Tenzing and the importance of tool development for research efficiency

1.The changing research process in team science

Methodological innovations and technological advancements have changed how scientific research is conducted. Today, for many projects individual researchers do not have sufficient knowledge and resources to perform all parts of the work. Consequently, these scientific projects are conducted by large teams of researchers [2]. For example, some multi-lab replications in the social sciences have dozens or even hundreds of participating researchers. When this many contributors are involved, seemingly trivial tasks, such as listing all contributors and their affiliations on the title page of a manuscript, turns into a time-consuming and error-prone endeavor [2]. The increasing numbers of project contributors have highlighted the importance of indicating who did what in a project. This, in turn, has further increased the documentation that must be done [3] (Holcombe, 2019). To reflect the shift toward giving equal acknowledgment to all who contributed to a paper, not just its writers, we will use the term ‘contributors’ rather than ‘authors.’

2.Why manuscript submission is so bothersome

Two main challenges arise when managing contributor information in large-scale projects. First, each contributor’s metadata - names, affiliations, and funding information - must be collected and properly managed. Traditionally, the corresponding author is solely responsible for this task. Many journals now require reporting of who contributed what to a research project, and even when journals do not require it, some research teams prefer to do it. One increasingly-popular way to document this information in a standardized and machine-readable format is by using the CRediT taxonomy [4]. The taxonomy outlines fourteen different types of contributions (see http://credit.niso.org/). As there are no widely-used tools, guidelines, or best practices on how to manage contributors’ metadata, researchers typically use inefficient ad hoc methods to collect this information, such as emailing each collaborator and then assembling the collected information directly in the manuscript. Such ad hoc approaches are tenable when the number of contributors is small, but they are error-prone and inefficient in larger projects. Furthermore, when corresponding authors do not collect contributors’ metadata until just before or at the time of submission to a journal, ad hoc approaches can delay the submission of the manuscript.

Second, contributor information is typically collated and formatted manually in manuscript sections such as the title page, the funding information section, and the contributor information section. Creating these outputs can be frustrating as different journals require different formats. For example, some journals require full contributor names while others prefer the use of initials. To the best of our knowledge, until tenzing there was no tool to facilitate or automate these tasks. Another source of frustration is that researchers often have to re-enter the same metadata cell by cell in the journal’s submission portal.

3.Tenzing and other tools can help

To facilitate managing contributor’s metadata and automate their reporting, we developed tenzing [1], an online app (https://martonbalazskovacs.shinyapps.io/tenzing/) and R package (https://github.com/marton-balazs-kovacs/tenzing) for documenting contributorship. Since the release of the first version, we have received feedback and feature suggestions from the research community, and professional user interface advice from ChaiOne (ChaiOne.com). Based on these inputs, we have updated the design and code of the app and released tenzing v0.2.0. (available at the previous link)¹.

To collect the contributors’ metadata, research teams are provided with a standardized Google spreadsheet that they can edit simultaneously (see Fig. 1). In the spreadsheet, each row contains the information for a different researcher. The columns are for their various metadata, presently: name, email address, affiliations, funding acknowledgment, and ORCID ID.

Fig. 1.

A snippet of the standardized spreadsheet that serves as the only input for tenzing. The spreadsheet is available at: http://bit.ly/tenzingTemplate.

Fig. 2.

A screenshot of the tenzing app. In the second step, users can choose between uploading a local file or pasting the URL of the filled-out spreadsheet.

The spreadsheet also contains fourteen columns corresponding to the fourteen contribution types of the CRediT taxonomy [4]. Researchers can indicate their (intended²) contributions according to the specific roles by clicking a checkbox. The definitions of each CRediT role, which are displayed when hovering over the label, help users report their contributions in a standardized manner.

When it is time to create a manuscript file, an author can load the filled-out contributors table into tenzing by pasting a link to the Google sheet into the tenzing app or, if they have downloaded it as a file, by uploading that file (e.g., in XLSX-format; see Fig. 2). Tenzing then validates the contributors table to ensure that the metadata are properly structured and no values are missing or duplicated.

Based on the contributors table, tenzing can create five different outputs with just one click for each. Three of these outputs are human-readable and designed for inclusion in manuscripts. They are the title page, the list of contributorship information, and one or more sentences that report funding information. The other two outputs are structured, machine-readable outputs, one in YAML [5] format and one in JATS-XML [6] format. Each output can be downloaded or copied to the clipboard by clicking a button.

The title page output provides the names and affiliations of all contributors in the annotated list format expected by many journals (see Fig. 3).

Fig. 3.

Screenshot of a title page created by tenzing. The app automatically detects if there are multiple first authors and creates a note by listing the names and highlighting the corresponding author.

The contributorship output lists each CRediT role together with the corresponding researchers’ full name or initials (see Fig. 4). In many journals, this text is expected to be part of a section with a name such as “Author Note” or “Authors’ Contributions.”

Fig. 4.

A screenshot of the contributorship output according to the CRediT taxonomy. Users can choose to list the CRediT roles after each corresponding name as well.

The funding output lists the full names or initials of contributors who received funding for the project and the name of the funder (see Fig. 5). This output is suitable for the funding information article section used by many journals.

Fig. 5.

A screenshot of the funding acknowledgment output. The app automatically lists the names of multiple contributors if they were supported by the same fund.

The YAML output (see Fig. 6) is for researchers who write their manuscript in R Markdown and use the papaja package [7] to create PDFs and Microsoft Word files in APA format.

Fig. 6.

A snippet of the YAML output which can be used in manuscripts written with the R package papaja.

The JATS-XML output is the format that publishers use to make CRediT information available alongside articles in a machine-readable fashion (see Fig. 7). Publishers generate it from the information that contributors enter into journal management and submission systems, which, as mentioned above, can be very laborious. Unfortunately, no publisher currently allows contributors to skip this step by accepting a pre-generated JATS-XML file such as that provided by tenzing. We consulted with several journals, publishers, and preprint servers who indicated that they support the concept, but at the time of writing, none of them has added this feature to their submission system.

Fig. 7.

A snippet of the JATS-XML output created with tenzing.

4.Reforming science with efficient tools

Whereas tenzing focuses specifically on documenting contributorship, it also showcases a general need for new tools that address the inefficiencies that emerge from changing research pipelines. By efficiency, we do not necessarily mean an increase in the number of scientific outputs, but rather allowing researchers to spend more of their time on tasks that improve the quality of their research. Efficient research practices may thereby reduce errors, as well as researcher frustration [8].

High-risk organizations and for-profit companies allocate substantial resources to automatization and to the development of standard guidelines to avoid mistakes, increase efficiency, and thereby increase their productivity [9,10]. Academic researchers in many sciences, in contrast, may not normally think of their work in terms of efficiency. This is unfortunate because efficiency may be an ethical requirement in light of the fact that researchers’ time is often funded by taxpayers [11].

The uptake of new tools that address inefficiency can be slow, because of the burden of learning how to use them. It is therefore important that new tools both facilitate the completion of routine tasks and are easy to master. Tools that facilitate routine tasks free up time and can facilitate the adoption of new best practices. In the case of tenzing, researchers may initially be attracted to the app for its ability to generate a title page and funding information, but continue on to use its functionality to document each author’s specific contributions using the CRediT taxonomy.

5.Tools developed by researchers for researchers

Tool development by researchers faces a number of obstacles. While researchers are often the ones who recognize the need for new or better tools, they often lack the skills, resources, and infrastructure to develop them. For-profit entities, on the other hand, have the necessary resources, but are less interested in keeping their services free-to-use for the research community [12,13] (Taylor, 2016; Posada & Chen, 2018). For researchers, acquiring the required skills, such as programming languages or user interface design, can be time-consuming and the contribution of this investment to career advancement is uncertain. Currently, tool development is not adequately rewarded by academia. Developers do not receive the same credit as researchers who conduct experiments or develop research methodologies, even though a good tool can have a substantial impact, by speeding knowledge accumulation thanks to the research efficiency gains. One reason developers often do not receive credit is that journal articles are the currency of academia, but few journals publish tool-focused articles. Instead, tools are often popularized via other means of communication. Furthermore, if a tool is introduced through the traditional route of a journal publication, these publications often are not cited by researchers who use the tools in their research [14,15].

Another challenge for developers is that tools, unlike research projects, can require continued attention long after the publication of the associated journal article - in particular, when the tool is widely-adopted. The work associated with a scientific study is completed upon publication in a journal. For tools, in contrast, publication in a journal may increase adoption and increase the demand for upgrades and continued maintenance to ensure continued stability and usability. Such maintenance work occupies developers without providing an opportunity to publish new journal articles. Thus, tool development is a long-term commitment that is rarely honored in the academic reward system and therefore can put developers at a career disadvantage - with the incurred disadvantage paradoxically more pronounced for widely-adopted tools than for less-adopted ones.

In the case of web applications, web hosting is another obstacle facing tool developers. For-profit companies continuously invest money to support their interactive web content. Researchers, on the other hand, often struggle to secure comparably-sustained funding. As a consequence researchers often rely on free services, which limit the speed or the number of concurrent users (e.g., the free Shiny server³ hosted by RStudio that serves tenzing).

Finally, the ability of new tools to increase research efficiency is often restricted by the lack of interfaces with other applications that researchers use. For example, tenzing should ideally interface directly with publishers’ submission management portals. While some of these portals have an application programming interface (API) that allows other entities to programmatically communicate with it, we do not know of any that support adding contributor metadata, not to mention CRediT information. Most submission management portals are owned by for-profit entities that may not be motivated to facilitate interfacing with free-to-use external tools [7,8].

6.Conclusions

In the present paper, we showcased how the tenzing app helps researchers to collect and structure contributor information. We argued that tools such as tenzing can play an important role in improving the efficiency and indirectly the reliability of scientific work. Presently, scientific tool development faces several challenges. It is either done by the researchers who are rarely trained for this task and struggle to guarantee the long-term maintenance of their tools, or it is done by professional corporations who are less interested in keeping their services free for the research community and interoperable. We hope that the issues this paper highlights would generate further discussions among scientific stakeholders and usher in a new age of research efficiency.