bizarre but exquisite Dr Brown. Unexpectedly by some small intervention the course of history was changed a bit along Fox’s adventures. In this fourth Scholarly Insight Report we have explored two innovative approaches to learn from OU data of the past, which hopefully in the future will make a large difference in how we support our students and design and implement our teaching and learning practices. In Chapter 1, we provide an in-depth analysis of 50 thousands comments expressed by students through the Student Experience on a Module (SEAM) questionnaire. By analysing over 2.5 million words using big data approaches, our Scholarly insights indicate that not all student voices are heard. Furthermore, our big data analysis indicate useful potential insights to explore how student voices change over time, and for which particular modules emergent themes might arise. In Chapter 2 we provide our second innovative approach of a proof-of-concept of qualification path way using graph approaches. By exploring existing data of one qualification (i.e., Psychology), we show that students make a range of pathway choices during their qualification, some of which are more successful than others. As highlighted in our previous Scholarly Insight Reports, getting data from a qualification perspective within the OU is a difficult and challenging process, and the proof-of-concept provided in Chapter 2 might provide a way forward to better understand and support the complex choices our students make. In Chapter 3, we provide a slightly more practically-oriented and perhaps down to earth approach focussing on the lessons-learned with Analytics4Action. Over the last four years nearly a hundred modules have worked with more active use of data and insights into module presentation to support their students. In Chapter 3 several good-practices are described by the LTI/TEL learning design team, as well as three innovative case-studies which we hope will inspire you to try something new as well. Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties, we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight. We hope that you see some potential in the two innovative approaches, and perhaps you might want to try some new ideas in your module. While a time machine has not really been invented yet, with the increasing rich and fine-grained data about our students and our learning practices we are getting closer to understand what really drives our students