![]() ![]() The final output of the analysis PaveTemp ver 0.1, in the form of continent wise pavement temperature data maps for Africa, Asia, Australia and Oceania, Europe, North America and South America is hosted at the domain and can be freely accessed through the website. The pavement temperature data obtained were further analyzed in the open-source GIS software QGIS, before being developed into interactive web maps using the JavaScript library, Leaflet.js. based on the Shell WMAPT, Strategic Highway Research Programme and long-term pavement performance seasonal pavement temperature models. Around 75 million daily air temperature data sourced from the United States National Climatic Data Centre-Climate Data Online for the past 30 years (1989–2018) was analyzed using the Python PANDAS data analysis tools and converted to WMAPT, low and high pavement temperatures etc. This project is envisioned to equip pavement designers from all over the world by providing quick and interactive access to air and pavement temperatures for over 13,000 stations worldwide in the form of geographic information system (GIS) data maps through the development of a web-based platform. for their binder selection and pavement design input requirements. While site-specific pavement temperature data are available (both online and offline) for certain parts of the world, pavement designers from most parts of the world are deprived of such data and have to resort to detailed analysis of regional climate data over the years to arrive at weighted mean annual pavement temperature (WMAPT), and the lowest and highest pavement temperatures etc. Inservice pavement temperature has considerable bearing on the selection of appropriate asphalt binder and in the determination of design asphalt modulus during the asphalt pavement design process. Additionally, we collected this information into an R metapackage, ForestAnalysisInR, an R Shiny-based solution that allows users to query the R packages we have identified to find those best suited for their analysis needs in a quick and efficient way. In these examples we used open-source data sets of our own selection. We present worked examples for a subgroup of R software packages for each category to demonstrate their potential and utility. We found more than 100 available packages which we systematically categorized by research category: community analysis dendrochronology forest mensuration and inventory hydrology informatics/IoT modeling phenology and remote sensing. Here we survey the available packages in the R programming language with specific utility for forest-related research. The lag in adoption of open-source software in forestry and forest ecology could be hindering collaboration, data sharing, and reproducibility. Open-source computing environments, such as R, Python, and Julia, increase the visibility and reproducibility of scientific research and foster collaborations through the removal of proprietary software restrictions. A direct comparison of the last decade of published research literature from the top 20 ecology and forestry journals shows that R is utilized in over 30% of the literature for ecology, yet in less than 10% of the forestry literature. Forestry and forest ecology research potentially lags behind related fields such as ecology, biodiversity, and conservation research in the employment of open-source software solutions, specifically the R programming language. ![]()
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