Table of Contents<\/p>
- 1<\/span> Why are research skills valuable?<\/a>
- 1.1<\/span> Research seems to have been extremely high-impact historically<\/a><\/li>
- 1.2<\/span> There are good theoretical reasons to think that research will be high-impact<\/a><\/li>
- 1.3<\/span> Research skills seem extremely useful to the problems we think are most pressing<\/a><\/li>
- 1.4<\/span> If you’re a good fit, you can have much more impact than the average<\/a><\/li>
- 1.5<\/span> Depending on which subject you focus on, you may have good backup options<\/a><\/li><\/ul><\/li>
- 2<\/span> What does building research skills typically involve?<\/a>
- 2.1<\/span> Academic research<\/a><\/li>
- 2.2<\/span> Practical but big picture research<\/a><\/li>
- 2.3<\/span> Applied research<\/a><\/li>
- 2.4<\/span> Stages of progression through building and using research skills<\/a><\/li><\/ul><\/li>
- 3<\/span> Personal fit is perhaps more important for research than other skills<\/a>
- 3.1<\/span> How much do researchers differ in productivity?<\/a><\/li>
- 3.2<\/span> What does this mean for building research skills?<\/a><\/li><\/ul><\/li>
- 4<\/span> How to evaluate your fit<\/a>
- 4.1<\/span> How to predict your fit in advance<\/a><\/li>
- 4.2<\/span> How to tell if you’re on track<\/a><\/li><\/ul><\/li>
- 5<\/span> How to get started building research skills<\/a>
- 5.1<\/span> Choosing a research field<\/a><\/li><\/ul><\/li>
- 6<\/span> Once you have these skills, how can you best apply them to have an impact?<\/a>
- 6.1<\/span> Which research topics are the highest-impact?<\/a><\/li>
- 6.2<\/span> Find jobs that use a research skills<\/a><\/li><\/ul><\/li>
- 7<\/span> Career paths we’ve reviewed that use these skills<\/a><\/li>
- 8<\/span> Learn more about research<\/a><\/li><\/ul><\/div>\n
\nIn a nutshell:<\/strong> Talented researchers are a key bottleneck facing many of the world’s most pressing problems<\/a>. That doesn’t mean you need to become an academic. While that’s one option (and academia is often a good place to start), lots of the most valuable research happens elsewhere. It’s often cheap to try out developing research skills while at university, and if it’s a good fit for you, research could be your highest impact option.<\/p>\n
\n\nKey facts on fit<\/h4>\n
You might be a great fit if you have the potential to become obsessed<\/em> with high-impact questions, have high levels of grit and self-motivation, are open to new ideas, are intelligent, and have a high degree of intellectual curiosity. You’ll also need to be a good fit for the particular area you’re researching (e.g. you might need quantitative ability).\n<\/div>\n<\/div>\n<\/div>\n
<\/span>Why are research skills valuable?<\/h2>\n
Not everyone can be a Norman Borlaug, and not every discovery gets adopted. Nevertheless, we think research can often be one of the most valuable skill sets to build \u2014 if you’re a good fit.<\/p>\n
We’ll argue that:<\/p>\n
- \n
- Research seems to have been extremely high-impact historically<\/a><\/li>\n
- There are good theoretical reasons to think that research will be high-impact<\/a><\/li>\n
- Research skills seem extremely useful to the problems we think are most pressing<\/a><\/li>\n
- If you’re a good fit, you can have much more impact than the average<\/a><\/li>\n
- And, depending on which subject you focus on, you may have good backup options<\/a>.<\/li>\n<\/ul>\n
Together, this suggests that research skills could be particularly useful for having an impact.<\/p>\n
Later, we’ll look at:<\/p>\n
- \n
- How to evaluate your fit for building research skills<\/a><\/li>\n
- How to get started building research skills<\/a><\/li>\n
- How you can use these skills to have an impact once you’ve started<\/a><\/li>\n<\/ul>\n
<\/span>Research seems to have been extremely high-impact historically<\/h3>\n
If we think about what has most improved the modern world, much can be traced back to research: advances in medicine such as the development of vaccines against infectious diseases, developments in physics and chemistry that led to steam power and the industrial revolution<\/a>, and the invention of the modern computer, an idea which was first proposed by Alan Turing in his seminal 1936 paper On Computable Numbers<\/em>.<\/p>\n
Many of these ideas were discovered by a relatively small number of researchers \u2014 but they changed all of society. This suggests that these researchers may have had particularly large individual impacts.<\/p>\n
\n ![\"Dr](\"https:\/\/80000hours.org\/wp-content\/uploads\/2016\/06\/1024px-Cholera_rehydration_nurses.jpg\")
Dr. Nalin helped to save millions of lives with a simple innovation: giving patients with diarrhoea water mixed with salt and sugar.<\/figcaption><\/figure>\n That said, research today is probably lower-impact than in the past. Research is much less neglected than it used to be: there are nearly 25 times as many researchers today as there were in 1930. It also turns out that more and more effort is required to discover new ideas, so each additional researcher probably has less impact than those that came before.<\/p>\n
However, even today, a relatively small fraction of people are engaged in research. As an approximation, only 0.1% of the population are academics, and only about 2.5% of GDP is spent on research and development<\/a>. If a small number of people account for a large fraction of progress, then on average<\/em> each person’s efforts are significant.<\/p>\n
Moreover, we still think there’s a good case to be made for research being impactful on average today, which we cover in the next two sections.<\/p>\n
<\/span>There are good theoretical reasons to think that research will be high-impact<\/h3>\n
There’s little commercial incentive to focus on the most socially valuable research. And most researchers don’t get rich, even if their discoveries are extremely valuable. Alan Turing made no money from the discovery of the computer, and today it’s a multibillion-dollar industry. This is because the benefits of research often come a long time in the future and can’t usually be protected by patents. This means if you care more about social impact than profit, then it’s a good opportunity to have an edge.<\/p>\n
Research is also a route to leverage. When new ideas are discovered, they can be spread incredibly cheaply, so it’s a way that a single person can change a field. And innovations are cumulative \u2014 once an idea has been discovered, it’s added to our stock of knowledge and, in the ideal case, becomes available to everyone. Even ideas that become outdated often speed up the important future discoveries that supersede it.<\/p>\n
<\/span>Research skills seem extremely useful to the problems we think are most pressing<\/h3>\n
When you look at our list of the world’s most pressing problems<\/a> \u2014 like preventing future pandemics or reducing risks from AI systems \u2014 expert researchers seem like a key bottleneck.<\/p>\n
For example, to reduce the risk posed by engineered pandemics<\/a>, we need people who are talented at research to identify the biggest biosecurity risks and to develop better vaccines and treatments.<\/p>\n
To ensure that developments in AI<\/a> are implemented safely and for the benefit of humanity, we need technical experts thinking hard about how to design machine learning systems safely and policy researchers to think about how governments and other institutions should respond. (See this list of relevant research questions<\/a>.)<\/p>\n
And to decide which global priorities we should spend our limited resources on, we need economists, mathematicians, and philosophers to do global priorities research<\/a>. For example, see the research agenda of the Global Priorities Institute at Oxford<\/a>.<\/p>\n
We’re not sure why so many of the most promising ways to make progress on the problems we think are most pressing involve research, but it may well be due to the reasons in the section above \u2014 research offers huge opportunities for leverage, so if you take a hits-based approach<\/a> to finding the best solutions to social problems, it’ll often be most attractive.<\/p>\n
In addition, our focus on neglected problems often means we focus on smaller and less developed areas, and it’s often unclear what the best solutions are in these areas. This means that research is required to figure this out.<\/p>\n
For more examples, and to get a sense of what you might be able to work on in different fields, see this list of potentially high-impact research questions, organised by discipline<\/a>.<\/p>\n
<\/span>If you’re a good fit, you can have much more impact than the average<\/h3>\n
The sections above give reasons why research can be expected to be impactful in general<\/em>. But as we’ll show below<\/a>, the productivity of individual researchers probably varies a great deal (and more than in most other careers). This means that if you have reason to think your degree of fit is better than average, your expected impact could be much higher than the average.<\/p>\n
<\/span>Depending on which subject you focus on, you may have good backup options<\/h3>\n
Pursuing research helps you develop deep expertise on a topic, problem-solving, and writing skills. These can be useful in many other career paths. For example:<\/p>\n
- \n
- Many research areas can lead to opportunities in policymaking<\/a>, since relevant technical expertise is valued in some of these positions. You might also have opportunities to advise policymakers and the public as an expert.<\/li>\n
- The expertise and credibility you can develop by focusing on research (especially in academia) can put you in a good position to switch your focus to communicating important ideas<\/a>, especially those related to your speciality, either to the general public, policymakers, or your students.<\/li>\n
- If you specialise in an applied quantitative subject, it can open up certain high-paying jobs, such as quantitative trading<\/a> or data science<\/a>, which offer good opportunities for earning to give<\/a>.<\/li>\n<\/ul>\n
Some research areas will have much better backup options than others \u2014 lots of jobs value applied quantitative skills, so if your research is quantitative you may be able to transition into work in effective nonprofits or government. A history academic, by contrast, has many fewer clear backup options outside of academia.<\/p>\n
<\/span>What does building research skills typically involve?<\/h2>\n
By ‘research skills’ we broadly mean the ability to make progress solving difficult intellectual problems.<\/p>\n
We find it especially useful to roughly divide research skills into three forms:<\/p>\n
<\/span>Academic research<\/h3>\n
Building academic research skills is the most predefined route. The focus is on answering relatively fundamental questions which are considered valuable by a specific academic discipline. This can be impactful either through generally<\/em> advancing a field of research that’s valuable to society or finding opportunities to work on socially important questions within that field.<\/p>\n
Turing was an academic. He didn’t just invent the computer \u2014 during World War II he developed code-breaking machines that allowed the Allies to be far more effective against Nazi U-boats. Some historians estimate this enabled D-Day to happen a year earlier than it would have otherwise. Since World War II resulted in 10 million deaths per year, Turing may have saved about 10 million lives.<\/p>\n
\n ![\"Alan](\"https:\/\/80000hours.org\/wp-content\/uploads\/2016\/06\/Alan_Turing_Aged_16.jpg\")
Turing was instrumental in developing the computer. Sadly, he was prosecuted for being gay, perhaps contributing to his suicide in 1954.<\/figcaption><\/figure>\n We’re particularly excited about academic research in subfields of machine learning relevant to reducing risks from AI<\/a>, subfields of biology relevant to preventing catastrophic pandemics<\/a>, and economics \u2014 we discuss which fields you should enter below<\/a>.<\/p>\n
Academic careers are also excellent for developing credibility, leading to many of the backup options<\/a> we looked at above, especially options in communicating important ideas<\/a> or policymaking<\/a>.<\/p>\n
Academia is relatively unique in how flexibly you can use your time. This can be a big advantage \u2014 you really get time to think deeply and carefully about things \u2014 but can be a hindrance, depending on your work style.<\/p>\n
See more about what academia involves in our career review on academia<\/a>.<\/p>\n
<\/span>Practical but big picture research<\/h3>\n
Academia rewards a focus on questions that can be decisively answered with the methods of the field. However, the most important questions can rarely be answered rigorously \u2014 the best we can do is look at many weak forms of evidence and come to a reasonable overall judgement. which means while some of this research happens in academia, it can be hard to do that.<\/p>\n
Instead, this kind of research is often done in nonprofit research institutes, e.g. the Centre for the Governance of AI<\/a> or Our World in Data<\/a>, or independently.<\/p>\n
Your focus should be on answering the questions that seem most important (given your view of which global problems most matter) through whatever means are most effective.<\/p>\n
Some examples of questions in this category that we’re especially interested in include:<\/p>\n
- \n
- How likely is a pandemic worse than COVID-19 in the next 10 years?<\/li>\n
- How difficult is the AI alignment problem going to be to solve?<\/li>\n
- Which global problems are most pressing?<\/li>\n
- Is the world getting better or worse over time?<\/li>\n
- What can we learn from the history of philanthropy about which forms of philanthropy might be most effective?<\/li>\n<\/ul>\n
You can see a longer list of ideas in this article<\/a>.<\/p>\n
Someone we know who’s had a big impact with research skills is Ajeya Cotra. Ajeya initially studied electrical engineering and computer science at UC Berkeley. In 2016, she joined Open Philanthropy as a grantmaker. Since then she’s worked on a framework for estimating when transformative AI might be developed<\/a>, how worldview diversification<\/a> could be applied to allocating philanthropic budgets, and how we might accidentally teach AI models to deceive us<\/a>.<\/p>\n
\n ![\"Ajeya](\"https:\/\/80000hours.org\/wp-content\/uploads\/2021\/01\/main_500.png\")
Ajeya was moved by many of the conclusions of effective altruism<\/a>, which eventually led to her researching the transformative effects of AI.<\/figcaption><\/figure>\n <\/span>Applied research<\/h3>\n
Then there’s applied research. This is often done within companies or nonprofits, like think tanks<\/a> (although again, there’s also plenty of applied research happening in academia). Here the focus is on solving a more immediate practical problem (and if pursued by a company, where it might be possible to make profit from the solution) \u2014 and there’s lots of overlap with engineering skills<\/a>. For example:<\/p>\n
- \n
- Developing new vaccines<\/li>\n
- Creating new types of solar cells or nuclear reactors<\/li>\n
- Developing meat substitutes<\/li>\n<\/ul>\n
Neel was doing an undergraduate degree in maths when he decided that he wanted to work in AI safety<\/a>. Our team was able to introduce Neel to researchers in the field and helped him secure internships in academic and industry research groups. Neel didn’t feel like he was a great fit for academia \u2014 he hates writing papers \u2014 so he applied to roles in commercial AI research labs. He’s now a research engineer at DeepMind. He works on mechanistic interpretability<\/em> research which he thinks could be used in the future to help identify potentially dangerous AI systems before they can cause harm.<\/p>\n
\n ![\"Neel](\"https:\/\/80000hours.org\/wp-content\/uploads\/2023\/05\/NeelNanda-e1684842910437.jpg\")
Neel’s machine learning research is heavily mathematical \u2014 but has clear applications to reducing the risks from advanced AI<\/a>.<\/figcaption><\/figure>\n We also see “policy research” \u2014 which aims to develop better ideas for public policy \u2014 as a form of applied research.<\/p>\n
<\/span>Stages of progression through building and using research skills<\/h3>\n
These different forms of research blur into each other, and it’s often possible to switch between them during a career. In particular, it’s common to begin in academic research and then switch to more applied research later.<\/p>\n
However, while the skill sets contain a common core, someone who can excel in intellectual academic research might not be well-suited to big picture practical or applied research.<\/p>\n
The typical stages in an academic career involve the following steps:<\/p>\n
- \n
- Pick a field. This should be heavily based on personal fit<\/a> (where you expect to be most successful and enjoy your work the most), though it’s also useful to think about which fields offer the best opportunities to help tackle the problems you think are most pressing, give you expertise that’s especially useful given these problems, and use that at least as a tie-breaker. (Read more about choosing a field<\/a>.)<\/li>\n
- Earn a PhD.<\/li>\n
- Learn your craft and establish your career \u2014 find somewhere you can get great mentorship and publish a lot of impressive papers. This usually means finding a postdoc with a good group and then temporary academic positions.<\/li>\n
- Secure tenure. <\/li>\n
- Focus on the research you think is most socially valuable (or otherwise move your focus towards communicating ideas or policy).<\/li>\n<\/ol>\n
Academia is usually seen as the most prestigious path…within academia. But non-academic positions can be just as impactful \u2014 and often more so since you can avoid some of the dysfunctions and distractions of academia, such as racing to get publications.<\/p>\n
At any point after your PhD (and sometimes with only a master’s), it’s usually possible to switch to applied research in industry, policy, nonprofits, and so on, though typically you’ll still focus on getting mentorship and learning for at least a couple of years. And you may also need to take some steps to establish your career enough to turn your attention to topics that seem more impactful.<\/p>\n
Note that from within academia, the incentives to continue with academia are strong, so people often continue longer than they should!<\/p>\n
If you’re focused on practical big picture research, then there’s less of an established pathway, and a PhD isn’t required.<\/p>\n
Besides academia, you could attempt to build these skills in any job that involves making difficult, messy intellectual judgement calls, such as investigative journalism, certain forms of consulting, buy-side research in finance, think tanks, or any form of forecasting.<\/p>\n
<\/span>Personal fit is perhaps more important for research than other skills<\/h2>\n
The most talented researchers seem to differ hugely in their impact compared to typical researchers across a wide variety of metrics and according to the opinions of other researchers.<\/p>\n
For instance, when we surveyed biomedical researchers, they said that very good researchers were rare, and they’d be willing to turn down large amounts of money if they could get a good researcher for their lab. Professor John Todd, who works on medical genetics at Cambridge, told us<\/a>:<\/p>\n
\n The best people are the biggest struggle. The funding isn’t a problem. It’s getting really special people[…] One good person can cover the ground of five, and I’m not exaggerating.\n<\/p><\/blockquote>\n
This makes sense if you think the distribution of research output is very wide \u2014 that the very best researchers have a much greater output than the average researcher.<\/p>\n
<\/span>How much do researchers differ in productivity?<\/h3>\n
It’s hard to know exactly how spread out the distribution is, but there are several strands of evidence that suggest the variability is very high.<\/p>\n
Firstly, most academic papers get very few citations, while a few get hundreds or even thousands. An analysis of citation counts in science journals<\/a> found that ~47% of papers had never been cited, more than 80% had been cited 10 times or less, but the top 0.1% had been cited more than 1,000 times. A similar pattern seems to hold across individual researchers<\/a>, meaning that only a few dominate \u2014 at least in terms of the recognition their papers receive.<\/p>\n
Citation count is a highly imperfect measure of research quality, so these figures shouldn’t be taken at face-value. For instance, which papers get cited the most may depend at least partly on random factors, academic fashions, and “winner takes all” effects \u2014 papers that get noticed early end up being cited by everyone to back up a certain claim, even if they don’t actually represent the research that most advanced the field.<\/p>\n
However, there are other reasons to think the distribution of output is highly skewed.<\/p>\n
William Shockley, who won the Nobel Prize for the invention of the transistor, gathered statistics on all the research employees in national labs, university departments, and other research units, and found that productivity (as measured by total number of publications, rate of publication, and number of patents) was highly skewed<\/a>, following a log-normal distribution.<\/p>\n
Shockley suggests that researcher output is the product of several (normally distributed) random variables \u2014 such as the ability to think of a good question to ask, figure out how to tackle the question, recognize when a worthwhile result has been found, write adequately, respond well to feedback, and so on. This would explain the skewed distribution: if research output depends on eight different factors and their contribution is multiplicative, then a person who is 50% above average in each of the eight areas will in expectation be 26 times more productive than average.<\/p>\n
When we looked at up-to-date data on how productivity differs across many different areas<\/a>, we found very similar results. The bottom line is that research seems to perhaps be the area where we have the best evidence for output being heavy-tailed.<\/p>\n
Interestingly, while there’s a huge spread in productivity, the most productive academic researchers are rarely paid 10 times more than the median, since they’re on fixed university pay-scales. This means that the most productive researchers yield a large “excess” value to their field. For instance, if a productive researcher adds 10 times more value to the field than average, but is paid the same as average, they will be producing at least nine times as much net benefit to society. This suggests that top researchers are underpaid relative to their contribution, discouraging them from pursuing research and making research skills undersupplied compared to what would be ideal.<\/p>\n
Can you predict these differences in advance?<\/h4>\n
Practically, the important question isn’t how big the spread is, but whether you could \u2014 early on in your career \u2014 identify whether or not you’ll be among the very best researchers.<\/p>\n
There’s good news here! At least in scientific research, these differences also seem to be at least somewhat predictable ahead of time, which means the people entering research with the best fit could have many times more expected impact.<\/p>\n
In a study<\/a>, two IMF economists looked at maths professors’ scores in the International Mathematical Olympiad<\/a> \u2014 a prestigious maths competition for high school students. They concluded that each additional point scored on the International Mathematics Olympiad “is associated with a 2.6 percent increase in mathematics publications and a 4.5 percent increase in mathematics citations.”<\/p>\n
We looked at a range of data on how predictable productivity differences are in various areas<\/a> and found that they’re much more predictable in research.<\/p>\n
<\/span>What does this mean for building research skills?<\/h3>\n
The large spread in productivity makes building strong research skills a lot more promising if you’re a better fit than average. And if you’re a great<\/em> fit, research can easily<\/em> become your best option.<\/p>\n
And while these differences in output are not fully<\/em> predictable at the start of a career, the spread is so large that it’s likely still possible to predict differences in productivity with some reliability.<\/p>\n
This also means you should mainly be evaluating your long-term expected impact in terms of your chances of having a really big<\/em> success.<\/p>\n
That said, don’t rule yourself out too early. Firstly, many people systematically underestimate their skills<\/a>. (Though others overestimate them!) Also, the impact of research can be so large that it’s often worth trying it out, even if you don’t expect you’ll succeed<\/a>. This is especially true because the early steps of a research career often give you good career capital for many other paths.<\/p>\n
<\/span>How to evaluate your fit<\/h2>\n
<\/span>How to predict your fit in advance<\/h3>\n
It’s hard to predict success in advance, so we encourage an empirical approach: see if you can try it out and look at your track record.<\/p>\n
You probably have some track record in research: many of our readers have some experience in academia from doing a degree, whether or not they intended to go into academic research. Standard academic success can also point towards being a good fit (though is nowhere near sufficient!):<\/p>\n
- \n
- Did you get top grades at undergraduate level (a 1st in the UK or a GPA over 3.5 in the US)?<\/li>\n
- If you do a graduate degree, what’s your class rank (if you can find that out)? If you do a PhD, did you manage to author an article in a top journal (although note that this is easier in some disciplines than others)?<\/li>\n<\/ul>\n
Ultimately, though, your academic track record isn’t going to tell you anywhere near as much as actually trying out research. So it’s worth looking for ways to cheaply try out research (which can be easy if you’re at college). For example, try doing a summer research project and see how it goes.<\/p>\n
Some of the key traits that suggest you might be a good fit for a research skills seem to be:<\/p>\n
- \n
- Intelligence (Read more about whether intelligence is important for research<\/a>.)<\/li>\n
- The potential to become obsessed with a topic (Becoming an expert in anything can take decades of focused practice<\/a>, so you need to be able to stick with it.)<\/li>\n
- Relatedly, high levels of grit, self-motivation, and \u2014 especially for independent big picture research, but also for research in academia \u2014 the ability to learn and work productively without a traditional manager or many externally imposed deadlines<\/li>\n
- Openness to new ideas and intellectual curiosity<\/li>\n
- Good research taste, i.e. noticing when a research question matters<\/em> a lot for solving a pressing problem<\/li>\n<\/ul>\n
There are a number of other cheap ways you might try to test your fit.<\/p>\n
Something you can do at any stage is practice research and research-based writing. One way to get started is to try learning by writing<\/a>.<\/p>\n
You could also try:<\/p>\n
- \n
- Finding out what the prerequisites\/normal backgrounds of people who go into a research area are to compare your skills and experience to them<\/li>\n
- Reading key research in your area, trying to contribute to discussions with other researchers (e.g. via a blog or twitter), and getting feedback on your ideas<\/li>\n
- Talking to successful researchers in a field and asking what they look for in new researchers<\/li>\n<\/ul>\n
<\/span>How to tell if you’re on track<\/h3>\n
Here are some broad milestones you could aim for while becoming a researcher:<\/p>\n
- \n
- You’re successfully devoting time to building your research skills and communicating your findings to others. (This can often be the hardest milestone to hit for many \u2014 it can be hard to simply sustain motivation and productivity given how self-directed research often needs to be.)<\/li>\n
- In your own judgement, you feel you have made and explained multiple novel, valid, nontrivially important (though not necessarily earth-shattering) points about important topics in your area.<\/li>\n
- You’ve had enough feedback (comments, formal reviews, personal communication) to feel that at least several other people (whose judgement you respect and who have put serious time into thinking about your area) agree, and (as a result) feel they’ve learned something from your work. For example, lots of this feedback could come from an academic supervisor. Make sure you’re asking people in a way that gives them affordance to say you’re not<\/em> doing well.<\/li>\n
- You’re making meaningful connections with others interested in your area \u2014 connections that seem likely to lead to further funding and\/or job opportunities. This could be from the organisations most devoted to your topics of interest; but, there could also be a “dissident” dynamic in which these organisations seem uninterested and\/or defensive, but others are noticing this and offering help.<\/li>\n<\/ul>\n
If you’re finding it hard to make progress in a research environment, it’s very possible that this is the result of that particular environment, rather than the research itself. So it can be worth testing out multiple different research jobs before deciding this skill set isn’t for you.<\/p>\n
Within academic research<\/h4>\n
Academia has clearly defined stages, so you can see how you’re performing at each of these.<\/p>\n
Very roughly, you can try asking “How quickly and impressively is my career advancing, by the standards of my institution and field?” (Be careful to consider the field as a whole, rather than just your immediate peers, who might be very different from average.) Academics with more experience than you may be able to help give you a clear idea of how things are going.<\/p>\n
We go through this in detail in our review of academic research careers<\/a>.<\/p>\n
Within independent research<\/h4>\n
As a very rough guideline, people who are an excellent fit for independent research can often reach the broad milestones above with a year of full-time effort purely focusing on building a research skill set, or 2\u20133 years of 20%-time independent effort (i.e. one day per week).<\/p>\n
Within research in industry or policy<\/h4>\n
The stages here can look more like an organisation-building career<\/a>, and you can also assess your fit by looking at your rate of progression through the organisation.<\/p>\n
<\/span>How to get started building research skills<\/h2>\n
- You’re making meaningful connections with others interested in your area \u2014 connections that seem likely to lead to further funding and\/or job opportunities. This could be from the organisations most devoted to your topics of interest; but, there could also be a “dissident” dynamic in which these organisations seem uninterested and\/or defensive, but others are noticing this and offering help.<\/li>\n<\/ul>\n
- The potential to become obsessed with a topic (Becoming an expert in anything can take decades of focused practice<\/a>, so you need to be able to stick with it.)<\/li>\n
- Intelligence (Read more about whether intelligence is important for research<\/a>.)<\/li>\n
- Pick a field. This should be heavily based on personal fit<\/a> (where you expect to be most successful and enjoy your work the most), though it’s also useful to think about which fields offer the best opportunities to help tackle the problems you think are most pressing, give you expertise that’s especially useful given these problems, and use that at least as a tie-breaker. (Read more about choosing a field<\/a>.)<\/li>\n
- The expertise and credibility you can develop by focusing on research (especially in academia) can put you in a good position to switch your focus to communicating important ideas<\/a>, especially those related to your speciality, either to the general public, policymakers, or your students.<\/li>\n
- How to get started building research skills<\/a><\/li>\n
- There are good theoretical reasons to think that research will be high-impact<\/a><\/li>\n
- 6.2<\/span> Find jobs that use a research skills<\/a><\/li><\/ul><\/li>
- 6<\/span> Once you have these skills, how can you best apply them to have an impact?<\/a>
- 4.2<\/span> How to tell if you’re on track<\/a><\/li><\/ul><\/li>
- 3.2<\/span> What does this mean for building research skills?<\/a><\/li><\/ul><\/li>
- 2.2<\/span> Practical but big picture research<\/a><\/li>
- 1.2<\/span> There are good theoretical reasons to think that research will be high-impact<\/a><\/li>
- 1.1<\/span> Research seems to have been extremely high-impact historically<\/a><\/li>