---
author:
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family: Edmunds
given: Scott
url: https://orcid.org/0000-0001-6444-1436
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date: '2021-10-29T00:00:00+00:00'
date_updated: '2025-12-06T10:12:51+00:00'
guid: http://gigasciencejournal.com/blog/?p=4197
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- alt: 'Banner from Open Access Week 2021, source;
www.openaccessweek.org'
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summary: '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.'
title: 'Open Access Week 2021: GigaScience''s 10 Examples of Open'
url: https://wayback.archive-it.org/22098/2025-05-01T17:13:42Z/http://gigasciencejournal.com/blog/gigascience-open-access-week-2021
version: v1
---
Banner from Open Access Week 2021, source;
www.openaccessweek.org
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).
**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)
**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).
GigaScience, like our sister GigaByte journal has
experimented with a number of platforms for publishing software in a
more reproducible manner.
**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)
**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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