rOpenSci

Stefanie Butland

Karthik Ram

rOpenSci's overarching mission is to promote a culture of transparent,
open, and reproducible research across various scientific communities.
All of our activities are geared towards lowering barriers to
participation, and building a community of practitioners around the
world. In addition to developing and maintaining a large suite of open
source tools for data science, we actively support the research
community with expert review on research software development, community
calls, and hosting annual unconferences around the world.

To further promote reproducible research activities within specialized
domains, we will soon be announcing our annual fellowship program. These
fellowships will provide support for collaborative research projects
that strengthen one or more of rOpenSci's core efforts. These activities
may include development of new domain-specific tools, or supporting
projects that are exemplars of reproducible research. Our early
fellowships are by invitation and an open call will be forthcoming in
early 2017.

::: {.float-left}
<figure class="m-0">
<img src="/img/team/nick-golding.jpg"
style="  object-fit: cover; object-position: center;  height: 250px;  width: 250px; margin-right: 15px"
data-zgotmplz="" alt="Dr Nick Golding, rOpenSci Fellow" />
<p>We are pleased to award the first fellowship to <a
href="https://scholar.google.co.uk/citations?user=peoal7wAAAAJ&amp;hl=en">Dr.
Nick Golding</a> to continue developing a framework for building and
sharing reproducible species distribution models (SDM). Nick Golding is
a Research Fellow in the <a href="https://qaeco.com/">Quantitative &amp;
Applied Ecology Group</a> at the University of Melbourne, Australia. He
develops statistical models and software to predict the distributions of
species and human diseases like those caused by the Zika and Ebola
viruses. Nick is particularly interested in improving these models with
information about traits, mechanistic relationships and population
dynamics.</p>
</figure>
:::

We interviewed Nick to learn more about him, the project, and what the
fellowship will help him and his collaborators accomplish.

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### Q: What is the Zoon Project?

The Zoon Project began in the Computer Science department in Oxford as
part of the EPSRC-funded [2020 Science
project](http://www.2020science.net/), and is a collaboration between [a
group of researchers](https://zoonproject.wordpress.com/user-panel/)
with interests in SDM, scientific software and building better research
communities. The name (Zoon) refers to an individual that is part of a
compound organism, like the polyps that make up jellyfish. It's a bit of
a geeky name (you can blame Greg McInerny for that), but it fits well
with the project's aim to improve collaboration in this area of science.

The main piece of the project is an R package:
[zoon](https://github.com/zoonproject/modules); that allows you to
develop your SDM analysis as a reproducible workflow. SDM involves
taking records on where species have been observed and the environmental
characteristics of those locations, representing these mathematically,
and applying algorithms to turn these into predictions of geographical
species distribution. The field of SDM within ecology has boomed in the
last fifteen years^[1](#fn:1){.footnote-ref role="doc-noteref"}^ ([free
pdf](https://www.researchgate.net/profile/Fabiana_Barbosa4/publication/279750919_Characteristics_of_the_top-cited_papers_in_species_distribution_predictive_models/links/559963f108ae21086d25abe2.pdf))
coinciding with the increase in data and the birth of machine learning.
The challenge is that the field is now a crowded and confusing place.
There are hundreds of different candidate models, and probably thousands
of papers on SDM methods, with each new method generally evaluated
against a different dataset. That poses a serious hurdle to the
community objectively evaluating the available methods and can be
baffling to newcomers to the field.

In other applications of machine learning, there are benchmark datasets
used by everyone in the community to evaluate methods (e.g.
[MNIST](http://yann.lecun.com/exdb/mnist/) and [many
more](https://archive.ics.uci.edu/ml/datasets)). There are also software
platforms (like [caret](https://topepo.github.io/caret/)) that enable
many of the candidate methods to be run from the same software, and
compared on the same dataset. We don't currently have either of these
things for SDM, which makes it really difficult to synthesize
information from the literature. The motivation for the Zoon Project is
to address this reproducibility challenge faced by the community.

![Zoon
workflow](/assets/blog-images/2016-12-12-ropensci-fellowship-zoon/zoon_workflow.png)

The Zoon package implements SDM analyses as workflows of bite-sized,
interoperable software modules. Each module represents one of the five
key steps in an SDM analysis.

1.  Get data on where species occur
2.  Get environmental data
3.  Pre-process data
4.  Plug data into a model
5.  Generate some outputs

Multiple modules can be used at each stage, allowing them to be combined
or compared and because modules can be switched in and out it's easy to
quickly see how an analysis would change if a different method or
dataset were used.

What makes the Zoon project unique is that all of these modules are
developed by the community (not the Zoon team) and held together in an
online repository, just like community-contributed R packages (but much
easier). Basically, each software module has to conform to a set of
input and output formats to make sure the modules are interoperable, and
also requires some basic documentation. The
[zoon](https://github.com/zoonproject/modules) R package helps users to
write, document, test and share these modules so that everyone, not just
experienced R developers, can contribute modules.

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

### Q: What's an example of a research question that can be addressed using the Zoon R package?

SDM is increasingly being used in epidemiology and public health
research. For example, the recent outbreak of Ebola in West Africa
occurred in a place people weren't really expecting, so being able to
anticipate where future outbreaks might start is pretty important.
During the outbreak, I worked with some colleagues to develop an SDM to
identify areas in Africa that are environmentally similar to those where
previous human outbreaks have started^[2](#fn:2){.footnote-ref
role="doc-noteref"}^
([code](https://github.com/SEEG-Oxford/ebola_zoonotic)). This used
location data for humans and animals infected with Ebola, environmental
covariates like vegetation, elevation, temperature and
evapotranspiration, and a machine learning method known in the SDM world
as Boosted Regression Tree modelling. We didn't have Zoon back then, but
it would have made that whole workflow quicker to put together, and
really easy to quickly evaluate the effects of changing model
assumptions on areas predicted to be at risk of future outbreaks.

![Figure 5B, Pigott et al
2014](/assets/blog-images/2016-12-12-ropensci-fellowship-zoon/elife-04395-fig5b-v2_crop.jpg)**Predicted
geographical distribution of the zoonotic niche for Ebola
virus.**^[3](#fn:3){.footnote-ref role="doc-noteref"}^ *The scale
reflects the relative probability that zoonotic transmission of Ebola
virus could occur at these locations; areas closer to 1 (red) are more
likely to harbour zoonotic transmission than those closer to 0 (blue).
(Figure 5B from Pigott et al 2014. CC BY 4.0)*

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

### Q: What would you like to accomplish with your fellowship?

We've already done most of the technical work in setting up the software
and online repository for Zoon, what we really need now is to build a
community of SDM users who can apply and contribute modules to the
online [repository](https://github.com/zoonproject/modules), as well as
SDM experts to help the community start to synthesize all of the
published research. During the term of the fellowship I'll be developing
an online hub for community-contributed SDM resources (SDM guides for
beginners and best practice guidelines around different aspects of SDM)
to make it easier for new SDM users to get started, and a forum where
SDM experts can discuss what these best practice guides should look
like.

**The Zoon Hub will include:**

- A single web-based portal for new users
- A basic introduction with tutorials to enable someone new to SDM to
  fit a model using the R package and share their workflow with others.
  The objective here is to make Zoon easy to use so new SDM users get
  started using software with reproducibility baked in.
- A series of expert-written best practice guides, covering contentious
  or nuanced topics in SDM methodology, like dealing with biases in
  occurrence data. Each of these guides will be backed up by
  reproducible examples in the form of Zoon workflows. We'll be
  soliciting feedback on these guides from SDM methods experts via an
  open online forum. We'll be encouraging any disputes about best
  practice to be resolved in an evidence-based way by sharing workflows
  that demonstrate a point.

We want new SDM users to try out the tutorials and guides and tell us
how they're working. We have a group of experts who will advise us on
best practices, but we'll invite others to tell us what's wrong with our
suggestions. We'd also love to hear advice on infrastructure from people
who live and breathe reproducible software. Our ultimate aim is to build
a self sustaining community suggesting, discussing, and implementing new
elements.

The rOpenSci fellowship allows us to complete this next step of Zoon
development, moving the discussion from one of opinions on which models
are best, to one of testable reproducible evidence-based answers.

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

### Q: How did you arrive at working in an environment where you apply computational statistical skills to epidemiology?

I started with an undergraduate degree in ecology from John Moores
University in Liverpool and then worked as research assistant, briefly
doing real biology (even some pipetting in a lab). I then did my PhD in
mosquito ecology at Oxford, during which time I steadily drifted from
field-based research to statistical ecology and particularly SDM. After
three years as a Postdoc in Oxford applying these methods to mapping
diseases, I moved to the University of Melbourne in February 2016, where
I'm a Research Fellow. My fellowship in Melbourne is about developing
new statistical methods and software to improve our ability to map
species and diseases.

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

### Q: How did you hear about rOpenSci?

It's hard to say, but I almost certainly first heard about [rOpenSci on
twitter](https://twitter.com/ropensci). I've followed members of the
team on there for a while, particularly because they're part of an
active online community interested in reproducibility in research.
rOpenSci has lots of really useful tools. Packages like
[rgbif](https://github.com/ropensci/rgbif/), which evolved into
[spocc](https://github.com/ropensci/spocc) are part of the core input
that goes into SDM models in Zoon.

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

### Q: If you could tell rOpenSci what tool to develop next, what would have the most impact on your research?

Unlike species occurrence data, there's currently no single portal to
access a wide array of different gridded environmental data (the maps of
climatic and environmental conditions that SDM needs to make prediction
maps). There are a couple of different data sources out there, with R
interfaces to some of them, but it would be great to have access to all
of them via a single R API.

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

**Want to learn more about Nick Golding and Zoon?**

- Nick will share more details about the Zoon Project, and outline how
  you can get involved, in a Spring 2017 Community Call.
- [Zoon on GitHub](https://github.com/zoonproject/modules)
- Read Nick's papers on [Google
  Scholar](https://scholar.google.ca/citations?user=peoal7wAAAAJ&hl=en).
- Follow Nick on [Twitter](https://twitter.com/_nickgolding_)

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

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

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

1.  ::: {#fn:1}
    Barbosa, F. G., & Schneck F. (2015). Characteristics of the
    top-cited papers in species distribution predictive models.
    Ecological Modelling, 313: 77-83.
    <https://doi.org/10.1016/j.ecolmodel.2015.06.014> [↩︎](#fnref:1){.footnote-backref
    role="doc-backlink"}
    :::

2.  ::: {#fn:2}
    Pigott DM, Golding N, Mylne A, Huang Z, Henry AJ, Weiss DJ, Brady
    OJ, Kraemer MU, Smith DL, Moyes CL, Bhatt S, Gething PW, Horby PW,
    Bogoch II, Brownstein JS, Mekaru SR, Tatem AJ, Khan K, Hay SI.
    (2014). Mapping the zoonotic niche of Ebola virus disease in Africa.
    Elife, 8(3), e04395.
    <https://doi.org/10.7554/eLife.04395> [↩︎](#fnref:2){.footnote-backref
    role="doc-backlink"}
    :::

3.  ::: {#fn:3}
    Figure 5. Pigott DM, Golding N, Mylne A, Huang Z, Henry AJ, Weiss
    DJ, Brady OJ, Kraemer MU, Smith DL, Moyes CL, Bhatt S, Gething PW,
    Horby PW, Bogoch II, Brownstein JS, Mekaru SR, Tatem AJ, Khan K, Hay
    SI. (2014). Mapping the zoonotic niche of Ebola virus disease in
    Africa. Elife, 8(3), e04395.
    <https://doi.org/10.7554/eLife.04395.009> [↩︎](#fnref:3){.footnote-backref
    role="doc-backlink"}
    :::
:::


rOpenSci’s overarching mission is to promote a culture of transparent, open, and reproducible research across various scientific communities. All of our activities are geared towards lowering barriers to participation, and building a community of practitioners around the world.

Postagens de Rogue Scholar

Announcing our first fellowship awarded to Dr. Nick Golding