Household and family structure in England and Wales (1851-1911): Continuities and change

AbstractThis article produces the first findings on changes in household and family structure in England and Wales during 1851–1911, using the recently available Integrated Census Microdata (I-CeM) – a complete count database of individual-level data extending to some 188 million records. As such, it extends and updates the important overview article published inContinuity and Changeby Michael Anderson in 1988. The I-CeM data shed new light on transitions in household structure and family life during this period, illustrating both continuities and change in a number of key areas: family composition; single parent families; living alone; extended households; childhood; leaving home and marriage patterns.</jats:p

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers

Representing Semantified Biological Assays in the Open Research Knowledge Graph

In the biotechnology and biomedical domains, recent text mining efforts advocate for machine-interpretable, and preferably, semantified, documentation formats of laboratory processes. This includes wet-lab protocols, (in)organic materials synthesis reactions, genetic manipulations and procedures for faster computer-mediated analysis and predictions. Herein, we present our work on the representation of semantified bioassays in the Open Research Knowledge Graph (ORKG). In particular, we describe a semantification system work-in-progress to generate, automatically and quickly, the critical semantified bioassay data mass needed to foster a consistent user audience to adopt the ORKG for recording their bioassays and facilitate the organisation of research, according to FAIR principles.Comment: In Proceedings of 'The 22nd International Conference on Asia-Pacific Digital Libraries

TIN-X:target importance and novelty explorer

Abstract Motivation The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. Results We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. Availability and Implementation http://www.newdrugtargets.org </jats:sec

Far away from the lamppost

[No abstract available

The fourteenth-century poll tax returns and the study of English surname distribution

The modern-day distributions of English surnames have been considered in genealogical, historical, and philological research as possible indicators of their origins. However, many centuries have passed since hereditary surnames were first used, and so their distribution today does not necessarily reflect their original spread, misrepresenting their origins. Previously, medieval data with national coverage have not been available for a study of surname distribution, but with the recent publication of the fourteenth-century poll tax returns, this has changed. By presenting discrepancies in medieval and nineteenth-century distributions, it is shown that more recent surname data may not be a suitable guide to surname origins and can be usefully supplemented by medieval data in order to arrive at more accurate conclusions