scholarly journals Why Watershed Analysts Should Use R for Data Processing and Analysis

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
R.D. (Dan) Moore ◽  
David Hutchinson
Keyword(s):  
Data Processing ◽  
Programming Languages ◽  
Watershed Management ◽  
Frequency Analysis ◽  
Quantitative Information ◽  
R Language ◽  
Watershed Delineation ◽  
Watershed Analysis ◽  
Baseflow Separation ◽  
Intensity Duration Frequency

Both the science and practice associated with watershed management involve the processing, presentation and analysis of quantitative information. In this article, the use of open source programming languages by watershed analysts is advocated. The R language, in particular, provides a rich set of tools for the types of data that are commonly encountered in watershed analysis. The utility of R is illustrated through three examples: intensity-duration-frequency analysis of rainfall data, baseflow separation, and watershed delineation and mapping.

FORMATION OF METASUBJECT COMPETENCIES IN THE COURSE “INFORMATION TECHNOLOGIES” BY MEANS OF THE BIG DATA PROCESSING LANGUAGE R

2019 ◽  
pp. 12-22
Author(s):  
D. M. Nazarov
Keyword(s):  
Data Processing ◽  
Information Technologies ◽  
Data Sets ◽  
Training Methods ◽  
Economic Knowledge ◽  
R Language ◽  
Currency Exchange ◽  
Business Informatics ◽  
Exchange Data ◽  
Processing Language

The article describes the training methods in the course “Information Technologies” for the future bachelors of the directions “Economics”, “Management”, “Finance”, “Business Informatics”, the development of metasubject competencies of the student while his use of tools for data processing by means of the language R. The metasubject essence of the work is to update traditional economic knowledge and skills through various presentation forms of the same data sets. As part of the laboratory work described in the article, future bachelors learn to use the basic tools of the R language and acquire specific skills and abilities in R-Studio using the example of processing currency exchange data. The description of the methods is presented in the form of the traditional Key-by-Key technology, which is widely used in teaching information technologies.

An Empirical Study on the Architecture Instability of Software Projects

2019 ◽  
Vol 29 (04) ◽  
pp. 515-545 ◽  
Author(s):  
Lerina Aversano ◽  
Daniela Guardabascio ◽  
Maria Tortorella
Keyword(s):  
Empirical Study ◽  
Programming Languages ◽  
Quantitative Information ◽  
Software Systems ◽  
Software Project ◽  
Software System ◽  
Software Projects ◽  
System Maturity ◽  
Multiple Releases ◽  
Initial Concept

Software architecture is an artifact that expresses how the initial concept of a software system has actually been implemented. However, changes to the requirement imply continuous modification of the software system and may affect its architecture. It is expected that when a software system reaches the mature state, the requirements for evolution decrease and its architecture becomes more stable. The paper analyzes how the architecture of a software system evolves during its life cycle, with the aim of obtaining quantitative information on its possible instability after it has been declared mature. The goal is to verify if the architectural instability decreases with the increase of the software system maturity and to identify the software components that are more unstable among multiple releases. The paper proposes metrics that measure the instability of the architecture of a software system and its components through different releases. Open source software projects classified as mature and active and related historical data are analyzed. The results of the empirical study point out that the instability of software projects continues to evolve even after they are declared mature. The proposed metrics give a useful support for investigating the instability of a software project, even if further factors can be analyzed. Furthermore, the study can be replicated on other software systems belonging to different domains and developed using different programming languages.

scholarly journals Polyglot Programming in Applications Used for Genetic Data Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Robert M. Nowak
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Data Processing ◽  
Programming Languages ◽  
High Performance ◽  
Genetic Data ◽  
Web Browser ◽  
Data Process ◽  
Genetic Data Analysis

Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

scholarly journals Towards Progressive Geospatial Information Processing on Web Systems: A Case Study for Watershed Analysis in Iowa

2021 ◽  
Author(s):  
Muneeb Shahid ◽  
Yusuf Sermet ◽  
Ibrahim Demir
Keyword(s):  
Data Processing ◽  
Web Application ◽  
Large Scale ◽  
Flow Direction ◽  
Geospatial Data ◽  
The Internet ◽  
Raster Data ◽  
Watershed Analysis ◽  
Client Side

Geographic Information Systems (GIS) are available as stand-alone desktop applications as well as web platforms for vector- and raster-based geospatial data processing and visualization. While each approach offers certain advantages, limitations exist that motivate the development of hybrid systems that will increase the productivity of users for performing interactive data analytics using multidimensional gridded data. Web-based applications are platform-independent, however, require the internet to communicate with servers for data management and processing which raises issues for performance, data integrity, handling, and transfer of massive multidimensional raster data. On the other hand, stand-alone desktop applications can usually function without relying on the internet, however, they are platform-dependent, making distribution and maintenance of these systems difficult. This paper presents RasterJS, a hybrid client-side web library for geospatial data processing that is built on the Progressive Web Application (PWA) architecture to operate seamlessly in both Online and Offline modes. A packaged version of this system is also presented with the help of Web Bundles API for offline access and distribution. RasterJS entails the use of latest web technologies that are supported by modern web browsers, including Service Workers API, Cache API, IndexedDB API, Notifications API, Push API, and Web Workers API, in order to bring geospatial analytics capabilities to large-scale raster data for client-side processing. Each of these technologies acts as a component in the RasterJS to collectively provide a similar experience to users in both Online and Offline modes in terms of performing geospatial analysis activities such as flow direction calculation with hydro-conditioning, raindrop flow tracking, and watershed delineation. A large-scale case study is included in the study for watershed analysis to demonstrate the capabilities and limitations of the library. The framework further presents the potential to be utilized for other use cases that rely on raster processing, including land use, agriculture, soil erosion, transportation, and population studies.

scholarly journals Free software as alternatives to commercial in application of Numerical methods and repetitive procedures

2020 ◽  
Vol 26 (3) ◽  
pp. 81-88
Author(s):  
Marko Todorović ◽  
Nebojša Bogojević
Keyword(s):  
Problem Solving ◽  
Numerical Methods ◽  
Data Processing ◽  
Programming Languages ◽  
Financial Incentives ◽  
Rapid Development ◽  
Free Software ◽  
Numerical Calculations ◽  
Computer Users ◽  
Educational Institutions

Computers with the help of proper software usage enables efficiency in work as well as the conducting of experiments, developments, simulations and data processing which allows specific conclusions to be drawn. The programming languages that have been developed in past decades gave computer users more opportunity to think outside the box. Due to the rapid development and complexity of the programming languages and their syntax, the demand for a much faster and simpler problem-solving tool was created in order to cater situations which require the need to have the tool created in the first place. Today, the most popular and sophisticated programme used in industrial environments is Matlab as it is applicable in various fields of sciences - from numerical calculations to complex simulations. However, Matlab is a commercial programme. In order for it to be used, financial incentives must be prioritised which often is a disadvantage for students and educational institutions as it is costly. In this paper a few free software solutions will be considered as alternatives to commercial software solutions.

scholarly journals Constructing and analysing dynamic models with modelbase v1.0 - a software update

2020 ◽  
Author(s):  
Marvin van Aalst ◽  
Oliver Ebenhöh ◽  
Anna Matuszyńska
Keyword(s):  
Mathematical Models ◽  
Programming Languages ◽  
Dynamic Models ◽  
Dynamic Properties ◽  
Quantitative Information ◽  
Label Propagation ◽  
Rate Equations ◽  
Disease Evolution ◽  
Metabolic Fluxes ◽  
Research Fields

AbstractBackgroundComputational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Unfortunately, despite community efforts leading to the development of SBML or the BioModels database, many published models have not been fully exploited, largely due to lack of proper documentation or the dependence on proprietary software. To facilitate synergies within the emerging research fields of systems biology and medicine by reusing and further developing existing models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent and reproducible is desired.Results and DiscussionWe provide here the update on the development of modelbase, a free expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow convenient analyses of structural and dynamic properties of the models. Specifying reaction stoichiometries and rate equations, the system of differential equations is assembled automatically. A newly provided library of common kinetic rate laws highly reduces the repetitiveness of the computer programming code, and provides full SBML compatibility. Previous versions provided functions for automatic construction of networks for isotope labelling studies. Using user-provided label maps, modelbase v1.0 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is continuously growing. Ranging from photosynthesis over tumour cell growth to viral infection evolution, all models are available now in a transparent, reusable and unified format using modelbase.ConclusionWith the small price of learning a new software package, which is written in Python, currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others, repeating and reproducing models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their label specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.

R Tutorial

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Programming Languages ◽  
Web Site ◽  
Electronic Version ◽  
Climate Data ◽  
R Language ◽  
Large Audience ◽  
R Project ◽  
Copy And Paste ◽  
The University ◽  
Enhanced Learning

This chapter is a tutorial on using R. To get the most out of it, you should open an R session and type the commands into the console as you read the text. You should be able to use copy-and-paste if you have access to an electronic version of the book. All code is available on the book’s Web site. Science requires transparency and reproducibility. The R language for statistical modeling makes this easy. Developing, maintaining, and documenting your R code is simple. R contains numerous functions for organizing, graphing, and modeling your data. Directions for obtaining R, accompanying packages, and other sources of documentation are available at http://www.r-project.org/. Anyone serious about applying statistics to climate data should learn R. The book is self-contained. It presents R code and data (or links to data) that can be copied to reproduce the graphs and tables. This reproducibility provides you with an enhanced learning opportunity. Here we present a tutorial to help you get started. This can be skipped if you already know how to work with R. R is the ‘lingua franca’ of data analysis and statistical computing. It helps you perform a variety of computing tasks by giving you access to commands. This is similar to other programming languages such as Python and C++. R is particularly useful to researchers because it contains a number of built-in functions for organizing data, performing calculations, and creating graphics. R is an open-source statistical environment modeled after S. The S language was developed in the late 1980s at AT&T labs. The R project was started by Robert Gentleman and Ross Ihaka of the Statistics Department of the University of Auckland in 1995. It now has a large audience. It is currently maintained by the R core-development team, an international group of volunteer developers. To get to the R project Web site, open a browser and, in the search window, type the keywords “R project” or directly link to the Web page using http://www.r-project.org/. Directions for obtaining the software, accompanying packages, and other sources of documentation are provided at the site.

Sign in / Sign up

Export Citation Format

Share Document