One may remember the first attempts at data sharing in functional neuroimaging, the fMRI data center (Van Horn and Gazzaniga, 2013), and the difficulty of getting and reusing data sent over on compact discs or DVDs. Creating a culture of data sharing has many advantages: it can lead to more rapid scientific discovery for basic science and clinical research, can improve data quality, reduce costs, and improve reproducibility, and is in some cases a requirement made by funding agencies (Poline et al., 2012; Poldrack and Gorgolewski, 2014; Madan, 2017a). Some researchers argue that it is an ethical imperative (Brakewood and Poldrack, 2013) to maximize a subject's contributions, especially in clinical trials (Bauchner et al., 2016). But just because the data is shared, it doesn't mean the data can be found.

First and -possibly- foremost, browsers are the doors to the four principles of FAIR (Findable, Accessible, Interoperable, and Reusable), a set of guidelines developed by stakeholders in academia, industry, and funding agencies to promote data reuse (Wilkinson et al., 2016). We review them briefly here in the context of the web technology:

Findable: In order for scientists to discover data that may be of use for their research questions, datasets need to be indexed within a central database server, with appropriate metadata such that search engine algorithms can efficiently perform queries, and most importantly, with a browser-based user interface for researchers to submit queries and display results.

A key feature of the FAIR principles is that when possible they should be applicable not only to humans, but also to machines. For instance, datasets should be findable by “bots” by being tagged with the appropriate machine-readable metadata.

Neuroimaging groups have developed web portals that make it easy for other researchers to query, explore, contribute, and share both raw data and derived data. The COINS web platform (Scott et al., 2011) provides data management tools, an intuitive user interface, and was built with an emphasis for PHI security and multisite collaborations. The LORIS platform (Das et al., 2012) includes a web portal for data management and data quality control with neuroimaging viewers. The LONI Image Data Archive (LONI-IDA) is a long-term, centralized, HIPAA-compliant relational database archive for researchers to upload and share their data (Van Horn and Toga, 2009); as of this writing, the LONI-IDA has provided over 50 million downloads and over 1 million uploads to the archive. Web application such as these reduce the technical overhead to find, share, and aggregate data, and should ideally become standard practice for all large data collection efforts in neuroimaging.

The accessibility (FAIR-ness) of derived data is key to meta- and mega- analyses. A prominent example is the ENIGMA project (Thompson et al., 2014), which disseminated standardized analysis scripts to be able to co-analyze (e.g., a mega-analysis) a set of individual center's results, by sharing derived data rather than raw data. A mega-analysis strategy is especially optimal in cases where raw data sharing is not feasible. For task and resting state fMRI, the NeuroVault (Gorgolewski et al., 2015) web application enables scientists to upload fMRI statistical maps (e.g., derived data) in the standardized MNI space, and link to their publications; this platform includes both volume and surface-based visualization, and can enable more accurate meta- and mega-analyses. For diffusion imaging, the Automated Fiber Quantification (AFQ) package (Yeatman et al., 2012) has an associated web-viewer (Yeatman et al., 2018) and vault¹ to easily share derived AFQ data in a standardized format. Building software that returns derived data in standardized formats and lowers barriers to sharing these derivatives with the neuroimaging community will facilitate meta- and mega- analyses in future years.

In the past, sharing data was a technical challenge (Van Horn and Gazzaniga, 2013); now, it is easier to share data even if the data are not part of a large consortium. The OpenNeuro web application enables researchers to upload and share their neuroimaging data as long as the data follow a community-developed standard to organize and describe neuroimaging datasets called the Brain Imaging Data Structure (BIDS) (Gorgolewski et al., 2016). Adopting standards for how data are stored enables sharing by reducing the overhead needed to curate heterogeneous datasets, and therefore promotes interoperability and reusability of data (Tenopir et al., 2011). Examples of standardized data formats outside of the neuroimaging field include the Open Geospatial Consortium (Castronova et al., 2013) and the Ecological Metadata Language (Fegraus et al., 2005).

In general, the FAIR principles do not stipulate how data sharing should be incentivized. The adoption of FAIR principles requires financial support as well as community adoption. While the OpenNeuro project has been funded by the NIH², the BIDS standard that it relies upon is, importantly, starting to be adopted by a wide community. The standard has recently been endorsed by the International Neuroinformatics Coordinating Facility (INCF)³, and is recommended by several journals. Funding agencies (e.g., the Wellcome Trust⁴) are increasingly asking that a wider set of research products are shared with the community to increase reuse and maximize the funding impact on research. The set of tools that facilitate the conversion of small datasets to BIDS format is also growing (see the BIDS starter kit⁵), which may mitigate the need for long-term funding. Concurrently, training material to educate researchers to adopt the BIDS format is being actively developed by ReproNim (e.g the “FAIR data” module⁶).

In the genomic community, the Bermuda principles (Contreras, 2011) led to the establishment of few large public databases, but the brain imaging community has been less unified. This led to a variety of large or small initiatives, such as ADNI (Mueller et al., 2005), BIRN (Keator et al., 2008), BrainMap (Laird et al., 2011), INDI (Mennes et al., 2013), OpenfMRI (Poldrack et al., 2013), OMEGA (Niso et al., 2016), OpenNeuro (Gorgolewski et al., 2017a), Schizconnect portal(Wang et al., 2016), Healthy Brain Network (Alexander et al., 2017) to name a few [for more, see (Eickhoff et al., 2016)], and more recently the funder-based National Data Archive. Specialized tools to discover these resources and their content are improving fast [see for instance Scicrunch (Grethe et al., 2014)].

Efforts have begun in the neuroimaging community to create centralized resources to find openly released neuroimaging datasets. A very simple yet valuable collection was collaboratively compiled on the social coding platform Github⁷. OpenMorph,⁸ (Madan et al., 2018), is a curated list of open access datasets that can be used to study brain morphology. It includes sample sizes, types of MRI modalities, the associated publications and a link to each project's web portal to download the data. Anyone can contribute to this collection by creating a GitHub⁹ account and editing the document. The DataLad (Halchenko et al., 2018) project has developed a crawler to index the data from various scientific data portals for a unified interface from which to download these datasets from the command line interface on their computers. DataLad also hosts a web application to interactively explore the various datasets that have been indexed. We hope to see more aggregation of open neuroimaging datasets in the future, with accessible web interfaces to query and explore all our resources.

More generally, platforms like Zenodo (https://zenodo.org), Dryad https://datadryad.org/, and the Open Science Framework https://osf.io give researchers generous storage for their datasets and assign digital object identifiers (DOIs) to datasets. This means that researchers who primarily collect data can get credit via citations, potentially alleviating concerns about “research parasites” (Longo and Drazen, 2016) that prevent some from openly sharing data. Our scientific culture is in part a roadblock to data sharing (Tenopir et al., 2011). Ideally, moving away from placing importance on only the first and last authors during grant and career reviews may incentivize data sharing and large collaborations. It is clear that technical challenges are not the only barrier to data sharing; we discuss the social and ethical challenges with data sharing in the “pitfalls” section. For an overview of the resources on data sharing, data analysis, and data collection, see Figure 1.

Even though the benefits of the web browser for scientific collaborations are evident, using the web for our research comes with some drawbacks or difficulties. Collaboration requires the sharing of data, and while some argue that data sharing is an ethical imperative (Brakewood and Poldrack, 2013; Bauchner et al., 2016), one must consider the risks of reidentification of our subjects, particularly for clinical research. True deidentification is difficult because of linked metadata (Narayanan and Shmatikov, 2008; de Montjoye et al., 2015). For example, in Narayanan and Shmatikov (2008), researchers identified pseudo-anonymized Netflix users by linking data with metadata from another website (IMDB). In de Montjoye et al. (2015), researchers proved that pseudo-anonymized credit card data could be reidentified provided just four spatiotemporal points. Research in differential privacy (Sarwate et al., 2014) might alleviate some of these risks; regardless, it is important that subjects are made aware of the risk in the consent process. The Open Brain Consent website²⁹ is a collaborative effort to provide resources that aid researchers in the IRB process for sharing data, writing the consent form, and tools for the anonymization of neuroimaging data.

Legal obligations concerning personal data handling are evolving and the recent European Union General Data Protection Regulation (GDPR) will likely change the requirements for participants control over their personal data. This will need to be considered at all stages of the research data lifecycle. While a full discussion on the legal and ethical aspects of data dissemination and reuse is out of the scope of this article [see for example (Marelli and Testa, 2018) on the GDPR] it is clear that legal and regulatory constraints are going to shape the implementation and use of web based data dissemination and retrieval tools, and this will require increased attention and human resources in the future. The challenge will be to constantly adapt our infrastructures and practices to the new regulations, which will require continuous software development.

Another drawback of using web technology for collaboration, in terms of sharing data, accessing cloud resources for analysis, and distributing work, is bit rot (Baker et al., 2006; Cerf, 2011). Bit rot refers to the eventual degradation of information stored on electronic media; for example, information stored on floppy disks is likely not accessible for most of us. Web technology is advancing rapidly: the browsers we use now look nothing like they did a decade ago. Some websites that were built in the past do not work with modern browser technology, and most websites from the past are not available to us anymore. A decade from now, many of the links presented in this article may no longer exist. Servers cost money, and domain names are charged annually. Software needs to be consistently maintained to be compatible with current technology. Efforts such as the Internet Archive³⁰ and Digital Object Identifier (DOI) system are working to preserve the information on the web, and in the case of DOI, provide persistent links to our research articles. But we need to work with funding agencies to ensure we have the resources to maintain scientific output, outside of our research articles, that depend on web technology. We also need to work with publishers to ensure our full scientific output, including the web technology that is used to produce it, can be fully preserved.

Finally, web-based research depends on a stable and fast internet connection. Such infrastructure may not be available to scientists in developing countries, which further drives inequalities and will decrease the diversity of our scientific community. It is important to keep this in mind when designing web applications, by optimizing websites for slow internet connections, and building offline support.

The small or medium size laboratory structure [5–15 people (Conti and Liu, 2015; Cook et al., 2015)] is still the predominant basic research structure in universities and research institutes, and these are mostly set up such that in person meetings are practical. Nevertheless, it is common that one or several members of the laboratory are temporarily located in another institution or building and the meeting will occur through web video conferences. The number of companies proposing free or paid services that may include capacity to share documents has multiplied during the past few years (the authors count at least 7 web video conference systems as of today, for instance Zoom³¹, Webex³², BlueJeans³³, Skype³⁴, Google Hangouts³⁵, appear.in³⁶, GoToMeeting³⁷, etc, as well as project management systems such as Trello³⁸ or Asana³⁹), allowing for unprecedented efficiency even in local communication. A key aspect of some of these communication tools is their capacity to record the meeting (audio-video) permitting delayed communication and traceability of discussion points, ideas or decisions, as well as scaling for larger groups. Another key aspect is that the use of these tools allow a group to immediately scale to non local members.

A neuroimaging or neuroscience researcher's work is heavily influenced, if not directed by, the search for funding and progression in academia career. As these mostly still depend on the quality and number of publications, it is clear that publishing activity is central to a researcher's academic life.

The current publishing industry is still very much influenced by how this activity used to be at the turn of the twentieth century, at a time when manuscripts had to be manually typed and printed, and distribution of journals was achieved through mail. Today, the article remains a standard for scholarly communication, even though an increasing number of researchers realize that the actual scholarship may actually reside in the code and data rather than the article⁴⁰. Jon Claerbout, a professor from Stanford University, argues that an article about a computational result is advertising, rather than scholarship. The actual scholarship is the full software environment, code and data, that produced the result (Donoho, 2010). The web has transformed the industry and is de facto the new media for scholarly communication, but somehow less rapidly and less radically than it could have. Most traditional journals are still shipping some printed copies of their editions, while a very large number of “on-line only” journals with an open access policy have emerged with a business model based on article processing charges (ACP), occasionally generating low quality content, but a highly profitable business (for a long list of questionable publishers, see the Beall's List⁴¹). We note that Beall's list does not necessarily have the level of granularity required as it can address general publishers rather than specific journals.

Even when the web is adopted as the communication media, the very large majority of the articles are based on HTML and PDF, with almost none of the modern visualization and interactive figure components that can be delivered by modern JavaScript libraries (e.g., D3.js⁴²). In neuroimaging, a number of open source, browser-based visualization tools have been developed. Javascript brain viewers like BrainBrowser (Sherif et al., 2015), papaya.js⁴³, XTK.js⁴⁴ (Haehn et al., 2014), and AMI library (Bernal-Rusiel et al., 2017) enable researchers visualize neuroimaging data in the browser. Interactive, linked data dashboards have been built as outputs of neuroimaging software, like ROYGBIV⁴⁵ (Keshavan et al., 2017b; Klein et al., 2017), AFQ-Browser⁴⁶ (Yeatman et al., 2018), and MRIQC has a web-based viewer to visually inspect outputs (Esteban et al., 2017). The Open Anatomy Browser⁴⁷ (Halle et al., 2017) hosts a variety of atlases with collaborative viewing. These tools have greatly simplified the process of building and sharing complex, interactive visualizations. For example, researchers may deploy an AFQ-Browser visualization of their data with two simple commands (afqbrowser-assemble, afqbrowser-publish). These interactive figures may go much beyond the convenience of a better view of the result; they allow to test for the potential robustness or sensitivity with data input or methods in a way that cannot be provided by static figures. In such a case, some parts of the scholarship need to be communicated by interactive figures, but few publishers are able to provide the infrastructure for hosting such “interactive articles”.

Recently, the rise of documents able to mix code and narrative such as R-markdown or Jupyter notebooks also provide researchers with new opportunities for communicating full fledged research objects. Some publishers already have embraced these new possibilities. For instance, eLife is working with Stencila⁴⁸, designed to be documents that “…are self-contained, interactive and reusable, containing all the text, media, code and data needed to fully support the narrative of research discovery” to foster more reproducible and reusable research, see eLife. In the near future, systems such as Binder (Jupyter et al., 2018) will allow not only to publish and review the computational documents but also provide with a container and the environment for a fully re-executable publication. The new web tools are not only key to provide us with ways of publishing a more complete set of research objects, they also allow for new review workflows to be implemented. For instance, Frontiers developed a platform that intended to make the interaction between reviewers and authors more efficient. Tools such as https://web.hypothes.is/⁴⁹ permit readers to annotate only specific parts of an article and may in the future be re-used by a review system. Such a review system could associate expert reviews and open community based reviews.

The web is also transforming how research communities meet for discussions by creating virtual conferences. A number of virtual conferences have been successfully organized in the past, removing the constraints of space and travels, while still allowing for questions and answer sessions monitored online (see for instance neuroscience-2018⁵⁰). A recent twitter conference was recently organized (the Brain Twitter Conference) which could scale easily to tens of thousands of participants. These events are much easier to organize in a short time and less costly if not free for attendees. They also are possible to attend by all researchers independently of possible travel and funding restrictions and are only limited by time zone constraints. For example, Chris Madan advocates using Twitter for science in (Madan, 2017b), and see his associated blog post⁵¹ on this topic.

In the same spirit, global Brainhack events gather locally groups of neuroinformaticians who collaborate on software development projects, and are also sharing courses and seminars across locations. The latest Brainhack⁵² event took place in 16 countries and gathered more than 1000 participants in 5 different time zones. The University of Washington hosts various week-long summer schools or“hack weeks” (e.g., Astrohackweek, Geohackweek, and Neurohackweek) to promote education and training, tool development, community building, and interdisciplinary research by combining pedagogy with project-based learning (the“hacking”) around a specific domain (Huppenkothen et al., 2018). They found that this combination is particularly effective at fostering collaborations and promoting best practices. Through collaborative web applications like GitHub, the projects started at these hackweeks have continued even after the events ended, and have resulted in measurable scientific output [for details, see Huppenkothen et al. (2018)]. Data analysis challenges hosted by conferences or symposia like MICCAI⁵³ bring researchers together to solve problems in the field, even if they cannot be present at the conference, and these groups collaboratively publish their results [for example see Commowick et al. (2018) for the results of a multiple sclerosis lesion segmentation challenge]. A curated list of biomedical image challenges can be found at https://grand-challenge.org/challenges/. We expect these types of events to be more frequent in the future, limiting the ecological, time, and cost impact of physical travels but offering the capacity for communication of research at a truly global scale.

Ultimately, research needs to go beyond the scientists and will need to be disseminated to the larger public which, through taxes, is funding a large part of it (Illes et al., 2010). The field of neuroimaging necessitates costly acquisition devices (MRI, PET, E/MEG), and has been particularly well funded, not only because of its potential for neuroscience, but also because the ideas were communicated well to the public and to funders. Communication is now largely operated and achieved by social media platforms such as Twitter, LinkedIn, Facebook, and blog platforms, to name a few. To read more about the advantages and disadvantages of social media use for scientific communication, see (Bik and Goldstein, 2013). Online resources that teach how to effectively communicate science are provided by the Alda-Kavli Center for Communicating Science⁵⁴. To consolidate the current consensus of knowledge, Wikipedia is probably the best resource; offering an introduction to functional magnetic resonance imaging through the consensus writing of many researchers (for example, see the Wikipedia article for fMRI).

Last, but certainly not least, web based education platforms are also re-inventing how training is performed in neuroimaging. The standard in person courses are now often replaced by on-line material (see ReproNim⁵⁵, Coursera online courses⁵⁶, EdX online courses⁵⁷), and a series of YouTube videos by Jeanette Mumford⁵⁸) and also Dirk Ostwald⁵⁹ as examples, amongst many good online materials. This allows laboratories to give some inverted classroom type of training by considering that formal lectures can be taken on-line but exercises or projects are best solved or supervised with direct interactions. It should be noted that on-line question and answer forums such as Neurostars⁶⁰ (with a tagging system similar to stackoverflow⁶¹), NITRC, and software tool email lists, are also key for the training of young researchers and boost efficiency. For a review of scientific web communcation tools, see Figure 2.

There are both limits and dangers associated with relying too much - possibly almost fully - on web technologies and browser enabled applications and workflows for research. Web communication does not necessarily allow the level of in depth interactions that are required to discuss a specific research question. In person meetings can be necessary both to organize projects and to advance the understanding of our scientific questions. In our experience, in person meetings are better at providing decision structures and at building trust, which are both necessary for the management of scientific projects.

Some of the dangers associated with the use of social media could also propagate to the scientific arena. For instance, while social media may be a great medium for quickly accessing or publicizing articles, it may also focus the attention on a specific cluster of the scientific community. This in turn may create research networks that are less permeable to different ideas, like a scientific echo chamber (Kim et al., 2017).

The immediate access to non -or poorly- peer-reviewed works may also amplify incorrect results that would not stand scrutiny under peer review. Take for example, a paper posted on the preprint server arXiv called “Automated Inference on Criminality using Face Images,” which received a lot of criticism from the scientific community⁶². Even though it was not peer reviewed, and as of this writing has not been published in a peer-reviewed journal, it nevertheless received a lot of alarmist press coverage. This can occur within the traditional literature, albeit at a slower pace. The neuroscience and public health communities are still contending with the spread of misinformation regarding a link between vaccines and autism (Del Vicario et al., 2016), despite the strong evidence to the contrary (Taylor et al., 2014). Scientific communication is our responsibility as scientists, not only to the scientific community but to the general public; we must be cautious of the immediacy of the web.

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.