Online Data Repositories Research Articles

773Triple elimination of mother-to-child transmission of HIV, syphilis and hepatitis B in the Pacific Islands

Abstract Background Without intervention, HIV, hepatitis B virus and syphilis can be transmitted from mother-to-child during pregnancy, birth and breastfeeding and these can cause adverse pregnancy and longer-term outcomes. The 22 Pacific Island countries and territories (PICTs) have among the highest global prevalence of syphilis and hepatitis B. Mother-to-child transmission remains a concern, particularly hepatitis B. Global and regional elimination targets have been set to eliminate mother-to-child transmission (EMTCT) however progress of PICTs towards meeting targets is unknown. Methods Data from 2000-2019 relating to EMTCT epidemiological, process and impact targets were extracted from peer reviewed literature, online data repositories and grey literature. Results 27 peer reviewed reports, data from online data repositories for 19 countries and twelve national demographic and/or health reports were collated. Existing online databases report data for only small number of EMTCT indicators and there are few peer-reviewed studies that report EMTCT coverage estimates for PICTs. Availability of data varied between countries and available data suggests that few countries are meeting EMTCT indicator targets. Conclusions Based on the current trach trajectory, PICTs will not reach triple elimination by 2030. Both interventions to improve EMTCT activity coverage and strengthened surveillance, monitoring and evaluation systems are required. Key messages Mother-to-child transmission of HIV, hepatitis B and syphilis is a major concern in the Pacific and increased efforts are required, including strengthened monitoring and evaluation systems.

Creation of a prototype biomimetic fish to better understand impact trauma caused by hydropower turbine blade strikes

Biomimetic model organisms could be useful surrogates for live animals in many applications if the models have sufficient biofidelity. One such application is for use in field and laboratory tests of fish mortality associated with passage through hydropower turbines. Laboratory trials suggest that blade strikes are especially injurious and often causes mortality when fish are struck by thinner blades moving at higher velocities. Dose-response relationships have been created from these data, but the exact relationship between fish mortality and the actual forces enacted on fish during simulated blade strike testing remains unknown. Here, we describe the methods used to create a prototype biomimetic model fish composed of ballistic gelatin and covered with a surrogate skin to better approximate the biomechanical properties of a fish body. Frozen fish were scanned with high-fidelity laser scanners, and a 3D-printed, reusable mold was created from which to cast our gelatin model. Computed tomography scan data, imaged directly or taken from online data repositories, were also successfully used to create CAD models for use in additive manufacturing of molds. One 3-axis accelerometer was embedded into the gelatin to compare accelerometer data to dose-response data from previous laboratory research on live fish. The resulting model (i.e., Gelfish) had a statistically indistinguishable tissue durometer to that of real fish tissue and preliminary blade strike impact testing suggested its overall flexibility was similar to that of live fish. Gelfish was designed with biofidelity as its guiding principle and our results suggest initial experimentation was successful. Future research will include replication of initial Gelfish test results, quantitative measurement of model flexibility relative to real fish, and inclusion of surrogate skeletal structures to enhance biofidelity. Use of more sophisticated sensors would also better quantify the physical forces of blade strike impact and help determine how said forces correlate with rates of mortality observed during tests on live fish.

COVID-19 waves in Africa: Effects of outbreak response and interventions

Introduction: Public health safety measures have been advocated for towards the effective containment of COVID-19. This study aimed to assess the effects of public health safety measures (PHSM) implemented during different phases of the COVID-19 pandemic in Africa. Methods: We conducted a retrospective analysis of the COVID-19 control measures implemented across Africa. We extracted and collated data on COVID-19 cases, deaths, and stringency level (SL) from online data repositories between 18 July, 2020 and 18 December, 2020 in Africa. Results: In the South African region, COVID-19 cases increased when there was partial closure of work sectors (SL = 41.67), but decreased with complete closure of work sectors (SL=46.30) in South Africa. In North Africa, COVID-19 cases increased when there was limited contact tracing (SL = 23.15), but decreased with comprehensive contact tracing (SL = 75.00) in Tunisia. In West Africa, COVID-19 cases increased when there was partial school closure (SL = 50.93), but decreased with complete school closure (SL = 65.74) in Nigeria. In East Africa, COVID-19 cases increased when contact tracing was limited (SL = 28.70), but decreased with comprehensive contact tracing (SL = 51.85) in Sudan. In Central Africa, COVID-19 cases increased when there was partial school closure (SL = 43.52), but decreased with complete school closure (SL = 56.48) in Congo. Conclusion: PHSM should be intensified to ensure the effective containment of COVID-19 in Africa. Evidence-based decisions should be made by policy makers and contextualized in each African country as the COVID-19 pandemic evolves.

Evaluation of the effect of border closure on COVID-19 incidence rates across nine African countries: an interrupted time series study.

BackgroundBorder closure is one of the policy changes implemented to mitigate against coronavirus disease 2019 (COVID-19). We evaluated the effect of border closure on the incidence rate of COVID-19 across nine African countries.MethodsAn interrupted time series analysis was used to assess COVID-19 incidence rates in Egypt, Tunisia, Democratic Republic of the Congo (DRC), Ethiopia, Kenya, Ghana, Nigeria, Senegal and South Africa (SA). Data were collected between 14 February and 19 July 2020 from online data repositories. The linear trend and magnitude of change were evaluated using the itsa function with ordinary least-squares regression in Stata with a 7-d deferred interruption point, which allows a period of diffusion post-border closure.ResultsOverall, the countries recorded an increase in the incidence rate of COVID-19 after border closure. However, when compared with matched control groups, SA, Nigeria, Ghana, Egypt and Kenya showed a higher incidence rate trend. In contrast, Ethiopia, DRC and Tunisia showed a lower trend compared with their controls.ConclusionsThe implementation of border closures within African countries had minimal effect on the incidence of COVID-19. The inclusion of other control measures such as enhanced testing capacity and improved surveillance activities will reveal the effectiveness of border closure measures.

MetaXplor: an interactive viral and microbial metagenomic data manager.

BackgroundEfficiently managing large, heterogeneous data in a structured yet flexible way is a challenge to research laboratories working with genomic data. Specifically regarding both shotgun- and metabarcoding-based metagenomics, while online reference databases and user-friendly tools exist for running various types of analyses (e.g., Qiime, Mothur, Megan, IMG/VR, Anvi'o, Qiita, MetaVir), scientists lack comprehensive software for easily building scalable, searchable, online data repositories on which they can rely during their ongoing research.ResultsmetaXplor is a scalable, distributable, fully web-interfaced application for managing, sharing, and exploring metagenomic data. Being based on a flexible NoSQL data model, it has few constraints regarding dataset contents and thus proves useful for handling outputs from both shotgun and metabarcoding techniques. By supporting incremental data feeding and providing means to combine filters on all imported fields, it allows for exhaustive content browsing, as well as rapid narrowing to find specific records. The application also features various interactive data visualization tools, ways to query contents by BLASTing external sequences, and an integrated pipeline to enrich assignments with phylogenetic placements. The project home page provides the URL of a live instance allowing users to test the system on public data.ConclusionmetaXplor allows efficient management and exploration of metagenomic data. Its availability as a set of Docker containers, making it easy to deploy on academic servers, on the cloud, or even on personal computers, will facilitate its adoption.

Intraspecific trait variation in plants: a renewed focus on its role in ecological processes.

Investigating the causes and consequences of intraspecific trait variation (ITV) in plants is not novel, as it has long been recognized that such variation shapes biotic and abiotic interactions. While evolutionary and population biology have extensively investigated ITV, only in the last 10 years has interest in ITV surged within community and comparative ecology. Despite this recent interest, still lacking are thorough descriptions of ITV's extent, the spatial and temporal structure of ITV, and stronger connections between ITV and community and ecosystem properties. Our primary aim in this review is to synthesize the recent literature and ask: (1) How extensive is intraspecific variation in traits across scales, and what underlying mechanisms drive this variation? (2) How does this variation impact higher-order ecological processes (e.g. population dynamics, community assembly, invasion, ecosystem productivity)? (3) What are the consequences of ignoring ITV and how can these be mitigated? and (4) What are the most pressing research questions, and how can current practices be modified to suit our research needs? Our secondary aim is to target diverse and underrepresented traits and plant organs, including anatomy, wood, roots, hydraulics, reproduction and secondary chemistry. In addressing these aims, we showcase papers from the Special Issue. Plant ITV plays a key role in determining individual and population performance, species interactions, community structure and assembly, and ecosystem properties. Its extent varies widely across species, traits and environments, and it remains difficult to develop a predictive model for ITV that is broadly applicable. Systematically characterizing the sources (e.g. ontogeny, population differences) of ITV will be a vital step forward towards identifying generalities and the underlying mechanisms that shape ITV. While the use of species means to link traits to higher-order processes may be appropriate in many cases, such approaches can obscure potentially meaningful variation. We urge the reporting of individual replicates and population means in online data repositories, a greater consideration of the mechanisms that enhance and constrain ITV's extent, and studies that span sub-disciplines.

Sustainable and inclusive – Evaluating urban sustainability indicators’ suitability for measuring progress towards SDG-11

The adoption of the sustainable development goals marks a transition in the global sustainability discourse to a growing focus on equity, with urban areas’ role in achieving sustainable and inclusive growth more explicit in sustainable development goal-11. Within this discourse, urban sustainability indicators could be used to monitor environmental quality and equity within individual cities, while promising to deepen our understanding of how urban areas contribute to global environmental sustainability. We examine 484 indicators of urban and regional environmental sustainability sourced from 40 indexes and online data repositories to determine their suitability for measuring both urban environmental performance and equity. Despite the large number of existing indicators related to urban environmental monitoring, we find that they are inadequate as tools for evaluating progress towards sustainable development goal-11’s integrated goal of sustainable and inclusive (i.e. equitable) urban areas, due to a lack of benchmarks, targets, and explicit measurement of equity considerations. Future research should emphasize data collection that can be disaggregated geographically to make it possible to measure distributional equity and establish locally appropriate benchmarks and realistic targets for urban sustainability indicators. Lastly, we argue that utilizing large-scale, high-resolution datasets has the potential to help overcome these data collection challenges.

Toward open and reproducible environmental modeling by integrating online data repositories, computational environments, and model Application Programming Interfaces

Cyberinfrastructure needs to be advanced to enable open and reproducible environmental modeling research. Recent efforts toward this goal have focused on advancing online repositories for data and model sharing, online computational environments along with containerization technology and notebooks for capturing reproducible computational studies, and Application Programming Interfaces (APIs) for simulation models to foster intuitive programmatic control. The objective of this research is to show how these efforts can be integrated to support reproducible environmental modeling. We present first the high-level concept and general approach for integrating these three components. We then present one possible implementation that integrates HydroShare (an online repository), CUAHSI JupyterHub and CyberGIS-Jupyter for Water (computational environments), and pySUMMA (a model API) to support open and reproducible hydrologic modeling. We apply the example implementation for a hydrologic modeling use case to demonstrate how the approach can advance reproducible environmental modeling through the seamless integration of cyberinfrastructure services.

The effects of publishing processes on scientific thought: Typography and typology in prehistoric archaeology (1950s-1990s).

In the last decades, many changes have occurred in scientific publishing, including online publication, data repositories, file formats and standards. The role played by computers in this process rekindled the argument on forms of technical determinism. This paper addresses this old debate by exploring the case of publishing processes in prehistoric archaeology during the second part of the twentieth century, prior to the wide-scale adoption of computers. It investigates the case of a collective and international attempt to standardize the typological analysis of prehistoric lithic objects, coined typologie analytique by Georges Laplace and developed by a group of French, Italian, and Spanish researchers. The aim of this paper is to: 1) present a general bibliometric scenario of prehistoric archaeology publishing in continental Europe; 2) report on the little-known typologie analytique method in archaeology, using publications, archives, and interviews; 3) show how the publication of scientific production was shaped by social (editorial policies, support networks) and material (typography features and publication formats) constraints; and 4) highlight how actors founded resources to control and counterbalance these effects, namely by changing and improving publishing formats.

Comparisons of Late Ordovician ecosystem dynamics before and after the Richmondian invasion reveal consequences of invasive species in benthic marine paleocommunities

AbstractA thorough understanding of how communities respond to extreme changes, such as biotic invasions, is essential to manage ecosystems today. Here we constructed fossil food webs to identify changes in Late Ordovician (Katian) shallow-marine paleocommunity structure and functioning before and after the Richmondian invasion, a well-documented ancient invasion. Food webs were compared using descriptive metrics and cascading extinction on graphs models. Richness at intermediate trophic levels was underrepresented when using only data from the Paleobiology Database relative to museum collections, resulting in a spurious decrease in modeled paleocommunity stability. Therefore, museum collections and field sampling may provide more reliable sources of data for the reconstruction of trophic organization in comparison to online data repositories. The invasion resulted in several changes in ecosystem dynamics. Despite topological similarities between pre- and postinvasion food webs, species loss occurred corresponding to a minor decrease in functional groups. Invaders occupied all of the preinvasion functional guilds, with the exception of four incumbent guilds that were lost and one new guild, corroborating the notion that invaders replace incumbents and fill preexisting niche space. Overall, models exhibited strong resistance to secondary extinction, although the postinvasion community had a lower threshold of collapse and more variable response to perturbation. We interpret these changes in dynamics as a decrease in stability, despite similarities in overall structure. Changes in food web structure and functioning resulting from the invasion suggest that conservation efforts may need to focus on preserving functional diversity if more diverse ecosystems are not inherently more stable.

Important Considerations when Using Models

Important Considerations when Using Models

CausalMGM: an interactive web-based causal discovery tool.

High-throughput sequencing and the availability of large online data repositories (e.g. The Cancer Genome Atlas and Trans-Omics for Precision Medicine) have the potential to revolutionize systems biology by enabling researchers to study interactions between data from different modalities (i.e. genetic, genomic, clinical, behavioral, etc.). Currently, data mining and statistical approaches are confined to identifying correlates in these datasets, but researchers are often interested in identifying cause-and-effect relationships. Causal discovery methods were developed to infer such cause-and-effect relationships from observational data. Though these algorithms have had demonstrated successes in several biomedical applications, they are difficult to use for non-experts. So, there is a need for web-based tools to make causal discovery methods accessible. Here, we present CausalMGM (http://causalmgm.org/), the first web-based causal discovery tool that enables researchers to find cause-and-effect relationships from observational data. Web-based CausalMGM consists of three data analysis tools: (i) feature selection and clustering; (ii) automated identification of cause-and-effect relationships via a graphical model; and (iii) interactive visualization of the learned causal (directed) graph. We demonstrate how CausalMGM enables an end-to-end exploratory analysis of biomedical datasets, giving researchers a clearer picture of its capabilities.

Health systems and global progress towards malaria elimination, 2000\u20132016

BackgroundAs more countries progress towards malaria elimination, a better understanding of the most critical health system features for enabling and supporting malaria control and elimination is needed.MethodsAll available health systems data relevant for malaria control were collated from 23 online data repositories. Principal component analysis was used to create domain specific health system performance measures. Multiple regression model selection approaches were used to identify key health systems predictors of progress in malaria control in the 2000–2016 period among 105 countries. Additional analysis was performed within malaria burden groups.ResultsThere was large heterogeneity in progress in malaria control in the 2000–2016 period. In univariate analysis, several health systems factors displayed a strong positive correlation with reductions in malaria burden between 2000 and 2016. In multivariable models, delivery of routine services and hospital capacity were strongly predictive of reductions in malaria cases, especially in high burden countries. In low-burden countries approaching elimination, primary health center density appeared negatively associated with progress while hospital capacity was positively correlated with eliminating malaria.ConclusionsThe findings presented in this manuscript suggest that strengthening health systems can be an effective strategy for reducing malaria cases, especially in countries with high malaria burden. Potential returns appear particularly high in the area of service delivery.

New filter approaches for feature selection using differential evolution and fuzzy rough set theory

Nowadays the incredibly advanced developments in information technologies have led to exponential growth in the datasets with respect to both the dimensionality and the sample size. This trend can be easily illustrated in popular online data repositories (e.g., UCI machine learning repository). The more growth in the datasets, the more challenging the data management becomes. This is because such datasets usually comprise a high level of noise as well as the necessary information. Therefore, the elimination of noisy features in the datasets is an important task to discover meaningful knowledge. Although a large number of feature selection approaches have been proposed in the literature to deal with noisy features, the need for the studies based on feature selection has not come to an end. In this paper, we propose differential evolution-based feature selection approaches wrapped around the principles of fuzzy rough set theory. In contrast to well-known feature selection criteria such as standard mutual information, standard rough set and probabilistic rough set, our approaches can also deal with real-valued variables without the requirement of discretization. Moreover, the feature subsets selected by our approaches can profoundly improve the classification performance compared to the recent particle swarm optimization approaches based on probabilistic rough set and the state-of-the-art filter approaches.

Online data repositories as educational resources? A learning environment covering formal and informal inferential statistics ideas in scientific inquiry

Statistical ideas play a vital role in scientific investigations. For students enrolling in physics-related courses at university, the need to interpret data is set from the start. Analysing graphical representations of data is seen as one way to acquaint students with statistical thinking without relying on pre-knowledge about formal statistics for applying more sophisticated methods like multiple regression. We designed a learning environment, which supports students in understanding the exploratory analysis of multivariate datasets as well as the concept of multiple regression. For phase one of the learning path, we work with exploratory data analysis using the software TinkerPlots, which has several major advantages in contrast to conventional software. Only in phase two formal inferential statistics is applied. We have chosen a context-oriented approach for this learning environment, using the particulate matter concentration in an Austrian city as topic. By providing data from online data repositories in a simplified way, students get the opportunity to work with real data. The amount of this data exceeds the number of measurements collected in typical training labs in an authentic and feasible way. In this article, the design of the intervention and a range of results originating from the triggered learning paths will be presented and discussed. To sum it up, we illustrate advantages and opportunities of the use of innovative software, online data repositories and informal statistics for a first introduction of methods of formal statistics like multiple regression.

Melanoma Explorer: a web application to allow easy reanalysis of publicly available and clinically annotated melanoma omics data sets.

Validating newly discovered biomarkers in large, publicly available data sets is often difficult and requires specialized computer programming skills. Melanoma Explorer is a web application that enables easy interrogation of melanoma omics data sets that are freely available in online data repositories with a point-and-click interface. Two use cases are demonstrated. First, the relationship of lysozyme mRNA expression is shown to be prognostic in two independent gene expression microarray data sets. Second, a figure from a journal article showing the relationship of tumour thickness and miR-382 abundance is reproduced. Melanoma Explorer is demonstrated to be a useful tool for reproducing results of published studies and providing additional evidence for biomarkers in independent data sets.

An open source web application for distributed geospatial data exploration

The number of online data repositories is growing and they are becoming increasingly difficult to navigate. Data are scattered among different repositories, or hidden on personal or institutional servers. To access data, users must search extensively and rely on site-specific tools. These hurdles substantially inhibit data findability and accessibility; in particular, those in the long tail of data. We developed an open source web application, Spatial Data Hub, that is a geospatial data index, connected to remote Internet sources. It allows simultaneous display and comparison of disparate datasets on a single map. It aims to promote all data equally and provide the flexibility to connect to any storage system, effectively making long-tail datasets as visible as those in large, established repositories. Its low barrier of entry allows scientists and organizations to easily add data throughout the research process; enhancing transparency, openness and reproducibility. This flexibility and functionality makes Spatial Data Hub a novel platform for researchers to promote their work, develop new hypotheses and create new collaborations.

Levels and building blocks\u2014toward a domain granularity framework for the life sciences

BackgroundWith the emergence of high-throughput technologies, Big Data and eScience, the use of online data repositories and the establishment of new data standards that require data to be computer-parsable become increasingly important. As a consequence, there is an increasing need for an integrated system of hierarchies of levels of different types of material entities that helps with organizing, structuring and integrating data from disparate sources to facilitate data exploration, data comparison and analysis. Theories of granularity provide such integrated systems.ResultsOn the basis of formal approaches to theories of granularity authored by information scientists and ontology researchers, I discuss the shortcomings of some applications of the concept of levels and argue that the general theory of granularity proposed by Keet circumvents these problems. I introduce the concept of building blocks, which gives rise to a hierarchy of levels that can be formally characterized by Keet’s theory. This hierarchy functions as an organizational backbone for integrating various other hierarchies that I briefly discuss, resulting in a domain granularity framework for the life sciences. I also discuss the consequences of this granularity framework for the structure of the top-level category of ‘material entity’ in Basic Formal Ontology.ConclusionsThe domain granularity framework suggested here is meant to provide the basis on which a more comprehensive information framework for the life sciences can be developed, which would provide the much needed conceptual framework for representing domains that cover multiple granularity levels. This framework can be used for intuitively structuring data in the life sciences, facilitating data exploration, and it can be employed for reasoning over different granularity levels across different hierarchies. It would provide a methodological basis for establishing comparability between data sets and for quantitatively measuring their degree of semantic similarity.

Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

Spatially distributed water-balance and meteorological data from the Wolverton catchment, Sequoia National Park, California

Abstract. Accurate water-balance measurements in the seasonal, snow-dominated Sierra Nevada are important for forest and downstream water management. However, few sites in the southern Sierra offer detailed records of the spatial and temporal patterns of snowpack and soil-water storage and the fluxes affecting them, i.e., precipitation as rain and snow, snowmelt, evapotranspiration, and runoff. To explore these stores and fluxes we instrumented the Wolverton basin (2180–2750 m) in Sequoia National Park with distributed, continuous sensors. This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies the hydrologic inputs and storage in a mostly undeveloped catchment. Clustered sensors record lateral differences with regards to aspect and canopy cover at approximately 2250 and 2625 m in elevation, where two meteorological stations are installed. Meteorological stations record air temperature, relative humidity, radiation, precipitation, wind speed and direction, and snow depth. Data are available at hourly intervals by water year (1 October–30 September) in non-proprietary formats from online data repositories (https://doi.org/10.6071/M3S94T).