Content from Introduction to DNA Data

Last updated on 2025-04-01 | Edit this page

Overview

Questions

  • Where do DNA data come from?
  • What do the look like?

Objectives

Biodiversity observation ain’t easy

“Observing biodiversity ain’t easy—too many species out there! By the time you figure out what you’re lookin’ at, it’s already somethin’ else!”

- Yogi Berra never said this

Why?

  • Time and Labor intensive
  • Expensive
  • Requires taxonomic expertise
  • Dominated by conspicuous or commercial species
  • Conditions dependent

There are many ways to observe biodiversity.

Strengths of DNA as biodiversity evidence

DNA helps, but it’s not a silver bullet

DNA at GBIF: how it is accessed and used

Spectrum of Use Cases

Spectrum of Approaches

Introduction to NCBI and MixS standard

It’s ok to not have it all figured out yet

Content from Example Datasets

Last updated on 2025-04-01 | Edit this page

Overview

Questions

  • How do you write a lesson using R Markdown and sandpaper?

Objectives

  • Explain how to use markdown with the new lesson template
  • Demonstrate how to include pieces of code, figures, and nested challenge blocks

Introduction

This is a lesson created via The Carpentries Workbench. It is written in Pandoc-flavored Markdown for static files (with extension .md) and R Markdown for dynamic files that can render code into output (with extension .Rmd). Please refer to the Introduction to The Carpentries Workbench for full documentation.

What you need to know is that there are three sections required for a valid Carpentries lesson template:

  1. questions are displayed at the beginning of the episode to prime the learner for the content.
  2. objectives are the learning objectives for an episode displayed with the questions.
  3. keypoints are displayed at the end of the episode to reinforce the objectives.

Challenge 1: Can you do it?

What is the output of this command?

R 

paste("This", "new", "lesson", "looks", "good")

OUTPUT 

[1] "This new lesson looks good"

Challenge 2: how do you nest solutions within challenge blocks?

You can add a line with at least three colons and a solution tag.

Figures

You can include figures generated from R Markdown:

R 

pie(
  c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), 
  init.angle = 315, 
  col = c("deepskyblue", "yellow", "yellow3"), 
  border = FALSE
)
pie chart illusion of a pyramid
Sun arise each and every morning

Or you can use pandoc markdown for static figures with the following syntax:

![optional caption that appears below the figure](figure url){alt='alt text for accessibility purposes'}

Blue Carpentries hex person logo with no text.
You belong in The Carpentries!

Math

One of our episodes contains \(\LaTeX\) equations when describing how to create dynamic reports with {knitr}, so we now use mathjax to describe this:

$\alpha = \dfrac{1}{(1 - \beta)^2}$ becomes: \(\alpha = \dfrac{1}{(1 - \beta)^2}\)

Cool, right?

Key Points

  • Use .md files for episodes when you want static content
  • Use .Rmd files for episodes when you need to generate output
  • Run sandpaper::check_lesson() to identify any issues with your lesson
  • Run sandpaper::build_lesson() to preview your lesson locally

Content from The MDT

Last updated on 2025-04-01 | Edit this page

Overview

Questions

  • How do you write a lesson using R Markdown and sandpaper?

Objectives

  • Explain how to use markdown with the new lesson template
  • Demonstrate how to include pieces of code, figures, and nested challenge blocks

Introduction

This is a lesson created via The Carpentries Workbench. It is written in Pandoc-flavored Markdown for static files (with extension .md) and R Markdown for dynamic files that can render code into output (with extension .Rmd). Please refer to the Introduction to The Carpentries Workbench for full documentation.

What you need to know is that there are three sections required for a valid Carpentries lesson template:

  1. questions are displayed at the beginning of the episode to prime the learner for the content.
  2. objectives are the learning objectives for an episode displayed with the questions.
  3. keypoints are displayed at the end of the episode to reinforce the objectives.

Challenge 1: Can you do it?

What is the output of this command?

R 

paste("This", "new", "lesson", "looks", "good")

OUTPUT 

[1] "This new lesson looks good"

Challenge 2: how do you nest solutions within challenge blocks?

You can add a line with at least three colons and a solution tag.

Figures

You can include figures generated from R Markdown:

R 

pie(
  c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), 
  init.angle = 315, 
  col = c("deepskyblue", "yellow", "yellow3"), 
  border = FALSE
)
pie chart illusion of a pyramid
Sun arise each and every morning

Or you can use pandoc markdown for static figures with the following syntax:

![optional caption that appears below the figure](figure url){alt='alt text for accessibility purposes'}

Blue Carpentries hex person logo with no text.
You belong in The Carpentries!

Math

One of our episodes contains \(\LaTeX\) equations when describing how to create dynamic reports with {knitr}, so we now use mathjax to describe this:

$\alpha = \dfrac{1}{(1 - \beta)^2}$ becomes: \(\alpha = \dfrac{1}{(1 - \beta)^2}\)

Cool, right?

Key Points

  • Use .md files for episodes when you want static content
  • Use .Rmd files for episodes when you need to generate output
  • Run sandpaper::check_lesson() to identify any issues with your lesson
  • Run sandpaper::build_lesson() to preview your lesson locally

Content from Suggested Reading and References

Last updated on 2025-04-01 | Edit this page

Overview

Questions

  • How can I learn more?

Objectives

  • Become familiar with useful references.

Background on the Science

  1. The ecologist’s field guide to sequence‐based identification of biodiversity (Creer et al, 2016)
  2. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA (Ruppert et al., 2019)
  3. Critical considerations for communicating environmental DNA science (Stein et al., 2024)
  4. How, What, and Where You Sample Environmental DNA Affects Diversity Estimates and Species Detection (Kirtane et al., 2024)
  5. A Guide to Environmental DNA Extractions for Non‐Molecular Trained Biologists, Ecologists, and Conservation Scientists (Rieder et al., 2024)

Data Management and Standards

  1. Publishing DNA‐derived data through biodiversity data platforms. (Finstad et al., 2023)
  2. A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics (Eloe‐Fadrosh et al., 2024)
  3. The MIEM guidelines: Minimum information for reporting of environmental metabarcoding data. (Klymus et al. 2024)
  4. Best practices for genetic and genomic data archiving (Leigh et al., 2024)
  5. Centering accessibility, increasing capacity, and fostering innovation in the development of international eDNA standards (Hirsch et al., 2024)

Policy

  1. National Aquatic Environmental DNA Strategy (eDNA Task Team, 2024)

Standards Reference Material

  1. Publishing DNA‐derived data through biodiversity data platforms. (Finstad wt al., 2023)

Darwin Core

  1. Quick Reference Guide (term search): https://dwc.tdwg.org/terms/
  2. DNA Derived Data Extension: https://rs.gbif.org/extension/gbif/1.0/dna_derived_data_2022‐02‐23.xml

MIxS

  1. Intro: https://www.gensc.org/pages/standards‐intro.html
  2. Term search: https://genomicsstandardsconsortium.github.io/mixs/term_list/

Data Management Tools

  1. GBIF Metabarcoding Data Toolkit (MDT): https://mdt.gbif.org/
  2. MDT User Guide: https://docs.gbif‐uat.org/mdt‐user‐guide/en/
  3. GBIF‐US MDT: https://mdt.gbif.us/ ; manager: Stephen Formel (sformel@usgs.gov)
  4. NOAA Omics Data Management Guide: https://noaa‐omics‐dmg.readthedocs.io/en/latest/

Community Pages

  1. GBIF: https://www.gbif.org/dna
  2. OBIS: https://obis.org/2024/10/22/obis‐edna/

References

  • Creer, S., Deiner, K., Frey, S., Porazinska, D., Taberlet, P., Thomas, W.K., Potter, C., Bik, H.M., 2016. The ecologist’s field guide to sequence‐based identification of biodiversity. Methods in Ecology and Evolution 7, 1008–1018. https://doi.org/10.1111/2041‐210X.12574

  • eDNA Task Team of the Interagency Working Group on Biodiversity of the Subcommittee on Ocean Science and Technology Committee on Environment of the National Science & Technology Council. 2024. National Aquatic Environmental DNA Strategy. https://www.whitehouse.gov/wpcontent/uploads/2024/06/NSTC_National‐Aquatic‐eDNA‐Strategy.pdf

  • Eloe‐Fadrosh, E.A., Mungall, C.J., Miller, M.A., Smith, M., Patil, S.S., Kelliher, J.M., Johnson, L.Y.D., Rodriguez, F.E., Chain, P.S.G., Hu, B., Thornton, M.B., McCue, L.A., McHardy, A.C., Harris, N.L., Reddy, T.B.K., Mukherjee, S., Hunter, C.I., Walls, R., Schriml, L.M., 2024. A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics, in: Setubal, J.C., Stadler, P.F., Stoye, J. (Eds.), Comparative Genomics: Methods and Protocols. Springer US, New York, NY, pp. 587–609. https://doi.org/10.1007/978‐1‐0716‐3838‐5_20

  • Finstad, A.G., Andersson, A., Bissett, A., Fossøy, F., Grosjean, M., Hope, M., Kõljalg, U., Lundin, D., Nilsson, H., Prager, M., Jeppesen, T.S., Svenningsen, C., Schigel, D., Abarenkov, K., Provoost, P., Suominen, S., Frøslev, T.G., 2023. Publishing DNA‐derived data through biodiversity data platforms. https://doi.org/10.35035/DOC‐VF1A‐NR22

  • Hirsch, S., Acharya‐Patel, N., Amamoo, P.A., Borrero‐Pérez, G.H., Cahyani, N.K.D., Ginigini, J.G.M., Hurley, K.K.C., Lopes‐Lima, M., Lopez, M.L., Mapholi, N., Ouattara, K.N., Pazmiño, D.A., Rii, Y., Thompson, F., Heyden, S. von der, Watsa, M., Yepes‐Narvaez, V., Allan, E.A., Kelly, R., 2024. Centering accessibility, increasing capacity, and fostering innovation in the development of international eDNA standards. Metabarcoding and Metagenomics 8, e126058. https://doi.org/10.3897/mbmg.8.126058

  • Leigh, D.M., Vandergast, A.G., Hunter, M.E., Crandall, E.D., Funk, W.C., Garroway, C.J., Hoban, S., OylerMcCance, S.J., Rellstab, C., Segelbacher, G., Schmidt, C., Vázquez‐Domínguez, E., Paz‐Vinas, I., 2024. Best practices for genetic and genomic data archiving. Nat Ecol Evol 8, 1224–1232. https://doi.org/10.1038/s41559‐024‐02423‐7

  • Kirtane, A., Howard, L., Beaver, C.E., Hunter, M.E., Luikart, G., Deiner, K., 2024. How, What, and Where You Sample Environmental DNA Affects Diversity Estimates and Species Detection. Environmental DNA 6, e70042. https://doi.org/10.1002/edn3.70042

  • Klymus, K.E., Baker, J.D., Abbott, C.L., Brown, R.J., Craine, J.M., Gold, Z., Hunter, M.E., Johnson, M.D., Jones, D.N., Jungbluth, M.J., Jungbluth, S.P., Lor, Y., Maloy, A.P., Merkes, C.M., Noble, R., Patin, N.V., Sepulveda, A.J., Spear, S.F., Steele, J.A., Takahashi, M., Watts, A.W., Theroux, S., 2024. The MIEM guidelines: Minimum information for reporting of environmental metabarcoding data. Metabarcoding and Metagenomics 8, e128689. https://doi.org/10.3897/mbmg.8.128689

  • Rieder, J., Jemmi, E., Hunter, M.E., Adrian‐Kalchhauser, I., 2024. A Guide to Environmental DNA Extractions for Non‐Molecular Trained Biologists, Ecologists, and Conservation Scientists. Environmental DNA 6, e70002. https://doi.org/10.1002/edn3.70002

  • Ruppert, K.M., Kline, R.J., Rahman, M.S., 2019. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Global Ecology and Conservation 17, e00547. https://doi.org/10.1016/j.gecco.2019.e00547

  • Stein, E.D., Jerde, C.L., Allan, E.A., Sepulveda, A.J., Abbott, C.L., Baerwald, M.R., Darling, J., Goodwin, K.D., Meyer, R.S., Timmers, M.A., Thielen, P.M., 2024. Critical considerations for communicating environmental DNA science. Environmental DNA 6, e472. https://doi.org/10.1002/edn3.472

Content from Introduction to DwC DNA extension

Last updated on 2025-04-01 | Edit this page

Overview

Questions

  • How do you write a lesson using R Markdown and sandpaper?

Objectives

  • Explain how to use markdown with the new lesson template
  • Demonstrate how to include pieces of code, figures, and nested challenge blocks

Content from Conceptual modeling of datasets

Last updated on 2025-04-01 | Edit this page

Overview

Questions

  • How do you write a lesson using R Markdown and sandpaper?

Objectives

  • Explain how to use markdown with the new lesson template
  • Demonstrate how to include pieces of code, figures, and nested challenge blocks

Introduction

This is a lesson created via The Carpentries Workbench. It is written in Pandoc-flavored Markdown for static files (with extension .md) and R Markdown for dynamic files that can render code into output (with extension .Rmd). Please refer to the Introduction to The Carpentries Workbench for full documentation.

What you need to know is that there are three sections required for a valid Carpentries lesson template:

  1. questions are displayed at the beginning of the episode to prime the learner for the content.
  2. objectives are the learning objectives for an episode displayed with the questions.
  3. keypoints are displayed at the end of the episode to reinforce the objectives.

Challenge 1: Can you do it?

What is the output of this command?

R 

paste("This", "new", "lesson", "looks", "good")

OUTPUT 

[1] "This new lesson looks good"

Challenge 2: how do you nest solutions within challenge blocks?

You can add a line with at least three colons and a solution tag.

Figures

You can include figures generated from R Markdown:

R 

pie(
  c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), 
  init.angle = 315, 
  col = c("deepskyblue", "yellow", "yellow3"), 
  border = FALSE
)
pie chart illusion of a pyramid
Sun arise each and every morning

Or you can use pandoc markdown for static figures with the following syntax:

![optional caption that appears below the figure](figure url){alt='alt text for accessibility purposes'}

Blue Carpentries hex person logo with no text.
You belong in The Carpentries!

Math

One of our episodes contains \(\LaTeX\) equations when describing how to create dynamic reports with {knitr}, so we now use mathjax to describe this:

$\alpha = \dfrac{1}{(1 - \beta)^2}$ becomes: \(\alpha = \dfrac{1}{(1 - \beta)^2}\)

Cool, right?

Key Points

  • Use .md files for episodes when you want static content
  • Use .Rmd files for episodes when you need to generate output
  • Run sandpaper::check_lesson() to identify any issues with your lesson
  • Run sandpaper::build_lesson() to preview your lesson locally