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0.2.3","direct":true,"kind":"imports","optional":false},{"id":6890378146,"package_name":"XML","ecosystem":"cran","requirements":"*","direct":true,"kind":"imports","optional":false},{"id":6890378147,"package_name":"testthat","ecosystem":"cran","requirements":"*","direct":true,"kind":"suggests","optional":false},{"id":6890378148,"package_name":"mockery","ecosystem":"cran","requirements":"*","direct":true,"kind":"suggests","optional":false}]},{"ecosystem":"docker","filepath":"apps/api/Dockerfile","sha":null,"kind":"manifest","created_at":"2023-01-13T16:23:25.303Z","updated_at":"2023-01-13T16:23:25.303Z","repository_link":"https://github.com/PecanProject/pecan/blob/develop/apps/api/Dockerfile","dependencies":[{"id":6890406690,"package_name":"pecan/base","ecosystem":"docker","requirements":"${IMAGE_VERSION}","direct":true,"kind":"build","optional":false}]}],"score":null,"created_at":"2023-09-11T11:54:35.455Z","updated_at":"2026-04-08T03:30:28.771Z","avatar_url":"https://github.com/PecanProject.png","language":"R","category":"Biosphere","sub_category":"Ecological and Environmental Modeling","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"[![GitHub Actions CI](https://github.com/PecanProject/pecan/workflows/CI/badge.svg)](https://github.com/PecanProject/pecan/actions)\n[![Slack](https://img.shields.io/badge/slack-login-green.svg)](https://pecanproject.slack.com/)\n[![Slack](https://img.shields.io/badge/slack-join_chat-green.svg)](https://join.slack.com/t/pecanproject/shared_invite/enQtMzkyODUyMjQyNTgzLWEzOTM1ZjhmYWUxNzYwYzkxMWVlODAyZWQwYjliYzA0MDA0MjE4YmMyOTFhMjYyMjYzN2FjODE4N2Y4YWFhZmQ)\n[![DOI](https://zenodo.org/badge/4469/PecanProject/pecan.svg)](https://zenodo.org/badge/latestdoi/4469/PecanProject/pecan)\n\n## Our Vision\n\n#### Ecosystem science, policy, and management informed by the best available data and models\n\n## Our Mission\n\n#### Develop and promote accessible tools for reproducible ecosystem modeling and forecasting\n\n## What is PEcAn?\n\nThe Predictive Ecosystem Analyzer (PEcAn) (see [pecanproject.org](http://pecanproject.org)) is an integrated ecological bioinformatics toolbox (Dietze et al 2013, LeBauer et al, 2013) that consists of: 1) a scientific workflow system to manage the immense amounts of publicly-available environmental data and 2) a Bayesian data assimilation system to synthesize this information within state-of-the-art ecosystems models. This project is motivated by the fact that many of the most pressing questions about global change are not necessarily limited by the need to collect new data as much as by our ability to synthesize existing data. This project seeks to improve this ability by developing a accessibe framework for integrating multiple data sources in a sensible manner.\n\nThe PEcAn workflow system allows ecosystem modeling to be more reproducible, automated, and transparent in terms of operations applied to data, and thus ultimately more comprehensible to both peers and the public. It reduces the redundancy of effort among modeling groups, facilitate collaboration, and makes models more accessible the rest of the research community.\n\nPEcAn is not itself an ecosystem model, and it can be used to with a variety of different ecosystem models; integrating a model involves writing a wrapper to convert inputs and outputs to and from the standards used by PEcAn. Currently, PEcAn supports over a dozen ecosystem models, with more being added all the time (see the _models_ folder for the most up-to-date list)\n\n## Documentation\n\nPEcAn documentation is available for both the [latest stable development](https://pecanproject.github.io/pecan-documentation/develop/) branch and the [latest release](https://pecanproject.github.io/pecan-documentation/latest/). Documentation for [earlier releases](https://pecanproject.github.io/documentation/) is also available.\n\nPackage-level reference documentation for PEcAn modules is generated using [pkgdown](https://pkgdown.r-lib.org/) and can be found [here](https://pecanproject.github.io/package-documentation/develop/)\n\n## Getting Started\n\nSee our [\"Tutorials Page\"](https://pecanproject.github.io/tutorials/) that provides self-guided tutorials, links to vignettes, and an overview presentation.\n\n### Notebook-based tutorials\n\nThe **recommended** way to get started with PEcAn is through the notebook-based tutorials. These provide reproducible, end-to-end examples of PEcAn workflows using rendered **Quarto** notebooks.\n\nCurrent tutorials include:\n- [Running a PEcAn workflow](https://pecanproject.github.io/pecan-documentation/develop/rendered-demo-notebooks/run_pecan.html)\n- [Uncertainty analysis tutorial](https://pecanproject.github.io/pecan-documentation/develop/rendered-demo-notebooks/uncertainty.html)\n\n### Installation\n\nComplete instructions on how to install PEcAn can be found in the [documentation here](https://pecanproject.github.io/pecan-documentation/develop/pecan-manual-setup.html). To get PEcAn up and running you can use one of the following methods:\n\n1. Use [Docker](https://pecanproject.github.io/pecan-documentation/develop/install-docker.html#install-docker). This is recommended, especially for development and production deployment.\n2. Install all of the PEcAn R packages on your own Linux or MacOS computer or server. This can be done by [installing from r-universe](https://pecanproject.github.io/pecan-documentation/develop/r-universe.html):\n\n```R\n# Enable repository from pecanproject\noptions(repos = c(\n  pecanproject = 'https://pecanproject.r-universe.dev',\n  CRAN = 'https://cloud.r-project.org'))\n# Download and install PEcAn.all in R\ninstall.packages('PEcAn.all')\n```\n\nSome functionalities will be limited without also installing the Postgres database ([BETYdb](https://pecanproject.github.io/pecan-documentation/develop/osinstall.html#install-bety)), though we are making steady progress toward removing this dependency.\n\n### Website\n\nVisit our [webpage](https://pecanproject.github.io) to keep up with latest news, version, and information about the PEcAn Project\n\n#### Web Interface demo (legacy)\n\nThe PEcAn web interface is considered a legacy interface and is **no longer** the recommended entry point for new users.\n\nNotebook-based tutorials provide reproducible, up-to-date examples of PEcAn workflows and are the recommended starting point for learning and exploration.\n\n## Publications\n\n* LeBauer, D.S., D. Wang, K. Richter, C. Davidson, and M.C. Dietze (2013). Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs. [doi:10.1890/12-0137.1](https://doi.org/10.1890/12-0137.1)\n* Wang, D, D.S. LeBauer, and M.C. Dietze (2013). Predicting yields of short-rotation hybrid poplar (Populus spp.) for the contiguous US through model-data synthesis. Ecological Applications [doi:10.1890/12-0854.1](https://doi.org/10.1890/12-0854.1)\n* Dietze, M.C., D.S LeBauer, and R. Kooper (2013). On improving the communication between models and data. Plant, Cell, \u0026 Environment [doi:10.1111/pce.12043](https://doi.org/10.1111/pce.12043)\n* Dietze, Michael C., Shawn P. Serbin, Carl Davidson, Ankur R. Desai, Xiaohui Feng, Ryan Kelly, Rob Kooper et al. \"A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes.\" Journal of Geophysical Research: Biogeosciences 119, no. 3 (2014): 286-300.\n* Viskari, Toni, Brady Hardiman, Ankur R. Desai, and Michael C. Dietze. \"Model-data assimilation of multiple phenological observations to constrain and predict leaf area index.\" (2015) [doi:10.1890/14-0497.1](https://doi.org/10.1890/14-0497.1)\n* Shiklomanov. A, MC Dietze, T Viskari, PA Townsend, SP Serbin. 2016 \"Quantifying the influences of spectral resolution on uncertainty in leaf trait estimates through a Bayesian approach to RTM inversion\" Remote Sensing of the Environment 183: 226-238\n* LeBauer, David, Rob Kooper, Patrick Mulrooney, Scott Rohde, Dan Wang, Stephen P. Long, and Michael C. Dietze. \"BETYdb: a yield, trait, and ecosystem service database applied to second‐generation bioenergy feedstock production.\" GCB Bioenergy (2017).\n\nA extensive list of publications that apply PEcAn or are informed by our work on [Google Scholar](https://scholar.google.com/citations?hl=en\u0026user=HWhxBY4AAAAJ).\n\n## Acknowledgements\n\nThe PEcAn project is supported by the National Science Foundation (ABI #1062547, ABI #1458021, DIBBS #1261582, ARC #1023477, EF #1318164, EF #1241894, EF #1241891), NASA Terrestrial Ecosystems, the Energy Biosciences Institute, Department of Energy (ARPA-E awards #DE-AR0000594 and DE-AR0000598), and an Amazon AWS in Education Grant.\n\nAny opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, NASA, or other federal agencies. PEcAn is a collaboration among research groups at the Department of Earth And Environment at Boston University, the Carl Woese Institute for Genomic Biology at the University of Illinois, the Image Spatial Data Analysis group at the National Center for Supercomputing Applications, the Department of Atmospheric \u0026 Oceanic Sciences at the University Wisconsin-Madison, and the Terrestrial Ecosystem Science \u0026 Technology group at Brookhaven National Lab.\n\nBETYdb is a product of the Energy Biosciences Institute at the University of Illinois at Urbana-Champaign. We gratefully acknowledge the great effort of other researchers who generously made their own data available for further study.\n\n## License\n\nUniversity of Illinois/NCSA Open Source License\n\nCopyright (c) 2012, University of Illinois, NCSA.  All rights reserved.\n\nPEcAn project\n\u003cwww.pecanproject.org\u003e\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the  \"Software\"), to deal with the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\n* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimers.\n* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimers in the documentation and/or other materials provided with the distribution.\n* Neither the names of University of Illinois, NCSA, nor the names of its contributors may be used to endorse or promote products derived from this Software without specific prior written permission.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. 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