scholarly journals An open source database for the synthesis of soil radiocarbon data: ISRaD version 1.0

2019 ◽  
Author(s):  
Corey R. Lawrence ◽  
Jeffery Beem-Miller ◽  
Alison M. Hoyt ◽  
Grey Monroe ◽  
Carlos A. Sierra ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Open Source ◽  
Soil Surface ◽  
Original Data ◽  
R Package ◽  
Direct Access ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Starting Point ◽  
Radiocarbon Data

Abstract. Radiocarbon is a critical constraint on our estimates of the timescales of soil carbon cycling that can aid in identifying mechanisms of carbon stabilization and destabilization, and improve forecast of soil carbon response to management or environmental change. Despite the wealth of soil radiocarbon data that has been reported over the past 75 years, the ability to apply these data to global scale questions is limited by our capacity to synthesis and compare measurements generated using a variety of methods. Here we describe the International Soil Radiocarbon Database (ISRaD, soilradiocarbon.org), an open-source archive of soils data that include data from bulk soils, or whole-soils; distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods; samples of soil gas or water collected interstitially from within an intact soil profile; CO2 gas isolated from laboratory soil incubations; and fluxes collected in situ from a soil surface. The core of ISRaD is a relational database structured around individual datasets (entries) and organized hierarchically to report soil radiocarbon data, measured at different physical and temporal scales, as well as other soil or environmental properties that may also be measured at one or more levels of the hierarchy that may assist with interpretation and context. Anyone may contribute their own data to the database by entering it into the ISRaD template and subjecting it to quality assurance protocols. ISRaD can be accessed through: (1) a web-based interface, (2) an R package (ISRaD), or (3) direct access to code and data through the GitHub repository, which hosts both code and data. The design of ISRaD allows for participants to become directly involved in the management, design, and application of ISRaD data. The synthesized dataset is available in two forms: the original data as reported by the authors of the datasets; and an enhanced dataset that includes ancillary geospatial data calculated within the ISRaD framework. ISRaD also provides data management tools in the ISRaD-R package that provide a starting point for data analysis. This community-based dataset and platform for soil radiocarbon and a wide array of additional soils data information in soils where data are easy to contribute and the community is invited to add tools and ideas for improvement. As a whole, ISRaD provides resources that can aid our evaluation of soil dynamics and improve our understanding of controls on soil carbon dynamics across a range of spatial and temporal scales. The ISRaD v1.0 dataset (Lawrence et al., 2019) is archived and freely available at https://doi.org/10.5281/zenodo.2613911.

scholarly journals An open-source database for the synthesis of soil radiocarbon data: International Soil Radiocarbon Database (ISRaD) version 1.0

2020 ◽  
Vol 12 (1) ◽  
pp. 61-76 ◽  
Author(s):  
Corey R. Lawrence ◽  
Jeffrey Beem-Miller ◽  
Alison M. Hoyt ◽  
Grey Monroe ◽  
Carlos A. Sierra ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Open Source ◽  
Soil Profile ◽  
Original Data ◽  
R Package ◽  
Direct Access ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Starting Point ◽  
Radiocarbon Data

Abstract. Radiocarbon is a critical constraint on our estimates of the timescales of soil carbon cycling that can aid in identifying mechanisms of carbon stabilization and destabilization and improve the forecast of soil carbon response to management or environmental change. Despite the wealth of soil radiocarbon data that have been reported over the past 75 years, the ability to apply these data to global-scale questions is limited by our capacity to synthesize and compare measurements generated using a variety of methods. Here, we present the International Soil Radiocarbon Database (ISRaD; http://soilradiocarbon.org, last access: 16 December 2019), an open-source archive of soil data that include reported measurements from bulk soils, distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods, samples of soil gas or water collected interstitially from within an intact soil profile, CO2 gas isolated from laboratory soil incubations, and fluxes collected in situ from a soil profile. The core of ISRaD is a relational database structured around individual datasets (entries) and organized hierarchically to report soil radiocarbon data, measured at different physical and temporal scales as well as other soil or environmental properties that may also be measured and may assist with interpretation and context. Anyone may contribute their own data to the database by entering it into the ISRaD template and subjecting it to quality assurance protocols. ISRaD can be accessed through (1) a web-based interface, (2) an R package (ISRaD), or (3) direct access to code and data through the GitHub repository, which hosts both code and data. The design of ISRaD allows for participants to become directly involved in the management, design, and application of ISRaD data. The synthesized dataset is available in two forms: the original data as reported by the authors of the datasets and an enhanced dataset that includes ancillary geospatial data calculated within the ISRaD framework. ISRaD also provides data management tools in the ISRaD-R package that provide a starting point for data analysis; as an open-source project, the broader soil community is invited and encouraged to add data, tools, and ideas for improvement. As a whole, ISRaD provides resources to aid our evaluation of soil dynamics across a range of spatial and temporal scales. The ISRaD v1.0 dataset is archived and freely available at https://doi.org/10.5281/zenodo.2613911 (Lawrence et al., 2019).

scholarly journals mixl: An open-source R package for estimating complex choice models on large datasets

2021 ◽  
Vol 39 ◽  
pp. 100284
Author(s):  
Joseph Molloy ◽  
Felix Becker ◽  
Basil Schmid ◽  
Kay W. Axhausen
Keyword(s):  
Open Source ◽  
R Package ◽  
Choice Models ◽  
Large Datasets

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

Soil salinization monitoring method evolution at various spatial and temporal scales in arid context: a review

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
Zarai Besma ◽  
Walter Christian ◽  
Michot Didier ◽  
Montoroi Jean Pierre ◽  
Hachicha Mohamed
Keyword(s):  
Soil Salinization ◽  
Temporal Scales ◽  
Monitoring Method ◽  
Spatial And Temporal Scales

scholarly journals A Comparison between One-Step and Two-Step Nesting Strategy in the Dynamical Downscaling of Regional Climate Model COSMO-CLM at 2.2 km Driven by ERA5 Reanalysis

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 260
Author(s):  
Mario Raffa ◽  
Alfredo Reder ◽  
Marianna Adinolfi ◽  
Paola Mercogliano
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Weather Forecast ◽  
Medium Range Weather Forecast ◽  
Spatial And Temporal Scales ◽  
Starting Point ◽  
One Step

Recently, the European Centre for Medium Range Weather Forecast (ECMWF) has released a new generation of reanalysis, acknowledged as ERA5, representing at the present the most plausible picture for the current climate. Although ERA5 enhancements, in some cases, its coarse spatial resolution (~31 km) could still discourage a direct use of precipitation fields. Such a gap could be faced dynamically downscaling ERA5 at convection permitting scale (resolution < 4 km). On this regard, the selection of the most appropriate nesting strategy (direct one-step against nested two-step) represents a pivotal issue for saving time and computational resources. Two questions may be raised within this context: (i) may the dynamical downscaling of ERA5 accurately represents past precipitation patterns? and (ii) at what extent may the direct nesting strategy performances be adequately for this scope? This work addresses these questions evaluating two ERA5-driven experiments at ~2.2 km grid spacing over part of the central Europe, run using the regional climate model COSMO-CLM with different nesting strategies, for the period 2007–2011. Precipitation data are analysed at different temporal and spatial scales with respect to gridded observational datasets (i.e., E-OBS and RADKLIM-RW) and existing reanalysis products (i.e., ERA5-Land and UERRA). The present work demonstrates that the one-step experiment tendentially outperforms the two-step one when there is no spectral nudging, providing results at different spatial and temporal scales in line with the other existing reanalysis products. However, the results can be highly model and event dependent as some different aspects might need to be considered (i.e., the nesting strategies) during the configuration phase of the climate experiments. For this reason, a clear and consolidated recommendation on this topic cannot be stated. Such a level of confidence could be achieved in future works by increasing the number of cities and events analysed. Nevertheless, these promising results represent a starting point for the optimal experimental configuration assessment, in the frame of future climate studies.

scholarly journals WORCS: A workflow for open reproducible code in science

Data Science ◽  
2021 ◽  
pp. 1-21
Author(s):  
Caspar J. Van Lissa ◽  
Andreas M. Brandmaier ◽  
Loek Brinkman ◽  
Anna-Lena Lamprecht ◽  
Aaron Peikert ◽  
...  
Keyword(s):  
Best Practices ◽  
Source Code ◽  
R Package ◽  
Open Science ◽  
Research Projects ◽  
Tabular Data ◽  
Step Procedure ◽  
Starting Point ◽  
Conducting Research ◽  
And Training

Adopting open science principles can be challenging, requiring conceptual education and training in the use of new tools. This paper introduces the Workflow for Open Reproducible Code in Science (WORCS): A step-by-step procedure that researchers can follow to make a research project open and reproducible. This workflow intends to lower the threshold for adoption of open science principles. It is based on established best practices, and can be used either in parallel to, or in absence of, top-down requirements by journals, institutions, and funding bodies. To facilitate widespread adoption, the WORCS principles have been implemented in the R package worcs, which offers an RStudio project template and utility functions for specific workflow steps. This paper introduces the conceptual workflow, discusses how it meets different standards for open science, and addresses the functionality provided by the R implementation, worcs. This paper is primarily targeted towards scholars conducting research projects in R, conducting research that involves academic prose, analysis code, and tabular data. However, the workflow is flexible enough to accommodate other scenarios, and offers a starting point for customized solutions. The source code for the R package and manuscript, and a list of examplesof WORCS projects, are available at https://github.com/cjvanlissa/worcs.

Challenges in urban flood management: travelling across spatial and temporal scales

2008 ◽  
Vol 1 (2) ◽  
pp. 81-88 ◽  
Author(s):  
C. Zevenbergen ◽  
W. Veerbeek ◽  
B. Gersonius ◽  
S. Van Herk
Keyword(s):  
Flood Management ◽  
Temporal Scales ◽  
Urban Flood ◽  
Spatial And Temporal Scales

Characteristics of the east Mediterranean dust variability on small spatial and temporal scales

2015 ◽  
Vol 120 ◽  
pp. 51-60 ◽  
Author(s):  
Yuval ◽  
Meytar Sorek–Hamer ◽  
Amnon Stupp ◽  
Pinhas Alpert ◽  
David M. Broday
Keyword(s):  
East Mediterranean ◽  
Temporal Scales ◽  
Spatial And Temporal Scales

scholarly journals tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Charlie M. Carpenter ◽  
Daniel N. Frank ◽  
Kayla Williamson ◽  
Jaron Arbet ◽  
Brandie D. Wagner ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Open Source ◽  
Data Structures ◽  
Negative Binomial ◽  
Rocky Mountain ◽  
R Package ◽  
Microbiome Analysis ◽  
External Data ◽  
Data Tables ◽  
Microbiome Data

Abstract Background The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa. Results We developed the R package “tidyMicro” to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts’ control over workflow. A complete vignette is provided to aid new users in analysis workflow. Conclusions tidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets.

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