Training Materials Archive
All of our training materials are archived on GitHub, which you can access from this page.
Training Series
These two-day workshops provided a hands-on introduction to open data science so you can work with data in an open, reproducible, and collaborative way. Participants learned data wrangling and visualization in R and RMarkdown while practicing reproducible workflows with RStudio and GitHub. These trainings were led through The Carpentries using training materials from the Ocean Health Index.
Access materials by workshop date:
Online tutorials and trainings in open data science, specifically for using R, RStudio, git, and GitHub and developed by our Ocean Health Index team.
2018 Trainings
This training covered techniques for building reproducible analysis workflows using the R programming language through a series of hands-on coding sessions. We will cover topics in data science, including reproducible analysis, version control, data modeling, cleaning, and integration, and data visualization both for publications and the web. The training took place at the University of Alaska, Fairbanks' International Arctic Research Center.
These trainings provided Arctic researchers with critical skills for the stewardship of data, software, and many other research products that are preserved at the Arctic Data Center. The trainings included an overview of best data management practices, data science tools, and concrete steps and methods for more easily documenting and uploading their data to the Arctic Data Center.
These trainings covered techniques for building reproducible analysis workflows using the R programming language, using Alaskan salmon brood data to demonstrate how heterogeneous data can be cleaned, integrated, and documented through RMarkdown. They also touched on other data science topics, including version control, data modeling, cleaning, integration, and data visualization.
This training provided data science training in RStudio and GitHub for coral reef researchers, focusing on data rescue, data integration, and team science, and integrating datasets in an exploratory way. Organized on behalf of the Coral Reef Science and Cyberinfrastructure Network (CRESCYNT), the workshops addressed issues consistently identified as challenges in the field of coral reef research and to broadly share lessons learned.
2017 Trainings
These trainings covered techniques for building reproducible analysis workflows using the R programming language, using Alaskan salmon brood data to demonstrate how heterogeneous data can be cleaned, integrated, and documented through RMarkdown. They also touched on other data science topics, including version control, data modeling, cleaning, integration, and data visualization.
These trainings provided Arctic researchers with critical skills for the stewardship of data, software, and many other research products that are preserved at the Arctic Data Center. The trainings included an overview of best data management practices, data science tools, and concrete steps and methods for more easily documenting and uploading their data to the Arctic Data Center.
This training helped participants build a foundation of data science skills for synthesizing data, weaving together several core concepts, including virtual collaboration; data management; data integration and exploration; scientific workflows and reproducible research; programming using agile and sustainable software practices; data analysis and modeling; and communicating results to broad communities. Programming languages learned include R and Python. The training was organized on behalf of The National Academy of Science’s Gulf Research Program.
Training materials
2016 Trainings
This training brought together Science for Nature and People Partnership (SNAPP) postdoctoral associates and technical liaisons to foster community and collaboration, as well as promote scientific computing and open science best practices. Topics included data modeling, manipulation and visualization; collaborative and open science principles and techniques; geospatial analysis; and coding best practices.
2014 Trainings
Open Science Codefest was a participant-driven event that brought together computer programmers and environmental scientists, who typically work separately, to collaborate, problem solve, code, and share skills. The unconventional, unstructured “unconference” was inspired by “hack-a-thons,” and the format of the event allowed flexibility for organic work flows and synergies between attendees, many of whom had not worked together prior to the event. A multitude of topics were covered across a wide range of interests chosen by the participants.
In partnership with University of North Carolina’s Renaissance Computing Institute (RENCI), this unique training for early career scientists taught software and technology skills needed for open, collaborative, and reproducible synthesis research. Participants received hands-on, guided experience using best practices in the technical aspects that underlie successful open science and synthesis – from data discovery and integration to analysis and visualization, and special techniques for collaborative research.
In collaboration with the National Science Foundation’s Advances in Biological Informatics (ABI) program, this workshop series aimed to improve infrastructure and capacity for scientists to create and share ecological community dynamics analyses and models through workflow systems. Participants created a toolbox that provides ecologists with tools for quantifying how communities change over time, helping to minimize data preparation and foster collaboration. Their approach built upon many informatics developments including Kepler, DataONE, and Ecological Metadata Language to advance ecological research.
2013 Trainings
This three-week intensive workshop in ecological analysis and synthesis gave participants hands-on experience using best practices in the technical aspects that underlie successful synthesis, including data discovery and integration, analysis and visualization, and special techniques for collaborative scientific research. The institute was supported generously by the Packard Foundation.