Mapping resource effectiveness across urban systems

Cities and their growing resource demands threaten global resource security. This study identifies the hotspots of imports in cities to redirect resources to where they are most needed, based on the system overall resource effectiveness to maximise the use of all resources available. This paper develops a taxonomy of resource-use behaviour based on the clustering patterns of resource utilisation and conversion across interconnected urban systems. We find high tendencies of consumer-like behaviour in a multi-city system because tertiary sectors are concentrated in urban areas while the producing sectors are located outside and hence, results in high utilisation but low output. The clustering taxonomy emphasises that the absence of producers in the system causes cities to rely on the imported resources for growth. Cities can be resource-effective by having a more diversified industrial structure to extend the pathways of resource flows, closing the circularity gap between the suppliers and consumers.

A classification for English primary schools using open data

England has statutory regulations in place that ensure state funded schools deliver broadly the same curriculum. However there still exists a wide range of contexts in which this education takes place, including: the management of schools; how the schools chose to spend their budgets; individual policies in regards to staffing, behaviour and attendance, and perhaps most importantly, the composition of the pupil population in the school. Given these contexts, one outcome of interest is the attainment profile of schools, and it is important that this performance is judged in context, for the benefits of pupils, parents and schools. To this end, this study develops a classification using contemporary data for English primary schools. The open data used captures aspects of the gender, ethnic, language, staffing and affluence makeup of each school. The nature of these derived groupings is described and made available as a mapping resource. These groupings allow the identification of “families of schools”, to act as a resource to foster better collaboration between schools and more nuanced benchmarking.

Collecting Data on Sexual & Reproductive Health in Humanitarian Settings

Unsafe abortion is a major contributor to maternal death and disability in humanitarian/ contexts, and women and girls are at considerable risk of experiencing forced marriage and sexual violence, as well as acquiring HIV and other sexually transmitted infections. Routine, reliable and rigorously collected data on sexual and reproductive health (SRH) in humanitarian settings are sparse globally irrespective of region or state of emergency. This problem is pronounced in politicized SRH issues such as abortion. This innovation has piloted a variety of strategies including data mapping, resource inventory, focus group/interviews, and other approaches to map challenges associated with collection and indicator development. Indicators have been developed, a toolkit with pre-programmed tablets to record information and detailed instructions for how to collect abortion information has been piloted, and a central database will be established to make SRHR data (particularly abortion and advocacy data) more widely available.

Assembling the Bathymetric Puzzle to Create a Global Ocean Map

Bathymetry data are fundamental ocean observations that are important for a variety of applications including exploration and research, habitat mapping, resource management, coastal and ocean resilience, and policy decisions. Despite the importance of these data, the majority of the ocean, and our planet, remains unmapped. As a result, we lack comprehensive integrated data and information products at the resolutions necessary to address fundamental questions about subaqueous environments. With the increasing availability of mapping technology, advances in computing and data science, and an evolving culture that embraces data sharing, there are new opportunities to produce high-quality, publicly available, integrated bathymetry data products. Coordinated efforts with grand aspirations to completely map the world's oceans come at a pivotal time as we confront global challenges related to a changing planet. Through coordination and collaboration across communities, scales, and sectors, we can accelerate toward delivering data and information products that are useful to society while developing strong collaborative relationships that will have long-lasting effects. The technical and collaborative approaches developed for completely mapping the world ocean can be applied to systematic mapping efforts in other subaqueous environments and can benefit initiatives such as Lakebed 2030.

Digital Resources: HGIS de las Indias

HGIS de las Indias is an open-access Spanish-language database and web platform on the temporal and spatial developments in the territorial organization and settlements of all Spanish America (from Nutka to the Malvinas) during the reign of the Bourbon dynasty until the eve of the independence movements (1701–1808). It consists of several components: a platform for visualization of the database in an interactive web application, an engine for the creation of base maps, and a repository for the raw data files that can be used in specialized software. Also, HGIS de las Indias has a feature that allows registered users to create spatial data sets from tabular data. Beyond its practical use as finding aid, data provider, and mapping resource, it aims at fulfilling an even more fundamental function of infrastructure. The unique resource identifiers (URIs) for places and territorial concepts in HGIS de las Indias can be used as identifiers across projects and text annotations. Also, there exist easy workflows to prepare research data with a spatial component in tabular form and connect it with the database. HGIS de las Indias may thus serve as a link between otherwise unconnected data sets and is itself integrated in more fundamental infrastructures like Pelagios or the World Historical Gazetteer that constitute a bridge to the wider world of the semantic web.

Dissecting the Genetic Basis of Variation in Drosophila Sleep Using a Multiparental QTL Mapping Resource

There is considerable variation in sleep duration, timing and quality in human populations, and sleep dysregulation has been implicated as a risk factor for a range of health problems. Human sleep traits are known to be regulated by genetic factors, but also by an array of environmental and social factors. These uncontrolled, non-genetic effects complicate powerful identification of the loci contributing to sleep directly in humans. The model system, Drosophila melanogaster, exhibits a behavior that shows the hallmarks of mammalian sleep, and here we use a multitiered approach, encompassing high-resolution QTL mapping, expression QTL data, and functional validation with RNAi to investigate the genetic basis of sleep under highly controlled environmental conditions. We measured a battery of sleep phenotypes in >750 genotypes derived from a multiparental mapping panel and identified several, modest-effect QTL contributing to natural variation for sleep. Merging sleep QTL data with a large head transcriptome eQTL mapping dataset from the same population allowed us to refine the list of plausible candidate causative sleep loci. This set includes genes with previously characterized effects on sleep and circadian rhythms, in addition to novel candidates. Finally, we employed adult, nervous system-specific RNAi on the Dopa decarboxylase, dyschronic, and timeless genes, finding significant effects on sleep phenotypes for all three. The genes we resolve are strong candidates to harbor causative, regulatory variation contributing to sleep.

Multiple genetic loci affect place learning and memory performance in Drosophila melanogaster

Learning and memory are critical functions for all animals, giving individuals the ability to respond to changes in their environment. Within populations, individuals vary, however the mechanisms underlying this variation in performance are largely unknown. Thus, it remains to be determined what genetic factors cause an individual to have high learning ability, and what factors determine how well an individual will remember what they have learned. To genetically dissect learning and memory performance, we used the DSPR, a multiparent mapping resource in the model system Drosophila melanogaster, consisting of a large set of recombinant inbred lines (RILs) that naturally vary in these and other traits. Fruit flies can be trained in a “heat box” to learn to remain on one side of a chamber (place learning), and can remember this (place memory) over short timescales. Using this paradigm, we measured place learning and memory for ∼49,000 individual flies from over 700 DSPR RILs. We identified 16 different loci across the genome that significantly affect place learning and/or memory performance, with 5 of these loci affecting both traits. To identify transcriptomic differences associated with performance, we performed RNA-Seq on pooled samples of 7 high performing and 7 low performing RILs for both learning and memory and identified hundreds of genes with differences in expression in the two sets. Integrating our transcriptomic results with the mapping results allowed us to identify nine promising candidate genes, advancing our understanding of the genetic basis underlying natural variation in learning and memory performance.

BioKEEN: a library for learning and evaluating biological knowledge graph embeddings

Summary Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies. Availability and implementation BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN Supplementary information Supplementary data are available at Bioinformatics online.

Mapping Archaeology While Mapping an Empire: Using Historical Maps to Reconstruct Ancient Settlement Landscapes in Modern India and Pakistan

A range of data sources are now used to support the process of archaeological prospection, including remote sensed imagery, spy satellite photographs and aerial photographs. This paper advocates the value and importance of a hitherto under-utilised historical mapping resource—the Survey of India 1” to 1-mile map series, which was based on surveys started in the mid–late nineteenth century, and published progressively from the early twentieth century AD. These maps present a systematic documentation of the topography of the British dominions in the South Asian Subcontinent. Incidentally, they also documented the locations, the height and area of thousands of elevated mounds that were visible in the landscape at the time that the surveys were carried out, but have typically since been either damaged or destroyed by the expansion of irrigation agriculture and urbanism. Subsequent reanalysis has revealed that many of these mounds were actually the remains of ancient settlements. The digitisation and analysis of these historic maps thus creates a unique opportunity for gaining insight into the landscape archaeology of South Asia. This paper reviews the context within which these historical maps were created, presents a method for georeferencing them, and reviews the symbology that was used to represent elevated mound features that have the potential to be archaeological sites. This paper should be read in conjunction with the paper by Arnau Garcia et al. in the same issue of Geosciences, which implements a research programme combining historical maps and a range of remote sensing approaches to reconstruct historical landscape dynamics in the Indus River Basin.