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Dear colleagues,
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<p>We are pleased to announce the release of Reproducible
Software Environment (Resen) version 2019.01. Resen is an open
source software platform that allows the geospace scientists
to use several Python libraries developed by the community, to
access and analyze data from various data providers including
Madrigal, and create reproducible results. For those new to
using Python, you can do a test run on the online Resen system
at <a class="moz-txt-link-freetext" href="https://ingeo.datatransport.org/home/resen/resen-online">https://ingeo.datatransport.org/home/resen/resen-online</a> by
signing up for an account. You will find tutorials available
for basic Python usage, and specifically to use geospace
software libraries. For experienced users, you can find
installation instructions on <a class="moz-txt-link-freetext" href="https://resen.readthedocs.io/">https://resen.readthedocs.io/</a> and
the download at <a class="moz-txt-link-freetext" href="https://github.com/EarthCubeInGeo/resen">https://github.com/EarthCubeInGeo/resen</a> to run
on your local machine. <br>
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<p><img alt=""
src="https://ingeo.datatransport.org/home/user/pages/05.resen/02.download-resen/resen_screenshot2.png"
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<p>Both online and offline, you are able to use JupyterLab to do
your analysis using available Python libraries, or install
your own libraries on the offline version if you need to. <br>
</p>
<p><b>Create computationally reproducible results:</b> A key
feature of Resen is the ability to containerize the code, data
and the underlying system configuration for a research result
you want to share for computational reproducibility - a figure
for your paper for example. This feature is currently
available only in the downloadable version. In Resen, you
begin your analysis in a 'bucket', which can be exported with
relevant code and data together as a .tar file, and uploaded
to a site like Zenodo for sharing with a journal or other
researchers. Resen buckets are different from simply uploading
code and data to Zenodo, as it exports the entire
containerized environment that includes software and data
versions, and which allows the end user to run the code
without concerning themselves with operating system or
dependency challenges. Currently, only Python environment is
supported in Resen, but we hope to include other open source
languages in future based on community feedback. <br>
</p>
<p><b>Available data and software:</b> You can find the list of
current geospace software packages installed via Resen on the
main InGeO website here -
<a class="moz-txt-link-freetext" href="https://ingeo.datatransport.org/home/resen/packages">https://ingeo.datatransport.org/home/resen/packages</a>. These
include tools to access geospace data from SuperDARN,
Madrigal, and MANGO. Besides these, you can use any other data
for processing with Resen by copying it into the relevant
bucket. If you are a data provider, and would like to include
a software interface for users to access data seamlessly in
Resen, please write to us. If you have developed a Python
library for geospace applications, and would like to include
in future Resen releases, please write to us. <br>
</p>
<p>Resen was developed under the NSF EarthCube and CSSI grants
to create a platform for geospace scientists to enable better
sharing of data and software. <br>
</p>
<p>Please feel free to write to us at <a
class="moz-txt-link-abbreviated" href="mailto:ingeo@sri.com"
moz-do-not-send="true">ingeo@sri.com</a> with any questions
and feedback. <br>
</p>
<p>Sincerely,</p>
<p>The InGeO team</p>
<p>Asti Bhatt, Ashton Reimer, Todd Valentic, Leslie Lamarche,
Pablo Reyes</p>
<p>SRI International<br>
</p>
<pre class="moz-signature" cols="72">--
Asti Bhatt
Center for Geospace Studies
SRI International
333 Ravenswood Ave
Menlo Park, CA 94025
Phone # +1-650-859-3424
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