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DTSTART;VALUE=DATE:20300101
DTEND;VALUE=DATE:20300102
DTSTAMP:20260418T222144
CREATED:20230322T193501Z
LAST-MODIFIED:20240404T141707Z
UID:10000424-1893456000-1893542399@prstats.preprodw.com
SUMMARY:Reproducible and collaborative data analysis with R (RACRPR)
DESCRIPTION:Delivered remotely (Portugal)\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nPre Recorded \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				\n\n\nThe computational part of a research is considered reproducible when other scientists (including ourselves in the future) can obtain identical results using the same code\, data\, workflow and software. Research results are often based on complex statistical analyses which make use of various software. In this context\, it becomes rather difficult to guarantee the reproducibility of the research\, which is increasingly considered a requirement to assess the validity of scientific claims. Moreover\, reproducibility is not only important for findings published in academic journals. It also becomes relevant for sharing analyses within a team\, with external collaborators and with one’s supervisor. During this three-day course\, the participants will be introduced to a suite of tools they can use in combination with R to make reproducible the computational part of their own research. A strong emphasis is given to collaboration\, and participants will learn how to set up a project to work with other people in an efficient way. \nOn day 1 the participants learn about the most important aspects that make research reproducible\, which go beyond simply sharing R code. This includes problems arising from the use of different packages versions\, R versions\, and operating systems. The concept of research compendium is introduced and proposed as general framework to organise any research project. Day 2 is dedicated to version control with Git and GitHub which are fundamental tools for keeping track of code changes and for collaborating with other people on the same project. We will cover both\, basic and more advanced features\, like tagging\, branching\, and merging. On day 3 the participants are introduced to literate programming using RMarkdown with the focus on writing a scientific article. The aim is to bind the outputs of the R analysis (i.e. results\, tables\, and figures) together with the text of the article. Participants will also learn how to use templates to fulfil requirements of different journals. \n\n\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is suitable for any MSc and PhD students\, postdocs and practitioners from any research field interested in collaborative projects and delivering reproducible results using R.\n			\n				\n				\n				\n				\n				Course Details\n				Last up-dated – 13:06:2023 \nDuration –  Approx. 18 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n  \n			\n				\n				\n				\n				\n				Teaching Format\n				\n\n\n\n\n\nOn each day\, participants will get an introduction to a different tool and practice its use together with the instructor. There will be lecture-style presentations to explain the different problems that make research not reproducible and provide possible solutions to the problem. Lectures will be alternated with hands-on sections guided by the instructor and group exercises to enhance collaboration skills. \n\n\n\n\n\n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				\n\n\nA basic knowledge of statistics is required. \n\n\n\n			\n				\n				\n				\n				\n				Assumed computer background\n				The participants are required to have some previous experience with R and should know the main data types and how to run commands to create basic plots.\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). Participants should also create a GitHub account in order to attend the second day of this course. Instructions on how to create the account and how to install Git will be provided during the first day. \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Day 1\n				Approx. 6 hours \n\n\n\n\n–  Intro to the reproducibility crisis\n–  Examples of problems arising from different Operating Systems\, R versions\, andpackage versions\n–  What happens when you start R\n–  RStudio projects\n–  Project organization\n–  Code style\n–  Reproducible R environment\n\n\n\n\n\n\n \n\n\n			\n				\n				\n				\n				\n				Day 2\n				Approx. 6 hours \n\n\n\n\n–  Intro to Git and Github\n–  Configure Git and GitHub\n\n\n\n\n\n\n\n\n–  Git basic from command line\n–  Create a local repository and push it on Github\n–  Craft a good commit\n–  Clone and fork a GitHub repository\n–  Craft a pull request\n–  Git branch\, merge\, and tag\n–  Git checkout\, reset\, and revert\n–  Use Git with RStudio\n–  Ignore files\n\n\n\n\n			\n				\n				\n				\n				\n				Day 3\n				Approx. 6 hours \n\n–  Literate programming\n–  RMarkdown to produce html\, word\, and pdf outputs\n–  Manage references with Zotero\n–  Use templates for word outputs\n–  Write your scientific article with RMarkdown\n–  Reference tables and figures in the text\n\n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Sergio Vignali\n					\n					Sergio Vignali is a postdoctoral researcher at the University of Bern (Switzerland)\, in the division of Conservation Biology of the Institute of Ecology and Evolution. His research focuses on spatial predictive models for animal movements and distributions. Sergio combines his strong scientific interest in animal ecology\, particularly birds\, with his computational and statistical background to develop new methodological approaches. He is the developer of SDMtune\, an R package to tune and evaluate species distribution models. Sergio is also an advocate of open source software and is committed to improving transparency and reproducibility in research. \nResearchGate GoogleScholar ORCID GitHub
URL:https://prstats.preprodw.com/course/reproducible-and-collaborative-data-analysis-with-r-racrpr/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:Previously Recorded Courses
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DTSTART;VALUE=DATE:20300101
DTEND;VALUE=DATE:20300102
DTSTAMP:20260418T222144
CREATED:20230322T195810Z
LAST-MODIFIED:20230727T114344Z
UID:10000426-1893456000-1893542399@prstats.preprodw.com
SUMMARY:Advanced Ecological Niche Modelling Using R (ANMRPR)
DESCRIPTION:Delivered remotely (Portugal)\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				Ecological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for mechanistic and correlative models\, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental\, topographical\, human) to species physiological data or geographical locations\, in order to identify the factors limiting and defining the species’ niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management. \nHave you built an Ecological Niche Model? If yes\, you have already encountered challenges on data preparation\, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. \nBy the end of 5-day practical course you will have the capacity to \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.\n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is orientated to PhD and MSc students\, as well as other students and researchers working on biogeography\, spatial ecology\, or related disciplines\, with experience in ecological niche models. \n			\n				\n				\n				\n				\n				Course Details\n				Last up-dated – 29:01:2021 \nDuration – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				The course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphic(http://www.r-project.org/).\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of ecological niche models and biogeography in general is required.\n			\n				\n				\n				\n				\n				Assumed computer background\n				Experience implementing ecological niche models using R is desirable.\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n			\n				\n				\n				\n				\n				Day 1\n				Approx. 7 hours \n\nENM guide: how to model.\nENM R packages.\nSources of environmental variables using dismo package.\nGetting species records with rgbif package.\n\n			\n				\n				\n				\n				\n				Day 2\n				Approx. 7 hours \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/sp Thin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n			\n				\n				\n				\n				\n				Day 3\n				Approx. 7 hours \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.\nComparing correlative models with AIC\, with ENMeval package.\nValidate models null models.\n\n			\n				\n				\n				\n				\n				Day 4\n				Approx. 7 hours \n\nMESS practice with Biomod2 package.\nVirtualSpecies SDMvspecies packages.\nMIGCLIM practice.\n\n			\n				\n				\n				\n				\n				Day 5\n				\nMechanistic model NicheMapper packages.\n\n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Neftali Sillero\n					\n					Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal website\nWork Webpage\nResearchGate\nGoogleScholar\n					\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n			\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)
URL:https://prstats.preprodw.com/course/advanced-ecological-niche-modelling-using-r-anmrpr/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:Previously Recorded Courses
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GEO:39.399872;-8.224454
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