And if you’re a PI? You might feel like you’re running a barely-contained chaos machine; juggling grant deadlines, teaching loads, and a dozen advisees who all need different kinds of support—but don’t always ask for it clearly.

The good news is that this kind of dysfunction isn’t personal. It’s structural. And most researchers simply don’t yet have the methods, tools or training to remedy them.

In this edition of The Scoop, we’re tackling the advisor–student relationship from both ends: why it often doesn’t work, what systems can actually help, and the actions you can take today to make things better—whether you’re a mentee or a mentor.

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Relationship problems?

The student-advisor relationship is one of the most important dynamics in academia—and often, one of the most dysfunctional. Many PhD students feel unsupported, isolated, or unsure where they stand. According to a 2019 Nature Survey, one out of every four graduate students would choose a different PI if they could. The situation is likely even more dramatic for non-doctoral students, who report even lower satisfaction across the board when it comes to supervision.

Advisors, on the other hand, are juggling overloaded calendars and lack the support and resources they need to ensure their own students’ success. The result? Misunderstandings, missed expectations, and missed opportunities. Poor mentorship has real-world implications for all parties, slowing collaboration and weakening research quality and outcomes.

But we don’t need to wait for a paradigm shift before students and advisors can efficiently produce research together. Simple, shared practices exist to help rebuild these relationships into effective research collaborations once more. Here we review the key issues in these academic relationships, and simple methods and tools that address them.

The feedback gap: Still waiting for your paper?

It’s the most common complaint from PhD students: “I sent them my paper two weeks ago and haven’t heard back.” When the feedback does arrive, it’s often too detailed or out of sync with what the student needs.

But advisors aren’t just sitting on drafts for fun. Most are operating with overflowing calendars, administrative responsibilities, and dozens of competing priorities. One researcher in South Africa estimated that it takes about 200-300 hours a year to supervise just one PhD student. This perpetual time-crunch leaves supervisors exhausted while students are invariably disappointed.

Although no one in this equation can change their workload, we can change how we work together. Starting with the basic premise of most advisor-advisee interactions: requests. Clearly defining what needs to be done is essential. When facing time constraints, prioritize real-value contributions within accepted timeframes to stay on track together.

👩‍🎓 Student tip: When you send work, include a focused request like:

“I’m looking for high-level feedback: do the arguments flow, is the framing clear? Please skip detailed grammar edits for now.”

👩‍🏫 Advisor tip: Can’t do a full review? Use a fast triage framework: structure, novelty, and readiness for submission. It’s better to provide 15% feedback on time than 100% feedback too late.

Tool to try:
🔧 Use the CRediT Taxonomy to define who’s responsible for which parts of a project. This is especially helpful to avoid blurred boundaries on multi-author papers. Or use tools like Trello or Asana for tracking project status and assigning tasks. Use them to turn “we should do this” into “who’s doing what, by when?”

Lab meetings: A weekly time sink?

Every lab has its weekly meeting—but how many of them are actually useful?

Student POV: “We just sit through one presentation a week and no one engages.”
Advisor POV: “I want students to feel connected and informed, but the format’s stale.”

There’s no one right way to run a lab meeting. Some labs stick with updates; others try new formats to keep things engaging. Rotating who leads each week can shift the dynamic, especially if students get a chance to shape the agenda. Mixing up the structure can also help meetings feel less like status reports and more like real intellectual exchange. For example, rotate between project updates and paper discussions, led by different members.

And for those who prefer to gather thoughts ahead of time, using a shared Notion board, Google Doc, or even a Slack or Discord channel to collect questions or topics can help steer the conversation in more useful directions.

In the end, the goal isn’t just to meet—it’s to connect in a way that’s actually meaningful for the group.

👨‍🎓 Student tip: Even if you can’t change the structure of lab meetings, you can shift how you engage with them. Try coming in with a specific question or issue you want to raise, even if it’s just in the discussion portion. If presenting, use the time to test out an idea or get early feedback on something you're stuck on—not just to give a progress report.

👩‍🏫 Advisor tip: Check in with your group now and then about what’s working (and what’s not) in lab meetings. Even a quick anonymous poll or casual ask can surface ideas you haven’t thought of—and it signals that you’re open to adapting.

Tool to try:
🔧 Experiment with a centralized location for collaborative notes and project tracking, like Notion, Discord, Slack or even just Google Docs. Record the meeting notes to make it more actionable and searchable.

Misaligned expectations: Am I on track?

One of the most quietly stressful parts of grad school is not knowing where you stand. Many students have no idea if they're making good progress because the expectations are often vague and unspoken.

This can feel like walking a tightrope with no safety net. Students may keep working, but without clear direction or a notion of what is “enough”. And when feedback is infrequent or only focused on immediate deliverables, it's hard to get a sense of the bigger picture. That doesn’t just make the day-to-day more stressful, but can impact long-term goals and even completion rates.

From the advisor side, what looks like neglect is often an attempt to give space. Many advisors intentionally take a hands-off approach, especially with senior students, thinking it's respectful or empowering. But without shared benchmarks or check-ins, students may end up feeling lost.

In a system where degrees are long, progress is nonlinear, and communication is often inconsistent, this mismatch can quietly erode confidence on both sides.

To ensure expectations are met, they must first be set. It’s the student’s responsibility to set goals and milestones, plan out dates and deadlines, and share this plan. The advisor can in turn review the plan, ensure its feasibility and support the student in meeting their objectives.

👨‍🎓 Student tip: Set milestones and progress checkpoints. Use a shared doc to list goals (e.g., lit review completed, first dataset analyzed, paper submitted, etc.). Take charge of your own plan, set meetings and request feedback.

👨‍🏫 Advisor tip: Ask the student to complete an Individual Development Plan (IDP) as a shared tool to align expectations. Your institution likely has its own template you can use. Or you can start with a template IDP from another university like Johns Hopkins or the University of Missouri.

Tools to try:
🔧 Develop a roadmap or long-term plan — whether a spreadsheet via Excel or Google Sheets with concrete dates & deadlines, or a simple Google Doc or Word document outlining your Individual Development Plan (IDP).

Isolation: Does your lab know what you do?

Academia is full of people working alone together. Even within the same lab, every researcher is buried in their own project with minimal overlap in methods, topics, or even reading habits. It's easy for a lab to become just a shared physical space (if even that given the popularity of remote work now!).

According to a 2023 study, 62% of students lack a “sense of belonging”, indicating isolation despite feeling generally supported by supervisors. Although it’s difficult to evaluate the real-world impact of this particular problem, it likely curbs collaboration and intellectual growth. If students and postdocs collaborate less, they won’t develop the peer learning culture that strong research environments thrive on. This throttles research progress and can put even more intellectual burden on the PI.

The key is to build camaraderie based on shared experiences and values, which provide genuinely helpful insights for all. This includes practices like structured peer review, skill sharing, encouraging rituals like sharing “lab wins”, encouraging exchanges among sub-groups or pairs, and providing ongoing structure via a shared space.

👩‍🎓 Student tip: Fuel your curiosity about the work of others in your lab and start asking questions. Ask lab peers to review your work. You can start small, with a single figure or a short section of writing.

👩‍🏫 Advisor tip: Model curiosity by encouraging questions and research sharing, which is the cornerstone of collaborative work. Set up rituals, events and opportunities for collaboration (like those listed above).

Tool to try:
🔧 Set up shared spaces like a chat channel (Slack, Discord), shared notes (Notion, Jupyter Notebooks, etc.), or shared literature libraries (Zotero or Litmaps).

Neither students nor advisors can change overnight. But both parties can take simple actions to enhance collaboration and meet their collective goals better.

PhD students: Even if you can’t instantly change your lab, you can change how you engage. Set structure where there is none. Ask for clarity where you’re lost. Remember, you’re your own project lead, and your lab is there to support you.

Advisors: Your students don’t need perfection. They need predictability, clarity, and the sense that their work matters. Small changes in how you give feedback, structure meetings, or encourage peer support can radically improve the lab experience.

💬 Got tips for improving student–advisor relationships? Drop them in the comments or reply to this email. We’d love to include your ideas in a future issue.

Resources

Empowering academic advisors: A wellness revolution at UH Hilo, Hawaiʻi CC, 2025

Doctoral Students Are More Satisfied with Their Advisor Relationships than Terminal Master’s Students Are, 2022

Sleeplessness and anxiety: PhD supervisors on toll of COVID pandemic, 2021

How scientists use Slack, 2017

Graduate survey: A love–hurt relationship, 2017

I was a terrible PhD supervisor. Don't make the same mistakes I did, 2016

How much time does it take to supervise a PhD student?, 2012

There are potentially hundreds of thousands of fake papers in circulation. As of January of this year around 55,000 have already been retracted. These articles can be well-camouflaged, avoiding scrutiny for years. You may have read one yourself in the past few weeks without even realizing it.

Fake papers aren’t just “bad science”, they compromise the work of real scholars. If you call upon these papers in your references for your next literature review, you risk undermining the quality of your own work. But given how hard they can be to spot — how can you avoid this mistake?

In this edition of The Scoop, we dive into fake papers and look at just how widespread they may be, as well as key signs and signals to look for to avoid using them in your own work.

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What are “fake papers” really?

What we refer to as “fake papers” are any published, academic work that falsifies data, methodology, authorship, or some other aspect. Paper mills are the main source of fake literature today. These business operate under the radar, allowing researchers to pay for finished manuscripts that they can submit under their own name. To ensure they pass the peer-review process, some researchers form stable connections or “peer review rings”, guaranteeing publication of the fraudulent work.

Why does it happen? The classic publish-or-perish mentality is again to blame. Pressure is high for researchers to produce work in peer-reviewed journals. Under these strains and demands, some researchers turn to unethical practices like paper mills in order to remain competitive.

Paper mill production has exploded in recent years. Five years ago, publishers were not seeing any ethical cases reported regarding fake papers. As of 2023, this skyrocketed to the thousands.

These fraudulent papers don’t just pose a risk to the academic quality of future work via unruly references. They compromise the entire research landscape and even real-world clinical and medical decisions.

As much as 3% of all medical publications may be the result of paper mills. Other disciplines face similar problems, with a full breakdown visible below.

Given the rise of such papers, it becomes increasingly important for researchers to distinguish paper mill products from genuine publications. Relying on peer-review or publisher quality is no longer an option, particularly as AI use only increases, further exacerbating the issue.

How to spot fake papers: Quiz time

Papers from paper mills can blend in really well.

Try to guess which of these 2024 papers is real:

Paper A: Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis

Paper B: Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis

Can you tell?

That’s the difficulty with paper mill work. It looks real, and needs to do so to make it through the system. However, as we dive into these papers in more detail, the evidence starts to build as to which one is real.

Key signs of fake papers

Here are the key indicators that a paper may have come from a paper mill. Keep these in the back of your mind when you explore literature. After you’ve gone through them, take a look back at the quiz, and see if you can figure it out:

1. Generic or strange titles

The first sign that a paper may be fake is the title. Real research is specific. Fake papers often have more generic titles. But, this isn’t always the case. Be sure to check multiple factors when trying to decide if a paper is fake or not.

For example, here’s the name of a paper under investigation and found to have a fake peer review: “The Connotation Between Perioperative Glycemic Control Approach and Sternal Wound Infection in Individuals With Diabetes Mellitus Experiencing Cardiac Surgery: A Meta-Analysis”. This is quite specific, and is an exception to the “generic” rule.

2. Check the journal

Some fake papers tend to target predatory journals which lack a vigorous peer review. But, this isn’t a sure-fire sign, because even reputable journals sometimes have fake papers make it through the process as well. You can check the list of predatory journals here or here.

3. References have fake, self-citing or outdated sources

Review the references to see their quality and validity. Are they valid? Are those papers connected to the field via citations and references? Are there some recent ones or are they all over five years old?

Tip: You can use Litmaps to instantly visualize the article’s references, and see their overall reference and citation counts. Just search for the article and click on “References” in that paper’s details to get a breakdown.

4. Authors aren’t real, or have different backgrounds

Paper mill articles may host an unusual set of authors, which can be a good indicator of the paper’s authenticity. Either the authors are not real, or their research backgrounds are not in the domain the paper is in. Explore each author individually to see if it checks out.

5. Figures are fabricated, inconsistent, or copies from elsewhere

Figures and plots in a paper may be another giveaway that the article is fraudulent.

Experiment with Google Search or Google Lens on the figures of the paper to see if they appear elsewhere on the Internet. Or, review the figures in detail to make sure the make sense with the paper’s contents.

In 2017, a journal realized that eight different papers contained the same image, resulting in retractions. One of the paper’s authors admitted to using a paper mill for that paper.

If you follow these steps to distinguish the example above, you’ll find not all these steps help in every case. Did you manage to guess which of the papers in our quiz above was real? In this case, it’s tricky. The fake paper is from a reputable journal (Cureus by SpringerNature), the abstract is well-worded, and even the authors appear to be valid and on-topic with respect to their backgrounds.

Thankfully, you don’t always need to guess. If an article has been retracted, you’ll see that information on the article page. If you use a tool like Litmaps to search for articles, once you click on the article title to read it, you’ll see that information right away. You can also confirm its retraction state on RetractionWatch.

The real challenge is anticipating which articles are fake, before the journals even notice themselves. After all, you want to use only high-quality, reliable sources in your own research.

How do you distinguish real studies from fake ones? Share any tips or tricks with the research community in the comments below. As this becomes only more difficult with AI and large-scale paper mills, it becomes more important than ever to work together to uphold academic integrity.

Resources

China’s supreme court calls for crack down on paper mills, March 2025

Bogus scientific papers are enriching fraudsters and slowing lifesaving medical research, March 2025

Paper mills: the ‘cartel-like’ companies behind fraudulent scientific journals, 2024

How big is science’s fake-paper problem?, Nov 2023

How Sydney cancer scientist Jennifer Byrne became a research fraud super sleuth, 2017

Publication and Research Integrity in Medical Research (PRIMeR)

​Potential predatory scholarly open‑access publishers database, Beall’s List

RetractionWatch

We’re rapidly accelerating towards a future where academic articles may be largely written by AI. If you’re scoffing at this reality, then take into consideration the newest wave of features: “AI detectors”, designed to evaluate what percentage of a written piece was based on AI, with the destiny of allowing educators to determine how much of a paper can be written by AI (10%, 40%, 100%). But this cat-and-mouse game will no doubt quickly evolve, as even more advanced text generation will make AI detection harder.

What does this all mean for the modern researcher? The one who seeks to truly understand their topic, create high-quality research, and take ownership of our collective knowledge? With tools that replace each part of the research process, this idea of a researcher may not be around much longer.

In this edition, we dive into the topic of using AI in your academic writing, the powerful features available, and the key reasons why we should and should not use it.

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AI writing assistants today

If you’re still playing catch up, you’ve entered the space at a great time. Today, there are several remarkable AI writing assistants, with varying degrees of automation. There are many tools in the field for academics, like Jenni AI and Grammarly. But, keep in mind that many of these tools simply use GPT tech.

Try out these tools and you’ll quickly feel that you’re optional in the writing process. Today, AI can make a draft of your paper, generate citations, and even change the tone or style to your liking.

These benefits aren’t just handy, but significantly alter the writing process. It’s no wonder an increasing number of researchers rely on these tools as a part of their process. One evaluation found that 17% of computer science papers and 6% of math and Nature papers contain AI-generated text.

But, collectively, we seem to have bypassed the should and moved quickly onto the how. You’ll find countless articles and workflows on incorporating these tools into useful workflows, but little to no attention regarding the long-term implications of these tools.

Long-term objectives matter. There’s increasing evidence that excessive reliance on these assistants can lead to a very different world, and one we may not recognize.

Why we should not use these tools

Despite the prevailing popularity of AI in research and writing, some scientists are still skeptical, and there are good reasons why. Some worry about dubious quality from AI generated text. Others are concerned that their content may be flagged by AI detectors (although this can be increasingly avoided by using AI detector evaluator tools). Some may be trying to follow the guidelines of their institution or journal.

Although these acute reasons demand attention and consideration, it’s the long-term unknowns that may pose the greatest risk.

Decline in research quality

The main argument goes something like this: When increasing amounts of text are AI-generated, a certain standard is created in overall writing. Researchers who use AI writing assistants can produce more papers faster, and thus have more opportunities to submit their work. This ultimately leads to more and more AI-based text, until it is a majority of written text.

On the face of it, this wouldn’t be a problem. However, there are issues with AI-based text that makes this an undesirable future. Publishers, in general, fear a future of low-quality work. Academic publications aren’t alone. This is a prevalent fear among all forms of writing including fiction. Moreover, there’s a realization amongst some writers that although AI can help improve bad writing, it sometimes makes good writing worse.

Decline in critical thinking

A growing concern about AI tools is how they compromise essential skills like creativity, critical thinking and writing. For example, one recent study found LLMs interrupts the depth to which a student explores a field. Another recent study from a survey of over 300 knowledge workers also had some stark findings when it came to critical thinking and AI. Those with a higher confidence in generative AI were associated with less critical thinking.

Although these studies are not specific to academic writing, the takeaways are likely relevant across all uses of AI in the future. As we grow more reliant on these automated tools and more confident in their capabilities, we may face a corresponding decline in our critical thinking skills, since we can outsource them.

Decline in academic integrity

Perhaps one of the most common criticisms of AI today, particularly in higher education, is the risk it poses to academic integrity. The majority of news articles on ChatGPT argue that it facilitates cheating and plagiarism. Teachers have to rapidly adapt assignments in order to contend with the fact that most assignments and exams can now be easily accomplished using the tool.

This issue is just as serious, if not more so, at more advanced levels of research. When graduate students or even senior academics today use such tools to automate the majority of all tasks, how much ownership will we still take in our work?

Why we should use these tools

Although there are plenty of intimidating risks and challenges surrounding AI in writing and research, there are just as powerful reasons to use these tools. Below are two of the most persuasive arguments, as well as guidelines on how to use generative AI for responsible academic writing.

Enhance research writing

Anyone who has played around with ChatGPT or AI writing assistants knows the feeling of relief when a well-formulated piece of text pops out based on a few jumbled thoughts. That efficiency and improved productivity is just the tip of the iceberg when it comes to improving our writing with automated tools.

When starting a paper, particularly from scratch and/or when dealing with writing in foreign languages, writing assistants are powerful aids. This is particularly true when outlining and drafting, as well as for later steps in the process like grammar and syntax checking. Additionally, researchers struggling to write in a non-native language can easily make their writing more coherent, clear and accurate.

Remain competitive in the future

Regardless of the results of the ethical debates taking place, AI tools will continue to be used, and at ever-increasing rates. This 2024 paper found an explosion in particular kinds of words in publications from PubMed since the advent of widespread LLM use. Although we don’t need any more evidence, it’s a clear signal that many researchers today are already using AI in their academic writing. Moreover, future reliance on these tools likely won’t abate, but can become harder to spot as technology and careful usage improves.

So, how can you remain competitive in a world where all your colleagues and peers are automating their work? Consider the common hook we read across the Internet: “If you’re not using ___, you’re falling behind.” Replace the blank with virtually any AI tool, and you’ll find plenty of results.

Although this alone isn’t a compelling reason to use these tools, it’s an observation of the natural human tendency to experiment and quickly adapt to a new status quo.

How to use AI for writing: Important guidelines

Most researchers today seek to use AI tools consciously by understanding how they work and using them for parts of the process that are best to automate. Now that a few years have passed, there are a number of available guidelines, rules and regulations for the usage of generative AI in writing.

New guidelines are continuously coming out for academic writing with AI, which provide useful tips and ethical considerations. This 2024 set of guidelines recommends these three principles:

1. Human vetting and guaranteeing: At least one author takes responsibility for the accuracy of the article

2. Substantial human contribution: Each author significantly contributes either in concept, analysis and AI-usage editing

3. Acknowledgement and transparency: Authors confirm usage of LLMs

Since many journals have different rules, some researchers seek to standardize AI-related guidelines. For example, these authors are attempting to compile a set of consistent guidelines for using ChatGPT in research that could become universal.

In the meantime, it’s best to confirm with the journal or venue you’re submitting to, as well as to review the latest ethical guidelines available online.

It’s a tricky game in today’s rapidly evolving research environment. You want to use the newest technology to remain competitive, but you can’t compromise your scholarly vigor or academic integrity. The good news is that the vast majority of researchers today are optimistic about AI. According to the latest surveys, 94% of researchers and 96% of clinicians think AI will help accelerate knowledge discovery, and most are happy to rely on these tools (so long as they’re backed by trusted content).

Although journal guidelines offer some guard rails for usage, they deal primarily with the acute and short-term implications of AI in academic research and writing. When it comes to long-term concerns about a decline in critical thinking, innate writing skills, or academic integrity, we simply don’t know.

How do you responsibly use AI in your writing? What guidelines do you use, whether personally or institutionally? Share your thoughts with the research community in the comments below.

Resources

Is AI making us stupider? Maybe, according to one of the world’s biggest AI companies, Feb 2025

The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers, 2025

Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry, 2024

Guidelines for ethical use and acknowledgement of large language models in academic writing, 2024

The promise and perils of using AI for research and writing, 2024

Why AI Makes Bad Writing Better but Good Writing Worse, 2024

Most Researchers Use AI-Powered Tools Despite Distrust, 2024

Delving into ChatGPT usage in academic writing through excess vocabulary, 2024

Generative AI will not make you a better writer – it will destroy creative writing as a way of expressing the human experience, 2023

What makes the biggest difference, however, isn’t doing this just at the start of the year. The key is a strategy for maintaining this attitude over the coming weeks and months. Plenty of organizational systems and tools exist to support better time management strategies, but which ones actually work?

Here, we explore five popular work management tools used across industry and academia to help researchers accomplish their work more effectively. Read on to ensure 2025 is your most productive year yet.

Top time management tools for academics

Below we outline five tried-and-true methods and tools for students, academics, and professionals trying to manage research projects. Some techniques emphasize long-term planning, while others support day-to-day tasks and organization.

The best time management strategy for you will likely be a combination of some of these. For example, maybe you’ll set up a Kanban board to track work on your next paper and categorize specific tasks using the Eisenhower Matrix. You may combine that along with a Gantt chart of your entire project over the next three years, to identify important deadlines and timeframes.

Kanban board

👍 Pros: Organize tasks, stay on top of work

👎 Cons: Lacks clear prioritization or managing multiple projects effectively

A kanban board visualizes your work in one central place, and clearly delineates tasks based on their progress (i.e. “to do”, “in progress”, “completed”, etc.).

Kanban was developed over 40 years ago by an industrial engineer at Toyota, Taiichi Ohno, who wrote about it in the book Toyota Production System in 1988. Back then, Kanban served to enhance manufacturing efficiency. Now, many references to Kanban refer to its use in software development, and in particular, in agile project management.

However, you don’t need to be a software or industrial engineer to benefit from the organizational ease of Kanban. Simply set up a board, whether physical (with paper, sticky notes, and pens) or virtually (using free software like Trello) for the project you’d like to track.

The “Getting Things Done” (GTD) system

👍 Pros: Prioritize work, reduce cognitive load

👎 Cons: Not a system for managing projects or big-picture goals

GTD is a system of organizing work, developed by a productivity consultant and published in his book in 2001. The aim of this system is to reduce the cognitive load of managing work and always knowing what to work on next. Instead, by maintaining clear organization and hierarchy, you can always easily identify what’s the next task to complete.

The key steps which make GTD different to other systems are:

1. Capture: Record and maintain all your to-dos in a single tool or space

2. Clarify: Define the next step for each; if there are no steps, categorize it as either trash, reference, or something to put on hold

Once these initial steps are complete, you can integrate GTD into your existing system for managing work. This PhD student, for example, shows how to implement a GTD-inspired work management system in Notion.

The Eisenhower Matrix

👍 Pros: Prioritize tasks, reduce workload

👎 Cons: Not a system for managing projects or big-picture goals

The Eisenhower Matrix is a simple method to prioritize tasks. Although developed by Stephen Covey, the matrix gets its name from a quote from U.S. President Eisenhower in 1954, "I have two kinds of problems, the urgent and the important. The urgent are not important, and the important are never urgent."

To use the Eisenhower Matrix in your work, take each task you need to do and evaluate it using the matrix guidelines. You’ll find that many tasks fall into three of the four quadrants which release your obligation of actually doing the task (at least for now). This helps cut down on “less urgent” work dramatically and allow you to break down a long list of to-dos into a more manageable set.

Although this method provides a useful way to choose the next step for each bit of work you have, it doesn’t help when it comes to the big-picture organization. Like the GTD strategy, you can layer this matrix atop an existing productivity workflow you already use.

Gantt chart

👍 Pros: Long-term planning

👎 Cons: Can get complex and hard to manage; not a system for day-to-day work

The Gantt chart is unique among the tools here due to its age. Designed in the early 20th century by an engineer, Henry Laurence Gantt, the Gantt chart has been widely known since the 1920s. At the time, it was a radical new idea and was used to plan huge projects in the U.S., like the Hoover Dam and interstate highway network.

The idea, although revolutionary at the time, is simple. Create a graphical representation of different tasks over time to help you meet deadlines and complete a project. The modern Gantt chart tends to use tools like Excel or Google Sheets in order to visualize tasks over time.

Here is an example chart for an entire Ph.D. (available here). It includes everything from the stages of thesis development to the timelines for completing and submitting manuscripts.

Agile methodology

👍 Pros: Long-term planning, short-term organization

👎 Cons: Can be difficult to maintain in a non-agile environment

Although popular in software development and in industry in general, agile methodology can be applied to academic projects too. Agile was developed in the early 2000s by a group of software developers aiming to bring software products to market faster.

Since then, agile has exploded in popularity and is the dominant method used by most software developers. The agile philosophy is outlined in the manifesto. The core philosophy of agile is to break work down into iterative, short cycles (called sprints) and incorporate regular feedback to create the best product at each step.

Agile can easily be taken out of the software world and incorporated into academia and research projects. This PhD student used agile techniques to keep their thesis on track. For example, he’d hand in a portion of the thesis (i.e. a chapter or section) at 2-3 week intervals. By working in this way, he avoided any significant re-writes or alterations. As he says, “almost none of the 40K words in my intermediate submission [had] to be rewritten.”

Alternatively, you can use agile methodology to change how you even think of your work. This academic adopted the agile idea of defining clear tasks that can be accomplished in relatively short timeframes. For example, rather than aiming to “write two chapters this year”, she would set the goal “In this month, I'm going to write the conference paper that will eventually become part of x chapter”.

The main challenge you may face with agile is its dependence on your entire team functioning similarly. For example, you may finish a cycle of work only to find your supervisor or colleague hasn’t yet responded to your last iteration’s work.

Some days, research can be a gripping ride, keeping you on the edge of your seat for the results of your recent analysis or the acceptance of a paper by a prestigious journal. But most days, it is a methodical and sometimes even repetitive pursuit. It’s easy to get bogged down by the to-dos, and get lost answering low-priority emails or mindlessly re-running the same statistical tests.

Experimenting with time management tools may not sound enticing, but the payoff can be great. Not only can you cope with the slow days better, but you can create an internal accountability system to help meet your goals — goals we’re usually excited for in January but have mostly forgotten about by March.

Do you have a system you swear by to keep your research in order? Share with us and the rest of the Litmaps community below!

“It just weighs on me,” she said. And I’m sure she’s not alone.

This feeling of drowning in work is pervasive, and rarely abates for many researchers, regardless of where you are in your career, or whether you’re approaching a holiday break or not.

But as 2024 comes to an end, it’s the perfect time to take a break from thinking about what you didn’t yet do, to really appreciating all you did do over the last calendar year.

We cover two unique ways to review your work (using Litmaps and ChatGPT) to help you see your research with a new perspective. By reviewing your work from a bird’s-eye perspective, you can feel a sense of renewed motivation for 2025.

Here are two ways to review your research, using Litmaps and ChatGPT.

See your research impact with Litmaps

You can clearly visualize your research impact in 2024 by using visual literature maps of your work. By viewing recent work based on your publications, you can directly observe the research your work supported.

You can review the real-time impact of your work using Litmaps, our online literature mapping and discovery tool. Litmaps helps you discover literature and manage your literature review process, as well as visualize research and share ideas.

Here’s how to use Litmaps to review your research impact:

1. Create a Litmap with your existing publications

2. Run Explore to discover new papers connected to your work.

3. Use the Date Filter to get papers published in 2024, to see your recent influence

4. Review the suggested articles

5. Add related articles to your map (use “More Like This”) and explore second-degree connections—works that don’t cite you directly but build on your ideas.

I performed this example review on Lina Quan, a professor of Chemistry at Virginia Tech.

The Litmap below shows her work (pulled down from Scopus) in orange. The white circles at the bottom right are relevant, connected articles published in 2024 from Q1 high-impact journals that rely on her work. In total, Litmaps found 27 high-impact articles published in 2024 that relied on Quan’s work.

After reviewing these articles, I saved the suggested articles which connect to her work with the Tag “Quan Impact”.

The result is the Litmap below, where we can clearly see the dozens of articles published in 2024 that are highly connected to Quan’s research.

This type of review can help you to understand the impact a researcher (or even a whole group) has on their field.

If you’re still early on your research journey, this method can be adjusted to work for poster presentations or other works. You can create custom articles in Litmaps (for posters, unpublished work, recently submitted papers, etc.), and add any relevant citations and references to them manually.

What does your Map look like?

Make my Litmap

Reset your research mindset with ChatGPT

If you’re using the New Year as a time to set resolutions for your work, then consider using ChatGPT to help guide you.

When given the right data and information, we can use ChatGPT like a simulated career coach, asking it to help us meet our goals and find the issues we’ve overlooked in our own work.

Here’s how to use ChatGPT for resolution setting:

1. Share a summary of your 2024 work with ChatGPT (e.g. “I submitted 2 papers, attended 3 conferences, etc.)

2. Share any digital sources with ChatGPT (e.g. papers, notes, etc.)

3. Tell it your 2025 goals ("Publish 3 papers and secure funding for a new project.")

4. Ask for actionable advice and feedback. For example, use prompts like:

   1. “Give me ten concrete steps I can take the first month of 2025 to set up my entire year for success to achieve my goals.”

   2. “Give me 3 tasks per week for each week in January 2025.”

   3. “Based on everything I told you, what do you think are my biggest blockers to meeting my goals and my biggest opportunities for growth?”

The more specific details you provide, the better you can leverage its ability to help understand your approach to research and thus advise you on what you may change to improve. If your case is vague, it’ll provide equally generic advice.

Doing this myself, I found some excellent advice from ChatGPT. It pointed out where I was wasting too much time and what inhibited me from reaching my goals.

Tip: Ask for actionable, concrete advice you can take now. Below is an example output.

As 2024 comes to an end, we all begin to reflect on our year. Too often, we’re worried about what we don’t accomplish, which is why it becomes essential to review what we did do, to provide ourselves a mental reset and reinstantiate our creativity.

What are your reflections on the research you’ve done this year?

Share with us in the comments below. 👇

At the least, we’re convinced you have been an industrious researcher because the Litmaps community has collectively made over 2 million Litmaps this year!

Thank you for being a part of the Litmaps community, and we look forward to continuing to support your research in 2025.

As AI continues to reshape the academic landscape, ensuring you stay one step ahead is essential. Join the Academic Language Experts’ upcoming free course: AI Tools Boot Camp for Researchers: Core Essentials.

This course offers practical, hands-on training with AI tools to help streamline your literature review, conduct research more efficiently, and write and edit your papers.

Learn More

Students, academics, professionals and users with titles we can hardly imagine use Litmaps. With researchers at thousands of different universities, institutions and companies across the world, it’s hard to capture the diversity of research conducted by our users.

But, we can still try!

Here, we showcase two users who have found Litmaps essential to their work. My hope is that this can give you new ideas on how to approach your own research.

Whether you’re an undergraduate student starting your first literature review, or a senior professor reviewing your life’s work, I hope Litmaps can help you in your research journey.

Debika is a Professor of Business at Southwestern University who researches the impact of technological innovations on marketing strategy. She teaches a seminar for first year students at SU, designed to prepare them for the research they’ll need to do during their degree.

Debika is passionate about having students see how ideas in research connect, and to understand scholarly networks. Litmaps is central to her teaching strategy, providing a platform with which to clearly teach fundamental research concepts like seeing how literature connects, identifying key authors or concepts, and seeing the research in different ways (by citations/references, author, or semantic similarity; the algorithm options).

She use Litmaps as a teaching aid to help make research less intimidating. She also feels that equipping students with expertise in an advanced tool like Litmaps provides them a critical skill for the rest of their academic journey. Her seminar students even chose to include visual Litmaps demonstrating their work in their final presentations at their first symposium, on topics as varied as The Desire to be Scared While Watching Movies to ACL Reconstruction.

If you’d like to use Litmaps with your class, you can sign up as a Team or reach out to us for assistance. See our classroom resources here.

Tyler Smith is a graduate student at the University of Ottawa and is in the final stretch of his PhD. Over the last five years, he’s been studying immune cell metabolism in contexts such as bacterial infection and heart disease. He’s spent countless hours running experiments, culturing and infecting primary immune cells with pathogens like Salmonella, to understand how metabolic pathways impact disease progression.

During experimental days, he’ll cram in as much reading as he can, and often uses Litmaps to quickly dive into topics. He finds Litmaps especially useful for exploring new topics outside his comfort zone. He’ll throw in a few key papers he knows, and start iteratively searching using the More like this feature, and “just keep hitting refresh!”

Tyler also tries to keep his search as focused as possible, and will often use the keyword filter to specify the particular protein he’s interested in at the moment.

As is the case for most graduate students, Tyler is excited to take the next steps in his research journey and to finally wrap up his PhD. Having run hundreds of experiments already, he recognizes the challenge in deciding “what is the end of this story and the beginning of the next story?”

We absolutely love hearing about how you use Litmaps. That’s why this isn’t even the first time we’ve done a letter like this (see our last one here).

If you find Litmaps useful and would like to share your story, get in touch with us. We’re eager to hear what you have to share.

Message us with the text “Here’s my research story…” and we’ll reach out to feature you in our future newsletters.

Upcoming Research Tools Webinar

As AI continues to reshape the academic landscape, staying one step ahead is essential.

Join the upcoming AI Tools Boot Camp for Researchers course in January, 2025.

Learn practical, hands-on training with essential AI tools to streamline your research process so you can more efficiently write and edit your papers.

Whether you're a seasoned researcher or a new doctoral student, this boot camp can empower you with cutting-edge AI tools to enhance your productivity and research output.

Learn More

“Most of our students still aren’t past ChatGPT.”

Although there are hundreds of AI-powered tools available (plenty of which use GPT), most researchers and students are still learning how to navigate the landscape, facing questions like:

• What tools should we be using?

• How do they work?

• Are they reliable?

If you have these questions too, then this Webinar may be just what you’re looking for.

The AI Literature Review Webinar

The AI Literature Review Webinar tackles questions about AI tools in research head-on. It provides a clear, proven strategy for incorporating the best available tools into your next literature review. From searching for papers, to taking notes, all the way to writing up your final paper, you’ll learn key techniques to save time and accelerate your research.

The Webinar is hosted by Ilya Shabanov, creator of the Effortless Academic, who has helped help 1,000s of researchers through his courses. His grounded approach to AI tools balances new tech with reliable research methods.

With Litmaps as one of the tools in his workflow, we’re excited to be supporting another one of his Webinars (check it out below!).

Join the Webinar for Free

We’re offering our Litmaps users special access to this Webinar!

Sign up for an Annual Litmaps Pro Subscription and attend this $40 Webinar for FREE*.

Use the code WEBINAR at checkout

*Only Annual subscriptions will get a free ticket. Monthly subscriptions will just get 25% off the ticket price. You must use the code WEBINAR at checkout.

Claim Free Ticket

But such success doesn’t just happen by accident.

In this edition of The Scoop, we interviewed the author of Stuff Matters: a materials scientist, Mark Miodownik. Miodownik’s contagious passion for his subject has given birth to several books, the UCL Institute of Making, and a lifelong commitment to learning and sharing what he’s learned with others.

We’re stoked to share Miodownik’s journey, the lessons he’s found along the way, and what it means for researchers all along the academic path, whether students, academics or professional researchers.

Seeds of inspiration

Miodownik has been sharing his science for as long as he’s been learning it. Since he was an undergraduate student, he’s been giving talks to students and learners, explaining materials science concepts via lecture and practical example.

His books are a reflection of his own way of learning, his passion for his subject, and a desire to share it with others.

Teach to learn

Miodownik has been teaching for as long as he has been learning materials sciences.

“I’m a slow learner, I really take ages,” Miodownik explains, and teaching has always been his outlet to better help himself learn. Indeed, learning by teaching is a fundamental pedagogical concept, and deeply integrated into academic systems with roles like teaching assistants. Miodownik always appreciated the questions he would get from his own students, especially those he wouldn’t think of on his own and that helped him to gain new perspectives. Teaching is sharing, and both enable better learning.

Keep the bigger picture in mind

But, “learning slow” is also a conscious choice. Miodownik has maintained an affinity for understanding the bigger picture. When he was an undergraduate, he took a leaf out of the books of his friends in design and art, who valued looking at the whole. “I really enjoyed doing research in materials,” explains Miodownik, “but didn’t want to get trapped in a lab and get to the end of my life feeling like all I did was develop one material. I needed to understand the material world more broadly.”

It’s this perspective that made Miodownik go down different paths with materials, and also helped shape his popular book, Stuff Matters. Instead of just diving into the science behind a material (say, chocolate, metal or plastic), he considers each with historical, anthropological and cultural lenses. Although viewing things this way may have “slowed him down”, the payoff was a bigger perspective and the ability to still be amazed by these materials in the world.

Fuel by passion

There are few better motivators for creative pursuits than passion. Miodownik keeps this value close to heart, as he says, “That’s my guiding principle: am I thrilled?”. He admits this may not be the best characteristic for a classically successful academic, often expected to work with a more targeted focus on a specific niche for decades. Nonetheless, this attitude of following his excitement has fueled his various projects, books, and pursuits.

Miodownik’s open approach and passion for materials science is reflected in the UCL Institute of Making, which he founded with his colleagues in order to inspire people of all backgrounds to understand and create materials. And when the institute says all backgrounds, they mean it. Unlike many organizations which allow only certain students or academics, the Institute of Making welcomes all university folks, from librarians to security guards to researchers of all backgrounds.

“There’s no hierarchy,” explains Miodownik, because “That’s the thing about materials, they’re democratic. They’re open to everyone. That makes a certain vibe in the place.”

However, this attitude isn’t without challenges. By supporting such a broad audience, the planning, preparation and execution of its events can take more time. But this trade-off is well worth it and fosters an excitement for exploration among all the participants who pass through the doors.

How to write a best-seller

If you’re thinking of writing your own book, or curious what really sets apart a best seller in the world of scientific nonfiction, there’s a few lessons to be learned from Stuff Matters.

What sets Stuff Matters apart is its focus on story. Storytelling is essential to make science communication more compelling. As Miodownik puts it, “It [Stuff Matters] is light on the science and heavy on the stories. Stories themselves are how the message is carried.”

Audiences react well to stories, and it’s one of the key characteristics of the book that Miodownik believes has kept so many general readers engaged.

“Unless you’re one of the 5% of people who want to know about a new topic,” says Miodownik, “then after Chapter Two you’re like ‘Yeah, I got it’. Whereas Stuff Matters was never for those people… I didn’t want to write a niche book. I wanted to write for the 95% of people.”

Miodownik was driven to inspire the layperson in just what makes himself so impressed, amazed and intrigued by the material world that we’re surrounded by.

Miodownik even took this idea of storytelling one step further by making each chapter not just about a different material, but maintaining the character of that material in voice. This eccentricity even created some arguments with his editor at the time, when he tried to insist the chapter on plastics should contain a film script (read the book to learn why this makes so much sense!).

“Then we both [editor and I] thought the book’s a bit mad… but I think the madness is necessary to get people to feel the extraordinariness [of the materials],” shares Miodownik. This commitment to each chapter’s character makes the book a bit more rough stylistically, but may also make it more memorable and sticky for readers.

In addition to story-focused writing, Miodownik also followed the typical logistical advice for most science writers, like self-promoting, being his own ambassador, and staying passionate about his book throughout the publication process by avoiding looking at reviews and ratings. We cover some of these recommendations in a previous article where we interviewed another materials scientist author, here.

The special perks of writing books

Miodownik hasn’t only written books, but also done TV series and documentaries, sharing his scientific knowledge for a broad audience. But, when compared with these other mediums, books seem to hold one key advantage: longevity.

Miodownik finds that books seem to be one of the longest-lasting forms of communication, perhaps due to their physical nature. They have their own way of evolving through recommendations, translations, and continued momentum. Authors have the opportunity to bear witness to how their creations evolve over time, even without their participation.

“The book becomes this discussion between people in which you’re not involved. I just find that marvelous. It’s a really under-appreciated aspect of the physicality of books. It becomes this thing you can give to someone. It’s an important present, saying ‘I’m thinking about this. Why don’t you read it and we can have a discussion about it?’” - Miodownik

Successful books continue to give back after years and years. Even now, Stuff Matters continues to be translated into other languages and recommended among friends. Books are catalysts for new and meaningful conversations, creating their own worlds and cultural movements. It’s this evolution that is so rewarding to see as an author, and which drives Miodownik to continue to write and contribute to the literary world today.

Are you inspired to write your own book one day? What’s hindering or helping you? Share with the community below to help or encourage other researchers!

Whether it’s curiosity about other fields, interest in potential collaborators, or an openness to new ideas, simply leaning into our curiosity may be the most underrated quality for scientific success. In this newsletter, we’ll uncover exactly how this trait unlocks new research potential, and what simple habits students and researchers can adopt to better tap into more transformative science.

Can expertise narrow your mind?

It’s no surprise that when we’re new to a subject, we ask many questions, poking around in the dark to try and create our understanding of a new field. In no time at all though, most of us develop an intuition regarding the general constraints and methods of the field, and immediately begin confining ourselves to them. This is what allows meaningful understanding and contribution, but also has the double-edged effect of stifling creativity.

Kraković uses the analogy of the Grandmasters in chess. The greatest chess players in the world have both exceptional skill, hard-won by many hours of training and study, and an unwavering connection to their own creativity. Similarly, the most successful researchers strike the same balance between knowing the rules and yet still thinking outside the box.

However, this balance is not so easy to master. Research shows — and Kraković’s personal experience validates — that experience and expertise tends to gradually reduce creativity. Intuitively, it makes sense. Learning the key ideas, constraints and challenges of a field requires an entirely different mindset than that which seeks to break those rules.

Kraković even noticed this change in himself shortly after starting his PhD at EPFL, where he examines how the immune and nervous systems of Drosophila melanogaster flies interact. In the first few months, he asked many questions and conducted many experiments, until he started to develop a stronger intuition of what would and wouldn’t work. Quickly enough, he was running far fewer experiments. Although this intuition was critical in understanding his field and helped him to make his research strategy more targeted, it can also be demoralizing. Especially when junior researchers continue to let their ideas go unchecked. This is where cultivating an attitude of openness comes in.

Use curiosity to get creative

Becoming an expert is an essential step to making meaningful contributions. But as we’ve seen, it comes at the cost of out-of-the-box thinking which can break old patterns and lead to radical, new discoveries. From Kraković’s interviews with professors, biotech entrepreneurs, and postdocs, we can peek into how staying curios helps fuel a productive and enjoyable research career.

Here, we’ll dive into the key habits that enable open-mindedness and maintain connection to creativity, by tapping into curiosity. Not only does this enable better research, but helps structure a way of approaching science that leads to long-term growth.

Be curious about your experiments

The more expertise one develops in a field, the easier it is to get an intuition for what will and won’t work. But, more often than not, that intuition leans in favor of what won’t work. This is where it pays to zoom out, to think less, and just perform the experiment. The idea here is to stay curious about what an experiment may reveal instead of always anticipating every result.

Kraković recalls a recent lull he hit in his own research. Although he had many ideas for a range of experiments to run on his Drosophila flies, none felt compelling enough to pursue. That is, until he spoke with a colleague who helped him separate the negative-leaning intuition about what wouldn’t work from what could work. It became clear that several of his experiments were worth a try.

In those cases where experiments can be done easily, it can thus be advantageous to just run the experiment instead of always thinking out a solution beforehand.

“The more you talk to people, the more they tell you why it [an idea] won’t work… And you hear that about 20 times, and you start closing yourself in a box. Every now and then you need to talk to someone in science, but maybe not in your field, to tell you to be brave and try that experiment anyways.” - Kraković

Be curious about people

Although the different scientists and professionals Kraković interviewed have had different careers, goals and paths, all shared one commonality when it came to their research success stories: the power of relationships and networking.

Students and junior academics can often be initially off-put by the term networking. As Kraković shares, “when you imagine networking, you immediately imagine people trying to get something from you,” but the reality is entirely different. Instead, the scholars and professionals he’s interviewed, as well as Kraković himself, stress the importance of simply being curious about others.

“The power of just meeting people without the intention of getting anything from them. Just meeting them for being cool and interesting. Long-term doing that has benefited them [the interviewed researchers] the most in their careers. Like 99% of people I’ve talked to so far have said this. And that’s one thing, as students, we don’t really utilize.” - Kraković

There are several practical and actionable ways to bring this advice into reality. For one, consciously participating in conferences can transform the experience from one of passive attendance, to a real opportunity to build brand new connections. The curious researcher even prepares beforehand, reviewing what posters and presentations may occur, to see what’s worth attending and who they’d like to speak with.

Be curious beyond your niche

Stoking the flames of your scientific curiosity is one of the most accessible and vastly underrated ways of tapping into inner creativity and “zooming out” of your local pocket of research. Certainly you don’t need to start a podcast, but it can help.

Kraković started the Better Biologist Podcast in order to satisfy his own curiosity, as well as support early-career researchers. By exploring questions he is fascinated by with his guests from the biotech industry and academia, he “feeds himself” when it comes to learning, and address the pressing questions other students also have.

In fact, this attitude of curiosity for topics beyond one’s specialty is a common trait among many great scientists. Schrödinger, like many well-known scientists, was quite a Renaissance Man, active in a range of disciplines from physics to philosophy to German poetry. Today, such characters are less common in our era of specialization. But with the explosion in platforms sharing science, there’s an imminent revival of students and researchers tapping into inspiration from various domains.

Practically speaking, there are hundreds of ways to tap into your curiosity and fields of interest. Kraković shares one easy way to not only get a broader perspective, but to also meet interesting people and build new connections: attending guest lectures. Many institutions have weekly seminars with visiting speakers. These are an excellent way to pique your curiosity in a new subject, form new connections and even learn about potential opportunities for collaboration or work.

Whether it’s through podcasts, blogs, social media or real-life lectures, the bottom line is simply tapping into your curiosity in order to expand your field of awareness in order to stimulate your creativity and bring renewed passion into your work.

With curiosity so foundational to good science and creativity, it’s surprising how easy it is to lose this instinct when overcome by the research grind. As is evident by the range of experiences Kraković has examined through his interviews, the most passionate and successful researchers don’t neglect their curiosity. Instead, they nurture it and maintain an open mind. In this way, they can bring in fresh perspectives, collaborators and energy into their scientific work.

How do you tap into your curiosity and fuel your research creativity? Share your experiences with us below!

Resources

Should You Be Doing Science or Sharing It? June 2024

Don't think, just do the experiment May 2024

How the Best Scientists Avoid Burnout May 2024

How I lost and found my scientific creativity March 2024

The role of memory in creative ideation 2023

Curiosity made the cat more creative: Specific curiosity as a driver of creativity 2019

Inflexibility of experts—Reality or myth? Quantifying the Einstellung effect in chess masters 2008

The Expert Mind 2006

Between pressure to publish and doubting if scientists even should be sharing so much publicly, there are several issues getting in the way. In this edition of The Scoop, we’ll get an insider’s lens into exactly how researchers are overcoming these challenges and taking matters into their own hands when it comes to sharing their work. From our recent conversation with Jonathan Tonkin, head of the Tonkin Lab at the University of Canterbury, New Zealand, we examine how sharing research is just as important as contributing to it.

Sharing science: key issues

Sharing discoveries, educating the public and joining public debates with an expert angle are essential to ensure scientific contributions make a real-world impact. But most researchers today still don’t take on these roles.

There are several reasons why the scientific community is largely still reluctant. For one, there’s still some hesitation among the scientific community to be outwardly opinionated. For another, there’s no reward system in place to provide the time and resources for scientists to do so. Despite these challenges, a growing number of researchers are making their way into the public sphere in order to make an impact.

Barriers to engagement

Some argue that activism and political engagement isn’t recommended. The role of scientists is to provide objective information, from which other people (i.e. policy makers) make informed decisions. Thus, scientists shouldn’t engage directly in activism. Although this sounds good in theory, most researchers would agree that this system has been somewhat ineffective. Moreover, Tonkin argues, no scientist is purely objective. Scientists are inherently passionate about their research, so they can’t be genuinely disconnected from the outcome.

Even if this hurdle is surpassed, researchers interested in making a dent in the public sphere need to spend considerable effort to get their work beyond the walls of academia. Today, this looks like online promoting, active social media engagement, and public speaking. This time-consuming endeavor is also rarely motivated in academic systems, making it hard for most to find the time to even start.

Breaking through the noise

Despite these challenges, an increasing number of scientists are still doing it. For Tonkin, the answer came down to personal reasons. Tonkin’s lab focuses on understanding and predicting the dynamics of ecological systems, and using this information to effect change. His passion for the environment not only drives his scientific endeavors but also fuels him to try and bring his work into the public domain.

The Tonkin Lab contributes to our understanding around climate change, biodiversity, the mismanagement of water, etc., but the rate of change in response to academic work is universally slow. The problems continue to persist. Thus, Tonkin felt a sense of urgency to act, “to do something more now beyond just publishing papers”.

Fast forward to today, where he’s invested over 100 hours into his Substack, Predirections, focused on educating the layman on the “biodiversity-climate nexus”.

Surprising systematic benefits

Although we’re treating them as distinct, sharing and doing science are fundamentally inseparable. The way science has been done has so radically changed and accelerated that the very structures we use would be incomprehensible to researchers a century ago. In the past (think early 1900s and before), science was done largely by “invisible colleges”, at a time when much work and collaboration happened informally. Today, the research landscape is dominated by enormous and wealthy institutions, with the vast majority of work being communicated in dense manuscripts and materials. This little science versus big science distinction was identified as early as 1963, and has only become more obvious in recent decades with 2 million+ formal publications a year and an army of tools to help researchers decode this wealth of information (Litmaps!).

But, with scientists entering the world of social media, blogging and other modes of informal communication, we’re seeing this narrative shift. These alternative science communication modes not only impact the public, but even the academic systems themselves. Acts like sharing early science and new ideas via newsletters, conducting Open-notebook science, and even just tweeting at strangers online about your research, shifts the focus from polished articles back to the sphere of spontaneous and informal discourse.

How to get started

Most researchers face the same challenges: limited time and an endless pressure to publish, publish, publish.

Nonetheless, even in this environment, many scientists do make the time to get their messages “out there”. Given the hundreds of hours it takes to build and maintain a strong public-facing presence, it can’t be a process without great motivation and reward.

The two keys to Tonkin’s perseverance are simple: make a commitment, and make it easy to stick to by ensuring it’s beneficial.

Make a commitment

Like many scientists, Tonkin used to dabble on Twitter (ie. #AcademicTwitter), but found it difficult to break out to a broader audience.

About three months ago, he made the personal commitment to publish one article per week on his new Substack, Predirections, for a year. He has been steadfast in his publishing since then, with a new article out roughly every week.

With a hard commitment like this, Tonkin has been able to set himself a structure in which to create freely, which is long attributed as the one of the key measures of success in creative and scientific endeavors. “Freedom through structure,” as Timothy Slater puts it.

Make sharing work for you

Science communication sometimes is viewed with an “us” versus “them” mentality: whether it’s the scientists versus the public, or even a distinction among scientists who carry the title “science communicator” and those who don’t. These distinctions aren’t helpful, according to Tonkin.

“I don’t think of myself as being a science communicator at all. I just like to share interesting and important science.”

Tonkin doesn’t view his blog just as a platform by which to communicate with the public. He views it as a beneficial outlet for enhancing his own writing, messaging and ideas. It’s the same method by which other prominent writers and researchers swear by, like Michael Shermer on his Substack, After Babel. By finding the process of sharing beneficial for their own work, these researchers are getting the best of both worlds.

“It’s a good process of writing as well… It helps you to refine your message… By going through the process of sharing your research in a way that reaches a broader audience, it helps you understand deeply what your messages are.” - Tonkin

So what should you do, really?

With limited time and an endless pressure to publish, it can be difficult for scientists to make the leap or to the realm of science communication. As we see with Tonkin, it often boils down to a personal decision. Even with high motivation though, the journey isn’t easy. Tonkin himself admits to spending more time than he’d like on his new blog.

He tries to reduce the work-load by lowering his expectations of perfectionism and by recognizing that his public-facing work isn’t the same as peer-reviewed articles. He also continues to associate his blog posts with the research he’s interested in pursuing, to ensure he sustainably gets the best out of both worlds.

As is the case for most writers and creators, external reward can also fuel additional energy to pour into the passion. Receiving positive comments and engagement make Tonkin feel that people are genuinely enjoying his work, and that it’s making a difference. Of course, we all enjoy higher self-esteem from positive social feedback. But, it’s not this alone which keeps Tonkin attached to his work. At the core, purpose and values overcome threats to our self-esteem (say, for a poorly received blog post), and its this we can tap into when we struggle to keep going.

If we’ve got you convinced, then here are a few ideas to try:

• Start a social media account (Twitter, TikTok, LinkedIn, Instagram, etc.)

• Blog, newsletter, etc.

• Open science notebook - conducting research “in public” so to speak. They not only increase public trust and understanding the scientific process, but help generate more reliable and vetted science. Being an early career researcher doesn’t exclude you either. You can check out platforms like OpenLabNotebooks, Open WetWare, Open Neuroscience, or even just your Jupyter Notebook.

So, can you really make an impact by sharing your work and research passions publicly? Or is the real question, can you really make an impact any other way?

We’re curious to hear your thoughts below, and what experiences you’ve had with sharing your research so far. With the core mission of Litmaps to accelerate impactful science, we are just as interested as how our tool supports scientific breakthroughs within academia and well beyond it.

Resources

Predirections Substack

Tonkin Lab

Open Lab Notebooks

Open WetWare

Open Neuroscience

On science and activism: can we be truly objective as scientists? May 2024

The importance of distinguishing climate science from climate activism May 2024

Social Media and the Production of Knowledge: A Return to Little Science? 2010

Little Science, Big Science 1963

This week, we’re hosting two Litmaps Webinars to support researchers, students, and entire organizations in accelerating their research using Litmaps. We’ll cover the best ways to use our tool to:

• Save time searching for literature

• Do a comprehensive literature review

• Find research gaps

• Collaborate with others

Our Upcoming Webinars

Are you a student or researcher curious about using Litmaps to find literature and discover research gaps faster?

→ Join our Literature Review with Litmaps Webinar May 29th

Are you exploring tools for your organization or team, and want to learn more about Litmaps for institutional use?

→ Join our Litmaps Webinar for Librarians May 28th

You’re welcome to join us for both as well. Read on to learn more about what to expect in each Webinar.

Literature Review with Litmaps Webinar

Do you still end up with 100s of opened tabs when doing a literature review? Are you overwhelmed by the sheer volume of literature? Or, perhaps you’d like to take your existing research to the next level and start using advanced search methods to find novel research gaps?

We’ll cover these use cases and more in our easy-to-follow Webinar this week. We’ll go into detail, demonstrating the application, and cover the best uses of Litmaps for your research.

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Litmaps Webinar for Librarians

​Are you looking for tools to better support your entire research group or institution?

Join this Webinar to learn how Litmaps can be used across institutions to enable more productive research and collaboration, organization-wide. ​We'll demonstrate the Litmaps tool, explain how we source data, and cover institutional licensing in detail. There will be plenty of time at the end for your questions too.

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June Trials

If you’d like to see how your entire organization performs with Litmaps, then sign up for a Litmaps Trial.

This June only, we’re offering free institutional trials, so your entire organization can get hands-on with our advanced search and literature review features.

Request a trial today, or ask us more at the upcoming Librarian Webinar.

Can’t join us for any of these Webinars or the timezones don’t work?

Don’t worry. We’ll continue hosting Webinars for the community, and switch up the timezones to serve researchers across the globe equally.

We also recently updated our YouTube channel with a new set of tutorial videos and answer to common questions. Check out Litmaps YouTube to watch easy step-by-step videos on how to use our tool.

Here are some of the easiest ways to get started with Litmaps:

Top 5 Ways to Use Litmaps

Accelerate your Literature Review with Litmaps

Find Research Gaps with Litmaps

How Litmaps Search Works

Thanks for being a part of the Litmaps community, and we hope to see you at our upcoming events!

The Litmaps Team

Scientific research has been pursued for millennia, and even the term research is 400 years old, stemming from the French, recherche, meaning “to search intensively”. Technologies throughout history have repeatedly revolutionized how research is done. By considering how these changes have happened in the past, we can look to the game-changing implications of AI with a broader perspective.

Here, we’ll look at literature reviews in particular, examining how they’ve evolved over time, and what the future may look like when most of the work can be done by AI.

Join us on May 29th for our Literature Review Webinar, where we’ll cover the best tips and tricks to accelerate your literature review using Litmaps.

Register for the Webinar

Literature Reviews: Past

Literature reviews, as we know them, only started appearing in a more formalized format around the 19th and 20th centuries. Before that, scholars would discuss and cite other works, but generally didn’t share official “reviews” as a preface for their own work. For example, in the work of Hippocrates, an ancient Greek physician in the 4th century BCE, previous treatments and ideas were criticized but no specific texts cited.

The first literature review

One of the earliest recordings of what we’d consider a modern day “literature review” is included in James Lind’s A treatise of the scurvy, published in 1753. At the time, the British Navy had been suffering from widespread issues with scurvy, and the causes were yet unknown. Lind recorded his clinical trials and potential cures, as well as “a critical and chronological view of what has been published on the subject”, i.e. a literature review.

“It became requisite to exhibit a full and impartial view of what had hitherto been published on the scurvy… before the subject could be set in a clear and proper light, it was necessary to remove a great deal of rubbish.” - James Lind, for his 1753 literature review

Although Lind tried his best, he appeared to still miss some sources. Later scholars pointed out how he overlooked “Woodall’s 1617 publication The Surgions Mate with its clear recommendation of equipping ships to the East Indies with lemon juice to prevent scurvy”.

This 18th century mistake is still seen today: Lind used only one “database”, the library at the Edinburgh College. Today, best practices still demand that researchers use several databases when conducting a literature review.

The first meta-analysis

Meta-analyses, a particular type of literature review which also involve conducting an analysis on the combined results of various studies, also date back to the 18th century. At that time, concerns over whether the smallpox vaccine caused more harm or good drove Thomas Nettleton, a doctor in West Yorkshire, to collect statistics on the mortality of the vaccine. Later, this data along with that collected from other sources, formed one of the first meta-analyses.

Literature reviews in the 20th century

By the 20th century, literature reviews were becoming more common. For example, Sigmund Freud’s 1905 book Jokes and Their Relation to the Unconscious acknowledges the state of the field in the introduction, and quotes other authors on the topic.

But how these reviews were conducted looked very different as compared to today. Researchers, whether students or senior researchers, would spend months poring over the available literature in their libraries. Barbara Tuchman, an eminent 20th century historian who examined what lead to World War I, surmised it as such:

“To a historian libraries are food, shelter, and even muse.” - Barbara Tuchman

These traditional and hugely time-consuming techniques of sifting through literature at libraries meant it was very easy to miss important and relevant work. Even Tuchman herself nearly missed out on a critical set of primary sources for one of her books; articles written specifically by the protagonist of her book:

“With no clue to their existence, I might never have found them [the articles], which would have been a serious omission for Stilwell’s biographer. This is the kind of thing that makes one shiver to think of what else one may be missing.” - Barbara Tuchman, 1981

Most students and researchers today would still acknowledge that missing sources still poses a challenge to contemporary literature reviews.

Literature Reviews: Present

Online databases and advanced search tools have radically transformed the literature review process for researchers. Instead of living out of libraries for weeks on end, researchers are able to conduct comprehensive searches in a matter of few hours. With the newest AI-driven tools, even reading these sources has been partially automated, so that each step of the research process is faster.

Instead of taking months to manually pore over books in libraries and weeks of reviewing the materials, it takes hours or even minutes to achieve each of these steps with modern tools (i.e. Litmaps).

The same goes for all the steps throughout the research process. Staying organized is now automated with reference managers and in-app tools (i.e. Tags in Litmaps). Double-checking for missed sources now comes down to taking a few minutes to re-run searches across databases and monitoring searches.

It’s worth noting that some researchers still rely on more traditional methods, despite the plethora of tools available. For example, historians relying on primary sources (like Tuchman’s example) may still need to do some research manually. Although libraries globally have invested in digitizing primary sources (i.e. those articles and 18th century literature reviews mentioned earlier), not all sources have been digitized. This library in Ireland documents what primary sources have been digitized, so researchers know what is and isn’t covered.

Literature Review: Future

Researchers today view AI as a promising technology to automate labor-intensive tasks in literature reviews, and, of course, research more broadly. Today’s tools already save researchers time in finding articles faster and reviewing relevant work, but it is perhaps only a matter of time until AI can automate the entire review process. AI isn’t yet at the level of reliability and quality necessary to replace researchers, but as technologies advance, it’s conceivable that such tools will make manual, human-written literature reviews obsolete.

In particular, this may make typical review articles less valuable. The kind and quality of the review will determine its value in a future AI-driven world. For example, systematic reviews and meta-analyses will continue to be valuable, because such reviews consider papers altogether, looking for potential biases, and surmising a new conclusion based on the combination of these works. The automation of such comprehensive studies may be a much longer way off.

The strongest believers in AI envision how it will transform every aspect of scientific research, far beyond automating literature reviews. AI can change everything from experimentation and scientific development, to determining how research is funded or who gets tenure. The rate at which this occurs depends on the state of technology, society and their interaction.

Some scholars believe that by 2030, AI will be able to automate and tasks requiring “routine intelligence”, such as transportation, customer service, and marketing. Many aspects of the literature review process fall under that umbrella, like gathering sources, reviewing papers, and writing summaries. As for generative AI, which could conceivably write entire systematic reviews, the predictions are still 20-40 years in the future.

These predictions may sound intimidating, but of all the fields seeing automation, reviewers may be the most grateful to see this evolution. After all, the vast majority of reviews are conducted to set the stage and motivate research contributions. Automation here is likely a welcome change, enabling researchers to focus more on addressing research gaps and coming up with groundbreaking solutions.

The way research gets done has evolved dramatically over the centuries. AI may seem radically revolutionary, but it may be just as shocking as search databases were to scholars who had been conducting all reviews manually for most of human history. Even in a future dominated by fully-automated AI-generated reviews, it’s unlikely that the biggest issues, like missing sources, will simply vanish. When comparing today’s issues (like missing sources in a review), key problems and challenges persist despite new tech, and only change in how they manifest.

As such, it becomes more important to accurately identify those challenges and biases in order to effectively use AI to fix, rather than exacerbate, those issues. Otherwise, the past is bound to repeat itself.

How do you view the place of AI in the big picture of literature reviews throughout time? Is it solving old problems or making new ones? Share your thoughts with us below!

Resources

Integrating AI in academic research – Changing the paradigm April 2024

Rating the robots: Artificial intelligence for literature reviews March 2024

Systematic Reviews—From 1753 to Today June 2023

Reflections on the history of systematic reviews 2018

Thomas Nettleton and the dawn of quantitative assessments of the effects of medical interventions. 2010

If you’re burned out, you may feel:

• “I am achieving less than I should.”

• “Run down and drained of physical or emotional energy.”

• “There is more work to do than I practically have the ability to do.”

However, burnout isn’t a necessary side-effect of success. Some of the most notable scientists in history succeeded by working not just hard, but smart. By balancing personal interests, hobbies and external outlets with their work, they enabled the most ground-breaking contributions, while supporting their mental health.

In this issue of The Scoop, we’ll examine how notable scientific figures like Richard Feynman and Barbara McClintock wove their personal and professional lives together, for the betterment of their lives and craft. Their commitment to both science and personal wellness hold valuable and perhaps surprising lessons for researchers today.

What is burnout?

Burnout refers to being over-worked to the point of feeling drained, hopeless, and even resentful. Herbert Freudenberger was one of the first psychologists to study burnout, and his 1974 paper on the subject has over 15,000 citations today. No field is immune to the phenomenon. Whether in a factory or university, any one can experience the symptoms and negative health consequences of burnout.

The very term “burnout” is perhaps part of the problem. It implies we are like engines running out of fuel or lightbulbs left on too long — inanimate objects expected to operate efficiently, no matter personal circumstances.

Today, plenty of resources help diagnose and relieve burnout. You can find recommendations on everything from staying hydrated to doing hobbies. However, a combination of systematic issues, culture, and individual limitations still leaves many people stuck in this cycle of over-work.

Burnout lessons from great scientists

Many of the most successful researchers and scientific contributors in history managed to flourish despite long hours and difficult circumstances. Most seemed to pursue a range of interests and hobbies, which evidently gave their lives balance and fueled their scientific careers. Here are some of their stories, and what we can learn from them.

Feynman: From bombs to bongos

Richard Feynman, one of the greatest physicists of all time, struggled with burnout after working intensely on the Manhattan project. Over the course of a few years in the 1940s, Feynman worked laboriously alongside other physicists on the first atomic weapons. He was so immersed in his work that even when his wife passed away, he spent only a few days off before going right back to work to witness the famous Trinity nuclear test.

After the project, Feynman was burned out. To cope with this, “he would go to the library and sit there for hours reading Arabian nights.” Feynman also found joy in drumming. After working on the bomb, he went on to work at Cornell where he discovered his interest in percussive instruments. He even took part in musicals and productions in the Drama Department.

Takeaway → Explore hobbies like reading, music and performance.

McClintock: Walking to new ideas

Barbara McClintock’s contributions to genetics led her to win the 1983 Nobel Prize in Physiology or Medicine. Even today, she remains the only woman in history to have received an unshared prize in that field. In the 1940s and 50s she identified DNA sequences known as “jumping genes”, or transposable elements (TE), which move from one location on the genome to another. Over the decades, scientists realized TEs were present in nearly all organisms, and make up about half of the human genome, and 90% of maize genome which McClintock originally studied.

During this creative period of her life, McClintock enjoyed walking on the grounds of Cold Spring Harbor Laboratory where she worked. According to her biography, “Rain or shine, from at least the early 1950s through the 1980s, alone or with a friend, she hiked through the woods... Out in nature but near the lab, these walks led to a string of minor discoveries that sparked her synthesis of the races of maize and controlling elements. On these walks, she integrated heredity, development, and evolution.”

Takeaway → Take some long walks to think and talk.

Schrödinger: Renaissance man

Erwin Schrödinger was best-known for his contributions to theoretical physics, particularly to the wave theory of matter and quantum mechanics. His 1926 paper introduced a novel, probabilistic model of the atom, and laid the foundation for much future work in the area of quantum mechanics (Schrödinger's equation).

What really set Schrödinger apart though was his remarkable curiosity and enthusiasm for a range of disciplines. In addition to physics, he “felt at home” in philosophy, literature, and even ancient Indian writings. He enjoyed German poetry, evidenced by quotations from poets like Goethe in his philosophical book “My View of the World”.

This “renaissance man/renaissance woman” attitude is certainly not limited to Schrödinger, and many contemporary researchers manage to exemplify this value. Dr. John Ioannidis, a well-known researcher in the field of meta-science, is also known for his published literary work in Greek and interest in poetry.

Takeaway → Indulge in your curiosity of different subjects.

Carl Sagan: Facts and fiction

Carl Sagan, one of the best known astronomers of the last century, had a remarkable career both within and outside of academia. In addition to his scientific success, he offered advice to NASA and the Apollo crew before journeys to the moon, helped define humanity’s presence via the Voyager Golden Records, and authored over a dozen science books geared towards inspiring the general public. Sagan’s attitude of adopting a range of projects serves as an excellent example of public speaker Alexander Den Heijer’s belief: “You often feel tired, not because you've done too much, but because you've done too little of what sparks a light in you.”

Even with such an ambitious career, Sagan also found time to relax. He spent time with his family (collaborating even professionally with his wife), wrote fiction (Contact), and explored many ideas beyond astronomy (see his notes below).

Takeaway → Diversify your projects; indulge in your curiosity of different subjects.

Rosalind Franklin: Summiting for success

Rosalind Franklin studied DNA in the 1950s, before anyone understood the structure or chemical makeup of DNA. It was her DNA photograph, taken using X-ray diffraction, that helped set James Watson and Francis Crick on the right trajectory to develop the double helix model. Franklin ended up writing a supportive paper to Watson and Crick’s model, without ever knowing how her photograph helped the two with their discovery. In addition to her photographic contribution, Franklin also published over a dozen papers on the molecular structure of viruses, all in her brief 10-year career.

Franklin was a fan of physical activity and the outdoors. She was an avid hiker and traveler, spending weekends hiking with friends from her laboratory and vacations walking and cycling. She would travel and hike throughout Europe, and was captivated by French culture, evidenced by her mastery of the French language and cuisine.

Takeaway → Enjoy the outdoors, exercise, and travel.

Although the most successful scientists in history no doubt worked incredibly hard, they managed to also balance their personal and work lives effectively, ensuring the sustainability of their productivity. It can be tempting to equate hours worked with value, but most contemporary productivity guidance tends to discourage this mindset. As Feynman, McClintock, Schrödinger, Sagan and Franklin all show, successful scientific careers aren’t just compatible with fulfilling personal lives, they are fueled by it.

How do you deal with burnout?

Share your thoughts below to help your peers and colleagues balance their work-life better, for better research and scientific success.

Avoid burnout by tackling your research efficiently.

Use Litmaps to accelerate your literature review. Join our upcoming webinar to learn how.

RSVP For Our Webinar

Resources

The Busy Professor by Dr. Timothy Slater

Burnout to Brilliance by Jess Stuart

Rest: Why You Get More Done When You Work Less by Alex Soojung-Kim Pang

The Burnout Society by Byung-Chul Han

Are We Living in a Burnout Society?, April 2024

How Burnout Became Normal — and How to Push Back Against It, April 2024

Fortunately, this hasn't always been the case, and in the last decade serious strides have been made to address these issues. By taking lessons from the past, and promoting new priorities for the future, metascientists and the scientific community are rethinking the structures and drivers of how research is funded.

In this issue of The Scoop we’ll dive into the key issues with research funding and publishing today, and what exciting solutions are already addressing it. Our recent interview with Stuart Buck, who’s participated in the evolution of metascience over the last decade and now directs the Good Science Project, sheds much-needed light on the prevailing issues and solutions when it comes to improving the situation.

What’s wrong with research funding today

It’s not just how research is funded or what research is funded. When we start lifting the cover on the world of research funding, we quickly see a myriad of interconnected issues: existing challenges, unseen biases, underfunding replication research that ensures quality, and the reduction in high-risk, high-reward work. Here are the key problems in research funding and what that means for the rest of science.

Bureaucracy

Most researchers are aware of the role government plays in funding research. Today, about half of all research at U.S. universities is funded by the federal government. Other countries also have a significant portion of research funded by the government (31% in Australia in 2018, 68% of all public R&D in France in 2014, 30% of all R&D in Germany).

This central approach to funding results in a couple key issues. One is basic bureaucracy. Researchers often spend more time dealing with paperwork than actually getting research done. To get a window into this world, consider that the National Institutes of Health’s (NIH) guide for “preparing and submitting applications” is 157 pages long. Ironically, even attempts to reduce paperwork end up having an inverse effect. Burdensome bureaucracy is a barrier to quickly funding and supporting high-impact research. As we’ll discuss later, this is so hard to change that attempts to get around this turn instead to making entirely new, streamlined organizations (the ARPA-everything movement).

Lack of institutional diversity

Even if the bloated bureaucracy could be successfully addressed, far bigger issues remain. Most funding is in the hands of just a few organizations, so that there’s little opportunity to introduce a radical diversity of perspectives when it comes to what kind of research is funded. This, as Buck and those in The Good Science Project say, constitutes a lack of institutional diversity. Not only may it limit the kinds of approaches pursued in a field, it also inhibits novel, high-risk and experimental research.

Bias towards “safe” research

Many remarkable discoveries and critical research breakthroughs of the past would never have happened in today’s funding climate. Today, researchers must first prove their research before even getting funding. A strong emphasis on preliminary data and findings doesn’t make room for truly novel and unexpected findings. It’s difficult to address proposals based on their “transformative” potential, since genuinely novel discoveries aren’t guaranteed or obvious. As Nobel winner Roger Kornberg puts it, “discoveries are by their nature unanticipated, completely unknown.”

It’s not only transformative research that’s compromised in the current funding paradigm. Lack of replication and reproducibility in research is still a hot topic, and tightly connected to funding as well. In the same way that high-risk research isn’t easy to sell, replication work isn’t rewarded well in the current system. However, the replication crisis has received widespread attention in recent years and many general and domain-specific organizations have cropped up to address it.

How funding is changing, for the better

Over the last decade, many organizations have cropped up to address the quality of scientific research. One such initiative is the Good Science Project, lead by Stuart Buck, which focuses on raising public awareness of funding and publishing issues, as well as promoting better values for future research. The organization promotes a set of guiding principles for what a good funding system entails, and allows researchers to be added as signatories.

Equally important to communicating with researchers at large, the Good Science Project also serves to inform research organizations and entities on how to make good, sustainable changes in funding.

Creating institutional diversity and innovation

Increasing the diversity of research projects enables truly innovative, high-risk work to get funded. It also limits the biases of any one prevalent research funder from influencing the future of research in that field. Several huge philanthropies and government agencies tend to dominate the research funding landscape. For example, of the $USD686 million invested into cancer research in Canada between 2016-2020, $338 million (about half) was awarded by a single entity, the Canadian Institute for Health Research. For biomedical researchers in the U.S., about 50% of research is funded by the NIH. This means that any particular preferences, biases or research priorities of these institutions strongly influences the present and future work of the field.

There are many ideas for reshaping institutional diversity, trying to mix up who makes the decisions, who is eligible, and ultimately, what gets funded. Organizations can try “re-granting” programs, by asking a third-party institution to take a portion of funding and decide its fate. The advantage would be that if these “re-granted” funds end up funding highly successful projects, the original institution can use that information to identify better selection methods for the future and identify gaps in their own selection criteria.

Another idea may be to even experiment with introducing elements of randomness, like lottery systems. Experimenting with a lottery-system, for all applicants that meet a certain threshold of requirements, leaves some decision-making to chance. It may enable unexpected breakthroughs, where otherwise unintentionally biased decision-making would continuously reward the same types of applicants.

Even the structures of organizations play a role. Since most academic institutions are structured similarly, this may have systematic effects which hinder a diversity of decision-making. The Good Science promotes smart restructuring, by reviewing how entities like the NIH are set-up and what direction to take with reform.

It’s ideas like these that Buck and his colleagues explore, write about and share with organizations.

New funding is already here

Although these ideas sound theoretical, new funding options are already emerging. The NIH runs the High-Risk, High-Reward Research program, which funds higher-risk research that wouldn’t be competitive in traditional peer-review. Canada created the New Frontiers in Research Fund (NFRF) a few of years ago, which also focuses on high-risk research. Last year, the NSF committed to experimenting with how it funds research.

For some time, the U.S. has also been creating a slew of research agencies based off of DARPA, the Defense Advanced Research Projects Agency, which has been enabling successful research since the 1960s. Even the UK set up its ARPA-inspired Advanced Research and Invention Agency (ARIA) in 2022. This “ARPA-everything” movement has lead to ARPA-Health in the U.S., which is already funding innovative projects with aggressive timelines, like the living knee replacement project at Columbia.

These changes constitute exciting transformations in the existing funding paradigm, but some solutions bypass the system altogether. Decentralized science is an emerging movement, attempting to transform research funding by relying on blockchain technologies. It exists largely through independent decentralized autonomous organizations (DAOs) which focus on specific scientific fields. One of the newest DAOs is Hélice, which aims to accelerate research and decrease prices of bionic prosthetics and artificial organs. DAOs like this one, and the more centralized ResearchHub platform, turn towards alternative funding methods like crowdsourcing and quadratic funding.

There are many opportunities for improvement when it comes to research funding, but the most essential change is simply trying. Funding bodies need to experiment in order to find out what works. This is one of the core missions of The Good Science project, and essential to transforming the funding system. Organizations simply need to experiment and actually put their ideas to the test. As Stuart Buck puts it, “The idea of trying new ideas. That, to me, is the most important thing to get right.”

Do you battle with these issues in funding research? How would you want research to be funded? Tell us in the comments below and join the conversation!

Resources

The Good Science Project

The Good Science Project Substack

ResearchHub

Do We Fund New Research or Not?, March 2024

Three thoughts on "high-risk" research, March 2024

Metascience Since 2012: A Personal History, 2023

How ARPA-H can master the subtle art of risk-taking, 2023

The Paperwork Reduction Act Is Terrible, and We Should Eliminate or Reform It, 2023

Pioneering the Future: Top 6 Upcoming Decentralized Science Tokens Transforming Industries, 2023

Is Decentralised Science better science?, 2023

The Reproducibility Crisis Isn't New, But This Way of Fixing It Is, 2023

The rise of ‘ARPA-everything’ and what it means for science, 2021

Although this is often considered beneficial to researchers since they are provided assistance throughout the patenting and development process, it also has potential disadvantages. This system can result in unethical power struggles, like the recent case with Katalin Karikó whose research was patented by the University of Pennsylvania (which consequently profited $1.2 billion from it), while Karikó herself was forced out of the decision-making process and institution.

Not only are these policies questioned by the community on ethical grounds, they may also have bigger implications for scientific productivity overall, both within and beyond academia.

In this issue, we dive into the current issues and drivers of IP and innovation in universities, what it means for researchers, and what solutions loom on the horizon.

The Karikó Case

Katalin Karikó’s work and contributions on mRNA was critical to the creation of the mRNA vaccines ultimately used to save countless lives against the SARS-CoV-2 virus during the global pandemic. Although she is well-recognized for this achievement, the events and subsequent profiting from her achievements are widely recognized as unethical.

Karikó and a colleague developed modified RNA (that would ultimately lead to the mRNA COVID vaccine and a Nobel Prize) in 2005, without much recognition at the time. It wasn’t until the pandemic that these results would come into the global spotlight. Before then, this work really wasn’t recognized. In fact, Karikó was demoted four times at Penn. The real kicker is that the university held the rights to Karikó’s work, and later controlled the patent process. Even when Karikó and her colleagues wanted to purchase the patent for their own work themselves, they couldn’t. The university already entered a licensing arrangement with Cellscript. The result? In 2022, Penn profited over USD$1.2 billion from the Pfizer-BioNTech Vax, long after Karikó had been pushed out of the university.

The Karikó case is complex, but even at a cursory glance, it’s clear the unfortunate and unethical loopholes that exist in the current system. With universities owning both the IP and patenting power for their researchers’ work, they can profit from their achievements without rewarding their contributors appropriately.

How did we get here?

Back in the 1950s, a significant portion of innovative and ground-breaking research actually was in the hands of private companies. In the 1960s, for example, DuPont (a big chemical company) published more than MIT in the Journal of the American Chemical Society. Strong anti-monopoly laws at the time meant businesses couldn’t just acquire each other’s inventions, and so many invested into research and development. As these policies loosened in the 70s and 80s, along with an increase in university presence, this kind of research shifted back towards academia. Today, there are less companies investing the same resources into research, with biotech and tech firms being the general exception to the rule.

The 1980s saw another important change, the passing of the Bayh-Dole Act of 1980, which sought to protect the “inventions made by nonprofit organizations…”. This, combined with the fact that universities generally own their researchers’ IPs, means they have the patenting power. It’s this act, although well-intentioned, that can cause ethically questionable cases like Karikó’s.

However, this shift of patenting power to academic institutions may have even bigger consequences outside of academia. Some argue that it stifles industry-driven innovation, reduces corporate R&D, limits access to scientific knowledge, and pushes universities away from foundational science. Academic institutions may compromise their position as being the only entities flexible enough to pursue theoretical and basic science, which industry programs can’t afford to do.

Although this discussion focuses largely on the United States, similar trends can be seen in other parts of the world. China has a similar Bayh-Dole Act in place, and Norway, among other European countries, recently made this shift in the early 2000s. In the case of Norway, some studies find that there was a significant drop in startup and patenting activity from university researchers, largely attributed to the change. Thus, issues appear to surface from both sides: universities aren’t helping researchers patent enough, but successful patenting inevitably competes with industry innovation.

Where do we go from here?

Some argue that researchers should have the right to patent their own discoveries. They can maintain the ability to work with their university’s Technology Transfer Office, but by their own choice. Currently, researchers are required to go through these entities, which manage the IP and patent process, and help transfer discoveries into industry.

Some remorse the loss of “Professor’s Privilege” systems. These systems left IP and patenting rights in the hands of professors, not institutions, but have largely been abolished. One study found a “decline in the technological importance and the value of the patents owned and managed by universities in the countries abolishing the professor’s privilege.” Sweden is one of the few countries left that still maintains the policy, and seems to have a higher rate of academic entrepreneurship as a result. But, bringing Professor’s Privilege policies back into the mainstream is likely not feasible. Most universities have already moved away from the system (Italy, for example, just abandoned it last year) and some argue that institutions owe it to their taxpayers to oversee the innovation of federally funded research.

Another solution in this area focuses on enhancing collaborations between academia and industry to help close the innovative gaps between the two. For example, Roche’s new Institute of Human Biology serves to bring together scientists from both academia and industry in order to conduct research more effectively. In fact, it’s an investment on Roche’s part in “useful scientific knowledge”, rather than creating new drugs. Universities are also often eager to foster more industry collaboration’s, like University of Wisconsin’s new program which will make it easier to build industry partnerships. Of course, these programs still don’t address the individual researcher’s IP rights to their own work, but may help increase innovation in both industry and academia.

Although “enhancing collaborations” doesn’t sound exciting, it may be an essential factor in ensuring future innovation. In the last few decades, universities and public research institutes produce most scientific knowledge, but the withdrawal of companies from their own research means they are less able to understand and use the advancements in public science. That might partially explain why big companies are more reluctant to invest in university-driven discoveries. Instead, spin-offs tend to go towards small or medium-sized companies. Thus, fostering better collaborations is a key step in ensuring the success of both entities.

So, should you own your IP?

Although some believe scientists should own their own IP and have patenting power, there are many connected issues in and outside of academia that could be impacted by such a change. One thing is clear: something needs to change. Cases like Katalin Karikó’s illustrate the loopholes in the existing system that can unethically exclude researchers while profiting their institutions.

There are various indicators that patenting power in the hands of universities may contribute to various issues: waning innovation, decreased academic entrepreneurship, less disruptive research, and declining productivity in higher ed. But this is a deeply-connected system, with researchers’ IP, patenting and licensing regulations, university influence, industry investments and even open science all in play. Influencing one of these may impact other parts of the system in potentially unexpected and unintentional ways.

Do you think researchers at universities should own their own IP? Why or why not? Share your thoughts with us below!

Resources

Why Are We Screwing Over Researchers Who Make Innovative Discoveries?, Feb 2024

The Ecosystem: patent licensing makes way for partnerships in university tech transfer, Feb 2024

Universities are failing to boost economic growth, Feb 2024

The Effect of Public Science on Corporate R&D, 2023

ACADEMIC TO ENTREPRENEUR, Unlocking the Potential of UK Spinouts, 2023

Papers and patents are becoming less disruptive over time (Nature), 2023

‘Disruptive’ science has declined — and no one knows why (Nature), 2023

Sweden ‘can teach US about inclusive innovation’, 2022

University Innovation and the Professor's Privilege (Norway), 2018

University innovation and the ‘professor’s privilege’, 2016

We recently interviewed Ilya Shabanov, creator of The Effortless Academic, to examine how AI tools like ChatGPT have evolved over the last year, and what role these tools do and should play in academia. Shabanov has been investigating academic tools over the last year, building a community of like-minded researchers through Twitter. He started by sharing his expertise in productive note-taking techniques and tool usage, but has since evolved to consider the wide range of technologies available to academics. Today, his platform, The Effortless Academic, gives considerable attention to using AI in research.

Here, we dive into what researchers need to know about using AI tools and ChatGPT in research today.

A new way to view AI tools

Since its inception at the end of 2022, ChatGPT has taken the world by storm, and scientific research along with it. The original problems that surfaced have mostly settled, only to be replaced by a new wave of concerns. The regulations across publishers and its banned use in peer review reflect this caution. However, many researchers are also optimistic. The majority of academics agree that generative AI will help researchers with repetitive tasks and reduce language barriers, which can radically improve research collaborations and opportunities worldwide.

If the scientific community’s reaction is mixed between extreme caution and hopeful, futuristic thinking, then Ilya Shabanov is somewhere in the middle: optimistic about the novel applications of AI in research, and distinctly aware of the key drawbacks and issues. He explores the world of AI-powered research, where new applications surface almost daily. According to him, there is no shortage of tools to explore, but only a fraction appear to provide genuine value: “Most tools are not very useful, but they’re easy to build, and since with AI you can build tools in a few days, we’ve seen many come and go. There’s a cambric explosion of tools (so to speak) but only a few tool families will evolve to see the light of day.”

The majority of tools and uses of generative AI (i.e. ChatGPT) focus on improving writing and reading. But Shabanov feels this is just the tip of the iceberg, and the most impactful uses of generative AI are still being overlooked.

The best use cases for researchers

Improving writing and evaluating written work are two of the most common uses of AI tools today, and are often central to the most heated debates, like if AI should be banned in peer review or how AI can reduce the trustworthiness and quality of scientific work.

Although improving writing and reading are no doubt useful, Shabanov feels these applications are limited in scope. The majority of the scientific community appears to be unaware of the fundamentally transformational applications available, which we dive into below.

Learn a new topic, as an expert

One of the most exciting uses of generative AI is the ability to quickly jump into a new field. This is equally salient for novice student researchers entering a field for the first time as it is for mature researchers learning a new domain. For Shabanov, the ability to jump into a new topic quickly and solve cross-domain questions is remarkable.

How to do this in practice varies based on use case. The most basic methods involve researchers interacting directly with ChatGPT and asking it questions. On the other end of the spectrum is custom GPTs, where users can train a personalized bot to answer any question on a topic, given a specific set of data. These AI bots can be given specific texts, books, papers, etc. to help them solve specific tasks better. For example, a bot can be trained to recommend the best statistical tests for your data. The key limiting factors when it comes to developing these assistants are the user’s creativity and “prompt literacy”.

“AI enables you to wedge into these cross-domain questions much faster… Before that, you’d have to read a couple books. No PhD student or let alone a professor has time for that.” - Ilya Shabanov

Fill knowledge gaps and find supporting evidence

AI tools are highly effective at helping researchers find resources and examples. This is particularly useful when trying to find supporting evidence or alternative approaches to a given method. For example, Shabanov recommends the prompt "find evidence for/against" for any document or text in order to critically analyze it from multiple perspectives (using Consensus). Note, this isn’t equally effective across all AI tools/bots. For example, Consensus, SciSpace and ScholarAI are expressly trained on millions of scientific papers, whereas the default ChatGPT is not.

By leveraging AI’s ability to study large corpuses of information and learn facts, researchers can close gaps in knowledge more easily than manually searching. Shabanov cites an example in his field of ecology, to determine what models exist for predicting population growth. Although dozens of possible algorithms may exist, identifying all of these in the literature manually is an arduous process. Plus, a more novice researcher may not even know what to search for. Instead, he queries ChatGPT (see image below).

Here, ChatGPT provides the top algorithms, and serves as a starting point from which to dive deeper into each recommendation.

Writing

The majority of AI-assisted tools today focus primarily on helping researchers write better and faster. Scientific writing has clear, specific rules. AI can easily identify how text is written and adapt it to this style. Shabanov shares his techniques for using AI to help improve how one writes. For example, he may input a text and ask AI, based on certain rules of writing, where he could improve. In fact, AI could be treated like an initial peer reviewer, asked to review articles before they are even submitted.

However, it’s worth noting that the time-saving capabilities of these tools are often offset by paying additional penalties regarding the quality and reliability of the results. As Shabanov shares, “Since the technology isn’t highly reliable, I pay for that time through accuracy, which, in science, is often unacceptable.”

What to watch out for in ChatGPT, in 2024

Many researchers were initially skeptical about ChatGPT’s responses because of its hallucinations. Although the issue persists, it has improved significantly with ChatGPT-4, which has a significantly lower rate of hallucinations compared to 3.5 or Bard (according to some studies). This is particularly true when models are expressly trained on specific corpuses of knowledge. The community also appears to discuss this problem less often. As Shabanov shares, “In 2024, barely anyone talks about it [hallucinations], and there are ways around it. You have connections to the Internet, to scientific databases. The biggest thing is not hallucinations, the biggest thing is that it’s incomplete. That it misses certain things.”

Shabanov stresses that AI tools can serve as an effective starting point to dive into a new topic, but that the information cannot be assumed to be fully complete.

Most users today are already aware of the other key issues in AI models, like how they exhibit and exacerbate various biases. A 2023 study identified political biases in ChatGPT, and just a couple weeks ago Google’s Gemini AI came under fire for some generated images. This issue is particularly hard to solve, as we attempt to create perfect models using data from an imperfect world.

Poor results are one problem, but when these inaccuracies start to permeate the academic system, they become an even bigger issue. The community was in uproar after the image below was circulated: an inaccurate AI-generated image which wasn’t caught in peer-review and ended up in a published article. Researchers who specialize in assessing the reliability of scientific papers, like Elizabeth Bik, are especially cautious of these new technologies, fearing that “Generative AI will do serious harm to the quality, trustworthiness, and value of scientific papers.”

It’s essential to note that the limitations and disadvantages of AI tools vary greatly. Students, in particular, should be aware of AI tool limitations, since they are more likely to use free or limited versions, compared to their supervisors and colleagues who have access to better resources. For example, ChatGPT4 provides significantly better results than 3.5, but with its $20/month price-tag, many students can’t afford it.

Ideally, advisors and senior researchers take responsibility to ensure their students are educated in how to appropriately use these tools. But, they may also not know the answers themselves. This is where the variety of webinars and workshops available on these topics should be given consideration, as they provide a reliable way to stay up-to-date on the ever-evolving landscape of AI tools.

Although regulations about AI’s use in peer review and publication have been formed, the dust is still far from settling when it comes to the impact of AI in research. The scientific community is abuzz, with both avid supporters and cautious skeptics contributing to discussions. Some researchers, like Ilya Shabanov, are trying to shift the community’s perspective from one focused exclusively on the biases and inaccuracies of AI towards a more optimistic and inclusive view of how AI can support and accelerate research.

Shabanov believes these tools can radically transform how science gets done. He encourages researchers to start experimenting, explore the best use cases, and be creative. Although it’s tempting to ask ChatGPT to simply summarize a paper, there are far bigger fish to fry.

“Use it to leverage the knowledge it has and close the knowledge gaps that you have, finding these jigsaw puzzles pieces that fit into one another.” - Ilya Shabanov

Do you use AI in your research? Are you worried about the future of scientific research? Or excited? Share your thoughts with us below.

Resources

Is ChatGPT making scientists hyper-productive? The highs and lows of using AI Feb 28

A ridiculous AI-generated rat made it into a peer-reviewed journal Feb 16

AI hallucinations: The 3% problem no one can fix slows the AI juggernaut Feb 7

AI and science: what 1,600 researchers think Sept 2023

So, are academics really leaving Twitter? And, if so, where did they go?

We dive into these questions in this week’s newsletter, looking at the state of Academic Twitter, and examine the implications for the loss of these voices on the platform. Plus, we’ll look at the alternative platforms academics are trending towards and see which one stands a chance at filling the gap.

What’s Academic Twitter, and what happened to it?

Academic Twitter is the common phrase used to describe the scientific community and corresponding content on Twitter. The #AcademicTwitter tag appeared shortly after the platform started. The widespread adoption meant that Twitter was generally the primary social media choice for academics where they’d discuss research, share findings and stay up-to-date in their field.

That was the case until two years ago when Elon Musk acquired the site in 2022, rebranded it to X, and made some unpopular changes. Chief among these changes was the new account verification system which many users felt compromised the availability of reliable information. Although Musk’s decisions were based on premises of enabling free speech, some users strongly opposed these decisions, fearing the rise in misinformation and heightened aggression.

Many believed academics would abandon the platform, while others already anticipated the struggle of attempting to find a suitable replacement. After almost two years, these predictions indeed came to pass. Many academics left the platform. A recent survey of 9,200 scientists found that over half have reduced their time on X and 7% quit altogether. This begs the question, what now?

Abandoning Twitter: The repercussions

Some users left Twitter following the Musk changes, because they felt the platform failed to uphold their values. Although this motivation is understandable, the large-scale implications may mean that the overall impact could cause more harm than good. A drop in engaged users who focus on evidence-based reasoning and scientific inquiry could make the misinformation issues on the platform worse.

Some academics, like Timothy Caulfield, a science communicator and law scholar, wonders if this loss of experts can create “a massive echo chamber that can spread misinformation in a way that is very harmful to society?” Indeed, it may. Some researchers believe that misinformation is spreading even faster since the platform’s changes. Thus, the academics who left the platform because of these changes may have been the most critical users in counteracting the negative impact, if they had stayed active.

Without a competitive alternative, many academics jump ship only to find there is nowhere to swim. To date, no platform offers the same networking capabilities and reach into the academic community that were (and, to some degree, still are) achievable with Twitter. It also means the next generation of researchers need to either do what is good for their career and potentially compromise their values, or miss out on the benefits of large-scale networking.

Although the decline of the scientific community has certainly been felt, it continues to power on. Twitter itself still has 450+ million active users, and Academic Twitter continues, just at a smaller scale. Large accounts like AcademicChatter, The PhD Place, PhD Voice, and PhD Forum continue to remain active.

Nonetheless, many still anticipate some platform shift and are attempting to figure out when and where that will happen. We’ll take a look at some of the most promising alternatives next.

Twitter Alternatives

Although academics are leaving Twitter, it’s unclear where they are going and what platform will fill this burgeoning void. Below are some of the most discussed competitors and their estimated monthly active user (MAU) counts. The ✅ denotes platforms where the academic community appears to be trending towards, although not necessarily in significant numbers.

✅Bluesky (1.6 million MAU) : Some academics believe Bluesky to be the best replacement for Twitter. Originally operating on an invite-only scheme, the platform opened its doors to everyone earlier this month. Its novel algorithmic approach makes it appear more egalitarian, which some academics are happy to see. However, with just a couple million active users, it’s still a far cry from Twitter’s scale.

✅Mastodon (1 million MAU): Mastodon is an open source, decentralized social networking platform that grew in popularity following the Musk acquisition of Twitter. Although some academics find the platform appealing, it is also far from the scale necessary to compete with Twitter.

✅LinkedIn (310 million MAU): LinkedIn is the most competitive platform, in terms of active users and a similar community presence, but lacks the culture that Academic Twitter had. LinkedIn is viewed as more professional, and less apt to enable the casual and spontaneous discussions that Twitter enables. You’ll find a growing academic community, but with a greater focus on support and resources, rather than sharing work.

Substack Notes (35 million MAU): Substack Notes were, like Bluesky and Mastodon, released at the same opportune time, and appeared as another potential alternative to Twitter. Although the long-form Substack content does provide a compelling platform for academics to share ideas and gain notoriety, neither it nor Notes seems to be adopted by the academic community at large yet.

Threads (130 million MAU): Threads, released last year, was purported to be Twitter’s biggest competitor. Although it appears to compete with the scale of Twitter, the academic community has largely ignored it. Prominent academic accounts either don’t exist or are far from the follower count they have on Twitter or LinkedIn.

Note, other platforms like Spoutible and Spill, with under one million users, weren’t considered, due to their small volume.

Overall, although some of these platforms may seem like compelling options, none have effectively filled the gap left behind from the demise of Academic Twitter. LinkedIn, Mastodon, and Bluesky so far appear to be the most promising candidates, although none are yet functioning as fully-fledged “replacements”.

No platform has yet attained the same community engagement and scale as Twitter when it comes to serving an academic audience. But, if users continue to avoid Twitter, this might change. A continuous cascade of users switching over to these smaller platforms may eventually culminate in a fully-fledged academic community that doesn’t rely on Twitter.

Although Academic Twitter has undergone its share of struggles, it nonetheless continues to serve as a valuable centralized area where researchers can connect to one another and influence public discussion. As Professor Inger Mewburn, author of the Thesis Whisperer, puts it: “Academic Twitter was the experience of everybody in a very hierarchical system having a voice.” Although the community has seen a significant decline in participation and visibility over the last year or so, it has nonetheless continued to exist in the post-X world. What the future looks like and whether another platform will usurp it as the primary social media platform for academics remains to be seen.

Did you leave Academic Twitter? Where did you go? If you stayed, what do you think of it now?

Resources

What is Bluesky? Everything to know about the app trying to replace Twitter, Feb 2024

News Organizations Are Leaving Twitter. What About You?, Jan 2024

More thoughts on the enshittification of academic social media, Nov 2023

Void left by decline of academic Twitter ‘will be hard to fill’, Sept 2023

Thousands of scientists are cutting back on Twitter, seeding angst and uncertainty, Aug 2023

Environmental users abandoned Twitter after Musk takeover, Aug 2023

Column: Scientists used to love Twitter. Thanks to Elon Musk, they’re giving up on it, Aug 2023

8 facts about Americans and Twitter as it rebrands to X, July 2023

More Academics Take Flight From Twitter as Restrictions, Alternatives Grow, July 2023

Twitter just closed the book on academic research, July 2023

Misinformation superspreaders are thriving on Musk-owned Twitter, Feb 2023

Academics can easily leave Twitter’s town square, but it will be much harder for their institutions, Nov 2022

Twitter Will Lose More than 32 Million Users Worldwide by 2024 Amid Turmoil, Dec 2022

Cara Biasucci believes that understanding behavioral ethics in ourselves and others gives us the power to create a better, kinder and more ethical world. That’s why she and her colleagues Robert Prentice and Lazaro Hernandez run Ethics Unwrapped, a free online platform that teaches the fundamental concepts of ethics to students across the world.

In this edition of The Scoop, we take a look at what it takes to create a platform like Ethics Unwrapped. Biasucci gives a glimpse into the remarkable impact that these lessons in behavioral ethics may have for our world, and the hardships she and her colleagues face in this unique endeavor.

“It’s so important to be able to hear each other out when we have differing values, and also to understand our commonality. If you’re a human being, you’re subject to these biases and pressures. There’s no way that you’re not.” - Cara Biasucci

A brief history of behavioral ethics

It wasn’t until relatively recently that beliefs regarding man’s superior rational and logical thinking was questioned. Up until the early 20th century, “we’ve been thinking that rationality is the thing that separates human beings from the rest of the animal kingdom…” Biasucci says. This changed when Daniel Kahneman and Amos Tversky discovered the notion of behavioral economics. They were pioneers in cognitive bias, realizing that humans don’t make rational choices. Instead, we function off a System 1 and System 2 paradigm, which are two distinct ways of making decisions. System 1 thinking results in automatic, unconscious decision making and supports most of the choices we make throughout the day. Only System 2 thinking demands slow, logical and conscious decision-making.

This realization flipped the traditional notion of humans as rational beings on its head. With the vast majority of our choices made by our reflexive, intuitive and emotionally-charged framework, we’re using rational and logical thinking only a fraction of the time.

This why behavioral ethics is so important, and the primary teaching objective of Ethics Unwrapped. Understanding our behaviors and how these connect to ethical decision making is our greatest leverage in creating a better world. This understanding is further heightened through the intersection of other related domains like biology, neuroscience and psychology, which have all in recent times contributed significantly to research in this area.

“It takes the conversation out of the realm of the theoretical. It’s not enough to know moral frameworks. We’re facing ethical issues all the time. We’re not recognizing them as ethical issues because we have blindspots. We are rationalizing our intuitive responses to these situations, and not actually using deliberate logical thinking to work our way through to the most ethical decision that we can.” - Biasucci

Behavioral ethics in the classroom

Traditionally, ethics was taught in the context of philosophy or religious studies. This bode well in a world where many students pursued liberal arts educations and had a broader exposure to philosophy. As education has increased its focus on specialization, often as the cost of liberal arts, this has meant less opportunities for students to learn ethics.

This widespread lack of education is part of the reason why Biasucci was driven to found Ethics Unwrapped almost 11 years ago. With over 2,000 universities and hundreds of thousands of site visitors a month, the platform has served a real need in educating students globally.

Teaching ethics, the new way

When Biasucci began her work with Ethics Unwrapped, she knew the most important first step: understanding what was already out there.

Rather than jumping in and making content straight away, she interviewed many educators across the United States in an attempt to understand how ethics was being taught. It was from these interviews she gained key insights, like how students wanted to “see people who looked like them talking to them. They didn’t want to see a bunch of people who look like their parents talking to them about ethics.”

Moreover, she purposefully sought out instructors from various disciplines. “If we’re gonna create publicly available content, I want it to be useful across disciplines,” Biasucci said. Today, that dream lives on clearly, with Ethics Unwrapped videos being watched by students across all disciplines, from law school to music appreciation to chemistry.

Choosing video as the primary medium was a natural choice. Biasucci already had a decade of filmmaking experience and anticipated that short form videos would be the perfect conduit for teaching ethical concepts. She wanted to make “the videos short, so that teachers could use them in a variety of different ways, either inside the classroom to introduce a topic, or outside the classroom as preparation for discussion.” Going to the site today, you’ll find some videos under a minute, and longer ones maxing out at around seven minutes.

Those domain experts continue to be a pivotal part of the platform today. Biasucci works with academics across the country to come up with new, research-based content. These collaborators are just as passionate as Biasucci when it comes to communicating these important topics. She believes, “We all do this because we believe in the same thing, which is elevating ethics education, with the effort to make our world a more humane, friendlier, kinder, more conscious world.“

Students response

Years later, the crucial choices Biasucci and her collaborators made early on bore fruit. In their survey of over 8,500 students across the University of Texas campuses and colleges, over 90% of students said that watching an Ethics Unwrapped video helped them understand and apply ethics. Even more salient was the huge portion of respondents that specifically commented about the value of the student interviews within videos. Many videos, like this one on sports, use student interviews as the primary medium for how ethical concepts are taught in the Ethics Unwrapped world. Seeing the lessons in real-world situations from relatable sources made the content concrete and engaging, according to the survey respondents.

When reflecting on the 800+ survey comments on the Ethics Unwrapped content, Biasucci finds that the responses are split roughly down the middle. Half of the students resonate deeply with their peers presenting information, while the other half are particularly drawn to the animations and narrations. This seven-minute introduction to moral emotions demonstrates how easily these videos communicate even relatively complex and multifaceted topics. Corresponding materials, like a case study of the Wells Fargo 2016 fiasco, provide real-world examples of the same concepts, giving students concrete examples of how these ethical concepts influence some of the biggest scandals.

A persistent challenge: staying free

The accessibility of Ethics Unwrapped is both its greatest asset and most challenging requirement to work around. The platform is entirely free, and Biasucci views this as critical to its mission. When they started, there wasn’t a comparable source providing the same content in such an accessible manner. Clearly, Ethics Unwrapped filled a much needed gap. In fact, this wasn’t just limited to universities. In addition to the thousands of students and individuals across the world watching their content, over 1,000 companies have also used the platform in order to help educate employees. The openness of the platform has also been a crucial factor in appealing to a wide range of viewers across the world.

However, keeping any platform strictly free isn’t easy. For as long as Ethics Unwrapped has been around, securing finances have been a struggle. In fact, the project began with a finite budget, designed to be spent over a 3-year period. Since then, additional sources of funding have been acquired through other grants and opportunities, keeping the project alive and well today.

In an increasingly polarized world, understanding behavioral ethics is a crucial first step to bridging gaps between dissenting groups. By acknowledging our innate biases and self-serving behaviors, we are then empowered to step beyond those limitations.

“If you’re a human being, you’re subject to these biases and pressures. There’s no way that you’re not. And to be able to give each other a little bit of mercy, a little bit of slack, and recognize we all operate under this umbrella of imperfection…” - Biasucci

That’s what inspires Biasucci, her colleagues and collaborators to continue working on Ethics Unwrapped. Through the common language of behavioral ethics, everyone, and especially the next generation of world leaders and decision-makers, can understand each other better and make better choices.

Did you learn or teach behavioral ethics throughout your academic career? How do you think it should be incorporated into education? Share your thoughts and join the discussion below.

Some projects, like the Zealandia Te Māra a Tāne ecosanctuary, located in Wellington, New Zealand, take this one step further. With a 500-year mission to ecologically restore the area, this project gives an entirely new meaning to “long-term planning”.

As Chief Executive of Zealandia, Danielle Shanahan brings a unique perspective to research methods, values and best practices. In our recent interview, she gave me a glimpse into what it takes to manage a venture like Zealandia, and the various insights gained which apply just as well to researchers across domains and organizations.

What is Zealandia?

Zealandia is an ecosanctuary located in New Zealand, with the mission of ecologically restoring the valley it’s located in. That includes re-introducing and enabling wildlife and vegetation to flourish, gaining knowledge that can be applied elsewhere and restoring “the indigenous character of the valley”.

Due to its relatively urban location in the city of Wellington, its influence on the region is widely-known and celebrated. It has over 140,000 visitors per year and hundreds of local volunteers. The increase in native bird populations throughout the region has been largely attributed to the sanctuary’s work over the last three decades.

Lessons learned, for all academics

No matter the domain, many researchers face the same challenges with their long-term pursuits. Whether that’s interpersonal struggles, budgeting, or losing sight of the big picture, there is some degree of friction, which naturally increases in proportion to the size and scope of a research initiative.

Although Zealandia has a unique goal and remarkable timeline when it comes to its ecological objectives, the challenges and lessons learned are clearly applicable to researchers in other domains and circumstances. Here are a few of those key takeaways, based on my brief chat with Danielle Shanahan.

Embrace a long-term vision

Working with a 500-year timeline in mind means frequent reflection on the scale and sustainability of practically all choices. Take something as mundane as file systems. At Zealandia, grappling with the best way to store data in the long-term is a serious challenge, taking significant effort and long-term thinking. This perspective has driven Shanahan to give genuine attention towards what she wants to achieve in the time she has.

“How do I want to see the world differently when I exit it?” - Shanahan

→ Find a long-term vision for your research. By considering the far-reaching impacts and potential legacy of your work, you can better guide your research objectives and help stay focused on the bigger picture.

Rethink the value of publishing

With a project as impactful and practical as Zealandia, it’s easy to imagine how the research conducted there extends outside of academia. Although Shanahan acknowledges the value of publishing research in peer-reviewed journals, she also finds that it’s not always the best mechanism when it comes to sharing scientific findings.

Of course, this can be a challenge. Shanahan herself came from academia, and saw publishing as a clear measure of success. But over her 7+ years at Zealandia, she’s seen the enormous value that comes from sharing research findings with the right audience, in order to ensure it has the best intended impact.

What are these other venues? Depending on the project, it may be better to share findings as a report, a public-facing blog, or (in the case of Zealandia’s ecological work) directly with other sanctuaries. By relying on local networks, media, and news, the discoveries made at Zealandia have a much higher chance of encouraging real world change in other contexts, as compared to limiting information-sharing to the academic domain.

Shanahan shares a recent example of researchers experimenting with a novel proof-of-concept for the veteranization of pine trees. Instead of cutting some trees down, researchers cut them in specific way that enables native kākā birds to nest in them. Although it’s a novel application of this technique, there’s no clear value in spending concerted efforts on publishing this procedure in a journal. Instead, the team would opt to share the process on social media, and other methods targeted towards the audiences most likely to benefit via reports.

→ Not all research needs to be published in peer-reviewed journals. Assess the best platform for sharing your research findings. Especially if your work has practical applications, consider blogs, social media, and detailed reports to effectively reach your audience and create a more targeted impact.

Prioritize people and connections

No matter what I talked about with Shanahan, the conversation often came back to the value of relationships. Whether it’s trying to engage a community, in order to get their support for a research initiative, finding collaborators, or supporting staff, the priority is the people.

“That’s my most important job — looking after the people and the place.” - Shanahan

When it comes to working with the community, Shanahan observes that people are far more willing to engage when they understand why a certain initiative is happening.

→ Invest in real life connections and conversations (e.g. conferences), especially for practical outcome-focused research.

Maintain open communication

Investing in people and relationships may sound great, but how does it happen in practice? Trust and honesty are key components to maintaining human connection, and even more so in times of difficulty or conflict. A great example is how Zealandia handles weasel incursions — events that make communication with the broader audience intimidating.

In order to effectively preserve wildlife within the ecosanctuary, Zealandia is fully enclosed with a 2.2 meter (7 foot) tall fence, plenty of traps, and strict biosecurity rules. Nonetheless, pests sometimes still get in. Shanahan acknowledges that although its tempting to wait until an issue (like a weasel incursion) has resolved itself before informing the broader audience, it’s better to share these events promptly. “Early, open and transparent” is the primary philosophy for communication at Zealandia, which Shanahan believes fosters better community trust.

“I think talking about the fact that research is hard, that there’s no easy answers, I actually think that’s interesting to people. The mystery is interesting to people. And I think really hearing that journey is what creates buy-in.” - Shanahan

→ Be honest and open with stakeholders and audience members, especially those who contribute to or support your research. Consider Zealandia’s communication philosophy of “early, open, and transparent.”

Taking a step back, it’s easy to see the remarkable benefits of working with such a long-term mission as Zealandia’s. It allows contributing researchers, like Shanahan, to more easily appreciate the big picture, to focus more on how research gets done, and to even reflect on each person’s individual contributions throughout their life. Although these values come swiftly to the forefront for very long-term projects like this one, they are equally salient across all research domains.

Do you think the lessons from an ecosanctuary with a 500-year plan transfer to your own work? What are the barriers you face in long-term strategy and planning? Share your perspective in the comments, or get in touch. The Scoop is always seeking new perspectives on the state of academia, research and more.

This conundrum isn’t new, but the endlessly increasing tuition may finally be hitting a wall with the advent of digital technologies. I recently interviewed, Michael D. Smith, a Professor of Information Technology and Public Policy at Carnegie Mellon University and author of The Abundant University. Smith dives into why our current system of higher education is unsustainable and how emerging digital technologies are disrupting the system. Smith argues that these technologies are paramount to delivering better education, and that universities would be wise to recognize this as an opportunity for growth and re-evaluation of their mission.

Cost crisis in higher education

The cost of higher education has quadrupled compared to inflation over the last 50 years in the United States. Smith argues that this trend is unsustainable, with most Americans unable to afford college. The continual increase in costs, driven by the need to maintain physical infrastructures and compete with technological advancements, is not viable long-term.

Moreover, the current system largely reinforces existing class divisions. Those born into the top 1% are 77 times more likely to attend elite universities, compared to those born in the bottom 20%. Since elite college degrees are treated as the highest quality “brand” on the job market, this exacerbates socioeconomic divisions.

Smith argues that the only way to make these systems both morally and economically sustainable is to embrace digital technology. This is the topic of his latest book, The Abundant University.

“If our goal is conveying knowledge (and I think it is), then can we use new technologies to facilitate part of that distribution of knowledge in a way that might even be better than what we achieve in the classroom for some tasks? That’s what I’m trying to get us to think about.” - Michael Smith

Technological disruption as a solution

There are three key scarcity factors that continue to keep universities in business, despite the almost unbearable costs: limited seats in the classroom, access to scarce faculty experts, and access to scarce, valuable credentials. Digital technologies, which enable remote learning, address the first two. With unlimited seats and direct access to some of the best teachers in the world right at one’s fingertips, universities no longer have a monopoly on these two factors. Their final stronghold is the final factor: the perceived value and continued demand of degrees.

Smith argues that this is already changing. The university students attend is often treated as a “brand” when they later approach employers. However, brand is only a proxy for quality. If prospective employers had more information about the actual quality of credentials, they would be able to move away from brand-name recognition when it comes to degrees. Smith even envisions review-based markets, like AirBnB or Amazon, but where the degree and credentials themselves are reviewed. Ultimately, this would dissolve the final scarcity factor, allowing more online learning methods to be quality credentials when entering the job market.

If this comes to fruition, universities that don’t readily adopt such technologies may be left flailing. As someone who studies and teaches technological disruption, Smith often bears witness to how models that don’t adapt die out (think Blockbuster, Britannica, CDs/DVDs, etc.). Success happens when industries do recognize the potential of new technology and use it to fulfill their underlying mission better. In the entertainment industry, that meant developing content for streaming services and other retail platforms that have replaced older modes to better fulfill the underlying mission. In higher education, this may involve taking advantage of remote learning to improve education and opportunities across the board.

Unprecedented teaching innovations

The adoption of digital technologies in education means new possibilities for teaching in more collaborative environments. With the existing system, it’s challenging to effectively portray multiple perspectives in the classroom. With the continual and significant decrease in ideological and political viewpoint diversity in higher education over the last few decades, it gets even harder (according to Jonathan Haidt’s and Greg Lukianoff’s latest work).

By remotely connecting teachers of different opinions, students have the opportunity to learn a wider range of perspectives and build their critical thinking skills based on multiple views.

“Instead of saying we’re going to read chapter one from this book, I could say I’m going to bring in the author of this book and we’re going to pepper her with questions.” - Michael Smith

This extends beyond content, to even how a lesson is delivered. Smith shared with me an example of an online course that allowed students to listen to explanations on the same content by different instructors. Some students, like his daughter, immediately gravitated towards instructors of their own demographic background. Although having a diversified teaching workforce to improve student performance has long been a subject of discussion, never has it been so achievable as with remote learning.

Resistance to remote learning

Many people feel that remote learning isn’t on par with in-person education. As such, plenty of institutions are still resistant to its adoption. However, Smith is adamant that not everything needs to be remote. Instead, the priority is shifting the mindset from one of “finding reasons why it can’t work” to instead considering opportunities for improvement. Although some institutions like ASU and Georgia Tech have adopted fully remote learning programs, via their online degree programs, the adoption of digital technology is a spectrum. In many cases, some combination of in-person and remote learning could mean significant improvements.

Smith also believes that taking advantage of digital technology can benefit other aspects of higher education, and particularly research. The more teaching responsibilities are redistributed, the more time professors will have to devote to research. Already tenure-track research faculty juggle too many responsibilities. By at least alleviating teaching loads, digital technology may play a key role in giving researchers more time to produce credible and reproducible research.

Despite the profound potential benefits of adopting digital technology and remote learning, some resistance remains. The key to getting beyond the initial opposition is to recognize the potential improvements to be gained, as well as to study the industries that failed to react to disruption. Smith believes that this tech can enable universities to pursue their core mission better, by improving teaching, access and opportunities for students of all backgrounds.

What have been your experiences with remote learning, and how do you see it disrupting higher education? Share your thoughts in the comments below.

As it stands, teachers are already adopting more remote learning practices. In fact, we at Litmaps are working on a course that instructors can use in the classroom to teach fundamental literature discovery concepts using our tool. Pre-register today to get notified as soon as the course is live!

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