Incorporating Legacy Soil pH Databases into Digital Soil Maps

Soil data and reliable soil maps are imperative for environmental management. conservation and policy. Data from historical point surveys, e.g. experiment site data and farmers fields can serve this purpose. However, legacy soil information is not necessarily collected for spatial analysis and mapping such that the data may not have immediately useful geo-references. Methods are required to utilise these historical soil databases so that we can produce quantitative maps of soil propel-ties to assess spatial and temporal trends but also to assess where future sampling is required. This paper discusses two such databases: the Representative Soil Sampling Scheme which has monitored the agricultural soil in England and Wales from 1969 to 2003 (between 400 and 900 bulked soil samples were taken annually from different agricultural fields); and the former State Chemistry Laboratory, Victoria, Australia where between 1973 and 1994 approximately 80,000 soil samples were submitted for analysis by farmers. Previous statistical analyses have been performed using administrative regions (with sharp boundaries) for both databases, which are largely unrelated to natural features. For a more detailed spatial analysis that call be linked to climate and terrain attributes, gradual variation of these soil properties should be described. Geostatistical techniques such as ordinary kriging are suited to this. This paper describes the format of the databases and initial approaches as to how they can be used for digital soil mapping. For this paper we have selected soil pH to illustrate the analyses for both databases.

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.

Aligning sequences to structures

Most newly sequenced proteins are likely to adopt a similar structure to one which has already been experimentally determined. For this reason, the most successful approaches to protein structure prediction have been template-based methods. Such prediction methods attempt to identify and model the folds of unknown structures by aligning the target sequences to a set of representative template structures within a fold library. In this chapter, I discuss the development of template-based approaches to fold prediction, from the traditional techniques to the recent state-of-the-art methods. I also discuss the recent development of structural annotation databases, which contain models built by aligning the sequences from entire proteomes against known structures. Finally, I run through a practical step-by-step guide for aligning target sequences to known structures and contemplate the future direction of template-based structure prediction.

High throughput profile-profile based fold recognition for the entire human proteome

BACKGROUND: In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power.In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible. RESULTS: We show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains. CONCLUSION: This study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE.

Data-mining chess databases

This is a report on the data-mining of two chess databases, the objective being to compare their sub-7-man content with perfect play as documented in Nalimov endgame tables. Van der Heijden’s ENDGAME STUDY DATABASE IV is a definitive collection of 76,132 studies in which White should have an essentially unique route to the stipulated goal. Chessbase’s BIG DATABASE 2010 holds some 4.5 million games. Insight gained into both database content and data-mining has led to some delightful surprises and created a further agenda.

Implications for DNA identification arising from an analysis of Australian forensic databases

Previous analyses of Australian samples have suggested that populations of the same broad racial group (Caucasian, Asian, Aboriginal) tend to be genetically similar across states. This suggests that a single national Australian database for each such group may be feasible, which would greatly facilitate casework. We have investigated samples drawn from each of these groups in different Australian states, and have quantified the genetic homogeneity across states within each racial group in terms of the "coancestry coefficient" F(ST). In accord with earlier results, we find that F(ST) values, as estimated from these data, are very small for Caucasians and Asians, usually <0.5%. We find that "declared" Aborigines (which includes many with partly Aboriginal genetic heritage) are also genetically similar across states, although they display some differentiation from a "pure" Aboriginal population (almost entirely of Aboriginal genetic heritage).

Citation and peer review of data: moving towards formal data publication

This paper discusses many of the issues associated with formally publishing data in academia, focusing primarily on the structures that need to be put in place for peer review and formal citation of datasets. Data publication is becoming increasingly important to the scientific community, as it will provide a mechanism for those who create data to receive academic credit for their work and will allow the conclusions arising from an analysis to be more readily verifiable, thus promoting transparency in the scientific process. Peer review of data will also provide a mechanism for ensuring the quality of datasets, and we provide suggestions on the types of activities one expects to see in the peer review of data. A simple taxonomy of data publication methodologies is presented and evaluated, and the paper concludes with a discussion of dataset granularity, transience and semantics, along with a recommended human-readable citation syntax.

Environmental and ecological economics in the 21st century: an age adjusted citation analysis of the influential articles, journals, authors and institutions

We investigate the influence of articles, authors, journals and institutions in the field of environmental and ecological economics. We depart from studies that investigated the literature until 2001 and include a time period that has witnessed an enormous increase of importance in the field. We adjust for the age effect given the huge impact of the year of an article's publication on its influence and we show that this adjustment does make a substantial difference — especially for disaggregated units of analysis with diverse age characteristics such as articles or authors. We analyse 6597 studies on environmental and ecological economics published between 2000 and 2009. We provide rankings of the influential articles, authors, journals and institutions and find that Ecological Economics, Energy Economics and the Journal of Environmental Economics and Management have the most influential articles, they publish very influential authors and their articles are cited most. The University of Maryland, Resources for the Future, the University of East Anglia and the World Bank appear to be the most influential institutions in the field of environmental and ecological economics.