Ben Raymond

Michael Sumner

### Antarctic/Southern Ocean science and rOpenSci

Collaboration and reproducibility are [fundamental to Antarctic and
Southern Ocean science](https://doi.org/10.1038/d41586-018-05369-6), and
the value of data to Antarctic science has long been promoted. The
[Antarctic Treaty](https://www.ats.aq/e/ats.htm) (which came into force
in 1961) included the provision that scientific observations and results
from Antarctica should be openly shared. The high cost and difficulty of
acquisition means that data tend to be re-used for different studies
once collected. Further, there are many common data requirement themes
(e.g. sea ice information is useful to a wide range of activities, from
voyage planning through to ecosystem modelling). Support for Antarctic
data management is well established. The SCAR-COMNAP Joint Committee on
Antarctic Data Management was established in 1997 and remains active [as
a SCAR Standing Commitee](https://www.scar.org/data-products/scadm/)
today.

Software development to support Antarctic data usage is growing, but
still lags behind the available data, and some common tasks are still
more difficult than we would like. Starting in late 2017, the
[Scientific Committee on Antarctic Research](https://www.scar.org/) has
been collaborating with rOpenSci to strengthen the Antarctic and
Southern Ocean R/science communities. Our focus is on data and tasks
that are common or even unique to Antarctic and Southern Ocean science,
including supporting the development of R packages to meet Antarctic
science needs, guides for R users and developers, active fora for open
discussions, and strengthening connections with the broader science
world.

### First steps in building the community

Our initial efforts have focused on two things:

1.  compiling a list of existing resources likely to be useful to the
    community: see the [task
    view](https://github.com/SCAR/ropensci/tree/master/task_view). This
    document outlines some relevant packages, including some that are
    under active development.
2.  improving core functionality centred around three key tasks that
    many researchers find problematic:
    - getting hold of data. The physical environment is important to a
      large cross-section of Antarctic science, and environmental data
      often come from satellite, model, or similar sources,
    - processing those data to suit particular study interests, such as
      subsetting or merging with field or other data,
    - producing maps for exploratory analyses or publications.

A [couple](https://github.com/ropensci/antanym) of
[packages](https://github.com/ropensci/bowerbird) have been formally
peer-reviewed and onboarded into rOpenSci, with others in development.
Example usage of a few of these packages is demonstrated below.

### Get involved

Please get involved!

- contribute your Antarctic R knowledge, your Antarctic [use
  case](https://discuss.ropensci.org/c/usecases) for a package, or ask a
  question in the dedicated [Antarctic and Southern
  Ocean](https://discuss.ropensci.org/c/antarctic) category of
  rOpenSci's discussion forum,

- make [a suggestion](https://discuss.ropensci.org/c/antarctic) ---
  perhaps for Antarctic-related functionality that you feel is missing
  from the current R ecosystem?

- contribute an Antarctic R package, or improve the documentation or
  code of an existing one. See the [task
  view](https://github.com/SCAR/ropensci/tree/master/task_view) as a
  starting point,

- join the \#antarctic rOpenSci Slack channel for R users and developers
  --- contact us at <antarctic@ropensci.org> for an invitation to join.
  Slack is a great space in which to have conversations with the
  rOpenSci community, or to give us feedback in a less-public manner,

- participate in the [broader rOpenSci community](/community/). Follow
  on [Twitter](https://twitter.com/rOpenSci), read the [blog](/blog/),
  and check out the [ecosystem of R packages](/packages/).

The administrative contacts for this initiative are Ben Raymond, Sara
Labrousse, Michael Sumner, and Jess Melbourne-Thomas. Contact us via
<antarctic@ropensci.org>, or find us on Slack or Twitter.

------------------------------------------------------------------------

### Demo

A lightning trip through the land of `bowerbird`, `raadtools`, `SOmap`,
and `antanym`. First install some packages, if needed.

::: {.highlight}
``` {.chroma tabindex="0"}
## make sure we have the packages we needreq <- setdiff(c("dplyr", "ggplot2", "remotes"), installed.packages()[, 1])if (length(req) > 0) install.packages(req)## and some github packagesreq <- c("ropensci/antanym", "AustralianAntarcticDivision/blueant",         "AustralianAntarcticDivision/raadtools",         "AustralianAntarcticDivision/SOmap")req <- req[!basename(req) %in% installed.packages()[, 1]]if (length(req) > 0) remotes::install_github(req)
```
:::

Let's say that we have some points of interest in the Southern Ocean ---
perhaps a ship track, or some stations where we took marine samples, or
as we'll use here, the [track of an elephant seal](http://www.meop.net/)
as it moves from the Kerguelen Islands to Antarctica and back again
(Data from IMOS 2018^[1](#fn:1){.footnote-ref role="doc-noteref"}^,
provided as part of the `SOmap` package).

::: {.highlight}
``` {.chroma tabindex="0"}
library(dplyr)library(ggplot2)data("SOmap_data", package = "SOmap")ele <- SOmap_data$mirounga_leonina %>% dplyr::filter(id == "ct96-05-13")with(ele, plot(lon, lat))
```
:::

<figure>
<img src="/img/blog-images/2018-11-13-antarctic/get_track_data-1.png" />
</figure>

#### Fetching our environmental data

Very commonly, we want to know about the environmental conditions at our
points of interest. For the remote and vast Southern Ocean these data
typically come from satellite or model sources. Some data centres
provide extraction tools that will pull out a subset of data to suit
your requirements, but often it makes more sense to cache entire data
collections locally first and then work with them from there.

[bowerbird](https://github.com/AustralianAntarcticDivision/bowerbird)
provides a framework for downloading data files to a local collection,
and keeping it up to date. The companion
[blueant](https://github.com/AustralianAntarcticDivision/blueant)
package provides a suite of definitions for Southern Ocean and Antarctic
data sources that can be used with `bowerbird`. It encompasses data such
as sea ice, bathymetry and land topography, oceanography, and
atmospheric reanalysis and weather predictions, from providers such as
NASA, NOAA, Copernicus, NSIDC, and Ifremer.

Why might you want to maintain local copies of entire data sets, instead
of just fetching subsets of data from providers as needed?

- many analyses make use of data from a variety of providers (in which
  case there may not be dynamic extraction tools for all of them),
- analyses might need to crunch through a whole collection of data in
  order to calculate appropriate statistics (temperature anomalies with
  respect to a long-term mean, for example),
- different parts of the same data set are used in different analyses,
  in which case making one copy of the whole thing may be easier to
  manage than having different subsets for different projects,
- a common suite of data are routinely used by a local research
  community, in which case it makes more sense to keep a local copy for
  everyone to use, rather than multiple copies being downloaded by
  different individuals.

In these cases, maintaining local copies of a range of data from
third-party providers can be extremely beneficial, especially if that
collection is hosted with a fast connection to local compute resources
(virtual machines or high-performance computational facilities).

Install the package if needed:

::: {.highlight}
``` {.chroma tabindex="0"}
library(remotes)install_github("AustralianAntarcticDivision/blueant")
```
:::

To use `blueant`, we first choose a location to store our data. Normally
this would be a persistent location (perhaps on shared storage if
multiple users are to have access to it), but for the purposes of
demonstration we'll just use a temporary directory here:

::: {.highlight}
``` {.chroma tabindex="0"}
my_data_dir <- tempdir()
```
:::

We'll focus on two sources of environmental data: sea ice and water
depth. Note that water depth does not change with time but sea ice is
highly dynamic, so we will want to know what the ice conditions are like
on a day-to-day basis.

Download daily sea ice data (from 2013 only), and the ETOPO2 bathymetric
data set. ETOPO2 is somewhat dated and low resolution compared to more
recent data, but will do as a small dataset for demo purposes:

::: {.highlight}
``` {.chroma tabindex="0"}
library(blueant)src <- bind_rows(    sources("NSIDC SMMR-SSM/I Nasateam sea ice concentration", hemisphere = "south",            time_resolutions = "day", years = 2013),    sources("ETOPO2 bathymetry"))result <- bb_get(src, local_file_root = my_data_dir, clobber = 0, verbose = TRUE,                 confirm = NULL)
```
:::

    ##  ## Sat Nov 10 01:00:48 2018 ## Synchronizing dataset: NSIDC SMMR-SSM/I Nasateam sea ice concentration ##  ##  [... output truncated]

Now we have local copies of those data files. The sync can be run daily
so that the local collection is always up to date - it will only
download new files, or files that have changed since the last download.
For more information on `bowerbird`, see the [package
vignette](https://ropensci.github.io/bowerbird/articles/bowerbird.html).

Details of the files can be found in the `result` object, and those
files could now be read with packages such as `raster`. However, we are
collecting data from a range of sources, and so they will be different
in terms of their grids, resolutions, projections, and variable-naming
conventions, which tends to complicate these operations. In the next
section we'll look at the `raadtools` package, which provides a set of
tools for doing common operations on these types of data.

#### Using those environmental data: raadtools

[`raadtools`](https://github.com/AustralianAntarcticDivision/raadtools)
is suite of functions that provide consistent access to a range of
environmental and similar data, and tools for working with them. It is
designed to work data collections maintained by the
`bowerbird`/`blueant` packages.

Load the package and tell it where our data collection has been stored:

::: {.highlight}
``` {.chroma tabindex="0"}
library(raadtools)set_data_roots(my_data_dir)
```
:::

Define our spatial region of interest and extract the bathymetry data
from this region, using the ETOPO2 files we just downloaded:

::: {.highlight}
``` {.chroma tabindex="0"}
roi <- round(c(range(ele$lon), range(ele$lat)) + c(-2, 2, -2, 2))bx <- readtopo("etopo2", xylim = roi)
```
:::

And now we can make a simple plot of our our track superimposed on the
bathymetry:

::: {.highlight}
``` {.chroma tabindex="0"}
plot(bx)lines(ele$lon, ele$lat)
```
:::

<figure>
<img src="/img/blog-images/2018-11-13-antarctic/plotbathy-1.png" />
</figure>

We can also extract the depth values along our track using the
`extract()` function in `raadtools`:

::: {.highlight}
``` {.chroma tabindex="0"}
ele$depth <- extract(readtopo, ele[, c("lon", "lat")], topo = "etopo2")
```
:::

Plot the histogram of depth values, showing that most of the track
points are located in relatively shallow waters:

::: {.highlight}
``` {.chroma tabindex="0"}
ggplot(ele, aes(depth)) + geom_histogram(bins = 100) + theme_bw()
```
:::

<figure>
<img src="/img/blog-images/2018-11-13-antarctic/histbathy-1.png" />
</figure>

This type of extraction will also work with time-varying data --- for
example, we can extract the sea-ice conditions along our track, based on
each track point's location and time:

::: {.highlight}
``` {.chroma tabindex="0"}
ele$ice <- extract(readice, ele[, c("lon", "lat", "date")])## points outside the ice grid will have missing ice values, so fill them with zerosele$ice[is.na(ele$ice)] <- 0ggplot(ele, aes(date, ice, colour = lat)) + geom_path() + theme_bw()
```
:::

<figure>
<img src="/img/blog-images/2018-11-13-antarctic/temp2-1.png" />
</figure>

Or a fancy animated plot, using `gganimate` (code not shown for brevity,
but you can find it in the [page
source](https://raw.githubusercontent.com/ropensci/roweb2/master/content/blog/2018-11-13-antarctic.md)).
The hypnotic blue line shows the edge of the sea ice as it grows over
the winter season, and the orange is our elephant seal:

<figure>
<img src="/img/blog-images/2018-11-13-antarctic/plotanim-1.gif" />
</figure>

#### Mapping

Creating maps is another very common requirement, and in the Southern
Ocean this brings a few challenges (e.g. dealing with the dateline when
using polar-stereographic or similar circumpolar projections). There are
also spatial features that many users want to show (coastlines, oceanic
fronts, extent of sea ice, place names, etc). The in-development
[`SOmap`](https://github.com/AustralianAntarcticDivision/SOmap) package
aims to help with this.

::: {.highlight}
``` {.chroma tabindex="0"}
library(SOmap)SOauto_map(ele$lon, ele$lat, mask = FALSE)
```
:::

<figure>
<img src="/img/blog-images/2018-11-13-antarctic/somap1-1.png" />
</figure>

Or a full-hemisphere map:

::: {.highlight}
``` {.chroma tabindex="0"}
## first transform our track to polar-stereographic coordinateslibrary(sp)library(raster)ele_sp <- elecoordinates(ele_sp) <- c("lon", "lat")projection(ele_sp) <- "+proj=longlat +ellps=WGS84"ele_sp <- spTransform(ele_sp,            CRS("+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0             +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"))## plot the base map with ocean fronts shownSOmap(fronts = TRUE)## add current marine protected areasSOmanagement(MPA = TRUE, mpacol = "darkblue")## add our trackplot(ele_sp, col = "darkgreen", add = TRUE)
```
:::

<figure>
<img src="/img/blog-images/2018-11-13-antarctic/somap2-1.png" />
</figure>

#### Place names

SCAR maintains a gazetteer of place names in the Antarctic and
surrounding Southern Ocean^[2](#fn:2){.footnote-ref
role="doc-noteref"}^, which is available to R users via the [`antanym`
package](https://docs.ropensci.org/antanym/):

::: {.highlight}
``` {.chroma tabindex="0"}
library(antanym)
```
:::

    ## The Composite Gazetteer of Antarctica is made available under a CC-BY license.## If you use it, please cite it:## Composite Gazetteer of Antarctica, Scientific Committee on Antarctic Research.##   GCMD Metadata (https://data.aad.gov.au/aadc/gaz/scar/)

::: {.highlight}
``` {.chroma tabindex="0"}
xn <- an_read(cache = "session", sp = TRUE)
```
:::

There is no single naming authority for place names in Antarctica, and
so there can be multiple names for a single feature (where it has been
given a name by more than one country). We can trim our names list down
to one name per feature (here, preferentially choosing the UK name if
there is one):

::: {.highlight}
``` {.chroma tabindex="0"}
xn <- an_preferred(xn, origin = "United Kingdom")
```
:::

How many place names do we have?

::: {.highlight}
``` {.chroma tabindex="0"}
nrow(xn)
```
:::

    ## [1] 19742

OK, we can't show all of these --- which ones would be best to show on
this map? Let's ask antanym for suggestions:

::: {.highlight}
``` {.chroma tabindex="0"}
xns <- an_suggest(xn, map_scale = 20e6, map_extent = c(-180, 180, -90, -40))## transform to our map projection and take the first 10 namesxns <- as_tibble(spTransform(xns, projection(Bathy))) %>% head(10)## add them to the mapSOmap()plot(ele_sp, col = "darkgreen", add = TRUE)with(xns, points(x = longitude, y= latitude, pch = 16))with(xns, text(x = longitude, y= latitude, labels = place_name,               pos = 2 + 2*(longitude > 0)))
```
:::

<figure>
<img src="/img/blog-images/2018-11-13-antarctic/antanym2-1.png" />
</figure>

### Next steps

There is plenty more that these and other R packages can do for
Antarctic and Southern Ocean science. See the package READMEs and
vignettes for more examples, keep an eye out for future developments ...
and get involved!

### References

::: {.footnotes role="doc-endnotes"}

------------------------------------------------------------------------

1.  ::: {#fn:1}
    IMOS (2018) AATAMS Facility - Satellite Relay Tagging Program -
    Delayed mode data,
    <https://catalogue-imos.aodn.org.au/geonetwork/srv/en/metadata.show?uuid=06b09398-d3d0-47dc-a54a-a745319fbece> [↩︎](#fnref:1){.footnote-backref
    role="doc-backlink"}
    :::

2.  ::: {#fn:2}
    Composite Gazetteer of Antarctica, Scientific Committee on Antarctic
    Research. GCMD Metadata
    (<https://data.aad.gov.au/aadc/gaz/scar/>) [↩︎](#fnref:2){.footnote-backref
    role="doc-backlink"}
    :::
:::


Antarctic/Southern Ocean science and rOpenSci  Collaboration and reproducibility are fundamental to Antarctic and Southern Ocean science, and the value of data to Antarctic science has long been promoted. The Antarctic Treaty (which came into force in 1961) included the provision that scientific observations and results from Antarctica should be openly shared.

The Antarctic/Southern Ocean rOpenSci community

Imperial College London

Henry Rzepa

One of the delights of wandering around an undergraduate chemistry
laboratory is discussing the unexpected, if not the outright impossible,
with students. The \>100% yield in a reaction is an example. This is
sometimes encountered (albeit only briefly) when students attempt to
recrystallise a product from cyclohexane, and get an abundant crop of
crystals when they put their solution into an ice-bath to induce the
crystallisation. Of the solvent of course! I should imagine 1000% yields
are possible like this.

What the students are not expecting is that cyclohexane has such a high
melting point, higher than that of water! n-Octane for example melts at
-57°C (and most of us have seen those travelogues in the antarctic where
the petrol tanks need to be warned to prevent freezing), so why is that
of cyclohexane so much higher? That it might be strange is shown by the
melting points of the series:

1.  benzene, +5.5°C
2.  cyclohexadiene, -89°C
3.  cyclohexene, -97°C
4.  cyclohexane, +6.5°C.

Benzene one might explain because it famously stacks in a herring-bone
fashion, with the relatively electropositive hydrogen attracted to the
π-cloud on the face.

::: {#attachment_3279 .wp-caption .aligncenter style="width: 182px"}
![](http://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2010/12/benzene.jpg "benzene"){.size-full
.wp-image-3279 loading="lazy" decoding="async"
aria-describedby="caption-attachment-3279"
onclick="jmolInitialize('../Jmol/',true);jmolSetAppletColor('yellow');jmolApplet([600,600],'load wp-content/uploads/2010/12/BENZEN13.mol;zoom 90;spin 2;');"
width="172" height="173"
srcset="https://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2010/12/benzene.jpg 344w, https://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2010/12/benzene-150x150.jpg 150w, https://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2010/12/benzene-298x300.jpg 298w"
sizes="(max-width: 172px) 100vw, 172px"}

The crystal structure of benzene. Click for 3D
:::

Clearly, this explanation cannot hold for cyclohexane, which has no
π-face. What does the crystal look like?

::: {#attachment_3284 .wp-caption .aligncenter style="width: 202px"}
![](http://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2010/12/cyclohexane.jpg "cyclohexane"){.size-full
.wp-image-3284 loading="lazy" decoding="async"
aria-describedby="caption-attachment-3284"
onclick="jmolInitialize('../Jmol/',true);jmolSetAppletColor('yellow');jmolApplet([600,600],'load wp-content/uploads/2010/12/CYCHEX.mol;zoom 90;spin 2;');"
width="192" height="168"
srcset="https://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2010/12/cyclohexane.jpg 384w, https://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2010/12/cyclohexane-300x262.jpg 300w"
sizes="(max-width: 192px) 100vw, 192px"}

Crystal structure of cyclohexane. Click for 3D
:::

If one inspects the structure closely, one can find quite a few H...H
contacts at about 2.4Å and they are arranged in a particularly rigid
three-dimensional manner. The maximum attractive force resulting from
van der Waals, or dispersion interactions between two hydrogens is
thought to occur at \~2.4Å. Perhaps cyclohexane is a prime (possibly THE
prime) example of the influence of this (under-rated) interaction? A
molecule covered in Velcro no less. By the way, can you spot the
connection with the [previous
post](http://www.ch.ic.ac.uk/rzepa/blog/?p=3235){target="_blank"}?

------------------------------------------------------------------------

Postscript: Below is a so-called non-covalent-analysis (NCI) of
cyclohexane as packed into a crystal lattice. The coordinates are
obtained from a neutron diffraction structure. The green regions
indicate weakly attractive zones.

::: {#attachment_9180 .wp-caption .aligncenter style="width: 389px"}
![Click for
3D.](http://www.ch.imperial.ac.uk/rzepa/blog/wp-content/uploads/2010/12/cx.jpg){.wp-image-9180
loading="lazy" decoding="async"
aria-describedby="caption-attachment-9180"
onclick="jmolInitialize('../Jmol/');jmolSetAppletColor('black');jmolApplet([450,450],'load wp-content/uploads/2013/01/CYCHEX_den.cub.xyz;isosurface wp-content/uploads/2013/01/CYCHEX_den.cub.jvxl;zoom 100;');"
width="379" height="254"}

Click for 3D.
:::

::: {.wp_orcid_field}
[https://orcid.org/0000-0002-8635-8390](https://orcid.org/0000-0002-8635-8390){target="_blank"
rel="author"}
:::


One of the delights of wandering around an undergraduate chemistry laboratory is discussing the unexpected, if not the outright impossible, with students. The &gt;100% yield in a reaction is an example. This is sometimes encountered (albeit only briefly) when students attempt to recrystallise a product from cyclohexane, and get an abundant crop of crystals when they put their solution into an ice-bath to induce the crystallisation.

Messages de Rogue Scholar

The Antarctic/Southern Ocean rOpenSci community

The melting points from benzene to cyclohexane: a prime example of dispersion forces in action?