“Open notebook” science is a practice in which research scientists record their work online and make it publicly available while conducting research in near-real time. Such research is thus completely open to the public and includes all its aspects such as raw data and any associated material. The approach was described by Bradley (2007), the first to use the term, as “no insider information”. Open notebook science makes the research process transparent and provides unsuccessful, not very significant, or unpublished outcomes (sometimes called “dark data”) to anyone interested (Goetz 2007).

Let’s take a look at the primary advantages of this practice, according to the literature. According to Schapira, Harding and Consortium (2019, p. 3), open lab notebooks can save time, resources, and knowledge. Making the information accessible quickly means that other researchers will be able to build upon the open results, making it possible for others to avoid spending time and resources on redundant experiments (Powell 2016). Open lab notebooks should include detailed protocols to achieve experimental replicability. The necessity of more transparent, replicable experiments has been discussed recently (for example, by Nature 2016 and Wallach et al. 2018). Negative data from unpublished research might additionally provide important insights (Mlinarić et al. 2017; Nimpf and Keays 2020). Open lab notebooks can also give experts a space for discussion, to (for example) spot discrepancies in an experiment and so on.

Early career researchers can use their notebooks to connect with peers and experts in the field. One can also add a link to one’s research notebook in an academic CV when applying to a position in order to showcase technical skills (Schapira, Harding and Consortium 2019).

Possible drawbacks of open notebooks (Harding and Consortium 2019, Sanderson 2008, Schapira 2018, Zirnask 2014):

• Possible data theft (being “scooped”): Risk can be mitigated by using repositories such as Zenodo, which assign a citable DOI (or other citable record) to a notebook.
• Difficulty publishing open notebook results in traditional peer-reviewed journals.
• Influencing other research projects before the research documented in the open notebook is complete and/or well-analysed (this is why there are experimental collaborations without open notebook use with strict rules to avoid data leakage and issues with influencing results).
• “Data deluge”: flooding the information space with a large amount of non-peer-reviewed material.
• Can be difficult without a smooth process: maintaining an open notebook should be executed effectively to avoid wasting too much time (as with a regular lab notebook).

What are the necessary characteristics of open notebooks? Harding (2019, p. 2) notes she designed her notebook “to be discoverable, accessible, clear, and detailed in its presentation, and to permit dialogue between readers and me, and to pave the way for collaborations.”

Examples of platforms for open notebooks:

Picking the right open notebook platform can be daunting. Every scientific field has a different environment with different data collection requirements and different data types (e.g., code, images, equations, value). Several open notebook options are listed below; you may wish to ask your mentor if they have a preference for a particular tool. One could additionally create a blog for an open notebook.

• https://openlabnotebooks.org/

To create an open notebook, contact the coordinator. Data are uploaded and stored on zenodo.org (maintained by CERN as a part of the OpenAIRE initiative). You can also link zenodo files to your ORCID profile.

• https://github.com/

Used by many open sourced software developers, among others. A code repository that allows parallel code editing.

• https://openwetware.org/

Join via a web form. Designated for biology and biological engineering.

• https://jupyter.org/

Free software, open standards, and web services for interactive computing across all programming languages.

Various interfaces (lab book, notebook, hub, Voilá) for sharing outputs.

Use https://nbviewer.org/ to make a jupyter notebook publicly shareable.

• https://open-neuroscience.com/

Open database for neuroscience projects.

If your research, for any reason, cannot be made public, another option for organisation and cooperating/sharing with other researchers or within teams, or managing protocols is the use of electronic laboratory notebooks. There are many options to choose from such as: https://rmarkdown.rstudio.com/, https://www.labarchives.com/, https://www.benchling.com/, and https://www.elabftw.net/ (among others).

Openly published protocols: If you do not wish to share your process but would like to publish and share a protocol which you have designed, a tool like https://www.protocols.io can be considered. After publishing a protocol on protocols.io you obtain a digital object identifier (DOI). A DOI can be used in a manuscript so that it can, if the article is approved for publication in a scientific journal, be published (automatically, if a DOI is used upon article submission) at a later date.

Resources:

Bradley, Jean-Claude. (2007). Open notebook science using blogs and wikis. Nature Precedings. https://doi.org/10.1038/npre.2007.39.1

Goetz, Thomas. (2007). Freeing the dark data of failed scientific experiments. Wired, 15(10). Available from: https://www.wired.com/2007/09/st-essay-3/ 

Harding, Rachel J. (2019) Open notebook science can maximize impact for rare disease projects. PLoS Biol, 17(1). https://doi.org/10.1371/journal.pbio.3000120

Mliarić, Ana, Horvat, Martina. & Šupak Smolčić, Vesna. (2017). Dealing with the positive publication bias: Why you should really publish your negative results. Biochem Med, 27(3). https://doi.org/10.11613/BM.2017.030201

Nature. (2016). Reality check on reproducibility. Nature, 533(7604), 437-437. https://doi.org/10.1038/533437a

Nimpf, Simon & Keays David A. (2020). Why (and how) we should publish negative data. EMBO Rep., 21(1). https://doi.org/10.15252/embr.201949775

Powell, Kendall. (2016). Does it take too long to publish research? Nature, 530, 148-151. https://doi.org/10.1038/530148a

Sanderson, Katherine. (2008). Data on display. Nature, 455(18), 273. https://doi.org/10.1038/455273a

Schapira, Matthieu. (2018). Open lab notebooks to increase impact and accelerate discovery . Springer Nature. Available at: https://researchdata.springernature.com/posts/29655-open-lab-notebooks-to-increase-impact-and-accelerate-discovery

Schapira, Matthieu & Rachel J Harding. (2019). Open laboratory notebooks: good for science, good for society, good for scientists. F1000Res, 8(87). https://doi.org/10.12688/f1000research.17710.2

Wallach, Joshua D, Boyack, Kevin W. & John P. A. Ioannidis. (2018) Reproducible research practices, transparency, and open access data in the biomedical literature, 2015-2017. PLoS Biology, 16. https://doi.org/10.1371/journal.pbio.2006930

Zirnask, Mart. (2014). Are open notebooks the future of science? UT Blog. Available at: https://blog.ut.ee/are-open-notebooks-the-future-of-science/

I propose that all journals should, in principle, try their best to have authors and institutions make their raw data open in a public database or on a journal web site upon the publication of the paper, in order to increase the reproducibility of published results and to strengthen public trust in science. Currently, the data sharing policy of Molecular Brain only ‘encourages’ all datasets on which the conclusions of the manuscript rely to be either deposited in publicly available repositories (where available and appropriate) or presented in the main paper or additional supporting files, in machine-readable format (such as spread sheets rather than PDFs) whenever possible. Building on our existing policy, we will require, in principle, deposition of the datasets on which the conclusions of the manuscript rely from 1 March 2020. Such datasets include quantified numerical values used for statistical analyses and graphs, images of tissue staining, and uncropped images of all blot and gel results.

In my own recent experience with journal editing, I can say that having the ability to access underlying data for any study would provide me with more confidence when considering the results discussed in a paper, regardless of discipline.

Miyakawa does recognize the need for more robust data infrastructures for supporting raw data provision by authors and calls for better infrastructures for this:

…institutions, funding agencies, and publishers should cooperate and try to support such a move by establishing data storage infrastructure to enable the securing and sharing of raw data, based on the understanding that ‘no raw data, no science.’

Read more:

Miyakawa, T. No raw data, no science: another possible source of the reproducibility crisis. Mol Brain 13, 24 (2020). https://doi.org/10.1186/s13041-020-0552-2

2. …& a brand new resource from the US National Academies: https://www.nap.edu/catalog/25116/open-science-by-design-realizing-a-vision-for-21st-century

International research data networks are critical for progress in many scientific domains and underpin efforts to promote Open Science. At the same time, many of these networks are fragile and the responsibilities for their support and performance are frequently distributed across a variety of different actors. This report explores the challenges and enablers for the effective functioning of international research data networks.