Maëlle Salmon

Andrew MacDonald

Kara Woo

Carl Boettiger

Jeff Hollister

How do you get the maximum value out of a dataset? Data is most valuable
when it can easily be shared, understood, and used by others. This
requires some form of metadata that describes the data. While metadata
can take many forms, the most useful metadata is that which follows a
standardized specification. The [Ecological Metadata Language
(EML)](https://knb.ecoinformatics.org/#external//emlparser/docs/index.html)
is an example of such a specification originally developed for
ecological datasets. EML describes what information should be included
to describe the data, and what format that information should be
represented in.

There are many benefits to writing metadata according to a specification
like EML:

- **Understand data**: thorough metadata allows you, your collaborators,
  and anyone else to learn more about what the data represents,
  when/where/how it was collected, and how it can be used.
- **Discover data**: data repositories like the [Knowledge Network for
  Biocomplexity](https://knb.ecoinformatics.org/#data) use EML to allow
  users to search for datasets based on things like creator, location,
  date, or species.
- **Integrate data**: with structured, machine-readable metadata
  describing exactly what the fields in a dataset represent, it is
  possible to combine multiple datasets, even if the datasets themselves
  have different structure.

Despite these benefits, it's not always fun to write standardized
metadata. Although there's a very good package for helping you create
the EML, [rOpenSci's EML package](https://github.com/ropensci/eml),
documenting the data can be quite tedious. Furthermore, before you share
the data on a public repository that enforces EML, the only prize you
get is a happy conscience, which isn't very tangible. In our
[unconf](https://unconf17.ropensci.org/) project, we created immediate
gratification for EML users: a package that transforms the non-human
readable EML file into a pretty documentation website for any dataset!

## What's EML, exactly, and why can't humans read it?

As we mentioned above,
[EML](https://en.wikipedia.org/wiki/Ecological_Metadata_Language) is a
metadata standard originally created for the ecologists. In practice
it's a set of XML schema documents, telling you what you need to
document (e.g. the dataset creator, geographic coverage of the data,
etc.) and how to build those documents (format of the XML). The [`EML` R
package](https://docs.ropensci.org/EML/) provides tools to create and
read EML without needing to learn about XML thanks to helper functions
and extensive documentation. Although its name contains the word
*ecological*, one can use EML for documenting datasets from any field.
One of our team members uses it for documenting epidemiological
datasets, because everything she wants to document is present in the
standard.

After creating EML documentation for your data, you get an XML document
that's, well... not very human readable.

![raw
eml](/assets/blog-images/2017-08-01-emldown/screenshot_raw_xml.png)

In the `EML` package there's a function called `eml_view` relying on the
[`listviewer` package](https://github.com/timelyportfolio/listviewer) to
produce an interactive view of the XML in the Viewer pane of say,
RStudio, which allows one to check some things quickly but which is far
from being a user-friendly representation of the metadata. Our goal with
`emldown` was to bridge this gap and provide a quick and easy way to
take EML and convert it to a more user-friendly web page.

## What does emldown do with your EML?

After installing the package from Github, you can apply it to your
EML...

::: {.highlight}
``` {.chroma tabindex="0"}
devtools::install_github("ropenscilabs/emldown")library("emldown")render_eml(path_to_my_eml)
```
:::

and you get something like this!

![emldown](/assets/blog-images/2017-08-01-emldown/screenshot_emldown.png)

This format is much more likely to make you and your collaborators happy
because it's more engaging and easier to explore to find useful
information. Note how little effort you needed to invest into making it!
Viewing your metadata in this way makes it easy to read, and easy to
spot any errors.

The resulting website is based on [Bootstrap](https://getbootstrap.com/)
and has some interactive components:

![demo1](/assets/blog-images/2017-08-01-emldown/emldown_demo1.gif)

Geographic information turns into a map, made using
[leaflet](https://rstudio.github.io/leaflet/):

![demo2](/assets/blog-images/2017-08-01-emldown/emldown_demo2.gif)

Right now, we are able to capture some of the most common parts of
Ecological Metadata Language, including the Title, Abstract, Authors,
Keywords, Coverage (where in space and time the samples were taken), the
Data Tables and Units associated with these. Over time we plan to add
support for additional components of the EML specification.

![demo3](/assets/blog-images/2017-08-01-emldown/emldown_demo3.gif)

You can see a live example of a website created with `emldown`
[here](https://aammd.info/emldown/test.html)

## How can you contribute?

Use the package to transform the EML you have lying around on your PC
into a pretty website, and if you find a bug while doing so we'll be
happy to tackle it! Report any issue or feature request
[here](https://github.com/ropenscilabs/emldown/issues), or feel free to
contribute with code.

We're very happy to have been able to create this working package in two
days (and thankful for that opportunity, thanks rOpenSci!); and we
not-so-secretly hope that it will contribute to making writing good
metadata more attractive and therefore more common.


How do you get the maximum value out of a dataset? Data is most valuable when it can easily be shared, understood, and used by others. This requires some form of metadata that describes the data. While metadata can take many forms, the most useful metadata is that which follows a standardized specification. The Ecological Metadata Language (EML) is an example of such a specification originally developed for ecological datasets.

Messaggi di Rogue Scholar

emldown - From machine readable EML metadata to a pretty documentation website