Sec. Neural Technology
Volume 17 - 2023 | https://doi.org/10.3389/fnins.2023.1142317
Editorial: Open-access data, models and resources in neuroscience research
- 1Institute of Histology and Embryology “Aleksandar Ð. Kostić”, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
- 2Department of Biomedical Sciences, Florida State University, College of Medicine, Tallahassee, FL, United States
- 3Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
Editorial on the Research Topic
Open-access data, models and resources in neuroscience research
Why open-access data?
Life science has traditionally not been known for the open sharing of data and models. When Hopfield, a pioneer in computational neuroscience, attended his first biology conference in 1970, he spoke of his experience: “One of the senior biochemists took me aside to explain why I had no place in biology. As he said, gentlemen did not interpret other gentlemen's data and preferably worked on different organisms. If you wish to interpret data, you must get your own.” (Hopfield, 2014). The culture has however shifted and making use of open-access data, models and resources has now become commonplace in neuroscience. There are many good reasons for this, including the high quality of the available data, along with increased reproducibility and transparency, and collaboration opportunities among researchers working in different institutions, countries, and continents (Choudhury et al., 2014; Pascu and Burgelman, 2022). The tendency of making use of publicly available data gained further momentum in the past few years when the COVID-19 pandemic led to a near halt in experimental laboratory work in some countries. Thus, remote working and remote analysis of open-access databases emerged as a solution for overcoming the lack of hands-on laboratory work and were recommended by several neuroscience committees and societies (Vlisides et al., 2021).
The most important reason for the increasing use of open-access data, models, and resources is perhaps simply the increased availability. During the previous decade, there has been a tremendous increase in the number of published neuroscience papers accompanied by raw experimental data, spurring the creation of numerous data-sharing initiatives and databases that provide, at no cost, a large number of datasets (Spires-Jones et al., 2016). These databases can be used for various forms of neuroscientific research, such as morphometric analysis of nervous system cells (Ascoli et al., 2007), neuroimaging analysis of MRI datasets (Poldrack and Gorgolewski, 2014; Markiewicz et al., 2021), as well as genomic, transcriptomic, and proteomic expression analysis (Pereira et al., 2014; Keil et al., 2018). Needless to say, multiple users for these databases leverages the value of the original investment both in time and money that was needed to create them.
The same progress has been seen for open-access models as well: In the past, when computational neuroscientists wanted to reproduce modeling results, they often had to re-implement models based on equations and tables of parameters from scientific papers. This obstacle to scientific progress and reproducibility was acknowledged early, and the ModelDB1 repository for sharing neuroscience models was founded in 1996 and has been continuously growing ever since (McDougal et al., 2017). More recently, we have also seen the rise of several new model repositories, both from large initiatives such as the Allen Brain Institute2 and the Human Brain Project and from community-based efforts, such as the Open Source Brain.3
The main idea behind this Research Topic was to bring to the attention of a wide audience the use of open-access data, models, and resources from different publicly available repositories to generate new insights. Thus, we welcomed research papers based on open-access data or models, as well as any form of new computational resource, software, or platform that could be used for neuroscience research. As a result, eight articles were published in this topic that cover diverse aspects of neuroscience research, ranging from basic to clinical neuroscience, exploration of genetic and MRI databases, and different software tools for handling connectomic, electrophysiological, and multi-modal data. As this Research Topic has shown, the greatest interest in using open-access data is still present in research based on the use of neuroimaging databases, primarily containing MRI images (Short et al.; Beauferris et al.; Saat et al.). This is not surprising as these types of data can be easily obtained from diverse and large populations of human subjects, both healthy and diseased. In this way, researchers are offered a large pool of data that can be analyzed and lead to new advances in diagnosing and managing neuropsychiatric and other disorders (Poline et al., 2012). Another important component of this topic was the papers in which novel software tools and/or algorithms were presented. These include a unified framework for handling in vivo high-density extracellular probes (Garcia et al.), large-scale data analysis in connectomic research (Plaza et al.), as well as a query tool that can be used to easily search and categorize published multi-modal papers (Li and Liang). Further, the topic includes original research providing novel insights into the possible genes underlying autism spectrum disorders (Li et al.), which again emphasizes the importance and potential usage of open-access data obtained from gene-expression studies. Although there is no doubt that open-access data will yield discoveries in the field of neuroscience, the question of protection and privacy of such data arises (Jwa and Poldrack, 2022). This remains one of the most important and controversial questions in the world of open-access data sharing, and this topic includes an opinion article on the current problems, obstacles, and policies regarding data sharing (Lathe).
Although open-access databases have been present in neuroscience research for more than a decade, their importance is growing and is likely to continue to grow in the coming years (Wiener et al., 2016). This may represent a game-changing option for early-career researchers, or researchers in countries with less-than-optimal science funding as it gives access to resources that would otherwise have been restricted to large and well-funded labs. The increasing availability of online computational resources, like the EBRAINS4 or Neuroscience Gateway,5 can also be expected to contribute to making high-quality scientific work within reach of a much larger population of aspiring scientists. These initiatives are not only helping researchers gain access to large-scale computational resources but furthermore, have commonly used neuroscientific software pre-installed. This makes it almost trivial for researchers to ensure that their simulation results are reproducible and interactable since the computational environment that produced the results can easily be fully specified and recreated.
Open-access data, models, and tools are valuable only as long as they are Findable, Accessible, Interoperable, and Reusable (FAIR) (Wilkinson et al., 2016). Achieving this requires a joint effort: On the one hand, those responsible for the database or repository must facilitate that it is easy to search through and access, both for humans and machines, and that interacting with it does not require the user to be an expert programmer. On the other hand, researchers must strive to be sufficiently proficient in handling the variety of software needed to interact with online databases and repositories. This underscores the importance of making bioinformatics training an essential feature in biomedical curricula, or else many of these databases will remain underutilized and will not reach their full potential (Akil et al., 2011).
Neuroscience is increasingly becoming the highly interconnected global endeavor it needs to be to overcome the challenges ahead. As further progress is made, we look forward to the increasing usage of ever-expanding databases, leading to further advances in neuroscience.
IZ wrote the first draft of the article. RN and TN revised the final version of the manuscript. All authors read and approved the final submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Akil, H., Martone, M. E., and Van Essen, D. C. (2011). Challenges and opportunities in mining neuroscience data. Science 331, 708–712. doi: 10.1126/science.1199305
Ascoli, G. A., Donohue, D. E., and Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies. J. Neurosci. 27, 9247–9251. doi: 10.1523/JNEUROSCI.2055-07.2007
Choudhury, S., Fishman, J. R., McGowan, M. L., and Juengst, E. T. (2014). Big data, open science and the brain: lessons learned from genomics. Front. Hum. Neurosci. 8, 239. doi: 10.3389/fnhum.2014.00239
Hopfield, J. J. (2014). Two cultures? Experiences at the physics-biology interface. Phys. Biol. 11, 053002. doi: 10.1088/1478-3975/11/5/053002
Jwa, A. S., and Poldrack, R. A. (2022). Addressing privacy risk in neuroscience data: from data protection to harm prevention. J. Law Biosci. 9, lsac025. doi: 10.1093/jlb/lsac025
Keil, J. M., Qalieh, A., and Kwan, K. Y. (2018). Brain transcriptome databases: a user's guide. J. Neurosci. 38, 2399–2412. doi: 10.1523/JNEUROSCI.1930-17.2018
Markiewicz, C. J., Gorgolewski, K. J., Feingold, F., Blair, R., Halchenko, Y. O., Miller, E., et al. (2021). The OpenNeuro resource for sharing of neuroscience data. Elife 10, e71774. doi: 10.7554/eLife.71774
McDougal, R. A., Morse, T. M., Carnevale, T., Marenco, L., Wang, R., Migliore, M., et al. (2017). Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J. Comput. Neurosci. 42, 1–10. doi: 10.1007/s10827-016-0623-7
Pascu, C., and Burgelman, J. C. (2022). Open data: the building block of 21st century (open) science. Data Policy 4, e15. doi: 10.1017/dap.2022.7
Pereira, S., Gibbs, R. A., and McGuire, A. L. (2014). Open access data sharing in genomic research. Genes 5, 739–747. doi: 10.3390/genes5030739
Poldrack, R. A., and Gorgolewski, K. J. (2014). Making big data open: data sharing in neuroimaging. Nat. Neurosci. 17, 1510–1517. doi: 10.1038/nn.3818
Poline, J. B., Breeze, J. L., Ghosh, S., Gorgolewski, K., Halchenko, Y. O., Hanke, M., et al. (2012). Data sharing in neuroimaging research. Front. Neuroinform. 6, 9. doi: 10.3389/fninf.2012.00009
Spires-Jones, T. L., Poirazi, P., and Grubb, M. S. (2016). Opening up: open access publishing, data sharing, and how they can influence your neuroscience career. Eur. J. Neurosci. 43, 1413–1419. doi: 10.1111/ejn.13234
Vlisides, P. E., Vogt, K. M., Pal, D., Schnell, E., Armstead, W. M., Brambrink, A. M., et al. (2021). Roadmap for conducting neuroscience research in the COVID-19 era and beyond: recommendations from the SNACC Research Committee. J. Neurosurg. Anesthesiol. 33, 100–106. doi: 10.1097/ANA.0000000000000758
Wiener, M., Sommer, F. T., Ives, Z. G., Poldrack, R. A., and Litt, B. (2016). Enabling an open data ecosystem for the neurosciences. Neuron 92, 617–621. doi: 10.1016/j.neuron.2016.10.037
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Sci Data 3, 160018. doi: 10.1038/sdata.2016.18
Keywords: open-access, open data (OD), neuroscience databases, neuroimaging data repository, data sharing, big data, data protection, data mining
Citation: Zaletel I, Nowakowski RS and Ness TV (2023) Editorial: Open-access data, models and resources in neuroscience research. Front. Neurosci. 17:1142317. doi: 10.3389/fnins.2023.1142317
Received: 11 January 2023; Accepted: 03 February 2023;
Published: 14 February 2023.
Edited and reviewed by: Michele Giugliano, International School for Advanced Studies (SISSA), Italy
Copyright © 2023 Zaletel, Nowakowski and Ness. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ivan Zaletel, email@example.com
†These authors have contributed equally to this work