NINDS's Building Up the Nerve

S5E2: Thriving in Team Science

• NINDS • Season 5 • Episode 2

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In the fifth Season of the National Institute of Neurological Disorders and Stroke’s Building Up the Nerve podcast, we help you strengthen your science communication skills with tools and advice to use throughout your career. We know that navigating your career can be daunting, but we're here to help—it's our job!

In the second episode of the season, we talk about Thriving in Team Science, focusing on how to build professional collaborations and guidelines to ensure success for all parties when working in team, especially across disciplines.

Featuring Bosiljka Tasic, PhD, Director, Molecular Genetics, Allen Institute for Brain Science; Heidi Baumgartner, PhD, Research Scholar, Stanford University and Executive Director, ManyBabies; and Lingfei Wu, PhD, Assistant Professor, University of Pittsburgh

Resources

Transcript available at http://ninds.buzzsprout.com/.

Lauren Ullrich:

[Intro music] Welcome to Season 5 of the National Institute of Neurological Disorders and Stroke's Building Up the Nerve, where we help you strengthen your science communication skills with tools and advice to use throughout your career. We know that navigating your career can be daunting, but we're here to help— it's our job![music fades]

Marguerite Matthews:

Hi, I'm Marguerite Matthews, Section Chief for Career Preparation in the Office of Programs to Enhance the Neuroscience Workforce, also known as OPEN, at NINDS.

Lauren Ullrich:

And I'm Lauren Ullrich, Section Chief for Career Advancement in OPEN, and we're your hosts today.

Marguerite Matthews:

In our last episode, we discussed composing scientific narratives. Today, we're going to talk about thriving in team science. This will focus on how to build professional collaborations and guidelines to ensure success for all parties when working in a team, especially across disciplines.[music] Joining us today are Dr. Heidi Baumgartner, Dr. Bosiljka Tasic, and Dr. Lingfei Wu. Let's start with introductions.

Heidi Baumgartner:

Hi, I'm Heidi Baumgartner and I'm a research scholar at Stanford University and the executive director of ManyBabies, a global consortium of developmental psychology labs. ManyBabies is a collaborative project for replication and best practices and developmental psychology research. Primary goal of ours is to bring researchers together to address difficult, outstanding theoretical and methodological questions about the nature of early development and how it's studied. We also emphasize open science practices in everything we do, including sharing our data sets and reproducible analysis pipelines, holding workshops and seminars on topics ranging from project management to how to conduct a power analysis, and transparently documenting project contributions. I'd describe my science communication style and philosophy with transparency, respect, and directness. Those first two I borrowed from the ManyBabies guiding principles, and I think the third one complements those first two nicely.

Bosiljka Tasic:

Hi, I'm Bosiljka Tasic. I'm the Director of Molecular Genetics at the Allen Institute for Brain Science in Seattle. In my team, we want to understand how the brain performs its functions to control various neural states and behaviors. We use mouse as the most versatile mammalian model system to measure various properties in individual cells of the brain. And then we use this information to group cells of similar properties into cell types, as well as to build genetic tools for experimental access. The three words that describe my communication style: direct, concise, and precise. And preferably interactive.

Lingfei Wu:

Hi, I'm Lingfei Wu. I'm assistant professor of information science at the University of Pittsburgh, and my research of teams starts at one of my career definitive research published on Nature as a cover story in 2019, where we analyzed 65 millions of teams in science and technology. And we demonstrate that in the past century, it was small teams that has driven the innovation in science and technology compared to large teams. Whereas large teams play more the role of developing existing ideas and consolidate them. And since then, my research has been focused on looking at the characteristics of teams, like the size, the shape, the structure, the collaboration distance. And, uh, again, we found my research contribute to something very compliment to this, I will call it enthusiastic in collaboration and team science as our time's zeitgeist, when everyone want to collaborate, my research consistently reveals there are cost of teams, and not just benefits, and I look forward to discuss more in this section.

Lauren Ullrich:

And what three words describe your science communication style or philosophy?

Lingfei Wu:

I will just use one word, speak in plain English, and it takes a long time to do so, especially as I work in the School of Computing and Information, and I'm typically work with, and I'm also consider myself as a data scientist. It's very hard to not talk about this machine learning jargons and programming language to explain things in intuitive way.

Marguerite Matthews:

Yes as someone who doesn't speak any computing languages, [laughter] I appreciate that.

Lingfei Wu:

Yes [music]

Lauren Ullrich:

Okay so we're going to start you off with an easy one. What is a team and why work on a team?[laughs]

Bosiljka Tasic:

So I would say a team is a group of people that have a common mission. And the main benefits of working in a team is that you have more minds that look at the same problem and a variety of expertise and experiences that can be brought together to deliver this common mission.

Heidi Baumgartner:

I think it's something that we just all intuitively know what it means, but in my group of ManyBabies along with some of our partner networks, we talk about big team science. And we use that term very loosely to mean endeavors where a comparatively large number of researchers sort of get together to pool their intellectual or material resources in pursuit of a common goal. So that's intentionally vague. That can mean lots of things to lots of people, but we try to be as inclusive as possible when we talk about that.

Lingfei Wu:

I think there are two answers from the practitioner perspective and the researcher perspective, right? Because I will assume that from more a practitioner perspective, it means something more dynamic. If you have a lab, you have a research group, you will typically just call it a team. But in the researcher perspective, it's very hard to define because people come and go in your labs and money, sometimes you have more money than other times to hire. So in terms of researcher perspective, we usually start from defining team as a project, right? Such as like all the authors that co-author on the paper or all the active contributors in a GitHub project. So this is what I call as uh project-oriented teams. But I think there's a gap here that requires more research to be done that extends the current focus of project based teams into more long term actual teams that represents the efforts and organizations of science.

Marguerite Matthews:

And along those lines, like, perhaps regardless of the types of teams that you're working in, how do you build collaborations that work for you to reach those goals, right? Like when you're thinking about all the different people you may need for this, like how do you make it-- not only, like, bring it together, but then make it functional, right? That everyone is getting what they need out of that collaboration. And how do you communicate across disciplines? I mean, Ling, you mentioned about being able to speak in plain English and not always using jargon. But maybe that's difficult when you're working with people who are also thinking about their own jargon and bringing that to the table. So can you all talk about the different types of approaches to that and also what may be some challenges in getting started that people may want to keep in mind as they're moving forward, and then hopefully ironing out some of those issues as they go along.

Bosiljka Tasic:

The first thing that comes to mind is do we all actually subscribe to the common mission? Do we understand that common mission equally well? Do the things that we maybe put out as the common mission, do they mean the same to all the members of the team? So I think, again, concise and precise communication are really important in defining the mission. And then the main challenges I feel that arise during the execution of the mission are frequently communication challenges. People coming from different fields of expertise, sometimes just trying to understand how other people do the work they do. And sometimes asking questions may be in different ways that not everybody is accustomed to. Maybe the intensity of interaction or the frequency of interaction or how frequently you challenge somebody can sometimes feel adversarial versus supportive. So I think communication challenges are really the most common ones in team science, or the ones that I encounter the most.

Lauren Ullrich:

Yeah, because when I think about teams and the challenges around that, like one of the most salient memories for me, was in grad school. I was doing cognitive neuroscience research and a couple of years in, I consulted with a biostatistician and I just felt like we were speaking two different languages. And I was like, but we both do science, you're a biostatistician, so why are we having so much trouble communicating? And that was really kind of surprising to me and led me to learn a lot more about the differences between different disciplines and that even what constitutes evidence can be different across different disciplines.

Bosiljka Tasic:

And I want to mention one more thing. I think recapping, having somebody who takes the role of recapping, restating what that person understood is the agreement or conclusion we arrived at. And then sort of inquiring again are we on the same page? I think that actually really helps. And I want to say it's just the moderator is actually somebody who really summarizes and sort of moves the communication to the next stage.

Lauren Ullrich:

Yeah, that's a great concrete tip because sometimes you're just going along and you say something and everyone's like nodding. And then several months later, you realize that you were just completely talking past each other and [laughter] had no idea what the other person actually meant. And it's better to find that out in the moment rather than when it's maybe too late to fix things.

Heidi Baumgartner:

So our projects kind of come together over time. Uh, our teams are formed over time. It's a very iterative and fluid process. So generally speaking, the way our projects work is a small group of people will propose a project idea that they are intending to lead the project effort. And once we've adopted that project as a ManyBabies project, we might start by reaching out to some folks who are experts in that topic and inviting them to get involved. And then we just publicize the project on listservs, social media, word of mouth, and we invite anybody who's interested in participating to join us. So we do that by having Zoom meetings. We work collaboratively on things like Google Docs. We have a Slack workspace where people can have more informal discussions. But the composition of our teams is, um, people are constantly coming and going depending on their career stage, their other priorities. And so we work a little differently than a traditional project might, where you have a more stable set of contributors. And that has worked well for us, although there are certainly challenges involved with that as well. But it's sort of just built into our ethos as an open network.

Marguerite Matthews:

And can you talk about those challenges?

Heidi Baumgartner:

Sure. So just the challenges of working with really large distributed teams. So um, there are some basics in that, like any collaborative effort, there can be sort of a diffusion of responsibility the more people you have involved. Uh, especially if you don't have a strong leadership group sort of steering the ship as it goes. So that can be hard to get a lot of people all pointed in the same direction. Different perspectives, which can be really great and lead to creative problem solving, new ideas. It can also lead to conflicts or stalemates if you have people that are disagreeing on the best course of action. So we're always trying to improve sort of our processes for keeping discussions to be constructive and making progress instead of just getting stuck on the same issues over and over again. And then lastly, I'll just say is that at least our method where we're inviting everybody to be involved and we try to do things sort of on a consensus based model is that it can slow things down considerably. So not only is it just hard to schedule a meeting with dozens of people who are across the world, giving people time to weigh in to co-work on documents, to consider changes, it just is a much slower process than a single lab study would be where one PI potentially is making all of the decisions and able to keep things moving in a faster way.

Lingfei Wu:

So, last year, we published a Nature paper in response to remote work. Because there was ongoing conversation of, shall we get back to office or shall we not? And to respond to that, we analyzed millions of teams in science and technology in the past decades, and what we observed there is that communication matters, especially in-person communication matters in remote work . Why? Our explanation is that to generate ideas, you need to put people together in the same room. There are many reasons like, there's something tacit knowledge we need to interact to communicate with each other. In terms of that, that could be emotional engagements that happens more intensively when we are in-person. But one reason or the other, our observation is quite robust. What we observe is when the same team of people doing the same topic, like the same composition of scientists, they publish the same topic paper, and when one of them move out, the collective engagement in research design and writing in terms of published papers has significantly declined. So the observation is that when people move away from each other, they are more likely to contribute to technical work, not conceptual work. The takeaway is that if your research is fundamentally innovative, or if you are in a stage that is still in the early idea generation stage, then you if you are not the same city, maybe at least visit each other. But as the project evolves, when it comes to more implement stage, and that requires technical, but not conceptual work, maybe it's time to see that on the backseat of remote technology.

Bosiljka Tasic:

I absolutely love having some really truly in-person time when we start a collaboration. I think once things are clear what we are doing and when the collaboration starts flowing, I think virtual is really a major plus, the fact that we now can easily meet, we don't have to travel it's extremely useful. It gets more difficult the bigger the group size becomes. And you frequently have just a few people sort of dominating and maybe some people come with questions, but having the large groups of people and having them all be engaged, I think that's a challenge that's there, you know, in person and virtually no matter what. But I do think that meeting people in person is really helpful. Once things are put into motion, virtual is great. And then also even chats, even just going back and forth on chats is useful. We use Microsoft Teams a lot because you have a way to put all the information at one place. All the documents, links. And then you also have a way to do chat. Though there is a danger. There is a danger that you have too many people chatting.[laughter] It's sort of a cacophony of chats! And with a ton of emails that I get, for example, and a ton of chats, I sometimes will choose not to read the details, just because it's too much. So I do think that the ability to easily contact each other is positive, but I think it needs to be employed judiciously.

Heidi Baumgartner:

Yeah, those are really interesting points. Our network operates almost entirely virtually. We have people all over the world. I think we're up to 50 countries represented in our network right now. But sometimes, you know, the project leadership team will have two or three people who are clustered at the same institution and can be discussing some of these early ideas in person. So I haven't looked at that in a systematic way, but I think that's an interesting way to think about how our projects sort of evolve from the very early stages. That said, we have adopted some sort of tools and procedures to try and maximize our team's success. And I agree that communication is the number one most important thing. So one thing that we have implemented for all of our projects is as soon as possible, every project develops a collaboration agreement that has clear things written out in terms of what the expectations are, discussions about what the various roles and responsibilities are for contributions, what contributions will merit authorship, what the planned outcomes of the projects are, whether that be one or more manuscripts, a dataset, maybe in some cases it's a physical product or intellectual property ownership or patents, that type of thing. And so getting that all outlined in a document as soon as possible in a transparent way is crucial, I think, to setting a team up for success. And we had a little pushback from some of our members when we started implementing these because people were very used to operating in a very informal setting within their own labs or with collaborators that they knew personally, and they thought that this might be an extra level of bureaucracy that we didn't need or might make people feel like they're operating in an environment of distrust. But since we've started using them, we haven't had any pushback from anyone at all. And I've become a strong advocate for them for collaborations of any size, not just big team science collaborations like ours. Looking back, I'm like oh, these would have been great to have in the labs I worked in as a student. And I think they are particularly protective of those who are lower down in the power hierarchy, like students or early career researchers, because, you know, it's very clear what you need to do to get authorship, where you're going to be in that authorship order. And it's not something that's being determined by the person in the most powerful position when you're getting ready to submit that manuscript to a journal. So that's the number one thing that I advocate for to get collaborations off to a good start and to sort of anticipate and head off issues before they become issues rather than having to deal with them later down the line.

Bosiljka Tasic:

We internally at Allen for any standardized procedure that's part of a pipeline and pipeline is like a set of procedures that you execute repeatedly to generate standardized types of data, we try to also deposit those to protocols.io. And even what may seem like trivial logistical things, like how do we communicate and share the data and reagent? Sometimes we spend a ton of time just sending emails back and forth, as opposed to from the beginning having let's say, a shared software platform where we'll put all the documents or you know, we need to sign a collaboration agreement to proceed with this.

Marguerite Matthews:

Yeah, definitely. I think setting the stage and the expectations ahead of time will probably save a lot of very awkward and difficult conversation. But, I think all of us here know that even with your best intentions and all the things you do to prevent issues, it doesn't always happen that way. But I would love to hear if you all have had experience with this to talk a little bit more about how do you address issues when they come up. When you have that communication breakdown or I know we agreed to this at the beginning, but because this project or sets of projects have taken a different direction you know, we may need to make some adjustments. So have you all had to deal with some of those more challenging types of communication or maybe even confrontation in some respects, if it is something that you feel like we really just cannot continue like this and we have come to an impasse and we need to figure out other ways around it.

Heidi Baumgartner:

So we've been lucky in ManyBabies that there haven't been a lot of discussions that have ended in an impasse that we're just not able to get by. We have some things in our collaboration agreement talking about how disagreements will be handled. I mentioned earlier, we try to do things on a consensus basis, but you know, there are some times when you just need to make a decision and move forward. So our project leaders are designated as sort of the final decision makers, but we have a process in there for people to express disagreements, either to me as the network director or to another neutral party. Uh, but again, we've been lucky in that it hasn't come to that for many of our projects, uh, people are generally pretty agreeable and are just happy to have the opportunity to voice their disagreements. I know from speaking with colleagues that other groups have played around with models for expressing dissent. My colleague, Nicholas Coles, who was the director of the Psychological Science Accelerator, played around with a sort of dissent model that was kind of akin to how the Supreme Court does it in the US[United States] where there's a majority opinion, but then members are able to write up a dissenting opinion that might get included in supplemental materials or somewhere so that people can feel like even if they don't sort of ascribe to the majority view, they can still be a participant and have their opinion heard. So we're always exploring options like that. But generally it's worked well just to give people a forum to voice disagreements and then to move forward from there.

Marguerite Matthews:

Sounds like you should open a consulting business and[laughter] get paid to help people figure that out because that all sounds amazing. I mean, that you've had so few, um, issues that you don't feel like you could kind of get over in a straightforward way. And it's nice that people have really bought into what has been established. And also, just as a side note, Every time you say ManyBabies, it takes my brain just a second to realize you're talking about an organization and not just like "mini babies." I mean, you might be talking about both, but...[laughter]

Heidi Baumgartner:

I've heard that before, and I'm not upset about it. I like that!

Marguerite Matthews:

It's awesome, actually! I have all these like cute baby faces, uh, like almost like a Gerber's commercial kind of going through my mind when you say it.

Lingfei Wu:

I can testify with my personal story about the importance of communication. So I have this great postdoc and I also have my PhD student, and one of my observation is that when last year when we were all work together, and my postdoc tend to disagree with me much more often in the lab meeting than in private discussions about the same topic, same research. At the first second, I was like"Why does this keep happening? Why we can't just have like weekly meetings, so like each of us just save time?" And I immediately realized that, uh, I was doing the same thing when I was postdoc! I'm much more awful than my postdoc. So when I was a postdoc, I remember the moment our PI was asking me to comments on a freshman researcher, young and passionate, she/he developed a package of Python and demonstrate that it can be used to analyze a set of new problems like language and networks. When I was nominated to come on that for my expertise in Python, I found that I'm just getting weird. Like, I kind of want to say this is a great project, but I kind of also was hesitating. And looking back, this is a very honest moment of me that in this kind of large, group meetings, that people have roles and identities. And if those subtle things like happening, and if those things are not well taken care of, there could be problems, right? So I found that it doesn't work to have only research group meetings. Such a thing is considered as a gold standard in organizing a lab that is people typically meet every week. And there will be someone presenting, someone reasoning, and people exchange ideas, comments. I find it doesn't work in my case. There are two reasons. One is because I'm just incapable of hosting a successful lab meeting. Another thing is that I find that it always works better when you talk to each members individually before you actually put them together in the same room.

Bosiljka Tasic:

I also wanted to mention one thing, which is maybe on communication, if you don't mind? How to attend to rigor or experimental design if you don't understand all the methods. I think in multidisciplinary teams this is a rule, not an exception. So I tend to encourage people to ask questions even though they are out of the field. And sometimes a question may be trivial and you just answer it. And with that, you also sort of build the trust, build the goodwill among the members of the team that you're willing to engage them even if the question is trivial. But sometimes questions are not trivial coming from outsiders. And that's actually the beauty that when you're receptive to questions from members of the team that don't have the expertise, sometimes they can be very insightful because they are unencumbered by the history of the methods of the field. They're unencumbered by"oh this cannot be done." Actually it can be revelatory.

Lauren Ullrich:

So thinking about the importance of communication, sometimes one of the more delicate things is, like, giving feedback on a project or an idea, and are there specific strategies or tools that one can use that might help your feedback be received better or be more constructive?

Bosiljka Tasic:

I think, educated by our fantastic HR that we have at Allen Institute, especially when trying to provide feedback, possibilities for improvement, try and to do it in private. Sometimes I do have to put a stop to behavior that I don't think is productive. And it can be delicate and it can be sort of heavy both for me and the person receiving the feedback. But one thing that I try to really emphasize in teams is we are in this together. And I try to sort of embody that feeling. In terms of communication styles and feedback, I think it's very important to understand that there are different communication styles and that people really communicate differently. Some people will just talk, and some people, unless you call on them, they will not say anything. And actually we have had workshops at the institute where I've learned that I just didn't know why some people were quiet, and they said,"Well, I don't speak until you ask." So I think learning how people communicate and trying to engage people in ways that feel comfortable for them is just very, very important.

Marguerite Matthews:

And also sometimes people need to be stretched a little, right? Like sometimes you have to be maybe a little uncomfortable in some of these moments. I mean, not all the time. I don't think completely taking someone out. But the nature of science is you have to be able to communicate with your own team, but you also have to be able to communicate with outsiders to talk about the importance of the science, to talk about how the science is being done. And some people just may need a little bit more coaching, but I definitely agree with you that learning people's styles allows you to help them be more comfortable when it becomes maybe a little uncomfortable.

Heidi Baumgartner:

I'm not an expert in this. I've just sort of learned by doing. Both, both being on the receiving end of feedback and attempting to give constructive feedback. And what I've found works for me is just being as direct as possible. I feel like when you couch things in idioms or try and sort of get at things in a indirect way, it can backfire sometimes in not communicating what you're trying to communicate, but also leaving the recipient sort of wondering "Am I doing the right thing? Was that a criticism or not? I don't know." And that's not to say feedback is criticism, it's positive feedback as well. So I found that I appreciate people who can communicate things to me in a very direct way. There are ways to directly communicate while being polite and constructive and kind. So again, I think just being as direct as possible tends to work out the best in my experience.

Marguerite Matthews:

So, when working in a team, this idea of, you know, getting credit, whether it's to be able to put on your CV, put in a grant, or to just show your contribution as a person on a team, how do you deal with the idea of credit? Who gets credit on a team? How do you decide? I mean, publication is one thing, but really even like in terms of being the face of a project or getting a chance to maybe even present like what does that look like? Because I imagine it's very different than when you are sort of an individual. I know many of us still work in teams as like individual scientists. And we're not necessarily on these team collaboration things. But how do you allow people to feel like they have a valuable contribution and that they're getting credit, whether it's through kudos on the side, but also more formal levels of credit. But what does that look like when you're working in teams? And do people who tend to work on these larger types of teams, are they going for credit? Or are they happy to just sort of be just another person embedded in this larger one name of a group?

Bosiljka Tasic:

That is a really important question. And I think it starts, at least for me, with hiring. First, when hiring people at the Allen Institute for Brain Science, I emphasize the type of work we do and how we reward great team players. I absolutely emphasize that leading a team effort is rewarded, but also being a major participant in a team effort is rewarded . And I do again, emphasize that we tend to have papers with many authors. And that shared positions of authorship are more a norm than an exception. I also feel that most people that apply to Allen Institute for Brain Science know we do large encyclopedic and atlasing projects, and they want to be part of that. And in large teams, it is important to have a balance of people who lead and people who follow. I do maybe clarify when I hire people, whether I expect them to be leaders or followers. And I know that may not be a standard thing that is clear when people are hired at other institutions, especially in academia.[mmhmm]

Heidi Baumgartner:

Yeah, this is a thing that we think about a lot and comes up a lot in discussions of team science. One thing that I've noticed in discussing this issue with individual members of our network, really at all career levels is that sort of getting your name on a manuscript sort of in the middle of a long list of names is not necessarily the primary motivator for a lot of the people that participate in our projects. They're there to learn new skills, to meet new people to form relationships with others in their field, to feel like they're contributing to the field. A lot of what we do is sort of methological improvement and rigor. So we think a lot about how credit shows up in things like authorship on a manuscript, but the feedback that I hear from a lot of our participants is that that's not their primary motivator because you're right, it's really hard to know what that's going to mean in terms of career advancement and things like that. That said, we do try to recognize and document contributions so that contributions of all types are documented and reported in a transparent way that people can then communicate via their CV or applications and things like that. So we use the Contributor Roles Taxonomy, also known as CRediT, to document all of the contributions to all of our projects. We have a form that we have embedded on our website, which then populates a table, which is also embedded on our website. If you go to ManyBabies.org/credit, you'll see what I'm talking about. So people report how they contributed to a project in each of 14 different categories. So that ranges from conceptualization to collecting data, to analyzing the data, to writing the manuscript and everything in between. And you can look at that table and see their primary contribution was collecting data versus the people who really were involved in every aspect of the project. And so you can get sort of a scaled idea of how each individual contributed by looking at that. And then we use a great open source tool called Tenzing. I believe the website for that is Tenzing.club. And that is a tool that some colleagues have developed which compiles that contributor information into a nice table that can be easily inserted into manuscripts or passed along to journals for metadata purposes. It even generates a really lovely title page for your manuscript. So if you've ever created a title page with more than five authors and you're dealing with sort of superscripts for affiliations and things like that. It generates all of that for you. It really makes keeping track of these large teams much easier than it used to be. So we use tools like that to really try and make our contributions extremely transparent and documented in a way that people can report. Again, that might not be the primary motivator for all of our contributors, but we want to make it possible for them to communicate how they've contributed and why their contributions mattered.

Lingfei Wu:

Yeah, I think the contribution and credit conflict is one of the most fundamental problems in practice and in research. So in research, it goes way back to sociologist R.K. Merton's research on the rewarding system in science since 1960s. He described this situation in a very intuitive way. He called it the problem of 13 chairs. It happens when some prestige academic society have conference, uh, for example, Royal Society in London. And then they have 13 chairs on the stage. So the sociologist Merton has this question "So who is sitting on the 14th chair?" So there's a decisive moment that you see there must be a hard cut. So there's pretty much no difference, zero difference between people who are sitting on the 13th chair and people sitting on the 14th chair. But there's a huge credit difference. Like people are not remembered if they are beyond this 13th ranking. So this is like old version. And nowadays we have our own version of 13 chairs, especially in biomedical research, that's called co-first authoring. And I and my team are doing a study on this research. We basically analyze how people actually contribute by analyzing their latest source code using this author-specific macro. So we know who writes which part. And what we found empirically is that indeed, when people are listed as co-first author, they share equal on average, uh, working load. Sometimes the second author actually write more. But whenever you come to the actual remembering part, you analyze how people remember and perceive the actual contributions just by glancing at the ranking of orders. And nobody will remember who's the second [laughs]. So this is the cause of the problem. I'm glad to see that Heidi and her colleague is doing a lot of detailed work to push forward the norm. Because apparently, the norm of credit allocation is way behind the reality of contribution. Science is growing complicated and bigger and needs more collaboration. But it's much harder to scale up credit than scale up contribution.

Lauren Ullrich:

Mmhmm Yeah, with the co-first author thing, I remember several years ago on Twitter, there was a whole kerfuffle because somebody asked the question "If you're the second co-first author, is it inappropriate to list yourself first in your CV?" And some people were like "Well, it's 'co-,' so we're equal." And some people were like "Absolutely not! That's scientific misconduct!"[laughs] Like, there's a first co-first author and a second co-first author, and you're misrepresenting it if you do it in a different way. So clearly, like, even these fine distinctions, like, they still matter to people, right? Like, it's very emotional thing of fairness and equity. So I'm glad that there are more tools out there to try and help people navigate these things.

Lingfei Wu:

Yeah, we would definitely need more. Just to add a thing about the citation practice. So in social science, you cite a multiple order paper as someone et al.[laughter] So there's no place for co-first author.

Marguerite Matthews:

It's hard though especially in the academic system, it's set up in such a way that it matters. You know, maybe you don't care if your name shows up second, like aesthetically, right? You don't look at it like "Ah! I'm second." But it means something if someone's judging you based on where your name is listed on that publication. And so I see why people can have very strong feelings about it because we are growing up in a system where those things do matter and I don't think there's been enough done more broadly to say these contributions matter. Now, if it comes down to like, maybe thinking more specifically about how you're doing that, are there other ways to show that you can contribute or you have contributed? I know some journals actually show like, what have you done on this paper, like what have you provided? And perhaps that's maybe showing a little bit more fairness to the authors. But yeah it's cutthroat because it has to be, like, "I cannot have my name second. I have to show myself first" because it matters in getting this job or it matters in getting this fellowship. Because someone else said so, not because I inherently care about this taxonomy.

Heidi Baumgartner:

Yeah. And in some places have rules about the X number of first authorship papers are a requirement to meet certain thresholds. So it's not just a prestige thing. It can actually matter for things like hiring and promotion and depending on the conventions of different institutions and countries.

Marguerite Matthews:

Even different disciplines they have different orders of things. Like being a first order is nothing. It's like, who are you? You're Jane Doe might as well be if you're listed first on the author um, list. But if you're last, like, it's not just like, this is my lab. It's like, I'm the one who did all the work cause I'm listed last. And so, yeah, I mean, it's, it's a very strange thing when you start thinking about some perhaps more interdisciplinary things. If you're not all within say the biomedical sciences, it may have also different backlash if someone's like, no, actually I need to be last cause like, that's what my professional setting requires.

Lauren Ullrich:

Well, best case scenario, you have one person where first author is the main one and one person from a discipline where last author is the main one, and then you both win.

Marguerite Matthews:

Yeah, there you go.

Heidi Baumgartner:

I was just gonna say that in addition to some of these challenges, you know, we do have co-first authors for a couple of our papers where truly a couple leads have co-led the project from the beginning and our co-first authors, and we sort of denote that. It's easy for us when we put it on our website to just put a couple asterisks and say these folks are the co-first authors, but some journal and indexing sites like the metadata doesn't capture that sometimes and so that's another issue where it's one thing to decide you have co-first authors, but then making sure that information is carried through to some of these sites that do indexing and these metrics, whether you love them or hate them in terms of determining who the top researchers are, uh, you know, it's not a given that that information gets carried over. So it's kind of a challenge all the way through.

Bosiljka Tasic:

It can be quite challenging. You have people from academia who really need a first author paper because they're looking for, let's say, an academic job afterward. And at the same time, you have somebody internally at the institute who could contribute it. I think in my opinion, and I could share it with their external PIs, and maybe we agree they contributed at the same amount of work, intellectual contribution, leadership. It can be quite difficult and it can be part of a sort of a negotiation. And frequently we have shared first authorships, even for people within the institute. Because somebody may be contributed a lot experimentally and the other person contributed a lot bioinformatically. And the project just would not exist without the two. But then, who is the first first? Who is the second first? I mean, I think we have to take it in good faith. We have to discuss it. And I usually try to get, I really, not usually, I almost always, right, to get a bind from everybody to discuss it openly, to discuss first individually and then openly in a group. Okay. And try to see that we have reached a consensus. That's the best we can do, especially in large, in large projects where you just have a lot of people who have really substantially contributed. So I can tell you in the latest paper we submitted to BioRxiv about a month ago, we have four co first authors.

Lauren Ullrich:

Hmm.

Bosiljka Tasic:

We just. It was very hard to not do that. And we just decided to do it. And the paper has about a hundred authors total.

Marguerite Matthews:

Wow.

Lingfei Wu:

To this credit problem, I have two quick points to add. The first point is that why is it so hard to scale up credit? And this goes back to the thing that Marguerite has mentioned before, because it is a collective memory problem. To have a contribution, you don't need to remember, like you know you have a contribution, or you only need to be remembered by people next to you, right? Your collaborators, they remember you, then you have contribution within the team. But to have credit, usually we refer to collective memory in a much larger settings, like to be remembered as active researcher in the field. Now you need to get into the much more competitive pool of collective memory, and then that's why credit cannot be scaled because we cannot remember too many names. And no society will remember too many names collectively either. So that's the fundamental challenge. And this is an important challenge to teams because when teams are getting larger, we want to involve more people, we want to have more people contribute. But on the other hand, the manage and allocation of credit is not at the team's hand. It's not at the team's PI's hand. It's happening in a much larger scale. So that's the first point. We do need all kinds of communication and team management tools, and we need to revolutionize the citation practice to adapt to this evolving reality of contribution, but I want to remind it's deeply hard. And this brings to the second point in terms of the professional and career consequence. The postdoc, right? So this is a large pool in the biomedical research. They probably even is the backbone of the actual science research and innovation in biomedical. But they are likely, especially when they're in large things, they're likely to be vulnerable to this credit allocation that eventually undermine their career, right? Like, how we can have large teams, but allocate the credit in the way that reflects their contribution, right? So that larger teams doesn't undermine, doesn't cloud the individual credit. But also this goes beyond just in one team at a time. For those people who constantly working in large teams, how can we grow their career? Especially for postdocs in medical research.

Lauren Ullrich:

Mmhmm So what are the benefits of working on a team? What does a team have above the sole, the lone genius model that, you know, we think about when we think about the scientist working alone in their lab? Like, what's the elevator pitch for being on a team, of any size?

Bosiljka Tasic:

I mean, sometimes individuals who are dedicated, extremely, and working by themselves or in a very small group, they can do wonders, so... For me, though, I always felt much more motivated to work, to contribute, when I'm part of the team. I also feel like I'm more responsible, I'm not only responsible for myself because, eh, for myself. Well, you know, something bad happens to me, [laughter] not a biggie, but if something happens to the team, "oh my God!" so that's why I feel like to me it was always very, very important or it created more motivation to do something, to achieve something if I'm doing it for others. I mean, for me, but also for others.

Heidi Baumgartner:

So our projects are all large collaborations and obviously I think that there are huge benefits of working on teams. Otherwise our network wouldn't exist. One great thing is that when we get a group of experts coming from ideally different sort of places on the theoretical spectrum, they can work together to sort of collaboratively come up with a design to test a theory or a hypothesis in a way that isn't biased towards one particular theoretical framework. So it sort of forces people to consider their biases and come up with sort of a best test that will pass muster, not just for their own expectations, but for those of their colleagues as well. So it's almost like the design of our studies go through an internal peer review before even reaching an outside reviewer. So I think that's great. Again, I touched on this earlier, but our collaborative projects are a great place for our members, especially students and early career researchers to meet each other, meet the sort of preeminent figures in the field and work closely with them, even if they're not located in the same lab. I've heard of many cases where grad students got involved in a project and then end up and go do a postdoc with one of the faculty members that they worked with on one of our projects. So that's great as well. And then our projects also allow for specialization in a way that single lab study don't often allow for. So when you're working in your own lab, you kind of have to be a jack of all trades. You have to do everything from creating the physical stimulae if you're doing a non-computer based task. You have to collect the data, you have to schedule the participants, you have to clean up the data, you have to, you know, really do everything. Whereas in our projects, we have contributors who will come in and just work on the analysis pipeline. So they'll work in a smaller group of folks who are working collaboratively via a tool like GitHub to create some code to clean up the data. And they can really hone those skills and contribute in the way that is most interesting to them, is the most efficient use of their time, and other folks can contribute in the way that is the most interesting to them. And so we get sort of people pitching into different aspects of the project, and it allows for people with specialized skills to contribute in that way, and it also allows for people to learn those skills in a context outside of their own lab. So those are just a few of the benefits that I see for our type of collaborative project.

Lingfei Wu:

Yeah. I wanna add upon the one benefit Heidi mentioned, uh, teams is the way to connect junior scholars with prominent figures in the field. And I wanna remind that this benefit of teams has particularly been powerful for those from developing countries. So we have a lot of global talent in Asia, in Middle East, in Africa, in South America. And a lot of them already done their Ph.D., so they have completed their formal training. And the reason why they volunteer to come to the U.S. and work with the best scientists here is because through these collaborations, they learn when they work. And I want to emphasize that if we take away all these teams and collaborations, this means we permanently shut down a door for those global talent, which could be a loss to the U.S. innovation competitiveness. It will be loss for their career path. So team is a social device for training. It's important for preparing our next generation scientists.

Marguerite Matthews:

Yeah, I really appreciate you highlighting that benefit Ling, and I think it's easy for many of us, especially in the United States to think more about global talent that is physically located here. And perhaps not appreciating that a lot of people cannot get here. But there may still be opportunities for collaboration, sharing of knowledge, expanding on specific skills and the unique opportunities that may be available in someone's home country or the space that they are at that place, gaining perhaps a different opportunity. Maybe they don't have, say really state of the art equipment, but they certainly have the brainpower. Perhaps they have participants that are able to contribute in different ways than what we may be getting here, um, within this realm. So I really do appreciate that.[music]

Lauren Ullrich:

So thank you all for sharing your wisdom today. And can I ask each of you for one last piece of parting advice for our listeners?

Heidi Baumgartner:

I'll reiterate something that's come up already. And I think transparency is the key, like documenting things clearly, having a collaboration agreement, having a lab manual that outlines procedures, expectations, processes. It's so valuable, and the sort of startup cost of creating those documents is well worth it in the end and it's going to save you a ton of time, it's going to prevent issues down the line. It's all about transparency and documentation as far as setting teams up for success.

Lingfei Wu:

Yeah I'll summarize my thinking with the benefits and costs of teams. We are familiar with some benefits. We are not familiar with other benefits. The benefits we are familiar with is a team's increased productivity because it encourages specialization and collaboration between specialized scientists. So it increase productivity, it promotes division of labor. Collectively in teams, we publish more papers, likely with a high impact. Teams are, as we discussed, training institutions. Teams are the second universities, the second chance for many young talent that want to be good scientists. So these are all good things. But we also want to be aware of the cost of teams in the way that teams can cloud credit allocation within the team members. If this is not addressed well, this could accelerate the bias, the existing systematic inequality in the context of teams could in the long term undermine junior scholars career progression, such as postdocs. So the bottom line is we need more discussion of this kind to talk about the benefits of teams and how we can encourage, build, effective team science and mechanisms in especially in biomedical research while minimizing costs.

Bosiljka Tasic:

So I would say a couple of things. One, define the mission clearly, work on the mission together, and then subscribe to the mission. Second, keep the communication channels open, even when things are tough, come with good will. With good will to understand, to solve problems, and be open minded to criticism. Don't feel bad if somebody criticizes you, but try to make the criticism, try to build the atmosphere that criticism is welcome, that there may be some trivial questions, but don't, discourage people to ask those. And try to make it enjoyable. Try to make the communication helpful and joyous as opposed to a drag. I actually tend to try to make jokes a lot, which may not be funny always, but I think they inject some lightheartedness in the conversation and open up the floor to questions. So don't take yourself too seriously!

Lauren Ullrich:

Words to live by for sure!

Marguerite Matthews:

Yea, seriously![laughter]

Lauren Ullrich:

And Marguerite, what's your advice?

Marguerite Matthews:

Well, I really enjoyed this discussion about the diversity of thought, diversity of perspective, diversity of skill, perhaps effort level, and all of the great things that come out of these more collaborative teams. But I think the advice that I would give is to ensure that working on a project or starting a project that may incorporate a team that's interdisciplinary, to really make sure that it aligns with your needs. If you are a person who needs to have credit, you need to have your name on the summer jam screen. Perhaps that's not the best approach. I mean, maybe you won't reach a certain level of expertise where you can tap in and out of different types of teams as your career goes on. But I think it's okay to be honest with what it is that you need. And if you are fueled by certain types of career positions or grant mechanisms or other types of awards that do reward people who are sort of singularly given credit or are able to stand out. Um, that may not be the right opportunity for you. So I would advise thinking about what your interests are, both in terms of scientific study and the types of questions you want to tackle, but also what you think you need in your career, which may change over time. It may be different when you're starting out earlier versus later on, um, and thinking about what that looks like. Because maybe we will not have figured out how to scale credit or how to think about what this looks like. But I think it's important to be honest with what your needs as the individual are, and if those would prevent you not just from not getting what you need from the team, but perhaps bringing the team down or creating some discord within a team because other folks are there looking for perhaps a different level of satisfaction from a professional standpoint. And what about you, Lauren? What's your advice?

Lauren Ullrich:

I think I'll build on that and just say maybe the theme of this season, maybe the theme of this podcast is whatever you're going to do, be thoughtful and deliberate about it. And like, think about it, know yourself, know the other people that you're working with and communicate with them. And that's not, like, it's not easy, always, but it's worth it. So the more that you can invest in yourself and your skills and the people around you and their skills, I think that we all benefit from people who come with that kind of attitude.[outro music] That's all we have time for today on Building Up the Nerve. So thank you to our guests this week for sharing their expertise. Thank you to Ana Ebrahimi, Mariah Hoye, Jimmy Liu, Joe Sanchez, and Tam Vo for production help. And thank you to Bob Riddle for our theme song and music. We'll see you next time when we tackle collaborating with partners in research. You can find past episodes of this podcast and many more grant application resources on the web at ninds.nih.gov.

Marguerite Matthews:

Follow us on X @NINDSDiversity. You can email us with questions at NINDSNervePod@nih.gov. And make sure you subscribe to the podcast on Apple podcasts or your favorite podcast app so you don't miss an episode. We'll see you next time.