rOpenSci

Stefanie Butland

Mark Padgham

Karthik Ram

Noam Ross

We're thrilled to be introducing a new member of our team. Mark Padgham
has joined rOpenSci as a Software Research Scientist working full-time
from Münster, Germany. Mark will play a key role in research and
development of statistical software standards and expanding our efforts
in software peer review, enabled by [new
funding](/blog/2019/07/15/expanding-software-review/) from the Sloan
Foundation. He will work closely with [Noam Ross](/authors/noam-ross/),
rOpenSci Leadership team member, and Scientist at EcoHealth Alliance and
[Karthik Ram](/authors/karthik-ram/), rOpenSci Project Lead.

Mark has made many contributions to rOpenSci prior to joining the team.
He has developed four peer-reviewed packages:
[osmdata](https://docs.ropensci.org/osmdata/) to import Open Street Map
data^[1](#fn:1){.footnote-ref role="doc-noteref"}^;
[osmplotr](https://docs.ropensci.org/osmplotr/) to make bespoke plots
with that data; [bikedata](https://docs.ropensci.org/bikedata/) to
access historical trip data from public bicycle-hire systems
^[2](#fn:2){.footnote-ref role="doc-noteref"}^, and most recently,
[mapscanner](https://github.com/ropensci/software-review/issues/330) to
print maps, draw on them, and scan them back in. Mark also contributed
to the development of
[stats19](https://docs.ropensci.org/stats19/)^[3](#fn:3){.footnote-ref
role="doc-noteref"}^. He has reviewed three packages:
[piggyback](https://github.com/ropensci/software-review/issues/220),
[tradestatistics](https://github.com/ropensci/software-review/issues/274),
and
[bomrang](https://github.com/ropensci/software-review/issues/121)^[4](#fn:4){.footnote-ref
role="doc-noteref"}^, authored [two blog posts](/authors/mark-padgham/),
and participated in rOpenSci [unconf18](/blog/2018/06/05/unconf18/).

*Note from Stefanie:* When I met Mark in Seattle at rOpenSci's unconf18,
I was struck by his personal commitment to "walking the talk" of getting
around by bicycle. I knew him only from his blog post on [getting data
from public bicycle rental systems](/blog/2017/10/17/bikedata/), and
there he was with colleagues, walking down the street with a
brightly-colored rental bike. He said he made a point of using such
clearly marked rental bikes when visiting other cities since they're
great conversation-starters about sustainable cities.

<figure>
<img src="/img/team/mark-padgham.jpg" width="250" height="250"
alt="Headshot of Mark Padgham" />
</figure>

> I've got a lot of concrete visions for extending testing beyond
> current computational or quantitative approaches. Convincing leaders
> in statistics and statistical software to update and innovate on
> current practices is certainly going to be challenging and very
> rewarding.

We talked with Mark to learn more about him, and his work.

## What do you want people to know about your background?

I am originally from Australia, have lived and worked in a few countries
along the way (including Japan and Austria), and now live in Germany. I
have an undergraduate degree in physics and maths, a PhD in ecology,
qualifications in climatology and meteorology, as well as official
teaching qualifications. All of these were obtained from universities in
Melbourne, Australia - each one from a different university. Following
my PhD, I worked in post-doctoral academic positions at the interface of
ecology and climatology, before changing country and continent to
Germany, and changing academic direction to the study of urban systems.

I have always said I'm dedicated to changing the world one walking trip
or one bike trip at a time to change the way people live life in cities,
but now I have a much more "meta" personal motivation. I develop
software in the hope that it will make the world a better place. It's
focussed on a particular kind of change. My software is primarily
motivated by a desire to make the understanding of cities easier and
more accessible - from accessing the raw data of street networks
(osmdata), to fine-scaled data on mobility (bikedata), to understanding
and mapping how, where, and when people move through cities (through the
[dodgr](https://github.com/ATFutures/dodgr) package).

## What is your favorite thing about R?

I like to use R (rather than another mainstream language) because R is
the best language to develop master control systems from your raw data
input through to documentation outputs, as websites, published reports,
dashboards and every step in between. They can all be controlled from
the one language. There's no other language that reaches that level of
master control system. It is an accolade of what RStudio has done to
transform the utility of the language.

When I sit down with R, the first question I ask myself is, how do I get
into C++ from here?

## What's an R thing you'd like to learn?

Yeah...lots of things \[*long pause while Mark tries to choose one
thing*\]. To really understand how the C-level language of R interfaces
with objects compiled from other languages and environments - that would
be really cool. And at the level of C code, how lazy-evaluation works.
Everyone's gotten so used to that but you don't even have to think about
it. That's a piece of amazing magic under the hood that I don't
understand.

## You've made so many concrete contributions to rOpenSci, as well as less-visible community contributions. What made you keep coming back to give more?

It comes back to my belief in software as a tool to make the world a
better place. Part of that is an active effort on the part of myself and
the others with whom I work and develop packages to develop a
comprehensive ecosystem. I make an active personal effort to develop a
comprehensive ecosystem of packages that presents an open source
alternative to closed source commercial environments. And that open
source alternative will hopefully help to openly transform the world.

It's in my own interest to continue to contribute to the community
because it's only in that way that what I have contributed continues to
maintain its value. Without ongoing contributions to rOpenSci that whole
thing stops at the point at which I left it and all I've got is to sit
back and hope something happens. That's worth nothing in comparison to
me continuing to actively strive to foster, cultivate, grow, expand and
enrich this ecosystem of tools in our little niche.

## What will you be working on and what do you think are some of the key challenges?

I'll be working on developing robust tools to assess packages. As an
inveterate coder, I love nothing more than to get up in the morning and
find myself still sitting there at 4:00 in the afternoon in my
"coding-hole". But a big part of the initial stuff, which I'm really
excited about, is about connecting various people who will ultimately be
involved in guiding the expansion of rOpenSci into the statistical
methods side of things. So it's much more people-centered than the
typical life of a 100% coder. Like so many rOpenSci projects, this aims
to be very community-driven.

I'll really enjoy being between two worlds trying to think in general
terms about developing this whole new endeavour \[statistical software
peer review\] and ensuring the development is as general and
generalizable as possible. While on the other side, having to be
involved with profoundly important brilliant academic experts in various
aspects of statistics & statistical software so it will be both very
niche and very general at the same time. One of the key challenges will
be working with experts in the statistical and software communities and
having to foster consensus of opinion on their part in realms that have
basically never been tried before. And on the other side, I've got a lot
of concrete visions for extending testing beyond current computational
or quantitative approaches. Convincing leaders in statistics and
statistical software to update and innovate on current practices is
certainly going to be challenging and very rewarding.

## What would you like to see in the statistical software standards or package review landscape in two years?

Actual testing of the statistical routines themselves. To date, all
systems for evaluating software rely on signature keys of what reliable
software might look like externally. It's like it's packaged up in a box
and you can only examine all the things on the outside of the box
because getting inside the box is almost impossible. But the R package
system is also quite unique in that way, that it allows you to
standardize things enough that you know what to look for and then pry
into that.

I'm excited about the opportunity to develop new packages in a new field
for me. All of my packages to date have been intended to meet very
concrete, applied needs, and to enable users to do things in R that were
not otherwise possible. I'm already cultivating a different mindset in
this position, towards developing packages that enable users to do
things that might already be possible, yet to do them better -- along
the lines of how Jim Hester's glue package (largely) replaces current
functionality with better alternatives. I'm excited to be thinking about
the language itself more deeply and the structure of the way R works.

Also, the generalizability of everything. At the end I imagine there is
this document that says "This is how you construct a new community of
software peer review", regardless of the academic field. That's
incredibly exciting.

Find Mark on [GitHub](https://github.com/mpadge),
[Twitter](https://twitter.com/bikesRdata),
[Website](https://mpadge.github.io/), [rOpenSci](/authors/mark-padgham/)

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

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

1.  ::: {#fn:1}
    osmdata Padgham M, Rudis B, Lovelace R, Salmon M (2017). "osmdata."
    The Journal of Open Source Software, 2(14),
    <https://doi.org/10.21105/joss.00305>. [↩︎](#fnref:1){.footnote-backref
    role="doc-backlink"}
    :::

2.  ::: {#fn:2}
    bikedata Padgham M, Ellison R (2017). "bikedata." The Journal of
    Open Source Software, 2(20),
    <https://doi.org/10.21105/joss.00471>. [↩︎](#fnref:2){.footnote-backref
    role="doc-backlink"}
    :::

3.  ::: {#fn:3}
    stats19 Lovelace R, Morgan M, Hama L, Padgham M, Ranzolin D, Sparks
    A (2019). "stats 19: A package for working with open road crash
    data." The Journal of Open Source Software, 4(33), 1181,
    <https://doi.org/10.21105/joss.01181>. [↩︎](#fnref:3){.footnote-backref
    role="doc-backlink"}
    :::

4.  ::: {#fn:4}
    bomrang Sparks AH, Padgham M, Parsonage H, Pembleton K (2017).
    "bomrang: Fetch Australian Government Bureau of Meteorology Weather
    Data." The Journal of Open Source Software, 2(17),
    <https://doi.org/10.21105/joss.00411>. [↩︎](#fnref:4){.footnote-backref
    role="doc-backlink"}
    :::
:::


We’re thrilled to be introducing a new member of our team. Mark Padgham has joined rOpenSci as a Software Research Scientist working full-time from Münster, Germany. Mark will play a key role in research and development of statistical software standards and expanding our efforts in software peer review, enabled by new funding from the Sloan Foundation.

Introducing Mark Padgham, rOpenSci’s new Software Research Scientist

We're delighted to announce that we have received new funding from the
[Alfred P. Sloan Foundation](https://sloan.org/). The \$678K grant,
awarded through the Foundation's Data & Computational Research program,
will be used to expand our efforts in software peer review.

Software peer review has become a core part of rOpenSci, helping improve
scientific software quality, drive best engineering practices into
scientific communities, and building community and collaboration through
open, constructive reviews. We're excited to expand our work in this
important area. Here's what we'll be doing in the next two years with
this support:

**New tools to manage the software peer-review editorial process:**
We've built our software peer review system on top of GitHub's issue
system, but as we grow, we need additional automation to make the
process simpler and more automated for authors, reviewers, and editors.
We will also be building more tools to test and measure code quality,
flag areas for reviewers and authors to focus on.

**Better training, documentation, and tools for adoption**: We want
other groups to be able to use peer-review, too, so we will be rolling
out more documentation and training on how to set up peer-review
communities, including setting up their own chatbot-based systems and
automated reports of software quality customized to their own standards.

**A new focus on statistical software:** rOpenSci's software ecosystem
has always focused on reproducibility and managing scientists' data
lifecycle. Now we aim to expand that work into the critical area of
statistical software. Over the next year, we'll be setting up a new
review board with expertise in statistical and computational issues in
implementing packages that fit models and estimate statistical
quantities. We'll be working with the new board and the broader
statistical software community to develop a set of agreed-upon standards
for statistical package implementation and testing, then launching a new
peer-review process and testing tools. This is an ambitious new
challenge for us, but we're excited to expand and engage our amazing
community of contributors to meet it. Look out for more announcements in
the coming months, including ways you can participate!

Speaking of which, we are hiring a new team member for this project! Our
new **"Software Research Scientist"** position will play a key role in
research and development of statistical software standards. Interested?
[See the job posting](/careers/)

Noam Ross of EcoHealth Alliance will be joining rOpenSci's leadership
team to co-lead these projects.

This grant continues our relationship between rOpenSci and the Sloan
Foundation. Our project has received five previous awards from the
foundation, including the initial grant that led to the formation of
rOpenSci as an organization. We are grateful to Sloan for their ongoing
support, especially to the Program Director Josh Greenberg for
championing our work, the other officers and reviewers who provided
feedback on our proposal. We'd also like to thank those who provided
advice and support, including Ben Bolker, Max Kuhn, Andrew Gelman, Jonah
S Gabry, and Leah Wasser. Mostly we'd like to thank our amazing
community of contributors and volunteers, without whom none of this
would be possible.


We’re delighted to announce that we have received new funding from the Alfred P. Sloan Foundation. The $678K grant, awarded through the Foundation’s Data &amp; Computational Research program, will be used to expand our efforts in software peer review.

Rogue Scholar Posts

Introducing Mark Padgham, rOpenSci’s new Software Research Scientist

rOpenSci Announces $678K Award from the Sloan Foundation to Expand Software Peer Review