Scott Edmunds

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alt="Open Access Week 2021" />
<figcaption>Banner from Open Access Week 2021, source;
www.openaccessweek.org</figcaption>
</figure>

This week is [International Open Access
Week](http://www.openaccessweek.org/), and with the
[theme](http://www.openaccessweek.org/profiles/blogs/opening-knowledge-and-building-structural-equity-this-week-beyond)
"It Matters How We Open Knowledge: Building Structural Equity". This
aims at highlighting the individual and collective action required
alongside the decisions, actions, and investments in knowledge sharing
to ensure that equity is foundational. Beyond just open licensing of
content (of which is foundational in what we do) there are many other
barriers that we've been trying hard to break to enable more equitable
and open science for all. Since we've launched we worked hard to open
science in many ways, from a technological perspective through many of
the novel editorial practices and integrations we've implemented, to
helping educate and amplify the voices of people working to open
science. Be that through our efforts in [community
genomics](http://gigasciencejournal.com/blog/community-genomes-gigatv/)
and citizen science projects (see [Bauhinia
Genome](http://bauhiniagenome.hk/)) or publishing reviews, commentaries
and guest blogs from open science practitioners.

This Open Access Week seems a good time to look back over these efforts,
and here is a list of 10 of our favourite *GigaScience* papers providing
examples of barriers we've tried to help break for more open science.  

**1. The inclusivity barrier**  
One rare positive of the COVID-19 pandemic has been the potential of
increasing inclusivity of attendance at academic conferences. Yet, the
mere existence of online conferences is no guarantee that everyone can
attend and participate meaningfully. We recently published a
collaboratively written Review from [Open Science Special Interest Group
of the
OHBM](https://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=3712)
identifying practices that purposefully encourage a diverse community to
attend, participate in, and lead online conferences.

Levitis et al. Centering inclusivity in the design of online
conferences---An OHBM--Open Science perspective, *GigaScience*, 2021,
**10**:8
doi:[10.1093/gigascience/giab051](https://doi.org/10.1093/gigascience/giab051)

**2. The lab barrier**  
You no longer need to be based in a wealthy research laboratory, or have
any laboratory at all, to do cutting edge genomics research. Back in
2018 we published our first example of a study taking DNA sequencing and
genomics to a completely new place: *in situ* genome sequencing in the
jungle. See the [DNA Day
blog](http://gigasciencejournal.com/blog/dnaday2018/) covering this
\#jungleomics.

Pomerantz A et al. Real-time DNA barcoding in a rainforest using
nanopore sequencing: opportunities for rapid biodiversity assessments
and local capacity building. *Gigascience*. 2018, **7**:4.
doi:[10.1093/gigascience/giy033](http://dx.doi.org/10.1093/gigascience/giy033).

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**3. The participation barrier**  
In the data driven era, not only in research but in our day-to-day
lives, people are creating more and more personal digitized data that
enables human-participant research in social sciences and personalized
medicine. In 2019 [we
published](https://doi.org/10.1093/gigascience/giz076) an open call from
Open Humans for contributors to their community-based platform for
participant led research from the social sciences to precision medicine
(see the [Q&A for
more](http://gigasciencejournal.com/blog/open-humans-qa/)).

Greshake Tzovaras B, et al. Open Humans: A platform for
participant-centered research and personal data exploration.
*GigaScience*. 2019.
doi:[10.1093/gigascience/giz076](https://doi.org/10.1093/gigascience/giz076)

**4. The transparency barrier**  
One of the most distinctive open science policies carried out by
*GigaScience* since our launch is our open, named peer review and we
have an [editorial](https://doi.org/10.1186/2047-217X-2-1) discussing
the benefits of this policy.

Edmunds SC, Peering into peer-review at *GigaScience*, *GigaScience*,
2:1, 2013,
doi:[10.1186/2047-217X-2-1](https://doi.org/10.1186/2047-217X-2-1)

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<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/alessandro-fazari-Ngo5FrmF97o-unsplash2.jpg"
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srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/alessandro-fazari-Ngo5FrmF97o-unsplash2.jpg 640w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/alessandro-fazari-Ngo5FrmF97o-unsplash2-300x194.jpg 300w"
sizes="(max-width: 640px) 100vw, 640px" width="640" height="413" />
</figure>

**5. The "Data not Available" barrier**  
Publishing sensitive medical data is no longer a barrier to transparent
open peer review, our first example of precious Controlled Access
medical data peer reviewed by named reviewers proving an example of how
'data not available' is no longer justifiable in many of these cases, at
least from the perspective of journals that like ourselves have broken
the "transparency barrier" (see above).

Gabriel AAG et al. A molecular map of lung neuroendocrine neoplasms.
*Gigascience*. 2020 9:11.
doi:[10.1093/gigascience/giaa112](http://dx.doi.org/10.1093/gigascience/giaa112).

**6. The reproducibility barrier**  
*GigaScience* has always been trying to push the boundaries of how we
disseminate reproducible research, and to adapt to the challenges of
dealing with experiments become more data-intensive. [In 2017 we
published our first
example](http://gigasciencejournal.com/blog/data-intensive-software-publishing-sailing-the-code-ocean-qa-with-ruibang-luo/)
showcasing the Code Ocean reproducible research platform that wraps and
encapsulates the data, code, and computation environment in a "Compute
Capsule" that can be interacted with through their platform for testing
and then running via your cloud computing provider if it is of interest.

Luo R, Schatz MC, Salzberg SL. 16GT: a fast and sensitive variant caller
using a 16-genotype probabilistic model. *GigaScience* 2017.
doi:[10.1093/gigascience/gix045](http://dx.doi.org/10.1093/gigascience/gix045)

**7. The collaboration barrier**  
Since our launch, on top of software papers we have seen more and more
complex tools utilizing machine learning approaches. To help with
reproducibility with machine learning-based tools, we recently published
a paper [integrating
Gigantum](http://gigasciencejournal.com/blog/gigantum-joins-giga-reproducibility-machine-learning-toolkit/),
an open source web application that aims for better collaboration,
sharing and making reproducible data science research even easier.
Working these tools into the review and publication process potentially
makes it easier to assess replicability and utility. With a slightly
different and more distributed approach to Code Ocean, via a DOI users
can run the published tool anywhere (laptop, GPU, on premises
infrastructures, and Public Cloud) -- eliminating problems that can
arise when collaborating via multiple infrastructures and contexts are
involved.

Vieira DV, et al, Driftage: a multi-agent system framework for concept
drift detection, *GigaScience*, **10**:6, 2021,
doi:[10.1093/gigascience/giab030](https://doi.org/10.1093/gigascience/giab030).

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<figcaption><em>GigaScience</em>, like our sister GigaByte journal has
experimented with a number of platforms for publishing software in a
more reproducible manner.</figcaption>
</figure>

**8. The "certified reproducibility" barrier**  
While we've worked on methods to make the increasingly complicated
computational research we publish easier to interact with and use, we've
also look at ways of improving their peer-review. Last year we published
the [first published
example](https://doi.org/10.1093/gigascience/giaa026) demonstrating a
new way of peer reviewing software articles: presenting a
[CODECHECK](https://codecheck.org.uk/) certificate. These independently
time-stamped runs are awarded a "certificate of reproducible
computation" and increase availability, discovery and reproducibility of
crucial artifacts for computational sciences.

Piccolo SR. et al., ShinyLearner: A containerized benchmarking tool for
machine-learning classification of tabular data, *GigaScience*,
**9**:40, 2020,
doi:[10.1093/gigascience/giaa026](https://doi.org/10.1093/gigascience/giaa026)

<figure class="wp-block-image size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/CodeCheck2020-1024x726.png"
class="wp-image-4204" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/CodeCheck2020-1024x726.png 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/CodeCheck2020-300x213.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/CodeCheck2020-768x545.png 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/CodeCheck2020.png 1368w"
sizes="(max-width: 512px) 100vw, 512px" width="512" height="363" />
</figure>

**9. The methodological comprehension barrier**  
Not just focusing on computation methods easier to scruitinise and
reuse, in 2016 we published [our first
examples](http://gigasciencejournal.com/blog/reproducible-research-resources-researching-parasites/)
where complicated "wet lab" methods for extracting DNA from tiny
parasites where broken down step-by-step and hosted in open access
repository of scientific methods and collaborative protocol-centered
platform protocols.io. Since then, with our team working with authors to
input their methods we have gathered a large collection of protocols in
our [protocols.io
workspace](https://www.protocols.io/workspaces/gigascience-press).

Mofiz E. et al., Genomic resources and draft reference assemblies of the
human and porcine scabies mites, *Sarcoptes scabiei* var. hominis and
var. suis. *GigaScience*. **5**:23. 2016.
doi:[10.1186/s13742-016-0129-](http://dx.doi.org/10.1186/s13742-016-0129-2)

**10. The third dimension barrier**  
To ease access to our published three-dimensional datasets and models we
have gone beyond just describing the data collection and findings by
providing downloadable, interactive files of everything in these study.
For interested citizen scientists out there, we've been doing things
like providing new interactive web-based viewers, video clips and 3D
printable file formats. In 2019 [we published our first
paper](http://gigasciencejournal.com/blog/sketchfab-3d-fossil-seabed/)
showcasing *GigaScience*'s complete integration with the Sketchfab 3D
viewer, which enabling readers to interact with the 3D fossils in the
paper and even view them via virtual reality headsets. Making our
published research far more accessible and interesting to the general
public.

Reid M, et al. A micro X-ray computed tomography dataset of fossil
echinoderms in an ancient obrution bed: a robust method for taphonomic
and palaeoecologic analyses. *Gigascience*. 2019 **8**:3.
doi:[10.1093/gigascience/giy156](http://dx.doi.org/10.1093/gigascience/giy156)

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The post [Open Access Week 2021: GigaScience's 10 Examples of
Open](http://gigasciencejournal.com/blog/gigascience-open-access-week-2021/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


This week is International Open Access Week, and with the theme “It Matters How We Open Knowledge: Building Structural Equity”. This aims at highlighting the individual and collective action required alongside the decisions, actions, and investments in knowledge sharing to ensure that equity is foundational.

Open Access Week 2021: GigaScience’s 10 Examples of Open

GigaScience Press

Nicole Nogoy

*GigaScience* has always had a focus on reproducibility rather than
subjective impact, and it can be challenging for our reviewers to judge
this, especially now that more and more tools are being created --
bringing data science to the masses.  This also means more efficiency
and ease is required especially when multiple collaborators and
contributors on a specific project are involved. As a journal with an
ethos on reproducibility, we also think about our reviewers -- are they
able to test and run the tools easily as part of peer review? 

Moving from experimenting with our [GigaGalaxy.net
server](http://gigasciencejournal.com/blog/rewarding-reproducibility-first-papers-in-our-galaxy-series-utilizing-our-gigagalaxy-platform/),
[Virtual
Machines](http://gigasciencejournal.com/blog/publishing-our-first-virtual-box-of-delights-to-aid-the-fight-against-heart-disease/),
and
[Docker](http://gigasciencejournal.com/blog/fermenting-reproducible-research-revolution/)
containers, *GigaScience* has been using [Code
Ocean](https://codeocean.com/) to bring much of this experience together
in [a platform that lets you scrutinise code, test it and then run it on
your own cloud computing
account](http://gigasciencejournal.com/blog/data-intensive-software-publishing-sailing-the-code-ocean-qa-with-ruibang-luo/),
if it is of interest. In addition, new tools have been created to make
the research article itself executable and reproducible -- [GigaScience
Press is now collaborating with Stencila in a new
ERA](http://gigasciencejournal.com/blog/gigabyte-executable-research-articles/)
(Executable Research Article). 

Working these tools into the review and publication process potentially
makes it easier to assess replicability and utility. However, we have
experienced some hurdles in carrying out reproducibility case studies
(see this example [published in PLOS
One](https://doi.org/10.1371/journal.pone.0127612)) and getting our
in-house data scientists involved in the review process-- one paper
costing us over \$1500 USD in AWS credits to recreate the workflows.
Given that collaboration and reproducibility often requires working
across different infrastructures and contexts; for example, laptop, GPU,
on premises infrastructures and public cloud, problems can arise as most
commonly used cloud-based Software as a Service (SaaS) infrastructures
make it difficult for collaboration, and can involve a huge monthly cost
on cloud credits -- as we have experienced.  

Joining our Giga reproducibility toolkit is
[Gigantum,](https://gigantum.com/) an open source ([MIT
licensed](https://github.com/gigantum/gigantum-client)) web application
that aims for better collaboration, sharing and making reproducible
research easier. However, what makes it different is that the Gigantum
workbench can be run anywhere (laptop, GPU, on premises infrastructures,
and Public Cloud) -- eliminating problems that can arise when
collaborating via multiple infrastructures and contexts are involved. In
addition, Gigantum is  not purely SaaS dependent and thus is potentially
cheaper (with not having to rack up cloud credit costs) -- the platform
allows users to cut the corn on cloud-only platforms. Tyler Whitehouse,
CEO of Gigantum, along with Dean Kleissas and Dav Clark (previously Head
of Data Science at Gigantum, and now Director of [Glass Bead
Labs](http://bead.glass/)), shared their views in an earlier blog about
"Making Reproducibility Reproducible" in a [Guest Post in
GigaBlog](http://gigasciencejournal.com/blog/gigablog-meets-gigantum-guest-post-from-tyler-whitehouse-dean-kleissas-and-dav-clark/).
They highlighted the need for better reproducible research, focused on
user experience, and ease of use. 

<figure class="wp-block-image size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Gigantum-summary-1024x612.jpg"
class="wp-image-3997" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Gigantum-summary-1024x612.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Gigantum-summary-300x179.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Gigantum-summary-768x459.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Gigantum-summary.jpg 1244w"
sizes="(max-width: 522px) 100vw, 522px" width="522" height="311" />
<figcaption>How Gigantum makes collaboration easier and more cost
effective</figcaption>
</figure>

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<figcaption>Learn more from this video from Gigantum</figcaption>
</figure>

*GigaScience* has been seeing a lot more complex tools utilizing machine
learning, and to help with reproducibility with machine learning-based
tools, Gigantum is a great platform to share the fruits of these, with
[a collection of examples](https://gigantum.com/explore) of many common
AI and machine learning tasks that users can copy and customize. As we
are always on the lookout for new platforms to test and integrate into
our open science workflow, it was a no brainer to try and integrate
Gigantum with our open peer review.   
The latest paper, [published in
*GigaScience*](https://doi.org/10.1093/gigascience/giab030) -- Driftage,
is our first paper having utilised the Gigantum platform as part of our
open peer review, and [reviewed by Dav
Clark](https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/gigascience/10/6/10.1093_gigascience_giab030/1/giab030_reviewer_2_report_original_submission.pdf?Expires=1625612531&Signature=vcgnMEwZ9mGAG3JNYCvuWGSWZryjFXKHZEsbeViT6PX77oZ6PHDkgzl4P0XTsTjVtqCP11zLPcNPW1qFseNn~QyOikzlT-pmBx40pA20zRTqS~NZBpXQBLEjdgbyqNbuTfEECVx5SC5fuBEVVtRsVVDmZRbKD4U-DHr9aMTuR-X5mTX~SY1epSC78LlvuhqLxrdcCsKYDCv~D89zdoTW5D-XsNfMawcndhaMwlfdt-Xa~OZnOV1JwnUUezVzk2YoiOfJi~3kQByLOH43PdAA6duUt05TTfTQQmldXfMsigmTTW7dYsvoi8UzInwXeNECnx-ePt8sJ~G4w3AqLewgsw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA).
Since Driftage is a more complex machine learning framework and with the
added complexity of being multi-agent based, this meant it was more
difficult for the authors to ensure reproducibility, since each agent is
independent on results; and thus, dynamic due to when each agent
communicates with others and a particular environment at a particular
point in time. Given this complexity, Driftage seemed to be the right
paper to ensure its reproducibility using Gigantum.  In addition, Figure
5 of the manuscript was also reviewed for reproducibility, via
[CODECHECK](https://codecheck.org.uk/), and the paper is our second
example, and overall 24th paper given a [certificate of independent
execution](https://doi.org/10.5281/zenodo.4310025) (see the new paper in
[F1000Research](https://f1000research.com/articles/10-253/v1)). You can
also read more about CODECHECK and our first example, [in our blog on
Certified
Reproducibility](http://gigasciencejournal.com/blog/codecheck-certificate/).
  As huge fans of citable DOIs, another added bonus is that the
[Driftage project in
Gigantum](https://gigantum.com/dmvieira/driftage-example-project/files)
[has a citable DOI](https://doi.org/10.34747/mp7n-3487) that can be
cited in future papers.

<figure class="wp-block-image size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Driftage-manuscript.jpg"
class="wp-image-3995" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Driftage-manuscript.jpg 1008w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Driftage-manuscript-300x185.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/06/Driftage-manuscript-768x473.jpg 768w"
sizes="(max-width: 514px) 100vw, 514px" width="514" height="316"
alt="Driftage machine learning framework" />
</figure>

Here, we have a Q&A with the Driftage lead author, [Diogo Munaro
Vieira](mailto:dvieira@inf.puc-rio.br) who shares his user experience on
Gigantum and the benefits of having a reproducible environment for
machine learning tools. 

**Q: Despite the editorial's suggestion to try it- what made you agree
to try the platform?**

The Gigantum platform helps a lot with the reproducibility of my figures
and tables. In the case of Driftage it was very easy because I just
needed to install the Driftage library in the Gigantum Jupyter
environment and reproduce the results. Now it's simple for anyone who
wants to get the same result without installing the whole environment.

**Q: What did they like about Gigantum? Did you find it easy to use?**

It's easy to use because it has a very intuitive user interface and
integration with git. So, my collaborators that are not comfortable with
the git interface can make changes and see results using the Gigantum
user interface. The best feature I saw was that when you run the Jupyter
notebook and build new results, it recognizes that a new figure or table
was created and automatically adds it to your results. It's kind of
magic!

**Q: Can you tell us more about the Driftage tool and what benefits do
you see having a** **reproducible environment in Gigantum?**

Driftage is a modular multi-agent framework to detect concept drifts
from batch or streaming data. It was made with Python and it is
available as a library that users can install and start building their
concept drift detectors. As a framework it's important to be reliable
and retrieve reproducible results. Driftage was very hard to enhance a
reproducible environment because of its multi-agent nature where each
agent is independent to decide your own results; and results can change
depending when each agent communicates with others and how the whole
environment is at that moment. Another point is that electromyography
(EMG) data should be analyzed as streaming data and it is a very good
data type for drift detection algorithm, since most of the time, what
changes is the variance of the data and not the spikes. So, the data
drift is very hard to detect just by looking. In the Driftage example we
chose the ADWIN algorithm that captures data variance changes very well.
Gigantum forces me to isolate each part of the architecture to guarantee
reproducibility on them removing the environment and communications
noises and making EMG streaming data static for reproducible analysis.

Using Gigantum for future work on Driftage should be simple because a
person just needs to install the library and implement their algorithm
for drift detection with their own data. Because of multi-agent nature
they need to evaluate each part of their own  implementation separately
and validate using Gigantum with a static sample of data or
generate data using a data generator.

**Q: How do you see Gigantum ensuring reproducibility of your work?**

Gigantum is very efficient at reproducing the work because it makes sure
that all the environment and dependencies will be the same. Of course,
as users, we need to put the correct version of extra dependencies in
our work avoiding some dependency updates and crash results, but the
base project is always the same environment. The CODECHECK process was
so good because a reviewer needed to understand and run my code ([see
the resulting certificate](https://doi.org/10.5281/zenodo.4310025)). It
was an enriching process because I needed to make some changes in my
current work, so that it is easy to run and reproduce.

**Q: Why do you think Gigantum was useful for this type of machine
learning work?**

More and more machine learning work needs explainability and
interpretability of its results because of privacy rules. The first step
for explainability and interpretability is reproducibility, where in
which Gigantum helps a lot. Another important collaboration of Gigantum
for machine learning work is the comparison with other works. When
someone makes a better algorithm to solve a problem, it needs to prove
that the machine learning algorithm is really better and Gigantum
isolates the environment for a fair comparison that will be available
for everyone  who wants to check. Driftage was an example algorithm with
ADWIN that we need to prove it's impacted by variance to make sure the
framework was not changing algorithm behavior.

**Further reading and playing: **

1.  Diogo Munaro Vieira, Chrystinne Fernandes, Carlos Lucena, Sérgio
    Lifschitz, Driftage: a multi-agent system framework for concept
    drift detection, *GigaScience*, Volume 10, Issue 6, June 2021,
    giab030, <https://doi.org/10.1093/gigascience/giab030>. 
2.  Diogo Munaro Viera, Chrystinne Fernandes, Carlos Lucena, Sergio
    Lifschitz (2021): Driftage Example: A Multi-agent Drift Detection
    Framework, Gigantum, Inc <https://doi.org/10.34747/mp7n-3487> 

The post [Gigantum Joins the Giga Reproducibility
Toolkit](http://gigasciencejournal.com/blog/gigantum-joins-giga-reproducibility-machine-learning-toolkit/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


<em>
 GigaScience
</em>
has always had a focus on reproducibility rather than subjective impact, and it can be challenging for our reviewers to judge this, especially now that more and more tools are being created – bringing data science to the masses.  This also means more efficiency and ease is required especially when multiple collaborators and contributors on a specific project are involved.

Gigantum Joins the Giga Reproducibility Toolkit

![ShinyLearner](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/giaa026fig7-300x206.jpeg){.alignright
.size-medium .wp-image-3275 loading="lazy" decoding="async" width="300"
height="206"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/giaa026fig7-300x206.jpeg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/giaa026fig7.jpeg 766w"
sizes="(max-width: 300px) 100vw, 300px"}This week [we
showcased](http://gigasciencejournal.com/blog/codecheck-certificate/) a
new way of peer reviewing software, testing code in an independent
manner and providing a CODECHECK *"certificate of reproducible
computation"* when the results in the paper can be reproduced. We've
[written a post on the CODECHECK
process](http://gigasciencejournal.com/blog/codecheck-certificate/)
featuring a Q&A with [CODECHECK](http://codecheck.org.uk/) founder
Stephen Eglan, and here we'll provide a follow up with a Q&A with the
author of the software that was subject to the
[CODECHECK](http://codecheck.org.uk/) certification process. Another
Stephen, author Stephen Piccolo talks here about his new paper on
ShinyLearner, a benchmarking tool for machine-learning classification
algorithms.

![ShinyLearner
author](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/brightspotcdn.byu_.edu_-150x150.jpg){.alignleft
.wp-image-3276 loading="lazy" decoding="async" width="135" height="135"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/brightspotcdn.byu_.edu_-150x150.jpg 150w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/brightspotcdn.byu_.edu_.jpg 200w"
sizes="(max-width: 135px) 100vw, 135px"}The paper stands out by making
this process very systematic and reproducible, and we ask Stephen about
why [classification
algorithms](https://dzone.com/articles/introduction-to-classification-algorithms)
are so important, why he uses reproducible and open research in his
work, and how the CODECHECK review process was from an authors
perspective. Stephen is an [Assistant Professor in the College of Life
Science](http://piccolo.byu.edu/) at Brigham Young University, working
in an open and reproducible way to integrate knowledge and techniques
across biology, computer science, medicine, and statistics.**  
**

**Why are there so many classification algorithms and why do they need
to be benchmarked?**

Computer scientists and statisticians have been creating and refining
classification algorithms [for
decades](https://doi.org/10.1111/j.1469-1809.1936.tb02137.x). Research
papers have been published on hundreds of these algorithms, and
implementations are available in the public domain for many of them. In
many research areas (biological or otherwise), classification algorithms
can help to identify patterns that distinguish two or more groups and
then be used to predict the group to which new observations belong.
However, it is difficult for scientists to know which classification
algorithm is best for a particular research application. In addition,
most algorithms support
[hyperparameters](https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)),
which allow the scientist to modify the algorithm's behavior, but it is
inefficient and bias-prone to tune these algorithms in an *ad
hoc* manner. Benchmark analyses address this problem by making the
algorithm/parameter comparisons systematic in nature. We can apply tens
or even hundreds of algorithm variations to benchmark datasets and
identify which tend to perform best.

**Why did you build ShinyLearner and what technical approaches did you
take to design it?**

We created [ShinyLearner](https://github.com/srp33/ShinyLearner) to make
it easier to perform benchmark comparisons. Several open-source software
libraries are available for performing classification analyses. But most
require the scientist to write computer code to perform analysis.
Generally, these libraries perform parameter tuning, but they provide
little insight into this process. We sought to provide a tool that
requires no coding to perform the analysis and that generates simply
formatted output files so users can more easily gain insight into
benchmark results. It encapsulates 4 open-source, machine-learning
libraries into a "software container" and provides a consistent
interface for working with any of them. The use of software containers
makes the installation process easier because it already includes all
the software you need to execute the analysis. All you need is to
install the [Docker](https://www.docker.com/) software (or a related
tool for working with containers) and download the container image. The
user then executes the software at the command line (terminal). To ease
this process, we created a web application that helps the user construct
the commands they need to execute. One more tidbit: within the software
container, we use a combination of 4 programming languages to piece
everything together: Python, R, Java, and bash scripting. Most projects
would limit themselves to 1-2 programming language, but we needed to
write code in these various languages to interface with the different
machine-learning libraries but also because in some cases, it seemed
more efficient to use one language over another to implement the desired
functionality. By using software containers, the end user doesn't need
to worry about this complexity or install (specific versions of )
runtimes for all of these languages.

**This was an open source tool that used many techniques for
computational reproducibility, so what drove you to follow open science
practices?**

Nowadays, most scientists will not take seriously a journal article
describing a piece of research software unless it is open source. They
want to be able to see the code and verify its design and functionality
directly. This is a great thing! I am still baffled when I see a
research article that describes software or an algorithm but doesn't
make the code available.

In [this paper](https://doi.org/10.1093/gigascience/giaa026), we also
followed open-science practices by sharing our analysis scripts (in
addition to the actual [ShinyLearner
software](https://github.com/srp33/ShinyLearner)). We wanted others to
be able to see the exact code we used to generate the figures in our
paper. [We used CodeOcean](https://doi.org/10.24433/CO.5449763.v1), a
cloud-computing service that provides a free tier for open science
projects \[see [previous
blog](http://gigasciencejournal.com/blog/data-intensive-software-publishing-sailing-the-code-ocean-qa-with-ruibang-luo/)
on the platform\]. We did this for the sake of transparency and because
we knew that when we submitted the paper to a journal, at least one
reviewer would want us to tweak our analysis. Because we had already
packaged it up nicely and made it available for others, it was also easy
for us to remember each step of the analysis and tweak what we needed to
tweak. Finally, we used open-science practices because that's what we
like to see in others.

**How was the process of undergoing a CODECHECK review as an author?**

For me, the [CODECHECK](http://codecheck.org.uk/) process was easy. The
reviewer set everything up. I just needed to verify that his review was
reasonable and had been configured properly \[see the [certificate
here](http://doi.org/10.5281/zenodo.3674056)\].

**From an author's perspective does the CODECHECK certificate seem a
good incentive and reward for making your work easy to use? Do you think
it may drive authors to improve the way they release and write up
software papers?**

![CODECHECK
certificate](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Screenshot-2020-04-06-at-4.27.42-PM-300x281.png){.alignright
.wp-image-3272 loading="lazy" decoding="async" width="222" height="208"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Screenshot-2020-04-06-at-4.27.42-PM-300x281.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Screenshot-2020-04-06-at-4.27.42-PM-768x721.png 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Screenshot-2020-04-06-at-4.27.42-PM-1024x961.png 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Screenshot-2020-04-06-at-4.27.42-PM.png 1068w"
sizes="(max-width: 222px) 100vw, 222px"}It's nice to have that stamp of
approval and peace of mind knowing that at least one outside person was
able to reproduce our work. I wasn't aware of this certificate before
submitting to this journal. So it wasn't a big motivation for me at the
time. But going forward, I would be motivated by the certificate because
it would tell me the journal is serious about following good scientific
practices. Few other journals make the extra effort to really verify
your code when it is open source. I would love to see this become
standard practice. It's more work for everyone in the short term, but in
the long run it's very beneficial. It won't always prevent research
misconduct or ensure that a scientific analysis is impactful, but it's a
simple way to encourage open science.

If authors knew that they would need to pass a
[CODECHECK](http://codecheck.org.uk/) from the beginning, it would
motivate them to follow practices early in the process that would
support open, reproducible science. This would benefit themselves but
also the broader community. I touch on this in more detail in an earlier
*Gigascience* paper ([Tools and techniques for computational
reproducibility](https://doi.org/10.1186/s13742-016-0135-4)).

*Read more in our [previous post on
CODECHECK](http://gigasciencejournal.com/blog/codecheck-certificate/).
Stephen Eglen presented CODECHECK at The 14th Munin Conference on
Scholarly Publishing 2019 and you can watch a [video recording
here](https://mediasite.uit.no/Mediasite/Play/8027873496dc465ebc4b9b3ab0338ad01d?playFrom=1772000).*

### **References**

Piccolo SR. et al.,  ShinyLearner: A containerized benchmarking tool for
machine-learning classification of tabular data, *GigaScience*, Volume
9, Issue 4, April 2020,
doi:[10.1093/gigascience/giaa026](https://doi.org/10.1093/gigascience/giaa026)

::: {.col-md-8}
::: {.blog-post}
Eglen SJ. CODECHECK Certificate 2020-001. Zenodo. 2020[
<http://doi.org/10.5281/zenodo.3674056>]{.ng-binding}
:::
:::

::: {.col-md-3 .col-md-offset-1}
::: {.panel-body}
Piccolo SR, Frampton MB. Tools and techniques for computational
reproducibility. *Gigascience*. 2016;5(1):30. Published 2016 Jul 11.
doi:[10.1186/s13742-016-0135-4](https://doi.org/10.1186/s13742-016-0135-4)
:::
:::

The post [Reproducible Classification. Q&A on ShinyLearner & the
CODECHECK certificate, pt.
2](http://gigasciencejournal.com/blog/shinylearner-codecheck/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


This week we showcased a new way of peer reviewing software, testing code in an independent manner and providing a CODECHECK
<em>
 “certificate of reproducible computation”
</em>
when the results in the paper can be reproduced.

Reproducible Classification. Q&amp;A on ShinyLearner &amp; the CODECHECK certificate, pt. 2

Mary Ann Tuli

**Web tools and FAIR principles at the core of Life Science publishing
in GigaDB  
***As we continue to push the envelope for publishing in the life
sciences we have been listening to our users about the things that
should be incorporated into the publication process. That is why we have
taken the opportunity to not only freshen the appearance of the
GigaScience Database (GigaDB) website, but also to include the addition
of widgets within the framework to allow for a wide range of additional
features to be incorporated directly within the GigaDB datasets.*

Here we describe the latest release of [GigaDB.org](http://gigadb.org/)
(as [presented](https://doi.org/10.6084/m9.figshare.6668678.v6) at [ISMB
2018 Chicago](https://www.iscb.org/ismb2018)) which has recently
undergone a complete overhaul to improve the user experience. Our team
has continued improve and develop the database over the years, but this
is the biggest update to the look of the platform since the 2014 (see
the publication [describing that version in Database
Journal](https://doi.org/10.1093/database/bau018)). Every page has been
redesigned to allow faster navigation and improved visibility of links
to related resources, including those hosted by external services as
well as those on our own servers.

![](http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/Screen-Shot-2018-07-02-at-15.31.05-1024x625.png){.size-large
.wp-image-2423 .aligncenter fetchpriority="high" decoding="async"
width="1024" height="625"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/Screen-Shot-2018-07-02-at-15.31.05-1024x625.png 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/Screen-Shot-2018-07-02-at-15.31.05-300x183.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/Screen-Shot-2018-07-02-at-15.31.05-768x469.png 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/Screen-Shot-2018-07-02-at-15.31.05.png 1114w"
sizes="(max-width: 1024px) 100vw, 1024px"}

 

On a practical point, we would like to direct our users to the updated
[terms of use](http://gigadb.org/site/term) , [privacy
statements](http://gigadb.org/site/term#privacy), and [personal data
policy](http://gigadb.org/site/term#personal) which have all been
updated.

## New Tools to explore and discover the great data we publish. {#new-tools-to-explore-and-discover-the-great-data-we-publish. style="text-align: left;"}

![new look
GigaDB](http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/map_browser.png){.aligncenter
.wp-image-2425 .size-full decoding="async" width="941" height="459"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/map_browser.png 941w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/map_browser-300x146.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2018/07/map_browser-768x375.png 768w"
sizes="(max-width: 941px) 100vw, 941px"}

The **Sample Map Browser** (above) allows users to discover samples that
we have data for by geographic location of that sample (only available
where submitters provide GPS coordinates). Handy for geographically
spread datasets such as camera trap data ([see
GigaBlog](http://gigasciencejournal.com/blog/jack-rabbits-lizards-and-squirrels-oh-my-camera-trap/)
for an example of a camera trap study we published).

Incorporation of the 3D visualisation widget from
[SketchFab](https://sketchfab.com/) (below) enables interactive
visualisation of surface-rendered reconstructions of 3D imaging data,
such as microCT scan reconstructions (see the [hermit crab examples in
GigaBlog](http://gigasciencejournal.com/blog/interact-with-the-crabs-in-your-computer-new-gigadb-imaging-widgets/)
that showcase this nicely).

::: {.sketchfab-embed-wrapper}
[Paragiopagarus Atkinsonae F
Paratype](https://sketchfab.com/models/0b576006076748488be9bb137c6635df?utm_medium=embed&utm_source=website&utm_campaign=share-popup){style="font-weight: bold; color: #1caad9;"
target="_blank" rel="noopener"}  
by
[GigaDB](https://sketchfab.com/GigaDB?utm_medium=embed&utm_source=website&utm_campaign=share-popup){style="font-weight: bold; color: #1caad9;"
target="_blank" rel="noopener"}  
on
[Sketchfab](https://sketchfab.com?utm_medium=embed&utm_source=website&utm_campaign=share-popup){style="font-weight: bold; color: #1caad9;"
target="_blank" rel="noopener"}
:::

We also have the ability to include [Code Ocean](https://codeocean.com/)
widgets to display the interactive code of datasets where this is
provided by the authors. This allows users to rerun the published
pipeline/code on different datasets, or with altered parameters. We now
have a couple of examples of software (what we call Technical Note)
papers utilising Code Ocean integration including the recent [Genome
Annotation Generator tool](https://doi.org/10.1093/gigascience/giy018)
(see widget below).

In future developments we wish to provide more tools for visualisation
of data with a view to increasing data accessibility and reuse.

If you have opinions about the new look GigaDB and tools that we could
offer please get in touch or leave your comments below. Practising what
we preach regarding open source, you can also comment and issue pull
requests on the [GigaDB-website GitHub
repository](https://github.com/gigascience/gigadb-website).

The post [A new look GigaDB. Data publishing gets more interactive and
FAIR.](http://gigasciencejournal.com/blog/new-look-gigadb/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


<strong>
 Web tools and FAIR principles at the core of Life Science publishing in GigaDB
</strong>
<em>
 As we continue to push the envelope for publishing in the life sciences we have been listening to our users about the things that should be incorporated into the publication process.
</em>

A new look GigaDB. Data publishing gets more interactive and FAIR.

Hans Zauner

**The year is almost over and it's time to review the highlights of
*GigaScience*'s 2017.**

::: {#attachment_2231 .wp-caption .alignright style="width: 310px"}
![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/reindeer-300x199.jpg){.size-medium
.wp-image-2231 loading="lazy" decoding="async"
aria-describedby="caption-attachment-2231" width="300" height="199"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/reindeer-300x199.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/reindeer-768x511.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/reindeer-1024x681.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/reindeer.jpg 1280w"
sizes="(max-width: 300px) 100vw, 300px"}

["Draft genome of the reindeer (Rangifer
tarandus)"](https://doi.org/10.1093/gigascience/gix102)  
Foto: [Alexandre
Buisse](https://commons.wikimedia.org/wiki/Rangifer_tarandus#/media/File:20070818-0001-strolling_reindeer.jpg)
CC-BY-SA 3.0
:::

2017 was as special year for *GigaScience*: a year of many firsts and we
celebrated our 5th birthday! An occasion to look back -- and forward, as
we did in our [anniversary
editorial](https://academic.oup.com/gigascience/article/6/9/1/4004834).
We marked the completion of our first five years in Prague, at ISMB, the
same meeting where *GigaScience* first came to life in 2012. Czech beer,
cake and our amazing guests (mainly recruited ad hoc from the BOSC
track), all coming together on a summer evening in a corner pub in
Prague: those were the ingredients for a [truly memorable birthday
party](http://gigasciencejournal.com/blog/bohemian-bashfully-baroque-birthdays-and-bingo-in-prague/).
Thanks again to all our guests for a special evening. We hope to see you
all again in 2022 for our 10th birthday party, wherever that will take
place!

Apart from celebrating, we also were one of the sponsors of the BOSC
track at ISMB, and were happy to witness that the Open Science movement
is in excellent health.

> Thanks to all the brilliant people who came and celebrated our 5th
> birthday at
> [\#ISMBECCB](https://twitter.com/hashtag/ISMBECCB?src=hash&ref_src=twsrc%5Etfw)
> tonight [pic.twitter.com/4MOMfAWfIe](https://t.co/4MOMfAWfIe)
>
> --- GigaScience (@GigaScience) [July 23,
> 2017](https://twitter.com/GigaScience/status/889266228455460865?ref_src=twsrc%5Etfw)

## **Going forward with OUP**

2017 was the first year with our new publisher, Oxford University Press.
During the transition, quite a lot of chores needed to be done behind
the scenes to continue the smooth running of the journal. We are looking
forward to further grow our radical open review, open data, open science
approach with our new publishing partner in 2018 and beyond.

## ***GigaScience* Competition and Prize Track**

::: {#attachment_2233 .wp-caption .alignright style="width: 235px"}
![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/4190569720_4b2c376c6a_z-225x300.jpg){.size-medium
.wp-image-2233 loading="lazy" decoding="async"
aria-describedby="caption-attachment-2233" width="225" height="300"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/4190569720_4b2c376c6a_z-225x300.jpg 225w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/4190569720_4b2c376c6a_z.jpg 375w"
sizes="(max-width: 225px) 100vw, 225px"}

["The first near-complete assembly of the hexaploid bread wheat genome,
Triticum aestivum](https://doi.org/10.1093/gigascience/gix097)"  
Foto: [D. Endresen CC-BY
2.0](https://www.flickr.com/photos/dag_endresen/4190569720)
:::

Another highly memorable event this year was the International
Conference on Genomics (ICG) meeting in Shenzhen in October. For the 7th
consecutive year we've attended our co-publisher BGI's annual meeting,
but this year we had a first: Our first ever [competition and prize
track](http://gigasciencejournal.com/blog/and-the-winner-is-our-icg12-prize-track-on-gigatv/),
to promote new, cutting edge research.

Six *GigaScience* authors were given free registration, accommodation
and travel to present their work. Among the strong contenders for the
prize was a  massively improved, [near complete wheat
genome](https://academic.oup.com/gigascience/article/6/11/1/4561661)
that made waves also beyond the wheat community. But as competitions go,
there could be only one winner, and the jury awarded the first prize to
Taras Oleksyk, who presented work on [the genome of a rather unique
mammal, *Solenodon
paradoxus*](https://www.biorxiv.org/content/early/2017/07/18/164574)
from the island of Hispaniola. Watch Oleksyk's award-winning
presentation, and all the other talks of the prize track, on our
[Youtube
channel](https://www.youtube.com/channel/UC1Xt1XQMEYqhZ002Phrpn6A).

For the prize track we also experimented with real-time open peer
review. All reviews at *GigaScience* are open after publication, but for
the ICG event we went one step further, in collaboration with our
friends at [AcademicKarma](http://academickarma.org/). Reviews could be
followed and contributed in real time via [a special
page](http://academickarma.org/conference/icg12). Transparent peer
review is a topic we are particularly interested in, of course. As [our
contribution to Peer Review
Week](http://gigasciencejournal.com/blog/peerrevwk17-update-peer-review-week-goes-transparent-and-interactive/),
we co-hosted a webinar, discussing preprints and open peer review with
AcademicKarma.

## **Some favorite articles**

::: {#attachment_2039 .wp-caption .alignleft style="width: 310px"}
![: William H. Piel and Antónia
Monteiro](http://gigasciencejournal.com/blog/wp-content/uploads/2017/05/anynana-300x256.png){.wp-image-2039
.size-medium loading="lazy" decoding="async"
aria-describedby="caption-attachment-2039" width="300" height="256"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/05/anynana-300x256.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/05/anynana.png 554w"
sizes="(max-width: 300px) 100vw, 300px"}

["A high-coverage draft genome of the mycalesine butterfly Bicyclus
anynana."](https://doi.org/10.1093/gigascience/gix035)   
foto credit: William H. Piel and Antónia Monteiro
:::

Only a subset of  articles could be presented within the timeframe of
the ICG prize track. Other highlights published last year once again
include some high quality genome assemblies. *GigaScience* continues to
be a favorite journal for advanced plant genomics, showcasing cutting
edge technology to improve assemblies; as evidenced not only by the new 
wheat assembly mentioned above, but also by a recent article on a
[ improved assembly of the wild strawberry
genome](https://academic.oup.com/gigascience/advance-article/doi/10.1093/gigascience/gix124/4739363?searchresult=1),
using a combination of optical maps and long reads.

In the animal department, we could not possibly write a mid-December
blog post without mentioning [the reindeer
genome](https://academic.oup.com/gigascience/article/6/12/1/4562806)
published recently, also [featured on
GigaBlog](http://gigasciencejournal.com/blog/a-christmas-gift-to-you-from-gigascience-scientists-have-unwrapped-the-reindeer-genome/).

Moving on to the insect world, we published [the genome of *Bicyclus
anynana*](http://gigasciencejournal.com/blog/the-genome-of-a-shape-shifting-butterfly/),
a charismatic butterfly and important model in evolution and ecology due
to its seasonal morphological varieties. The [data
note](https://doi.org/10.1093/gigascience/gix035) (with [supporting data
in GigaDB](http://dx.doi.org/10.5524/100280), as always) will help to
better understand this unusual developmental switch.

We also featured an arachnid species: * *The parasitic  *Tropilaelaps
mercedesae *threatens bee colonies in Asia, and the genome sequence,
[published in
*GigaScience*,](https://academic.oup.com/gigascience/article/6/3/1/3038223)
reveals some interesting features, including traces of horizontal gene
transfer from bacteria, as the authors explained in a [GigaBlog 
Q&A](http://gigasciencejournal.com/blog/getting-the-buzz-on-bee-parasites-author-qa-with-tatsuhiko-kadowaki/).

## **FAIR everything**

A certain four letter acronym was mentioned a lot wherever Open Science
was discussed in 2017, including BOSC and also the recent [FORCE
2017](http://gigasciencejournal.com/blog/force2017-in-berlin-changing-the-culture/)
meeting in Berlin:

***FAIR** -- **F**indable, **A**ccessible, **I**nteroperable,
**R**eproducible.* First drafted in 2015, there is wide agreement now
that these principles are a very good idea -- but how to
put [FAIR](https://www.force11.org/group/fairgroup/fairprinciples) into
practice? Javier Quilez and colleagues showed in a [didactical
*GigaScience*
commentary](https://academic.oup.com/gigascience/article/6/11/1/4557140)
how best practice can look like for a sequencing project (and what
errors are best avoided).

Our radical approach to openness and sharing would not have been
possible without the help of our reviewers, who agree to sign and
publish their reports and also take extra care that data and methods are
supporting the submissions. Thank you for your dedication and your
continued support!

Although we were quite busy in the first half of the year with the
transition to our new publisher OUP, we also introduced a few technical
novelties:

## **BioRxiv integration**

![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/Ipe-e1513672055322-168x300.jpg){.size-medium
.wp-image-2235 loading="lazy" decoding="async" width="168" height="300"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/Ipe-e1513672055322-168x300.jpg 168w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/Ipe-e1513672055322.jpg 387w"
sizes="(max-width: 168px) 100vw, 168px"}  
["Genome assembly of the Pink
Ipê" ](https://doi.org/10.1093/gigascience/gix125)

Not long ago posting unreviewed versions of research articles online was
seen as an avant-garde and slightly iffy thing to do as a biomedical
researcher, but preprints are becoming the new normal in the life
sciences. We always had supported preprints, we encourage our authors to
cite them and of course we never hold it against authors if they post a
preprint of their work prior to submitting to us.

To make it even easier to submit bioRxiv preprints, we now offer [a
shortcut connecting bioRxiv and
*GigaScience*](http://gigasciencejournal.com/blog/shortcut-from-biorxiv-to-gigascience/).

## Code Ocean

In addition to our  integration with bioRxiv, Publons and protocols.io,
we also [experimented with the code sharing platform Code
Ocean](http://gigasciencejournal.com/blog/data-intensive-software-publishing-sailing-the-code-ocean-qa-with-ruibang-luo).
Code Ocean takes reproducible code to a new level. Authors can upload
code and data in ten programming languages and Code Ocean provides an
interactive "Compute Capsule". They also assign a Digital Object
Identifier (DOI) to the algorithm, that allows the algorithm to be cited
in the reference sections of papers. See this paper by [Ruibang Luo and
colleagues](https://academic.oup.com/gigascience/article/6/7/1/3868354),
and the [Code Ocean entry cited
therein](https://codeocean.com/2017/06/08/16gt-colon-a-fast-and-sensitive-variant-caller-using-a-16-genotype-probabilistic-model/code), 
for an example how *GigaScience*-style open data publishing ties in with
Code Ocean's reproducible software capsules.

What do our authors think of this experiment? Ruibang Luo is as excited
as we are:

> "I think Code Ocean is awesome and I would like to use it for my
> future projects and I would like to recommend my colleagues."

##  RRIDs {#rrids}

Another software-related innovation you may have come across in the
methods sections of our papers are ["Research Resource Identifiers"
(RRIDS)](https://scicrunch.org/) we are encouraging our authors to
include.

The variety of software tools and databases used in life science
projects can get bewildering, and RRIDs are a handy way to keep track of
the tool zoo. Due to the standardized format, RRIDs mentioned in our
papers are machine-readable, allowing  for example annotation bots to
enhance and extract information automatically.

::: {#attachment_2234 .wp-caption .alignright style="width: 160px"}
![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/882px-Limnoperna_fortunei-150x150.jpg){.wp-image-2234
.size-thumbnail loading="lazy" decoding="async"
aria-describedby="caption-attachment-2234" width="150" height="150"}

["A hybrid-hierarchical genome assembly strategy to sequence the
invasive golden mussel Limnoperna
fortunei."](https://doi.org/10.1093/gigascience/gix128)  
Foto:[Boltovskoy](https://commons.wikimedia.org/wiki/File:Limnoperna_fortunei.jpg)/
CC-BY-SA
:::

## Citizen Science at the UN in Nairobi

In November, our executive editor Scott Edmunds was on a diplomatic
mission in Nairobi, Kenya: Before the main UN Environment Assembly, a
Science-Policy-Business Forum on the Environment took place, and as part
of this policy forum Scott participated in [discussions around Citizen
Science](http://gigasciencejournal.com/blog/citizen-smith-goes-to-nairobi-taking-citizen-science-to-the-un/),
also representing  [Citizen Science Asia](http://citizenscience.asia).
The gathering has set aim at promoting citizen science across the globe
-- and we will continue to do our bit. Just now, we published the
[genome assembly of the golden mussel *Limnoperna
fortunei*](https://academic.oup.com/gigascience/advance-article/doi/10.1093/gigascience/gix128/4750781?searchresult=1),
an invasive species. Following a [Community Genomics
approach](http://gigasciencejournal.com/blog/community-genomes-from-the-peoples-parrot-to-crowdfernding/)
that we've been promoting since our [Puerto Rican "peoples" parrot
genome](https://doi.org/10.1186/2047-217X-1-14), the project was
supported in part by crowfunding and also an exercise in public
participation, [as first author Marcela Ulioano da Silva
explains](https://fellowsblog.ted.com/i-crowdfunded-my-phd-research-this-model-could-change-the-world-28a079b7a0af).

## We Love CUDDEL(s) and BYO Data & Workflow Parties {#we-love-cuddels-and-byo-data-workflow-parties}

Some of you may recall our BBSRC UK-China partnering award funded [BYO
Data](http://gigasciencejournal.com/blog/aint-no-party-like-a-bring-your-own-data-party/)
party in our Hong Kong office in 2014 and thanks to the [positive
outcome](http://gigasciencejournal.com/blog/gigametabolomics-strikes-byo-data-parties-thematic-series/),
last month we hosted another hackathon in Hong Kong made possible with a
second BBSRC grant that we've called [CUDDEL](http://cuddel.net/)
('China-UK Data Dissemination in Metabolomics'. This year the focus was
on reproducible metabolomics workflows, and 19 participants squeezed in
our office from the ISA Team at the Oxford e-Research Centre, the EBI,
the Universities of Birmingham, Manchester (Common Workflow Language),
The Sainsbury Laboratory, BGI and the University of Melbourne. The
Oxford e-Research Centre has already [blogged on
this](https://www.oerc.ox.ac.uk/news/professor-susanna-assunta-sansone-explores-open-data-hong-kong),
and follow [our blog](http://gigasciencejournal.com/blog/) to see how
things progress from this event.

## More hands on deck at GigaDB

And finally, very good news for our repository GigaDB: Our data curators
Chris and Jesse got much needed help to curate the growing volume of
data submitted to GigaDB. Please welcome our new data curators Mary Ann
Tuli and Chris Armit. The bigger *GigaScience* team is prepared for your
submissions and another exciting year of open science publishing.

We wish all our readers, authors, reviewers and editorial board members
a Happy New Year!

::: {#attachment_2242 .wp-caption .aligncenter style="width: 530px"}
![](http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/dragonfish.jpeg){.wp-image-2242
.size-full loading="lazy" decoding="async"
aria-describedby="caption-attachment-2242" width="520" height="178"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/dragonfish.jpeg 520w, http://gigasciencejournal.com/blog/wp-content/uploads/2017/12/dragonfish-300x103.jpeg 300w"
sizes="(max-width: 520px) 100vw, 520px"}

["Draft genome of the Antarctic dragonfish, Parachaenichthys
charcoti"](https://academic.oup.com/gigascience/article/6/8/1/4004833?searchresult=1)
:::

The post [... and a Happy New
Year](http://gigasciencejournal.com/blog/and-a-happy-new-year/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


<strong>
 The year is almost over and it’s time to review the highlights of
 <em>
  GigaScience
 </em>
 ’s 2017.
</strong>
“Draft genome of the reindeer (Rangifer tarandus)”Foto: Alexandre Buisse CC-BY-SA 3.0  2017 was as special year for
<em>
 GigaScience
</em>
: a year of many firsts and we celebrated our 5th birthday! An occasion to look back – and forward, as we did in our anniversary editorial.

Publicaciones de Rogue Scholar

Open Access Week 2021: GigaScience’s 10 Examples of Open

Gigantum Joins the Giga Reproducibility Toolkit

Reproducible Classification. Q&A on ShinyLearner & the CODECHECK certificate, pt. 2

A new look GigaDB. Data publishing gets more interactive and FAIR.

… and a Happy New Year