Ecosystem science has many components, so does PEcAn! Some of those components where you can contribute includes but not limited to:
For trait ‘metadata’ tables … enable users to upload R data tables and csv files to sites, citations, variables, methods, treatments, experiments, tables There are three steps: 1. adding API endpoints 2. writing R functions that use them 3. then creating a shiny application.
Expected outcome:Add ‘post’ endpoints to API for metadata tables (sites, citations, variables, methods, treatments, experiments, ...). Write functions to take an appropriately formatted data table, google sheet, or csv and insert it into BETYdb. Create a Shiny application that will walk users through this process of uploading these tables.
Prerequisites: R is required, experience or willingness to learn PostgreSQL programming and R SHINY.
Contact person: David LeBauer, @dlebauer and Kristina Riemer,@Kristina RiemerData Ingestion Shiny App
Ecosystem modeling relies heavily on fusing data from multiple sources. Whether it be data to calibrate a model or benchmark a model result, they come from different sources that are varying in their formats and naming conventions. The difference in semantics creates a bottleneck as a central ontology does not exist to translate and relate the measurements from different sites, experiments, and/or databases. To alleviate this issue, this project’s goal is to improve upon the existing SHINY data ingestion app. Two main tasks will be to refine the existing interface and then to add a Machine Learning component to eas the process of matching ormats and variables as new data is added.
Expected outcome: A SHINY app that facilitates easy data ingestion and learns to suggest existing variable mathces in the database to the data that is being uploaded.
Prerequisites:Proficiency in R. Interest in data provenance, SQL (PostgreSQL preferred), and SHINY
Contact person: Tony Gardella @tonygardExtend API
Extend the PEcAn API package to full functionality. Flush out support for file I/O and transfer components using PEcAn THREDDS and redesign functionality to not rely on database connections.
Expected outcome: Easy to use API package allowing users to
Prerequisites: Knowledge of R
Contact person: Make interest known on Slack and we will find a match you with a mentorAdmin Dashboard
PEcAn exists as a distributed set of machines, but can take great expertise to handle and setting up and maintaining. To ease this process, this project entials building upon the existing dashboard so thatits becomes easier to add a node to the network and modify mahcine settings.
Expected outcome: Easy to use web interface to allow user to change config.php and machine settings of PEcAn
Prerequisites: Experience with R and PHP
Contact person: Rob Kooper, @kooperScientific Visualization
Our mission is to create an ecosystem modeling toolbox that is accessible to a non-technical audience (e.g., a high school ecology classroom) while retaining sufficient power and versatility to be valuable to scientific programmers (e.g. ecosystem model developers). However, the diversity of ecosystem models and associated analyses supported by PEcAn poses logistical challenges for presentation of results, especially given the wide range of targeted users. Web-based interactive visualizations can be a powerful tool for exploring model outputs and data as well as a fun learning tool in educational environments.
Currently, PEcAn has basic support for interactive visualizations of outputs using R Shiny. We are looking for a student interested in addressing any of the following areas:
Expected outcome: A more robust set of web-based interactive visualization tools for model simulations and user-provided data.
Contact person: Betsy Cowdery, @bcow and Hamze Dokoohaki @HamzeContainers
There are a number of project related to containers and PEcAn:
Prerequisites: R, Docker,SHINY, experience with git is a plus.
Contact person: Rob Kooper, @kooperExtend Analysis
PEcAn offers multiple analyses on top of a simple execution of an ecosystem model. Currently, you must write a custom script or start a run again from scratch if you would like to perform one of these analyses on an existing model run. To alleviate this problem, this project will entail creating a SHINY app that will facilitate the process of taking an existing model run and initating analyses on that existing run.
Expected outcome: A SHINY app that walks a user through selecting an existing workflow allowing a user to select from a set of analyses they can apply to that workflow.
Prerequisites: Experience with R required and knowledge of SHINY is preferred
Contact person: Tony Gardella @tonygardAdd Remote Data
PEcAn offers the ability to ingest multiple streams of data automatically into models. We are currently lacking automated ingestion of Remote sensing and large spatial data. This project will develop and improve upon PEcAn's ability to ingest these types of data.
Expected outcome: A SHINY app and/or set of functions that automates the process of ingesting remote sensing data into the PEcAn workflow.
Prerequisites: Experience with R required
Contact person: Shawn Serbin and Bailey Morrison, @Bailey BNL