BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:PR Statistics X-ORIGINAL-URL:https://prstats.preprodw.com X-WR-CALDESC:Events for PR Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20210328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20211031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20221030T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20230326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20231029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20250211 DTEND;VALUE=DATE:20250214 DTSTAMP:20260418T214737 CREATED:20240613T125347Z LAST-MODIFIED:20250205T170134Z UID:10000452-1739232000-1739491199@prstats.preprodw.com SUMMARY:ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, February 11th\, 2025\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 \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\nthrough the accompanying computer practicals via video link\, so a good internet connection is\nessential. \nTime Zone\nLisbon (Portugal) time\, i.e. UTC / GMT or BST\, depending on time of year (daylight saving time\nfrom last Sunday of March to last Sunday of October). Check online for the time conversion\ncorresponding to the course dates. However\, all sessions will be recorded and made available\,\nallowing attendees from different time zones to follow asynchronously. \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 This course focuses on the use of BART (Bayesian Additive Regression Trees) for modellingspecies’ geographical distributions based on occurrence data and environmental variables. BART is a relatively recent technique that shows very promising results in the field of species distribution and ecological niche modelling (SDM / ENM)\, as it produces accurate predictions (considering various aspects of model performance) without overfitting to noise or to special cases in the data. Additionally\, BART allows mapping the uncertainty and credible intervals associated to each local prediction. \nThe course includes a combination of theoretical lectures and hands-on practicals in R\, as well asopen discussions about models and data for SDM applications. The practicals go through acomplete worked example\, from data preparation to model output analysis\, with annotated Rscripts that can be adapted on-the-spot by participants to work on their own species of interest.Along the course\, the instructor is available for constant feedback and orientation on participants’; outputs and interpretations. \n \n \n \n \n \n Intended Audiences\n The course is aimed at students\, researchers and practitioners with an interest in implementing\nbest practices and state-of-the-art methods for modelling species’ distributions or ecological\nniches\, in an automated and reproducible way.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – 18 places \nDuration – 3 days \nContact hours – Approx. 12 hours live\, plus remote assistance via Slack from the first day to the\nweekday after the course. \nECT’s – Equal to 1.5 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n This course runs along 3 days\, each with a 4-hour live online session. Each session is divided into4 parts\, alternating between theoretical lectures and hands-on practicals. Annotated scripts areprovided and instructor assistance is available\, both during the live sessions (on Zoom) andwhenever possible the rest of the day (on Slack)\, until the weekday after the course.Live sessions will be video-recorded\, uploaded to a video hosting website as soon as possible aftereach session\, and remain available for one month after the course. \n \n \n \n \n \n Assumed quantitative knowledge\n Participants should know what species distribution or ecological niche models (SDM / ENM) are\,\nand ideally have some previous experience with the basics. Previous knowledge of Bayesian\nstatistics is not required.\n \n \n \n \n \n Assumed computer background\n Participants should have some previous experience with R\, including package installation and\nbasic data handling\, although commented scripts will be provided for the entire course.\n \n \n \n \n \n Equipment and software requirements\n \nParticipants must use a computer with a good internet connection\, a working recent version or R (and ideally also RStudio)\, and recent versions of some R packages whose installation instructions will be sent a few days before the course. A working webcam is desirable for enhanced interactivity during the live sessions. Some computation power is required for modelling large datasets\, although the provided example data (and suggested subsets of participants’ data) can run on an ordinary laptop. \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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\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 Tuesday 11th\n Classes from 14:00 – 18:00 \nDAY 1– Module 1a: Obtain and process data\, including species presences and environmental variables– Practical– Module 1b: Determine an adequate spatial resolution and extent for modelling– Practical \n \n \n \n \n \n Wednesday 12th\n Classes from 14:00 – 18:00 CET \nDAY 2– Module 2a: Build a species distribution model with BART and obtain predictions of environmentalfavorability\, with credibility intervals and associated uncertainty– Practical– Module 2b: Evaluate and cross-validate the model\, assessing various aspects of predictive ability– Practical \n  \n \n \n \n \n \n Thursday 13th\n Classes from 14:00 – 18:00 CET \nDAY 3 \n– Module 3a: Quantify variable contributions and try out different methods for selecting relevantvariables– Practical– Module 3b: Plot and map the species’ partial response to each variable– Practical \n \n \n \n \n \n \n \n \n Dr. Marcia Barbosa\n \n Márcia is an experienced researcher and instructor in biogeography and macroecology\, particularly in geographic information systems and species distribution modelling. She’s also a reviewer and editor for scientific journals and funding agencies\, and a promoter and developer of free and open-source software implementing transparency\, reproducibility and best practices. You can see her publication list at her website or at Publons/ResearcherID\, Scopus\, ORCID\, Google Scholar\, or ResearchGate. \nResearch Gate\n Google Scholar\n ORCID\n GitHub\nHomepage URL:https://prstats.preprodw.com/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/SDMB04.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250120 DTEND;VALUE=DATE:20250125 DTSTAMP:20260418T214737 CREATED:20240402T165424Z LAST-MODIFIED:20241216T141211Z UID:10000453-1737331200-1737763199@prstats.preprodw.com SUMMARY:ONLINE COURSE - Using Google Earth Engine in Ecological Studies (GEEE01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, January 20th\, 2024\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 – Western European Time (Portugal local time) – 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 Google Earth Engine (GEE) is a cloud computing platform for processing satellite imagery and other geospatial and observational data. GEE is currently the most complete and efficient platform for performing remote sensing analysis\, as it provides access to a large database of satellite imagery and the computational power needed to analyse these images. While other remote sensing programs require the user to have sufficient space and computing power available\, all data and processes in GEE are done in the cloud through Google's infrastructure. GEE provides a code editor that works with JavaScript and Python. GEE is the future of remote sensing. \nBy the end of the course\, participants should: Know the catalogue of spatial datasets provided by GEE. Know the most important satellite sensors for environmental studies in GEE. Know how to get remote sensing products from GEE. Process and develop new remote sensing (sub-)products in GEE. Classify satellite imagery in GEE. Perform different types of spatial analyses in satellite imagery in GEE. \n \n \n \n \n \n Intended Audiences\n The target audience for this training is students\, researchers\, technicians\, teachers\, or otherprofessionals who work in the areas of remote sensing\, environment\, geo-informatics\, geomatics\,geospatial engineering\, biology\, ecology\, and biogeography. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – Western European Time \nAvailability – 25 Places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n The topics will be presented and discussed in the theoretical classes to stimulate the interest of theparticipants and to provide the set of knowledge considered necessary and relevant for a completeunderstanding of the Google Earth Engine platform. In the practical classes\, participants are invitedto think about and solve a set of problems to consolidate the knowledge acquired in the theoreticallectures. The practical work proposed on the computer aims to train students in solving a set oftypical remote sensing problems related to the topics of the course program. The objective is forparticipants to obtain the necessary tools that will allow them to use Google Earth Engine and todeepen their knowledge autonomously. No data will be provided to the participants because all thenecessary data are stored in GEE. \n \n \n \n \n \n Assumed quantitative knowledge\n Solid knowledge of Geographical Information Systems and Remote Sensing is necessary. This course will suppose the attendees to know how to analyse remote sensing data. \n \n \n \n \n \n Assumed computer background\n No programming experience in JavaScript or Python will be necessary\, although having programming knowledge will be extremely useful. However\, this is not a course on programming. We will not teach JavaScript or Python but provide the necessary scripts to run analyses in GEE. \n \n \n \n \n \n Equipment and software requirements\n A laptop/personal computer\, a list of software you need to install will be sent the week before the course starts. \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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\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 \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 20th\n Classes from 13:00 to 18:00 \n Introduction to Google Earth Engine:o Understand the fundamentals of how Google Earth Engine operateso Visualize geospatial information in GEEo Know the catalogue of the most important satellite images in the environmental areao Build the first scripts in GEE and how to run themo GEE related platforms \n \n \n \n \n \n Tuesday 21st\n Classes from 13:00 to 18:00 \n Introduction to remote sensing products and analyses in Google Earth Engine:o Select\, visualize and access the metadata of satellite image data series (e.g.\, MODIS\, Landsat\, Sentinel)o Filter the data by spatial and temporal extentso Aggregate data over time\, space and bands using reducer functionso Build image composites and mosaicso Conduct cloud masking operationso Geospatial computations and operations (e.g.\, obtain terrain products from DEMs and calculate spectral indexes)o Import spatial data as Assets \n \n \n \n \n \n Wednesday 22nd\n Classes from 13:00 to 18:00 \n Process\, classify and analyse multispectral images:o Quality assessment of satellite imageso Learn the image classification algorithms available in GEEo Perform unsupervised classificationso Photo-interpretation of satellite images and build datasets of training areas (regions of interest)o Conduct spectral separability analyseso Perform supervised classificationso Evaluate final image classificationso Export outputs to Google Drive \n \n \n \n \n \n Thursday 23rd\n Classes from 13:00 to 18:00 \n Analyse time-series data and the temporal evolution of vegetation productivity and land useo Calculate spectral indiceso Analyse available and ready-to-use products of vegetation productivity proxies andLULC mapso Build and export time-series plots (e.g.\, seasonality plots\, anomaly analysis)o Conduct non-parametric trend analyses using time-series datao Time series modelling using linear regressionso Export outputs to Google Drive \n  \n \n \n \n \n \n Friday 24th\n Classes from 13:00 to 18:00 \n Learn advanced spatial modelling tools:o Import species databases into GEE (vector data or tabular databases) as assetso Search and import environmental variables for modelling procedureso Correlation analyses of environmental variableso Search information about modelling algorithms and classifiers available in GEEo Visualize and calibrate ecological niche models (e.g. Maxent)o Conduct modelling analyses and assess the predictive performance of the modelso Visualize model projections in GEEo Export model outputs to Google Drive \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)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology\, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology\, forestry\, spatial ecology\, andecoinformatics\, all addressed by explicitly considering the spatial component ofecological processes\, mainly applying spatially explicit modelling approaches\, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.preprodw.com/course/using-google-earth-engine-in-ecological-studies-geee01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2024/04/Screenshot-2024-04-02-at-17.51.29.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241202 DTEND;VALUE=DATE:20241205 DTSTAMP:20260418T214737 CREATED:20240404T125828Z LAST-MODIFIED:20241128T122417Z UID:10000456-1733097600-1733356799@prstats.preprodw.com SUMMARY:ONLINE COURSE – Introduction to Single Cell Analysis (ISCA01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 2nd\, 2024\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 – Central Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.\n \n \n \n \n \n \n \n \n \n \n About This Course\n Take your RNA-Seq analysis to the next level with single cell RNA-Seq. This technology allows insights with an unpredicted level of detail\, but that brings a new level of complexity to the data analysis. In this course\, we will learn about the most popular single cell platforms\, how to plan a scRNA-Seq experiment\, deal with some of the many pitfalls when analysing your data\, and effectively gain exciting\, and cell type specific biological insights \nBy the end of the course participants should: \n\nUnderstand the basic principles of popular single cell platforms and the pros and cons of the different technologies.\nBe able run standard software to process raw 10x Genomics and Parse Bioscience data and interpret the outputs\nUnderstand how to use the ‘Trailmaker’ to quickly analyse scRNA-Seq data.\nUnderstand the basics of the R Bioconductor ‘Seurat’ package\, and how to combine it with other tools.\nUnderstand how to perform appropriate data quality control and filtering.\nUnderstand how to cluster cells both within and between samples\, and identify possible cell types of individual cells and clusters\nUnderstand how to use statistically robust methods to compare gene expression between sample to identify cell type specific changes in gene expression and potential pathways of interest.\n\n \n \n \n \n \n Intended Audiences\n Academics\, post-graduate students or biotech employees working on\, or planning to work on any type of single cell RNA-Seq data. \n \n \n \n \n \n Venue\n Delivered Remotely \n \n \n \n \n \n Course Details\n Availability – 20 \nDuration – 2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Mixture of lectures covering the theory\, and practical sessions using the Linux command line and RStudio. Practical sessions are a mixture of demonstrations by the tutor and exercises to be completed independently. Data sets for computer practical sessions will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n Assumed quantitative knowledge\n Participants should have a basic understanding of transcriptomics and molecular biology \n \n \n \n \n \n Assumed computer background\n COMING SOON…\n \n \n \n \n \n Equipment and software requirements\n Participants should have basic experience of R\, RStudio and linux. \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 \nPLEASE READ – CANCELLATION POLICY \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\nDay 1 Classes from 9:30 – 3:30 \n\nBasic principles of popular single cell platforms and the pros and cons of the different technologies.\nImportant considerations when planning a scRNA-Seq experiment.\nRunning standard software to process raw 10x Genomics and Parse Bioscience data and interpretate outputs to perform an initial assessment of data quality.\nSample and library preparation issues that may affect your data\, and how these issues may be detected\nUse of the ‘Trailmaker’ tool to perform further analysis without the need for programming skills.\n\n\nDay 2 Classes from 9:30 – 3:30 \n\nUnderstand the basics of the R Bioconductor ‘Seurat’ package\, and how to combine it with other tools.\nNormalise data and cluster cells.\nPredict cell types of individual cells.\nClean and filter your data in a manner appropriate for your particular sample and tissue type.\n\n\nDay 3 Classes from 9:30 – 3:30 \n\nIntegration of and co-clustering of cells from multiple samples.\nIdentification of cell type marker genes and annotation of clusters.\nUse of statistically robust methods to compare gene expression between samples.\nIdentification cell type specific changes in gene expression and potential pathways of interest.\nDiscussion of participants individual projects (optional).\n\n\n \n \n \n \n \n Course Instructor\n \nEDINBURGH GENOMICS \n \n \n \n \n \n \n \n Frances Turner\n \n Through my work as a bioinformatician at Edinburgh Genomics\, I have many years experience of working with researchers from all areas of life sciences to help them get the most out of their high throughput sequencing data.\n \n \n \n \n \n \n \n read more\n This work ranges from bespoke data analysis and one-to-one training\, to running popular courses covering a range of applications. My particular focus is on RNA-Seq\, especially the exciting opportunities offered by long read and single-cell transcriptomics. \n \n \n \n \n \n \n \n Heleen De Weerd\n \n Heleen de Weerd has been a bioinformatician since 2010\, accumulating experience in both industry and academia and working with people from different backgrounds. \n \n \n \n \n \n \n \n read more\n Her expertise spans a wide array of topics with a special interest in genomic and the analysis of highly diverse samples. Since joining Edinburgh Genomics\, Heleen has focused on advancements in both short and long reads technologies and application of both to different research questions. She is passionate about sharing her experiences and helping people start their journeys with their data.\n \n \n \n \n \n \n \n Dr Kathryn Campbell\n \n Kathryn recently joined the Edinburgh Genomics team as the Genomics and Bioinformatics Training Coordinator. With a diverse background in bioinformatics and molecular biology\, she specializes in phylogenetics and viral classification. \n \n \n \n \n \n \n \n read more\n Her passion now lies in teaching and outreach\, where she brings extensive experience\, engaging with a broad range of audiences. Kathryn is dedicated to empowering learners through comprehensive training\, from sample preparation and sequencing to data analysis and interpretation. She is also committed to inspiring the next generation of biologists by working with primary and secondary schools to foster a love for science and genomics. URL:https://prstats.preprodw.com/course/introduction-to-single-cell-analysis-isca01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2024/04/single_cell_1.jpeg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241104 DTEND;VALUE=DATE:20241107 DTSTAMP:20260418T214737 CREATED:20240404T120253Z LAST-MODIFIED:20241015T150726Z UID:10000455-1730678400-1730937599@prstats.preprodw.com SUMMARY:ONLINE COURSE – Genome Assembly and Annotation (GAAA01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, November 4th\, 2024\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 – UK local time (GMT) – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \n \n \n \n \n \n \n \n \n \n \n About This Course\n Genome assembly is the process of piecing together fragments of DNA to recontruct the original genome. The genome provides crucial information for understanding genetic structure\, function and variation. \nIn recent years\, long-read sequencing technologies have revolutionized genome assembly. These long reads can span repetitive sequences and structural variations making genome assembly simpler but also reducing gaps and fragments in the genome\, resolve repeats\, help with the detection of structural variation as well as improved haplotype phasing. \nDuring this course we will look at data generated using PacBio and Oxford Nanopore\, discuss the pros and cons of both sequencing technologies and the effect they might have on genome assembly. During the course we will look at different tools available to generate assemblies\, focussing on de novo genome assembly. Polishing using short or long reads and the introduction of Hi-C sequencing can increase completeness of the genomes. At the difference steps during the assembly process we will look at the contiguity\, completeness and correctness of the generated genomes\, thereby evaluation the status of the genome. \nOnce a genome has been assembled the next step is annotation. Genome annotation involves identifying and mapping locations of genes and other functional elements within the sequenced genome. We will take a look at the differences between prokaryote and eukaryote genomes and the tools available for annotation. We will talk about steps to improve annotation once the automatic annotation has been made. \nBy the end of the course\, participants should: \n\nKnow the difference between Nanopore and PacBio data\nBe able to assembly genomes\nBe able to assess the generated genomes\nAssemble genomes integrating Hi-C data\nKnow how to annotated a genome\n\n \n \n \n \n \n Intended Audiences\n Academics and post-graduate students working on projects related to spatial data and applied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language \n \n \n \n \n \n Venue\n Delivered Remotely\n \n \n \n \n \n Course Details\n Availability – 22 \nDuration – 3 days \nContact hours – Approx. 16 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Intermediate-level lectures interspersed with hands-on mini practicals. Access to Linux VM and data sets for practicals will be provided by the instructors. Time will be available during the course for participants to ask questions regarding their own projects. \n \n \n \n \n \n Assumed quantitative knowledge\n Good familiarity of genomics studies. \n \n \n \n \n \n Assumed computer background\n Good familiarity with Linux will be helpful. \n \n \n \n \n \n Equipment and software requirements\n COMING SOON… \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 \nPLEASE READ – CANCELLATION POLICY \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\nDay 1 Classes from 10:00 – 15:30Data QC and preprocessing and genome assembly• Data QC and preprocessing using Nanopack• Genome assembly using Redbean\, Shasta\, Canu• PacBio assembly using hifiasm• Genome evaluation \nDay 2 Classes from 10:00 – 15:30Genome polishing and introduction into Hi-C• Polishing created genomes using Racon• Assembly using Hi-C data \nDay 3 Classes from 10:00 – 15:30Genome annotation• Genome annotation using Prokka• Look at genome annotation using AUGUSTUS/BRAKER \n\n \n \n \n \n \n \n \n Heleen De Weerd\n \n Heleen de Weerd has been a bioinformatician since 2010\, accumulating experience in both industry and academia and working with people from different backgrounds. \n \n \n \n \n \n \n \n read more\n Her expertise spans a wide array of topics with a special interest in genomic and the analysis of highly diverse samples. Since joining Edinburgh Genomics\, Heleen has focused on advancements in both short and long reads technologies and application of both to different research questions. She is passionate about sharing her experiences and helping people start their journeys with their data.\n \n \n \n \n \n \n \n Urmi Trivedi \n \n Urmi has been working as a Bioinformatician in research support role at Edinburgh Genomics\, a sequencing facility within The University of Edinburgh\, since 16 years. \n \n \n \n \n \n \n \n read more\n She is now leading the Bioinformatics team for almost three years\, overseeing a group of experts who are integral to the success of over 100 projects annually.The facility\, equipped with cutting-edge sequencing platforms\, is at the forefront of genomic research\, and Urmi’s team plays a critical role in ensuring the highest standards of data quality and analysis\, both for in-house and external projects. Urmi is also a passionate educator\, actively involved in designing and delivering training programs that empower the next generation of bioinformaticians. Her area of expertise is Genome assembly\, Genome annotation\, Metagenomics and metabarcoding. \n \n \n \n \n \n \n \n Dr Kathryn Campbell\n \n Kathryn recently joined the Edinburgh Genomics team as the Genomics and Bioinformatics Training Coordinator. With a diverse background in bioinformatics and molecular biology\, she specializes in phylogenetics and viral classification. \n \n \n \n \n \n \n \n read more\n Her passion now lies in teaching and outreach\, where she brings extensive experience\, engaging with a broad range of audiences. Kathryn is dedicated to empowering learners through comprehensive training\, from sample preparation and sequencing to data analysis and interpretation. She is also committed to inspiring the next generation of biologists by working with primary and secondary schools to foster a love for science and genomics. URL:https://prstats.preprodw.com/course/genome-assembly-and-annotation-gaaa01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2024/04/national-cancer-institute-JaoGCqzPgI0-unsplash.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241021 DTEND;VALUE=DATE:20241026 DTSTAMP:20260418T214737 CREATED:20231204T135919Z LAST-MODIFIED:20240514T140336Z UID:10000442-1729468800-1729900799@prstats.preprodw.com SUMMARY:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, 21st October\, 2024\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 \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 – `Central European Standard Time (CET) – 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 Metabarcoding has emerged as a pivotal technique\, rapidly expanding and revolutionizing the way we study biodiversity. From soil samples to aquatic environments\, metabarcoding provides insights into the diverse array of organisms present\, offering crucial information for conservation efforts and ecological research. However\, metabarcoding encounters intrinsic biases inherent in its methodology. Metabarcoding pipelines are designed to mitigate these biases\, and this course will offer insights into optimizing these pipelines for accurate and reliable results. With new techniques continuously evolving\, we’ll explore methodologies geared towards unraveling both inter and intra-species diversity while addressing the common challenges encountered in a methodology. Additionally\, we’ll navigate the landscape of methods enabling comprehensive biodiversity assessments\, alongside showcasing new machine learning approaches for inferring ecological quality status. This course will focus on the MJOLNIR3 pipeline and its theoretical framework. This R package is based on eight simple functions divided into four different blocks. For each function\, a comprehensive description of the process will be provided\, including alternatives from other pipelines and their basic command line usage. \n\nBy the end of the course\, participants will: \n\nGain a comprehensive understanding of the theoretical foundations underpinning metabarcoding pipelines.\nDevelop the ability to identify potential biases and effectively apply specialized software to mitigate them.\nAcquire proficiency in working across three distinct levels of coding requirements\, encompassing command-line operations and graphical user interface packages.\nDemonstrate a thorough comprehension of basic biodiversity analysis techniques\, spanning inter and intra-species levels.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students engaged in projects associated with DNA metabarcoding.\nApplied researchers and environmental managers seeking to implement DNA metabarcoding for ecosystem monitoring purposes.\nDNA metabarcoding specialists with expertise in Prokaryotic analysis\, seeking to comprehend the specific requisites essential for managing Eukaryotic data.\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 30 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage and linux command line. Intermediate-level lectures interspersed with hands-on mini practicals. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. Keep in mind that huge datasets can take hours of running time and subsets are recommended. Hands on will try to focus on the different format files to allow students to create their own pipelines. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of laboratory process. Basic knowledge of biodiversity analysis. \n \n \n \n \n \n Assumed computer background\n Basic familiarity with R and linux command line. \n \n \n \n \n \n Equipment and software requirements\n \nParticipants must use a computer with a good internet connection\, a working recent version or R (and ideally also RStudio)\, and recent versions of some R packages whose installation instructions will be sent a few days before the course. A working webcam is desirable for enhanced interactivity during the live sessions. Some computation power is required for modelling large datasets\, although the provided example data (and suggested subsets of participants’ data) can run on an ordinary laptop. \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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \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 Monday 21st\n  Classes from 09:30 – 17:30 (CET) \nDAY 1 \n– What is DNA metabarcoding and how to apply it to my research.– Basic metabarcoding terminology \n– Differences between sampling methods– Differences between different target organisms. Universal and specific primers: their pros and limitations. \n– COI and other markers. \n– Identifying the different biases in laboratory processes. PCR\, sequencing\, contaminations\, quimeras… \n– How to manage increasing data volumes. \n– Designing a Bias-Handling Strategy. Divide the pipeline in 4 stages: \n1- Demultiplexing and initial filters \n2- Units delimitation. From Denoising to clustering methods. \n3- Taxonomic assignment \n4- Final filtering steps \n– Industrial assembly lines and Nordic mithology as a metaphores for Metabarcoding pipeline \n– Get familiar with basic bash commands and R scripts. \n \n \n \n \n \n Tuesday 22nd\n Classes from 09:30 – 17:30 (CET) \nDAY 2 \n– Presenting MJOLNIR3 pipeline\, an R package to easy process metabarcoding data.Getting started with MJOLNIR3 pipeline \n– Understand the theory behind \n– Presentation and installation of the required software; conda\, obitools3\, cutadapt\, vsearch\, DnoisE\, SWARM\, lulu and dada2. \n– Demultiplexing\, initial filtering steps\, sequence quality\, pairing and dereplication and quimera detection. \n– Meet the gods RAN\, FREYJA and HELA. \n– Practical \n \n \n \n \n \n Wednesday 23rd\n Classes from 09:30 – 17:30 (CET) \nDay 3 \n– Alternatives to RAN\, FREYJA and HELA \n– The dada2 approach. \n– To denoise or to cluster. \n– Choosing the strategy. \n – Meet the god ODIN. \n– Practical \n– Alternatives to ODIN \n  \n \n \n \n \n \n Thursday 24th\n  Classes from 09:30 – 17:30 \nDay 4 \n– Alternatives to RAN\, FREYJA and HELA \n– The dada2 approach. \n– To denoise or to cluster. \n– Choosing the strategy. \n– Meet the god ODIN. \n– Hands on \n– Alternatives to ODIN \n \n \n \n \n \n Friday 25th\n Classes from 09:30 – 17:30 \nDay 5 \n– Taxonomic assignment \n– Know the different reference databases \n– Meet the god THOR \n– ecotag\, vsearch and other software \n– Practical \n– Alternatives to THOR \n– Final filtering steps \n– Meet the gods FRIGGA\, LOKI and face the final battle at the RAGNAROC \n– Practical \n– Understand the three levels of metabarcoding pipelines. How we go from command line and MJOLNIR3 package to the graphical user interfaces with SLIM as example \n \n \n \n \n \n \n \n \n Dr. Adrià Antich\n \n Coming soon… \nResearch Gate Google Scholar ORCID GitHub URL:https://prstats.preprodw.com/course/metabarcoding-pipelines-for-eukariotic-communities-mpec01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2023/12/jeff-griffith-ZqYPM8i60F8-unsplash-scaled.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20241007 DTEND;VALUE=DATE:20241011 DTSTAMP:20260418T214737 CREATED:20240404T115419Z LAST-MODIFIED:20241002T184044Z UID:10000454-1728259200-1728604799@prstats.preprodw.com SUMMARY:ONLINE COURSE – Introduction to Metabarcoding and Metagenomics Analysis (IMAM01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, October 7th\, 2024\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 – GMT (Edinburgh Local Time) – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \n \n \n \n \n \n \n \n \n \n \n About This Course\n Metabarcoding and metagenomics study genetic material recovered from environmental samples. Both methods provide a comprehensive view of microbial communities which are present in various ecosystems. The ability to identify organisms from traces of genetic material in environmental samples has reshaped the way we see life on earth. Especially for microorganisms\, metagenomic techniques have granted us unprecedented insight into the microbiome of animals and the environment more broadly \nMetabarcoding and metagenomics are both methods to study the composition of these complex communities. Where metabarcoding focusses on looking at a single or a combination of marker genes\,  metagenomics looks into everything within a community. \nDuring this course we will look at the differences and similarities between these two methods. We explain how to process the data using both short and long reads data\, we take a look at the pros and cons and some of the pitfalls. We will guide you through the different approaches to take when processing the data and walk you through using some of the tools which are considered to be golden standard in the field. You will have hands on experience processing real data. \nBy the end of the course\, participants should: \n\nUnderstand the basic concepts behind metabarcoding and metagenomics\nWork with both short and long read data for both metabarcoding and metagenomics\nBe able to use Qiime2 and NanoClust for analysis of metabarcoding\nKnow different methods (metaphlan\, humann) for marker based taxonomic and functional annotation of metagenomics data\nCreate and annotated metagenome assembled genomes (using megahit\, checkm\, gtdb-tk)\nBe able to annotated antibiotic resistance genes in metagenomics data\n\n \n \n \n \n \n Intended Audiences\n Academics and post-graduate students working on projects related to complex communities and applied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language \n \n \n \n \n \n Venue\n Delivered Remotely \n \n \n \n \n \n Course Details\n Availability – 25 \nDuration – 4 days \nContact hours – Approx. 22 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Intermediate-level lectures interspersed with hands-on mini practicals. Access to Linux VM and data sets for practicals will be provided by the instructors. Time will be available during the course for participants to ask questions regarding their own projects. \n \n \n \n \n \n Assumed quantitative knowledge\n Some familiarity of metagenomics will be helpful. \n \n \n \n \n \n Assumed computer background\n Good familiarity with Linux will be helpful. \n \n \n \n \n \n Equipment and software requirements\n COMING SOON… \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 \nPLEASE READ – CANCELLATION POLICY \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\nDay 1 Classes from 10:00 – 15:30Metabarcoding• Data QC and preprocessing of short reads metabarcoding• Filtering\, denoising and assiingment of taxonomy using Qiime2• Align sequencing and build phylogenetic tree• Calculate alpha and beta diversity• Introduction into ANCOM-BC• Long read 16S \nDay 2 Classes from 10:00 – 15:30Short read metagenomics• Host removal using KneadData • Taxonomic profiling using MetaPhlan• Functional profiling using HumanN• Antibiotic resistance gene screening \nDay 3 Classes from 10:00 – 15:30Short read metagenomics• Metagenome assembly using megahit• Contigs binning and generation of metagenome assembled genomes (MAGs)• De-replication of MAGs• Taxonomic classification of MAGs using GTDB-Tk \nDay 4 Classes from 10:00 – 15:30Long read metagenomics• Long reads metagenomics using the DIAMOND-MEGAN pipeline• Data QC and preprocessing of long reads• Metagenome assembly using metaFlye• Functional annotation using Prokka \n \n \n \n \n \n Course Instructor\n \nEDINBURGH GENOMICS \n \n \n \n \n \n \n \n Heleen De Weerd\n \n Heleen de Weerd has been a bioinformatician since 2010\, accumulating experience in both industry and academia and working with people from different backgrounds. \n \n \n \n \n \n \n \n read more\n Her expertise spans a wide array of topics with a special interest in genomic and the analysis of highly diverse samples. Since joining Edinburgh Genomics\, Heleen has focused on advancements in both short and long reads technologies and application of both to different research questions. She is passionate about sharing her experiences and helping people start their journeys with their data.\n \n \n \n \n \n \n \n Urmi Trivedi \n \n Urmi has been working as a Bioinformatician in research support role at Edinburgh Genomics\, a sequencing facility within The University of Edinburgh\, since 16 years. \n \n \n \n \n \n \n \n read more\n She is now leading the Bioinformatics team for almost three years\, overseeing a group of experts who are integral to the success of over 100 projects annually.The facility\, equipped with cutting-edge sequencing platforms\, is at the forefront of genomic research\, and Urmi’s team plays a critical role in ensuring the highest standards of data quality and analysis\, both for in-house and external projects. Urmi is also a passionate educator\, actively involved in designing and delivering training programs that empower the next generation of bioinformaticians. Her area of expertise is Genome assembly\, Genome annotation\, Metagenomics and metabarcoding. \n \n \n \n \n \n \n \n Dr Kathryn Campbell\n \n Kathryn recently joined the Edinburgh Genomics team as the Genomics and Bioinformatics Training Coordinator. With a diverse background in bioinformatics and molecular biology\, she specializes in phylogenetics and viral classification. \n \n \n \n \n \n \n \n read more\n Her passion now lies in teaching and outreach\, where she brings extensive experience\, engaging with a broad range of audiences. Kathryn is dedicated to empowering learners through comprehensive training\, from sample preparation and sequencing to data analysis and interpretation. She is also committed to inspiring the next generation of biologists by working with primary and secondary schools to foster a love for science and genomics. URL:https://prstats.preprodw.com/course/introduction-to-metabarcoding-and-metagenomics-analysis-imam01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2024/04/cdc-El76nrcRNw-unsplash-scaled.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240917 DTEND;VALUE=DATE:20240921 DTSTAMP:20260418T214737 CREATED:20240402T162713Z LAST-MODIFIED:20240910T124926Z UID:10000451-1726531200-1726876799@prstats.preprodw.com SUMMARY:ONLINE COURSE – Hidden Markov Models for movement\, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, September 17th\, 2024\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 \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 attendeesthrough the accompanying computer practicals via video link\, so a good internet connection isessential. \nTime Zone\nTIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. However\, all sessions will be recorded and made available\, allowing attendees from different time zones to follow asynchronously. \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 Hidden Markov models (HMMs) are flexible statistical models for time series observations driven by underlying states. Over the last decade\, HMMs have become increasingly popular within the ecological community as they allow to uncover behavioural state dynamics from noisy sensor data. For example\, a typical HMM-based analysis of say GPS locations or acceleration measurements could involve the investigation of internal (e.g. sex\, size\, age) and external (e.g. temperature\, habitat) drivers of behavioural state occupancy. \nThis workshop will introduce the HMM framework\, comprising a mix of theoretical lectures and hands-on practical components using R. In the theoretical sessions\, the following topics will be covered: motivation & overview basic HMM formulation fitting an HMM to data model selection & model checking state decoding incorporating covariates\, seasonality and random effects other model extensions \nThese techniques will be illustrated primarily using movement and acceleration data\, but are applicable also to other ecological time series data (e.g. capture-recapture). In the practical sessions\, we will focus on HMM analyses using the R packages moveHMM and momentuHMM\, but will also showcase the use of hmmTMB. Basic knowledge of the free software R is helpful\, but not required. \nA basic understanding of statistics and probability calculus\, as it would be taught in any introductory statistics class\, is required. By the end of the course\, participants will have a good understanding of what HMMs are and what they can be used for. Participants will also be prepared to tailor a suitable HMM to their data and to implement the corresponding analysis in R. \n \n \n \n \n \n Intended Audiences\n Academics and post-graduate students interested in adding HMMs to their methodological toolbox for analysing ecological data. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 25 places \nDuration – 3.5 days \nContact hours – Approx. 24 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n We will spend the mornings to learn about HMM methodology\, but will make these lectures asinteractive as possible\, with several R code snippets to try out and lots of time for questions. In theafternoons\, we will implement example HMM analyses in R. Some example data sets for the practical sessions will be provided by the instructors\, but participants are welcome to bring their own data. A Slack channel will be open to discuss any issues for which we may not have enough time in the sessions themselves. \n \n \n \n \n \n Assumed quantitative knowledge\n Basic understanding of statistics and probability calculus (e.g. probability distributions\, density functions\, conditional probability). \n \n \n \n \n \n Assumed computer background\n Basic familiarity with R is sufficient. In fact\, participants will be able to follow most of the workshopwithout prior knowledge of R. \n \n \n \n \n \n Equipment and software requirements\n \nParticipants must use a computer with a good internet connection\, a working recent version or R (and ideally also RStudio)\, and recent versions of some R packages whose installation instructions will be sent a few days before the course. A working webcam is desirable for enhanced interactivity during the live sessions. Some computation power is required for modelling large datasets\, although the provided example data (and suggested subsets of participants’ data) can run on an ordinary laptop. \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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\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 Tuesday 17th\n Day 1 – Classes from 08:30 – 16:00 \nThree 1-hour theory sessions: \n motivation & overview preliminaries: probability calculus & Markov chains the basic HMM formulationA 1-hour practical session: simulating data from an HMM \n \n \n \n \n \n Wednesday 18th\n Day 2 – Classes from 08:30 – 16:00Three 1-hour theory sessions: fitting an HMM to real data\, part I fitting an HMM to real data\, part II fitting HMMs to movement and acceleration dataA 2-hour practical session: fitting an HMM to real data \n  \n \n \n \n \n \n Thursday 19th\n Day 3 – Classes from 08:30 – 16:00Three 1-hour theory sessions: model selection & model checking state decoding covariatesA 2-hour practical session: complete HMM workflow \n \n \n \n \n \n Friday 20th\n Day 4 – Classes from 08:30 – 11:00Three 1-hour theory sessions: overview of extensions\, part I overview of extensions\, part II time discuss participants’ own data/questions \n \n \n \n \n \n \n \n \n Prof. Roland Langrock\n \n Roland is a professor of statistics and data analysis at Bielefeld University in Germany\, where he is heavily involved in the teaching of introductory statistics courses as well as advanced statistical methods. His research focuses on statistical method development for state-switching time series models\, in particular hidden Markov models\, as well as their applications primarily in ecology\, sports and economics. Within statistical ecology\, he has published extensively on the modelling of animal movement and general behaviour\, but also on capture-recapture and distance sampling.Google ScholarHomepage URL:https://prstats.preprodw.com/course/hidden-markov-models-for-movement-acceleration-and-other-ecological-data-hmmm01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2024/04/michael-blum-5MOScwaoYXM-unsplash-2-scaled.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240827 DTEND;VALUE=DATE:20240906 DTSTAMP:20260418T214737 CREATED:20221215T123244Z LAST-MODIFIED:20240223T142502Z UID:10000413-1724716800-1725580799@prstats.preprodw.com SUMMARY:ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR03) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday 27th August\, 2023\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 – CET – 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 \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 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. \nAt the start off the course\, 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. The course then moves on 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. Towards the end of the course the participants are introduced to literate programming using Quarto (the new scientific and publishing system recently released by RStudio) with the focus on writing a scientific article or report. 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 Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time zone – CET\n\nAvailability – 20 places \nDuration – 3 days \nContact hours – Approx. 20 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English\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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\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 Tuesday 27th\n Classes from 17:00-20:00 CET \n\n\n\nDAY 1 \n\nIntro to the reproducibility crisis\nExamples of problems arising from different Operating Systems\, R versions\, andpackage versions\nWhat happens when you start R\nRStudio projects\nProject organization\n\n\n\n\n\n\n \n\n\n \n \n \n \n \n Wednesday 28th\n Classes from 17:00-20:00 CET \nDAY 2 \n\n\n\n\nCode style\nReproducible R environment\n\n\n\n\n \n \n \n \n \n Thursday 29th\n Classes from 17:00-20:00 CET \nDAY 3 \n\nIntro to Git and Github\nConfigure Git and GitHub\nGit basic from command line\nCreate a local repository and push it on Github\nCraft a good commit\nClone and fork a GitHub repository\n\n \n \n \n \n \n Tuesday 3rd\n Classes from 17:00-20:00 CET \nDAY 4 \n\nCraft a pull request\nGit branch\, merge\, and tag\nGit checkout\, reset\, and revert\nUse Git with RStudio\nIgnore files\n\n \n \n \n \n \n Wednesday 4th\n Classes from 17:00-20:00 CET \nDAY 5 \n\nLiterate programming\nQuarto to produce html\, word\, and pdf outputs\nManage references with Zotero\nUse templates for word output\n\n \n \n \n \n \n Thursday 5th\n Classes from 17:00-20:00 CET \nDAY 6 \n\nWrite your scientific article with RMarkdown\nReference 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\nGoogleScholar\nORCID\nGitHub URL:https://prstats.preprodw.com/course/reproducible-and-collaborative-data-analysis-with-r-racr03/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2022/07/andrea-lightfoot-Pj6fYNRzRT0-unsplash-scaled.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20240415 DTEND;VALUE=DATE:20240427 DTSTAMP:20260418T214738 CREATED:20240404T164741Z LAST-MODIFIED:20240404T164749Z UID:10000457-1713139200-1714175999@prstats.preprodw.com SUMMARY:In Person Course - Advanced Python for Biologists - University of Glasgow DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, April 15th\, 2024\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 This course is being deliver by Martin Jones @ Python for Biologists\, you can book via his website here\nhttps://www.polyomics.gla.ac.uk/course-python_course_APR24.html URL:https://prstats.preprodw.com/course/advanced-python-for-biologists/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2024/04/Screenshot-2024-04-04-at-17.40.33.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20231002 DTEND;VALUE=DATE:20231007 DTSTAMP:20260418T214738 CREATED:20230721T124055Z LAST-MODIFIED:20230919T143549Z UID:10000431-1696204800-1696636799@prstats.preprodw.com SUMMARY:ONLINE COURSE – The Practice of RADseq: Population Genomics Analysis with Stacks (RADS02) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, October 2nd\, 2023\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 – Central Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \n \n \n \n \n \n \n \n \n \n \n About This Course\n This course is aimed at introducing researchers to the theory and practice of using reduced representation libraries – such as RAD sequencing – to preform population genomic analysis in non-model organisms. The course will center on running the software pipeline Stacks\, focusing on how the characteristics of the underlying molecular libraries result in weak or robust analytical results. Sessions will be live online\, consisting of a blend of lectures\, interactive demonstrations\, and lab practicals\, where participants will have the opportunity to ask questions throughout. Computation will be done on the Amazon AWS Cloud. \nBy the end of the course\, participants should be able to: \n\nNavigate the UNIX file system\, execute commands\, and interact with bioinformatic data files;\nUnderstand how to perform a de novo analysis – without a reference genome – including parameter optimization;\nUnderstand how PCR duplicates and other molecular library characteristics affect analysis;\nComplete a reference genome-based analysis;\nTake the outputs from Stacks to complete a Structure analysis (de novo)\, a genome scan based on FST(reference-based)\, and a private allele analysis.\n\n \n \n \n \n \n Intended Audiences\n Graduate students\, post-doctoral researchers\, or professionals who wish to learn how to analyze genomic RAD-based data. \n \n \n \n \n \n Venue\n Delivered Remotely \n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Data and analytical approaches will be presented in a lecture format to introduce key concepts. In the beginning\, participants will work interactively with the instructor to understand fundamentals. Once completed\, the course will shift into a lab practical format\, where the instructor introduces the lab\, then free time is given for participants to complete the lab with the instructor present to answer questions. At the end of each practical\, the instructor will go over the key ideas and results. \n \n \n \n \n \n Assumed quantitative knowledge\n Basic understanding of evolution (mutation\, drift\, selection\, migration\, HWE) and population-genomic concepts (e.g.\, FST\, population structure) is assumed. \n \n \n \n \n \n Assumed computer background\n No computational background knowledge is assumed\, however\, experience in UNIX and/or bioinformatics analysis will enable participants to move at a faster pace. \n \n \n \n \n \n Equipment and software requirements\n Students will need a laptop or desktop with a fast and reliable internet connection. The computer can run any operating system including MacOS\, Windows\, or Linux\, as we will connect\, via the terminal\, to our AWS instance on the Amazon Cloud. \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 \nPLEASE READ – CANCELLATION POLICY \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\nDay 1: 09:00 – 16:00 (Central Standard Time\, i.e.\, Chicago) \n\nInstructor and participant introductions\nLecture: Exploring the Genetics of Non-Model Organisms with RAD-seq\nIntroduction to the Cloud\nIntroduction to UNIX\, Part 1\n\nDay 2: 09:00 – 16:00 \n\nShort Lecture: Illumina error model\, FastQ files\, data quality control\, and sample multiplexing\nCleaning and demultiplexing RAD-seq data\nIntroduction to UNIX\, Part 2\nParticipant two-minute lightning talks\n\nDay 3: 09:00 – 16:00 \n\nShort Lecture: Parameter optimization and de novo assembly of RAD tags\nUnderstanding the de novo assembly algorithm\nHow to optimize assembly parameters in a de novo assembly\nDe novo assembly of RAD tags without a genome for a STRUCTURE Analysis\n\nDay 4: 09:00 – 16:00 \n\nReferenced-aligned RAD tags for genome scanning and identifying signatures of selection\nHow to perform an integrated analysis – applying de novo data to a related reference genome\n\nDay 5: 09:00 – 16:00 \n\nShort Lecture: Understanding DNA quality\, molecular library integrity\, and PCR duplicates\nExamining the effects of PCR duplicates in a bird dataset\nPerforming a private allele analysis in a hybrid zone\nOpen lab\, time for questions on participant provided data sets.\n\n\n  \n \n \n \n \n \n Course Instructor\n \nDr. Julian Catchen is an Associate Professor at the University of Illinois at Urbana-Champaign where he runs a population genomics lab that focuses on how the evolution of the genome affects underlying genomic architecture. He is the primary author of Stacks and has been involved in RADseq analysis since 2009\, working on projects in a variety of fishes\, birds\, and insects while applying a diversity of genomic analyses including defining population structure\, conducting genome scans\, private allele analysis\, and phylogenetics. \nLab Website: https://catchenlab.life.illinois.edu/Google Scholar: https://scholar.google.com/citations?user=YKnVJaAAAAAJ&hl=enResearch Gate: https://www.researchgate.net/profile/Julian-CatchenORCID: https://orcid.org/0000-0002-4798-660X URL:https://prstats.preprodw.com/course/the-practice-of-radseq-population-genomics-analysis-with-stacks-rads02/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2023/07/Screenshot-2023-07-21-at-13.30.55.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20230911 DTEND;VALUE=DATE:20230916 DTSTAMP:20260418T214738 CREATED:20230515T125812Z LAST-MODIFIED:20240403T160610Z UID:10000425-1694390400-1694822399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Advanced Ecological Niche Modelling (ENM/SDM) Using R (ANMR02) Deadline to register 28th August DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 11th\, 2023\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 \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 – 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 Have 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. By the end of 5-day practical course\, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”. \nEcological 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. \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 Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – 24 places \nDuration – 5 days \nContact hours – 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 graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of ecological niche models and biogeography in general is required\, thus we will assume the attendees know how to run an ecological niche model. \n \n \n \n \n \n Assumed computer background\n Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models\, please consider attending our basic course on PRStatistics (www.prstats.org). \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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\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 Monday 11th\n Classes from 09:30 to 17:30 \nDay One: \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 12th\n Classes from 09:30 to 17:30 \nDay Two: \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n \n \n \n \n \n Wednesday 13th\n Classes from 09:30 to 17:30 \nDay Three: \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 14th\n Classes from 09:30 to 17:30 \nDay Four: \n MESS practice with Biomod2 package. \n Validate models null models. \n VirtualSpecies virtualspecies packages. \n \n \n \n \n \n Friday 15th\n Classes from 09:30 to 17:30 \nDay Five: \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)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology\, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology\, forestry\, spatial ecology\, andecoinformatics\, all addressed by explicitly considering the spatial component ofecological processes\, mainly applying spatially explicit modelling approaches\, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.preprodw.com/course/advanced-ecological-niche-modelling-enm-sdm-using-r-anmr02/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2018/07/ANMR011.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20230116 DTEND;VALUE=DATE:20230307 DTSTAMP:20260418T214738 CREATED:20221017T154832Z LAST-MODIFIED:20221017T162301Z UID:10000417-1673827200-1678147199@prstats.preprodw.com SUMMARY:ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, January 16th\, 2022\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 \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 – GMT – 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 This course introduces the participants to the main concepts and methods of trait-based ecology. While traits have been used in ecology for a long time\, an approach explicitly based on traits has been increasingly introduced to almost all aspects of ecological research in the last two decades. In particular\, since the early 2000s\, methodological developments have really flourished\, up to a point that it is hard to keep track of such developments. In this course\, we will combine lectures providing an overview of the main principles and methods of trait-based ecology with practices using the statistical software R\, so that participants will acquire a knowledge of available R packages and customized functions\, and how to use them in the context of trait-based analyses. The course will span methods taking both species-level and community-level perspectives that can be applied to a large variety of organisms. Additional practical aspects that will be covered include the choice of the “right” traits for a given study\, what to consider when using trait data from data bases\, and how to design and optimize your own trait sampling campaign. The course is largely based on the book recently published by Cambridge University Press “Handbook of Trait-Based Ecology: From Theory to R Tools” and the accompanying R material. The book is not required for course participation. \n  \n  \n \n \n \n \n \n Intended Audiences\n Master and PhD students\, as well as post docs and established researchers new to the topic\, who are at the start of their own trajectory in trait-based ecological research. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time zone – GMTAvailability – 30 places \nDuration – 8 days (4 hours per day\, one day per week\, for 8 weeks) \nContact hours – Approx. 32 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \n \n \n \n \n \n Teaching Format\n The course will consist in 1 teaching block per week\, for 8 weeks. Each block will consist of approximately 3.5 hours of interactive live online sessions (at xx:xx GMT time)\, which will include theoretical lectures\, discussion\, and demonstrations of R code of selected packages and functions and approximately 4 hours of practical’s that each participant will do on their own schedule / time zone\, based on annotated self-explanatory R scripts. The instructor will be available for questions and help during Western European working hours and a bit beyond that\, depending on the participants’ time zones. Data sets and R codes for practicals will be provided\, so that participants can repeat and extend the methods demonstrated during the lectures\, at their own convenience. \n  \n  \n \n \n \n \n \n Assumed quantitative knowledge\n A basic knowledge of uni- and multivariate statistical analyses is assumed (correlation\, simple regression models\, unconstrained and constrained ordination\, e.g. PCA\, RDA). Without such knowledge the course can probably be followed for most parts\, but the practicals will be much less efficient for the student. \n \n \n \n \n \n Assumed computer background\n Participants should have basic experience in working with the R statistical environment\, preferably in connection with the R studio interface. They should be familiar with importing data to R\, installing and loading packages\, and basic plot functions. \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 \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \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 Monday 16th January\n Classes from 10:30 to 14:30\nInstructor Francesco Bello \nTheory\nIntroduction\, definitions\, response and effect\, functional groups\, trade-offs\, Gower distance. \nPractical\nPeople’s trait game\nGower distance\n \n \n \n \n \n Monday 23rd January\n Classes from 10:30 to 14:30\nInstructor Francesco Bello \nTheory\nCommunity Weighted Mean (CWM) and Functional Diversity (FD) \nPractical\nCWM & FD\n \n \n \n \n \n Monday 30th January\n Classes from 10:30 to 14:30\nInstructor Lars Götzenberger \nTheory\nResponse traits and environmental filtering \nPractical\nKleyer appendix\n \n \n \n \n \n Monday 6th February\n Classes from 10:30 to 14:30\nInstructor Carlos Pérez Carmona \nTheory\nCommunity assembly \nPractical\nBasics of null-models\n \n \n \n \n \n Monday 13th February\n Classes from 10:30 to 14:30\nInstructor Matty Berg & Carlos Pérez Carmona \nTheory\nIntraspecific trait variability \nPractical\nTrait overlap (trova)\, Trait variance\, CWM flex anova\n \n \n \n \n \n Monday 20th February\n Classes from 10:30 to 14:30\nInstructor Lars Götzenberger \nTheory\nPhylogeny \nPractical\nConservatism\, Phylogenetic diversity\, PICs\n \n \n \n \n \n Monday 27th February\n Classes from 10:30 to 14:30\nInstructor Marco Moretti & Francesco Bello \nTheory\nResponse & Effect traits \nPractical\nSelection/Complementarity\, Lautaret\, multitrophic\n \n \n \n \n \n Monday 6th March\n Classes from 10:30 to 14:30\nInstructor Lars Götzenberger & Carlos Pérez Carmona \nTheory\nMissing traits\, databases\, sampling traits \nPractical\nDatabases extraction\, sampling game\, data imputation URL:https://prstats.preprodw.com/course/online-course-trait-based-ecology-using-r-theory-and-practice-tber01-this-course-will-be-delivered-live/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/10/Screenshot-2022-10-17-at-17.03.22.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20221212 DTEND;VALUE=DATE:20221217 DTSTAMP:20260418T214738 CREATED:20220302T115332Z LAST-MODIFIED:20221019T151411Z UID:10000400-1670803200-1671235199@prstats.preprodw.com SUMMARY:ONLINE COURSE - Ecological niche modelling using R (ENMR04) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 12th\, 2022\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 – Western European Time – 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 \nThe course will cover the base theory of ecological niche modelling and its main methodologies. By the end of this 5-day practical course\, attendees will have the capacity to perform ecological niche models and understand their results\, as well as to choose and apply the correct methodology depending on the aim of their type of study and data. \nEcological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for similar mechanistic or correlative models\, empirical or mathematical approaches to the ecological niche of a species\, where different types of ecogeographical variables (environmental\, topographical\, human) are related with a species physiological data or geographical locations\, in order to identify the factors limiting and defining the species’ niche. ENMs have become popular due to the need for efficiency in the design and implementation of conservation management. \nThe 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 graphics (http://www.r-project.org/). Attendees will learn to use modelling algorithms like Maxent\, Bioclim\, Domain\, and logistic regressions\, and R packages for computing ENMs like Dismo and Biomod2. Also\, students will learn to compare different ecological niche models using the Ecospat package. \n  \n\n \n \n \n \n \n Intended Audiences\n \nThis course is orientated to PhD and MSc students\, as well as persons in researcher or industry working on biogeography\, spatial ecology\, or related disciplines. \n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – Western European Time \nAvailability – 24 Places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and applications of ENM. Practical lectures on most used ENM methods. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practicals will be provided by the instructor\, but participants are welcome to bring their own data. \n \n \n \n \n \n Assumed quantitative knowledge\n Basic knowledge in Geographical Information Systems and spatial analyses.\n \n \n \n \n \n Assumed computer background\n Familiarity with GIS software like QGIS. Ability to visualise shapefiles and raster files. Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots.\n \n \n \n \n \n Equipment and software requirements\n A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links \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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\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 \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 12th\n Classes from 09:30 to 17:30 \nElementary concepts on Ecological Niche Modelling \nModule 1: Introduction to ENM theory. Definition of ecological niche model; introduction to species ecological niche theory\, types of ecological niches\, types of ENM\, diagram BAM\, ENMs as approximations to species’ niches. \nModule 2: Problems and limitations on ENM. Assumptions and uncertainties\, equilibrium concept\, niche conservatism\, autocorrelation and intensity\, sample size\, correlation of environmental variables\, size and form of study area\, thresholds\, model validation\, model projections. \nModule 3: Methods on ENM. Mechanistic and correlative models. Overlap Analysis\, Biomod\, Domain\, Habitat\, Distance of Mahalanobis\, ENFA\, GARP\, Maxent\, Logistic regression\, Generalised Linear Models\, Generalised Additive Models\, Generalised Boosted Regression Models\, Random Forest\, Support Vector Machines\, Artificial Neural Network. \nModule 4: Conceptual and practice steps to calculate ENM. How to make an ENM step-by-step. \nModule 5: Applications of ENM. Ecological niche identification\, Identification of contact zones\, Integration with genetical data\, Species expansions\, Species invasions\, Dispersion hypotheses\, Species conservation status\, Prediction of future conservation problems\, Projection to future and past climate change scenarios\, Modelling past species\, Modelling species richness\, Road-kills\, Diseases\, Windmills\, Location of protected areas. \n \n \n \n \n \n Tuesday 13th\n Classes from 09:30 to 17:30 \nPrepare environmental variables and run ecological niche models with dismo package. \nModule 6: Preparing variables. Choosing environmental data sources\, Downloading variables\, Clipping variables\, Aggregating variables\, Checking pixel size\, Checking raster limits\, Checking NoData\, Correlating variables. \nModule 7: Dismo practice. How to run an ENM using the R package dismo. \n  \n \n \n \n \n \n Wednesday 14th\n Classes from 09:30 to 17:30 \nRun ecological niche models with Biomod2 package and Maxent. \nModule 8: Biomod2 practice. How to run an ENM using the R package Biomod2. \nModule 9: Maxent practice. How to run an ENM using the R packages dismo and Biomod2 as well as Maxent software. \n \n \n \n \n \n Thursday 15th\n Classes from 09:30 to 17:30 \nCompare ecological niche models with ecospat. \nModule 10: Ecospat practice. Compare statistically two different ecological niche models using the R package Ecospat. \nModule 11: Students’ talks. Attendees will have the opportunity to present their own data and analyse which is the best way to successfully obtain an ENM. \n  \n \n \n \n \n \n Friday 16th\n Classes from 09:30 to 17:30 \nRun ecological niche models with your own data. \nModule 12: Final practical. In this practical\, the students will run ENM with their own data or with a new dataset\, applying all the methods showed during the previous days. \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 websiteWork WebpageResearchGateGoogleScholar \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/ecological-niche-modelling-using-r-enmr04/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2021/09/pr-stats-stock-image-64562101-xl-2015.jpeg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20221212 DTEND;VALUE=DATE:20221216 DTSTAMP:20260418T214738 CREATED:20220310T151719Z LAST-MODIFIED:20230727T113948Z UID:10000377-1670803200-1671148799@prstats.preprodw.com SUMMARY:Ecological niche modelling using R (ENMRPR) 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\nPre Recorded\n \n \n \n \n \n \n \n \n \n \n About This Course\n \nThe course will cover the base theory of ecological niche modelling and its main methodologies. By the end of this 5-day practical course\, attendees will have the capacity to perform ecological niche models and understand their results\, as well as to choose and apply the correct methodology depending on the aim of their type of study and data. \nEcological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for similar mechanistic or correlative models\, empirical or mathematical approaches to the ecological niche of a species\, where different types of ecogeographical variables (environmental\, topographical\, human) are related with a species physiological data or geographical locations\, in order to identify the factors limiting and defining the species’ niche. ENMs have become popular due to the need for efficiency in the design and implementation of conservation management. \nThe 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 graphics (http://www.r-project.org/). Attendees will learn to use modelling algorithms like Maxent\, Bioclim\, Domain\, and logistic regressions\, and R packages for computing ENMs like Dismo and Biomod2. Also\, students will learn to compare different ecological niche models using the Ecospat package. \n  \n\n \n \n \n \n \n Intended Audiences\n \nThis course is orientated to PhD and MSc students\, as well as persons in researcher or industry working on biogeography\, spatial ecology\, or related disciplines. \n\n \n \n \n \n \n Course Details\n Last Up-Dated 15:03:2019 \nDuration – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and applications of ENM. Practical lectures on most used ENM methods. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practicals will be provided by the instructor\, but participants are welcome to bring their own data.\n \n \n \n \n \n Assumed quantitative knowledge\n Basic knowledge in Geographical Information Systems and spatial analyses.\n \n \n \n \n \n Assumed computer background\n Familiarity with GIS software like QGIS. Ability to visualise shapefiles and raster files. Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots.\n \n \n \n \n \n Equipment and software requirements\n A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links\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 \nPLEASE READ – CANCELLATION POLICY \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 \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Day 1 – Approx. 7 hours \nElementary concepts on Ecological Niche Modelling \nModule 1: Introduction to ENM theory. Definition of ecological niche model; introduction to species ecological niche theory\, types of ecological niches\, types of ENM\, diagram BAM\, ENMs as approximations to species’ niches. \nModule 2: Problems and limitations on ENM. Assumptions and uncertainties\, equilibrium concept\, niche conservatism\, autocorrelation and intensity\, sample size\, correlation of environmental variables\, size and form of study area\, thresholds\, model validation\, model projections. \nModule 3: Methods on ENM. Mechanistic and correlative models. Overlap Analysis\, Biomod\, Domain\, Habitat\, Distance of Mahalanobis\, ENFA\, GARP\, Maxent\, Logistic regression\, Generalised Linear Models\, Generalised Additive Models\, Generalised Boosted Regression Models\, Random Forest\, Support Vector Machines\, Artificial Neural Network. \nModule 4: Conceptual and practice steps to calculate ENM. How to make an ENM step-by-step. \nModule 5: Applications of ENM. Ecological niche identification\, Identification of contact zones\, Integration with genetical data\, Species expansions\, Species invasions\, Dispersion hypotheses\, Species conservation status\, Prediction of future conservation problems\, Projection to future and past climate change scenarios\, Modelling past species\, Modelling species richness\, Road-kills\, Diseases\, Windmills\, Location of protected areas.\n \n \n \n \n \n Day 2\n Day 2 – Approx. 7 hours \nPrepare environmental variables and run ecological niche models with dismo package. \nModule 6: Preparing variables. Choosing environmental data sources\, Downloading variables\, Clipping variables\, Aggregating variables\, Checking pixel size\, Checking raster limits\, Checking NoData\, Correlating variables. \nModule 7: Dismo practice. How to run an ENM using the R package dismo. \n \n \n \n \n \n \n Day 3\n Day 3 – Approx. 7 hours \nRun ecological niche models with Biomod2 package and Maxent. \nModule 8: Biomod2 practice. How to run an ENM using the R package Biomod2. \nModule 9: Maxent practice. How to run an ENM using the R packages dismo and Biomod2 as well as Maxent software.\n \n \n \n \n \n Day 4\n Day 4 – Approx. 7 hours \nCompare ecological niche models with ecospat. \nModule 10: Ecospat practice. Compare statistically two different ecological niche models using the R package Ecospat. \nModule 11: Students’ talks. Attendees will have the opportunity to present their own data and analyse which is the best way to successfully obtain an ENM. \n \n \n \n \n \n \n Day 5\n Day 5 – Approx. 7 hours \nRun ecological niche models with your own data. \nModule 12: Final practical. In this practical\, the students will run ENM with their own data or with a new dataset\, applying all the methods showed during the previous days. \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 websiteWork WebpageResearchGateGoogleScholar \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/ecological-niche-modelling-using-r-enmrpr/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2021/09/pr-stats-stock-image-64562101-xl-2015.jpeg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20221115 DTEND;VALUE=DATE:20221119 DTSTAMP:20260418T214738 CREATED:20220609T102652Z LAST-MODIFIED:20221108T151103Z UID:10000411-1668470400-1668815999@prstats.preprodw.com SUMMARY:ONLINE COURSE – Time Series Data Analysis (TSDA02) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, November 16th\, 2022\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 – GMT+1 – 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 This course covers introductory modelling for the analysis of time series data. The main focus of the course is on data observed at regular (discrete) time points but later modules cover continuously-observed data. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular forecast package. The second half of the course looks at Bayesian time series analysis which is extremely customisable to bespoke data analysis situations. \n \n \n \n \n \n Intended Audiences\n \nResearch postgraduates\, practicing academics\, or other professionals from any field who would like to learn about time series analysis and how it can help them derive superior insight from their data. \n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \nThe course will be divided into theoretical lectures to introduce and explain key concepts and theories. Afternoon practicals will be based on the topics covered in the morning lectures. \n\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of regression methods and generalised linear models. \nSome familiarity with R including the ability to import/export data\, manipulate data frames\, fit basic statistical models\, and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Assumed computer background\n Attendees should already have experience with R and be able to read csv files\, create simple plots\, and manipulate data frames. \n \n \n \n \n \n Equipment and software requirements\n EQUIPMENT AND SOFTWARE REQUIREMENTS\n\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 \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \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 Wedesday 16th\n \n\n\n9:30-10:30\nIntroduction\, example data sets\n\n\n10:30-10:45\nCoffee break\n\n\n10:45-11:45\nRevision: likelihood and inference\n\n\n11:45-12:00\nBreak\n\n\n12:00-13:00\nRevision: linear regression and GLMs\n\n\n13:00-14:00\nLunch\n\n\n14:00-14:45\nTutor-guided practical: Loading data in R and running simple analysis\n\n\n14:45-15:00\nCoffee break\n\n\n15:00-17:00\nSelf-guided practical: Using R for linear regression and GLMs’\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n Thursday 17th\n \n\n\n9:30-10:30\nAuto-regressive models and random walks\n\n\n10:30-10:45\nCoffee break\n\n\n10:45-11:45\nMoving averages and ARMA\n\n\n11:45-12:00\nBreak\n\n\n12:00-13:00\nIntegrated models and ARIMA\n\n\n13:00-15:00\nLunch\n\n\n15:00-15:45\nTutor-guided practical: the forecast package in R\n\n\n15:45-16:00\nCoffee break\n\n\n16:00-17:00\nSelf-guided practical: Fitting ARIMA models with forecast\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n Friday 18th\n \n\n\n9:30-10:30\nIncluding covariates: ARIMAX models\n\n\n10:30-10:45\nCoffee break\n\n\n10:45-11:45\nCreating bespoke time series models using Bayes\n\n\n11:45-12:00\nBreak\n\n\n12:00-13:00\nModel choice and forecasting using Bayes\n\n\n13:00-14:00\nLunch\n\n\n14:00-14:45\nTutor-guided practical: a walkthrough example time series analysis\n\n\n14:45-15:00\nCoffee break\n\n\n15:00-17:00\nSelf-guided practical: finding the best time series model for your data set\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n Tuesday 22nd\n \n\n\n9:30-10:30\nModelling with seasonality and the frequency domain (slides)\n\n\n10:30-10:45\nCoffee break\n\n\n10:45-11:45\nStochastic volatility models and heteroskedasticity (slides)\n\n\n11:45-12:00\nBreak\n\n\n12:00-13:00\nFitting Bayesian time series models (slides)\n\n\n13:00-14:00\nLunch\n\n\n14:00-14:45\nTutor-guided practical: fitting time series models in JAGS and Stan (code)\n\n\n14:45-15:00\nCoffee break\n\n\n15:00-17:00\nSelf-guided practical: start analysing your own data set with Bayes (worksheet)\n\n\n\n  URL:https://prstats.preprodw.com/course/online-course-time-series-data-analysis-tsda02/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/TSDA01.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20220713T160000 DTEND;TZID=Europe/London:20220713T163000 DTSTAMP:20260418T214738 CREATED:20220304T185953Z LAST-MODIFIED:20220628T122226Z UID:10000369-1657728000-1657729800@prstats.preprodw.com SUMMARY:FREE SEMINAR - Remote Sensing With Satellite Multi-Spectral Sensors\, drone RGB and Near Infrared cameras and Aircraft And Drone LiDAR Sensors (RSFS01) DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, July 20th\, 2022\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 \nFree seminar \n\n\nThis is a free ~30 minute seminar including a Q and A session at the end for our up-coming courses\, Remote Sensing with satellite multi-spectral sensors (RSMS01)\, Remote Sensing with drone RGB and Near Infrared cameras (RSWD01) and Remote Sensing with aircraft and drone LiDAR sensors (RSLD01). \n\n\nTime \n16:00 – 16:30 GMT \n\n\nSpeaker \n\n\nCourse Instructor Nelson Pires \nAbout these courses \n\nSatellite Remote Sensing has become a common tool to investigate the different fields of Earth and environmental sciences. The progress of the performance capabilities of the optoelectronic and radar devices mounted on-board remote sensing platforms have further improved the capability of instruments to acquire information about the Earth and its resources for global\, regional and local assessments. Disciplines such as agriculture\, hydrology\, and ecosystem studies have all developed a strong Remote Sensing component\, facilitating our understanding of the environment and its processes over a broad range of spatial and temporal scales.This 4-day course aims to provide participants with an integrated end-to-end perspective going from measurement techniques to end-user applications\, covering issues related to Remote Sensing\, Earth System Modelling and Data Assimilation as well as hands-on computing exercises on the processing of Earth Observation data. \n\n\n\nUnmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging/ranging and direct geo-referencing systems have been proven as a potential Remote Sensing platform that could satisfy the needs of a wide range of civilian applications. The continuous developments in direct georeferencing and Remote Sensing (i.e.\, passive and active imaging sensors in the visible and infrared range – RGB cameras and LiDAR) is providing the professional geospatial community with ever-growing opportunities to provide accurate 3D information used in environmental research to collect information about the Earth\, such as vegetation and tree species. This 4-day course aims to provide participants with an integrated end-to-end perspective going from measurement techniques to end-user applications\, covering issues related to LiDAR sensors coupled on aircraft and UAVs\, computing exercises on the processing of 3D point clouds to produce geospatial products. \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 \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 Instructor\n \n\n\n\n\n\nDr. Nelson Pires\nWorks at –\nTeaches – URL:https://prstats.preprodw.com/course/free-seminar-on-remote-sensing/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Free Seminars,Home Seminars ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/RSMS01-1.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220214 DTEND;VALUE=DATE:20220219 DTSTAMP:20260418T214738 CREATED:20190808T160414Z LAST-MODIFIED:20221019T153619Z UID:10000300-1644796800-1645228799@prstats.preprodw.com SUMMARY:ONLINE COURSE - GIS And Remote Sensing Analyses With R (GARM01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 14th\, 2022\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 – Western European Standard Time – 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 The course will cover the basics to perform spatial analyses using R as a Geographical Information System (GIS) platform and Remote Sensing as main data source. The course will provide a brief theoretical background of GIS tools and Remote Sensing data and techniques. By the end of this 4-day practical course\, attendees will have the capacity to search satellite imagery\, to manipulate Remote Sensing data\, to create new variables\, as well as to choose the best spatial tools and techniques to perform spatial analyses and interpret their results. \nThe 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 graphics (http://www.r-project.org/). Attendees will learn to use the Rpackage RSToolbox for Remote Sensing image processing and analysis such as calculating spectral indices\, principal component transformation\, or unsupervised and supervised classification. \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. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 25 places \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and applications of GIS and Remote Sensing.Practical lectures on most used spatial tools. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practical modules will be provided by the instructor\, but participants are welcome to bring their own data. \n \n \n \n \n \n Assumed quantitative knowledge\n Basic knowledge in Geographical Information Systems\, Remote Sensing\, and spatial analyses. \n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic 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\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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \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 Monday 21st\n Classes from 09:00 to 17:00Theory – Introduction to GIS.Practical – Introduction to GIS with R: Import and plot data.Theory – Coordinate systems.Practical – Projecting vectorial & raster files. \n \n \n \n \n \n Tuesday 22nd\n Classes from 09:30 – 17:00Theory – Vector database operations.Practical – Attribute and spatial queries: join/merge\, filter/subset\, select by attribute\, select bylocation\, summarize\, add/calculate new attributes (columns)\, plot attributes.Theory – Vector analyses.P: Vector analyses – buffer\, merge\, dissolve\, intersect\, union\, select\, calculate areas. \n \n \n \n \n \n Wednesday 23rd\n Classes from 09:30 – 17:00Theory – Raster GIS.Practical – Raster analyses: rasterize\, crop\, mask\, merge\, distance surface\, zonal statistics.Theory – Introduction to Remote Sensing. RS as main data source: RS sensors & variables.RS software.Practical – Getting and plotting RS data. Downloading\, reading\, and plotting RS data in R.Manipulating satellite data. \n \n \n \n \n \n Thursday 24th\n Classes from 09:30 – 17:00Theory – Working with RS variables. Image classification\, Vegetation indexes\, data fusion.Practical – Calculating RS variables with RStoolbox: Vegetation indexes and classificationmethods.Theory: Remote Sensing applications to biologyPractical: Statistical analyses with RS data. \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 websiteWork WebpageResearchGateGoogleScholar \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/gis-and-remote-sensing-analyses-with-r-garm01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/GARM01R.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20211213 DTEND;VALUE=DATE:20211218 DTSTAMP:20260418T214738 CREATED:20220425T145328Z LAST-MODIFIED:20220804T114533Z UID:10000408-1639353600-1639785599@prstats.preprodw.com SUMMARY:ONLINE COURSE - Remote Sensing With Aircraft And Drone LiDAR Sensors (RSLD01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 12th\, 2021\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 – GMT+1 – 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 \nUnmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging/ranging and direct geo-referencing systems have been proven as a potential Remote Sensing platform that could satisfy the needs of a wide range of civilian applications. The continuous developments in direct georeferencing and Remote Sensing (i.e.\, passive and active imaging sensors in the visible and infrared range – RGB cameras and LiDAR) is providing the professional geospatial community with ever-growing opportunities to provide accurate 3D information used in environmental research to collect information about the Earth\, such as vegetation and tree species. \n\n\nThis 4-day course aims to provide participants with an integrated \n\n\nend-to-end perspective going from measurement techniques to end- \n\n\nuser applications\, covering issues related to LiDAR sensors coupled on aircraft and UAVs\, computing exercises on the processing of 3D point clouds to produce geospatial products. \n\n \n \n \n \n \n Intended Audiences\n \nAny researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills. \n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 24 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \nThe course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with computing exercises on the processing of LiDAR data and point clouds. Afternoon practicals will be based on the topics covered in the morning lectures. \n\n \n \n \n \n \n Assumed quantitative knowledge\n \nFamiliarity with Geographic Information Systems and geospatial data (i.e.\, raster and vector data) could be useful\, but not mandatory. A basic understanding of physics radiation and proprieties of electromagnetic spectrum could be also useful\, but not required. \n\n \n \n \n \n \n Assumed computer background\n \nNo prior experience with LiDAR processing software\, point cloud data or any programming language is required. \n\n \n \n \n \n \n Equipment and software requirements\n \nAttendees of the course must use a computer with any Operating System installed (GNU/Linux\, MS Windows or MacOS). The course will use Open-Source software (FOSS) and some proprietary software which will be downloaded\, installed and configured during the lectures. \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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \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\nMonday 6th February – Classes from 10:00 to 17:00 \n\n\nModule 1: Fundamentals of Light Detection and Ranging (LiDAR) technique. Theoretical principles of a LiDAR systems. Electronic and sensor components. Main differences between spatial\, aerial and terrestrial platforms. The physics of laser signals: Introduction to discrete and full-waveform LiDAR and signal return analysis. Resolutions and precisions achieved. Advantages and disadvantages of the technique. Practice: Introduction to LiDAR data\, platforms and services. Overview of the available processing software and programming languages/libraries. \n\n\nTuesday 7th February – Classes from 10:00 to 17:00 \n\n\nModule 2: Interpretation of LiDAR data. Introduction to metrics/products such as Digital Elevation Models\, Digital Terrain Models and Canopy Height Models. Tree delineation approaches and algorithms (ex. Watershed Algorithm). Discrete versus full-waveform LiDAR data. Echo Decomposition for peak point extraction. Voxelisation of full-waveform LiDAR data. Introduction to binary files: Discrete and full-waveform LiDAR LAS files formats. Practice: Tridimensional point cloud processing and analysis. Filtering\, measuring and classification of LiDAR point clouds. \n\n\nMonday 13th February – Classes from 10:00 to 17:00 \n\n\nModule 3: Managing and exploring a LAS dataset. Visualization advanced techniques\, metadata analysis and content reports\, LiDAR points classification into ground points and non-ground points\, buildings and high vegetation classification. Coordinate Reference System transforms. LIDAR points triangulation into a TIN in order to create a Digital Elevation Model. Elevation contours extraction from a LiDAR point cloud and boundary polygon extraction. RGB colour sampled from an orthomosaic. \n\n\nTuesday 14th February – Classes from 10:00 to 17:00 \n\n\nModule 4: Different applications for LiDAR data: biodiversity monitoring\, forest health monitoring\, urban planning\, wood trade\, archaeology and heritage monitoring and automated driving. Other types of LiDAR systems: Space-based liDAR for measuring ice sheet mass balance\, cloud and aerosol heights. Bathymetric LiDAR for the study of underwater depth of ocean floors. Practice: Post-processing of LiDAR products\, Digital Terrain Model and elevation profile analysis. Measurements of distances\, areas and volumes. Integration with external geospatial data in a Geographic Information System (GIS). \n\n \n \n \n \n \n Course Instructor\n \nDr. Nelson Pires\n\n– Works at: University of Porto\, Portugal \n\n\n– Delivers: \n\n\nRemote Sensing with satellite multi-spectral sensors (RSMS01) \n\n\nRemote Sensing with drone RGB and Near Infrared cameras (RSWD01) \n\n\nRemote Sensing with aircraft and drone LiDAR sensors (RSLD) \n\n\nNelson holds a degree in Physics and Surveying Engineering\, a MSc and PhD degrees in Surveying Engineering from University of Porto. With more than 10 years of experience in teaching at higher education institutions and doing research work in several geospatial subjects. Past and recent research includes subjects in atmospheric corrections with high-precision Global Navigation Satellite Systems analysis\, aerial and close-range photogrammetric studies with drones for coastal monitoring and map production\, multi-spectral and SAR-imaging Remote Sensing for ocean wind-generated waves and ocean dynamics. \n\n\nORCID: https://orcid.org/0000-0002-6629-8060 URL:https://prstats.preprodw.com/course/online-course-remote-sensing-with-aircraft-and-drone-lidar-sensors-rsld01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/VGNR04R.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20210920 DTEND;VALUE=DATE:20210921 DTSTAMP:20260418T214738 CREATED:20220219T015845Z LAST-MODIFIED:20220804T113932Z UID:10000314-1632096000-1632182399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Remote Sensing With Satellite Multi-Spectral Sensors (RSMS01) This course will be delivered live DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 20th\, 2021\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 – GMT+1 – 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 Course Details\n \nSatellite Remote Sensing has become a common tool to investigate the different fields of Earth and environmental sciences. The progress of the performance capabilities of the optoelectronic and radar devices mounted on-board remote sensing platforms have further improved the capability of instruments to acquire information about the Earth and its resources for global\, regional and local assessments. Disciplines such as agriculture\, hydrology\, and ecosystem studies have all developed a strong Remote Sensing component\, facilitating our understanding of the environment and its processes over a broad range of spatial and temporal scales. \n\n\nThis 4-day course aims to provide participants with an integrated end-to-end perspective going from measurement techniques to end-user applications\, covering issues related to Remote Sensing\, Earth System Modelling and Data Assimilation as well as hands-on computing exercises on the processing of Earth Observation data. \n\n \n \n \n \n \n Intended Audiences\n \nAny researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills. \n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 24 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \nThe course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with computing exercises on the processing of Earth Observation data. Afternoon practicals will be based on the topics covered in the morning lectures. \n\n \n \n \n \n \n Assumed quantitative knowledge\n \nFamiliarity with Geographic Information Systems and geospatial data (i.e.\, raster and vector data) could be useful\, but not mandatory. A basic understanding of physics radiation and proprieties of electromagnetic spectrum could be also useful\, but not required. \n\n \n \n \n \n \n Assumed computer background\n \nNo prior experience with Remote Sensing software and data or any programming language is required. Familiarity with any digital image processing technique will be helpful\, but is not required. \n\n \n \n \n \n \n Equipment and software requirements\n \nAttendees of the course must use a computer with any Operating System installed (GNU/Linux\, MS Windows or MacOS). The course will use only Open-Source software (FOSS) which will be downloaded\, installed and configured during the lectures. \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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \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\nMonday 26th September – Classes from 10:00 to 17:00 \n\n\nModule 1: Fundamentals of Remote Sensing. Concepts of satellite orbits\, spacial resolutions\, temporal resolutions\, spectral and radiometric resolutions. Different types of sensors and processing levels of Earth Observation satellites. The physics of atmosphere and spectral signatures. Conceptual understanding of Remote Sensing\, where the participants will be able to identify its advantages and disadvantages. Introduction to data platforms\, software tools\, web portals\, and environmental monitoring applications. Practice: Introduction to Remote Sensing software. \n\n\nTuesday 27th September – Classes from 10:00 to 17:00 \n\n\nModule 2: Earth Observation Programmes. The National Aeronautics and Space Administration (NASA) LANDSAT Program and the European Space Agency (ESA) Copernicus/SENTINEL Program. History and Objectives. Satellite missions chronology. Different spatial\, temporal\, spectral and radiometric resolutions. LANDSAT Multispectral Scanner (MSS) and SENTINEL-2 Multispectral Instrument (MSI) sensor designs. Uses of Earth Observation satellite imagery for natural resources management\, climate change\, environmental disasters and ecology. Practice: Introduction to satellite image processing.  \n\n\nThursday 29th September – Classes from 10:00 to 17:00 \n\n\nModule 3: Remote Sensing for Vegetation Monitoring and Agricultural Applications. Satellite observations to assess a wide variety of geophysical and biophysical parameters\, including precipitation\, temperature\, evapotranspiration\, soil moisture\, and vegetation health. Band combination e index classification for vegetation monitoring. Remote Sensing data for agriculture monitoring\, specifically drought and crop monitoring. Practice: Supervised and unsupervised classification methods. \n\n\nFriday 30th September – Classes from 10:00 to 17:00 \n\n\nModule 4: Satellite Applications for Biodiversity Conservation. Specific applications and hands-on demonstrations of how to use Remote Sensing data to derive conservation policies and management decisions. Remote Sensing for Conservation and Biodiversity: Animal Movement\, Dynamic Habitat Index for Biodiversity\, Vegetation Carbon Stock Corridors and techniques for Land Change Detection. Land Management and Ecosystem Based Tools: Coral Reef Watch and MODIS NDVI Anomalies and Time Series. Practice: Image fusion and Pansharpening techniques. \n\n \n \n \n \n \n Course Instructor\n \n \n \n \n \n \nDr. Nelson Pires\n\n– Works at: University of Porto\, Portugal \n\n\n– Delivers: \n\n\nRemote Sensing with satellite multi-spectral sensors (RSMS01) \n\n\nRemote Sensing with drone RGB and Near Infrared cameras (RSWD01) \n\n\nRemote Sensing with aircraft and drone LiDAR sensors (RSLD) \n\n\nNelson holds a degree in Physics and Surveying Engineering\, a MSc and PhD degrees in Surveying Engineering from University of Porto. With more than 10 years of experience in teaching at higher education institutions and doing research work in several geospatial subjects. Past and recent research includes subjects in atmospheric corrections with high-precision Global Navigation Satellite Systems analysis\, aerial and close-range photogrammetric studies with drones for coastal monitoring and map production\, multi-spectral and SAR-imaging Remote Sensing for ocean wind-generated waves and ocean dynamics. \n\n\nORCID: https://orcid.org/0000-0002-6629-8060 \n\n  URL:https://prstats.preprodw.com/course/remote-sensing-with-satellite-multi-spectral-sensors-rsms01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/RSMS01.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20210125 DTEND;VALUE=DATE:20210130 DTSTAMP:20260418T214738 CREATED:20180703T125432Z LAST-MODIFIED:20221019T153030Z UID:10000280-1611532800-1611964799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Advanced Ecological Niche Modelling Using R (ANMR01) DESCRIPTION:Delivered remotely (Portugal)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, January 25th\, 2022\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 \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 Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 24 places \nDuration – 5 days \nContact hours – 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 PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \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 Monday 25th\n Classes from 09:30 to 17:30 \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 Tuesday 26th\n Classes from 09:30 to 17:30 \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 Wednesday 27th\n Classes from 09:30 to 17:30 \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 Thursday 28th\n Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nVirtualSpecies SDMvspecies packages.\nMIGCLIM practice.\n\n \n \n \n \n \n Friday 29th\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 websiteWork WebpageResearchGateGoogleScholar \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-anmr01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2018/07/ANMR011.jpg GEO:39.399872;-8.224454 END:VEVENT END:VCALENDAR