scholarly journals easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application

2021 ◽  
Author(s):  
Timothy A Crombie ◽  
Matteo Di Bernardo ◽  
Daniel E Cook ◽  
Erik Andersen
Keyword(s):  
Mobile Application ◽  
Large Scale ◽  
Data Entry ◽  
R Package ◽  
Environmental Data ◽  
Software Environment ◽  
R Software ◽  
Natural Sampling ◽  
Disparate Data ◽  
Ecological Sampling

Large-scale ecological sampling can be difficult and costly, especially for organisms that are too small to be easily identified in a natural environment by eye. Typically, these microscopic floral and fauna are sampled by collecting substrates from nature and then separating organisms from substrates in the laboratory. In many cases, diverse organisms can be identified to the species-level using molecular barcodes. To facilitate large-scale ecological sampling of microscopic organisms, we used a geographic data-collection platform for mobile devices called Fulcrum that streamlines the organization of geospatial sampling data, substrate photographs, and environmental data at natural sampling sites. These sampling data are then linked to organism isolation data from the laboratory. Here, we describe the easyFulcrum R package, which can be used to clean, process, and visualize ecological field sampling and isolation data exported from the Fulcrum mobile application. We developed this package for wild nematode sampling, but it is extensible to other organisms. The advantages of using Fulcrum combined with easyFulcrum are (1) the elimination of transcription errors by replacing manual data entry and/or spreadsheets with a mobile application, (2) the ability to clean, process, and visualize sampling data using a standardized set of functions in the R software environment, and (3) the ability to join disparate data to each other, including environmental data from the field and the molecularly defined identities of individual specimens isolated from samples.

scholarly journals easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0254293 ◽  
Author(s):  
Matteo Di Bernardo ◽  
Timothy A. Crombie ◽  
Daniel E. Cook ◽  
Erik C. Andersen
Keyword(s):  
Mobile Application ◽  
Large Scale ◽  
Data Entry ◽  
R Package ◽  
Environmental Data ◽  
Software Environment ◽  
R Software ◽  
Natural Sampling ◽  
Disparate Data ◽  
Ecological Sampling

Large-scale ecological sampling can be difficult and costly, especially for organisms that are too small to be easily identified in a natural environment by eye. Typically, these microscopic floral and fauna are sampled by collecting substrates from nature and then separating organisms from substrates in the laboratory. In many cases, diverse organisms can be identified to the species-level using molecular barcodes. To facilitate large-scale ecological sampling of microscopic organisms, we used a geographic data-collection platform for mobile devices called Fulcrum that streamlines the organization of geospatial sampling data, substrate photographs, and environmental data at natural sampling sites. These sampling data are then linked to organism isolation data from the laboratory. Here, we describe the easyFulcrum R package, which can be used to clean, process, and visualize ecological field sampling and isolation data exported from the Fulcrum mobile application. We developed this package for wild nematode sampling, but it can be used with other organisms. The advantages of using Fulcrum combined with easyFulcrum are (1) the elimination of transcription errors by replacing manual data entry and/or spreadsheets with a mobile application, (2) the ability to clean, process, and visualize sampling data using a standardized set of functions in the R software environment, and (3) the ability to join disparate data to each other, including environmental data from the field and the molecularly defined identities of individual specimens isolated from samples.

scholarly journals MendelianRandomization v0.5.0: updates to an R package for performing Mendelian randomization analyses using summarized data

2020 ◽  
Vol 5 ◽  
pp. 252
Author(s):  
Jim R. Broadbent ◽  
Christopher N. Foley ◽  
Andrew J. Grant ◽  
Amy M. Mason ◽  
James R. Staley ◽  
...  
Keyword(s):  
Data Visualization ◽  
Software Package ◽  
Mendelian Randomization ◽  
R Package ◽  
Robust Methods ◽  
Software Environment ◽  
R Software ◽  
Graphical Output ◽  
Web Resource ◽  
Randomization Analysis

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added to the package or updated in recent years. These features can be divided into four categories: robust methods for Mendelian randomization, methods for multivariable Mendelian randomization, functions for data visualization, and the ability to load data into the package seamlessly from the PhenoScanner web-resource. We provide examples of the graphical output produced by the data visualization commands, as well as syntax for obtaining suitable data and performing a Mendelian randomization analysis in a single line of code.

scholarly journals EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

2021 ◽  
Vol 11 (4) ◽  
Author(s):  
Germano Costa-Neto ◽  
Giovanni Galli ◽  
Humberto Fanelli Carvalho ◽  
José Crossa ◽  
Roberto Fritsche-Neto
Keyword(s):  
Large Scale ◽  
Cost Effective ◽  
Reaction Norm ◽  
R Package ◽  
Global Scale ◽  
Environmental Data ◽  
Fine Tuning ◽  
Literature Mining ◽  
Living Organisms ◽  
Quantitative Genomics

Abstract Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.

scholarly journals EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

2020 ◽  
Author(s):  
Germano Costa-Neto ◽  
Giovanni Galli ◽  
Humberto Fanelli Carvalho ◽  
José Crossa ◽  
Roberto Fritsche-Neto
Keyword(s):  
Large Scale ◽  
Cost Effective ◽  
Reaction Norm ◽  
R Package ◽  
Global Scale ◽  
Environmental Data ◽  
Fine Tuning ◽  
Literature Mining ◽  
Living Organisms ◽  
Quantitative Genomics

ABSTRACTEnvirotyping is an essential technique used to unfold the non-genetic drivers associated with the phenotypic adaptation of living organisms. Here we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically-smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.

scholarly journals MendelianRandomization v0.5.0: updates to an R package for performing Mendelian randomization analyses using summarized data

2020 ◽  
Vol 5 ◽  
pp. 252
Author(s):  
Jim R. Broadbent ◽  
Christopher N. Foley ◽  
Andrew J. Grant ◽  
Amy M. Mason ◽  
James R. Staley ◽  
...  
Keyword(s):  
Data Visualization ◽  
Software Package ◽  
Mendelian Randomization ◽  
R Package ◽  
Robust Methods ◽  
Software Environment ◽  
R Software ◽  
Graphical Output ◽  
Web Resource ◽  
Randomization Analysis

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added to the package or updated in recent years. These features can be divided into four categories: robust methods for Mendelian randomization, methods for multivariable Mendelian randomization, functions for data visualization, and the ability to load data into the package seamlessly from the PhenoScanner web-resource. We provide examples of the graphical output produced by the data visualization commands, as well as syntax for obtaining suitable data and performing a Mendelian randomization analysis in a single line of code.

scholarly journals The implementation of a mobile application to support community-based health workers

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
A C F Martins ◽  
P L Pereira ◽  
A C C N Mafra ◽  
J L Miraglia ◽  
C N Monteiro ◽  
...  
Keyword(s):  
Health Care ◽  
Mobile Application ◽  
Total Population ◽  
Health Workers ◽  
Data Entry ◽  
Population Data ◽  
Administrative Staff ◽  
Economic Data ◽  
Process Change ◽  
Community Based

Abstract Issue Real-time access to up-to-date population information is essential to the strategic planning of primary health care (PHC). In the Brazilian public health system community-based health workers (CHWs), working as part of PHC teams, collect health, demographic and socio-economic data from individuals mainly through paper-based forms that are later entered manually into electronic information systems. Mobile applications could help to improve the quality and speed of this process facilitating the CHWs day-to-day work while improving the access to the collected information. Description of the Problem During September of 2019, a mobile application installed in tablets for the collection of health, demographic and socio-economic data was deployed for 432 CHWs of 87 PHC teams in the southern region of São Paulo, Brazil, serving a total population of 283,324 individuals. During implementation, the acceptability and challenges faced by CHWs were evaluated and the time taken to complete data entry. Results Seventy-two hours of training were offered to CHWs and other 139 professionals including managers, nurses and administrative staff (AS). Some CHWs reported concerns about the process change and fear of not being able to operate the application, especially those unfamiliar to the technology. With training and team support, fear was transformed into satisfaction with the realization of the benefits of the system. The main infrastructure challenge was the need for installation of Wi-Fi routers in some health care units, in addition to the replacement 4.4% of damaged tablets. In four months 97.6% of the total population was registered in the application. Lessons A WhatsApp group was created to clarify AS doubts, receive suggestions and disseminate guidelines. AS remained as the reference point at healthcare units and data completeness still needs to be reinforced. Key messages A mobile application was well-accepted by CHWs and could facilitate the collection of population data. A tablet app proved to be a useful tool to generate information for the PHC teams.

scholarly journals Combining disparate data sources for improved poverty prediction and mapping

2017 ◽  
Vol 114 (46) ◽  
pp. E9783-E9792 ◽  
Author(s):  
Neeti Pokhriyal ◽  
Damien Christophe Jacques
Keyword(s):  
Bayesian Learning ◽  
Pearson Correlation ◽  
Gaussian Process Regression ◽  
Multidimensional Poverty ◽  
Environmental Data ◽  
Policy Makers ◽  
Multidimensional Poverty Index ◽  
Fine Grained ◽  
Disparate Data ◽  
Single Data

More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of “Big Data” to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84–0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication.

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