Ask HN: What can I do to accelerate scientific research?

This is a pipe dream of mine, but I would love a Wikipedia of null results: nullpedia! Nobody publishes null results bc they’re not exciting, but I think a lot of NSF money would be saved if “failed” experiments were aggregated somewhere in a searchable way.

There’s lot of questions. How to organize it? How to encourage participation? How to maximize usefulness while at the same time minimizing volunteer effort? How to encourage discussion (suggesting changes for a better exp design) rather than manipulation (stealing the seed of a bad experiment to publish at your better funded lab)?

I don’t know how to do it, but I think if done right people would really like it.

Provide funding for permanent positions. I'm currently in the last few months of my first postdoc in condensed matter physics (think superconductors, quantum computers etc.) but will move into industry next year for lack of perspectives towards a permanent position in a reasonable place. As far as I can tell, my research so far was not substandard and the software I wrote has enabled quite a few projects which otherwise would have not been possible or taken much longer. Most people I talk to (both inside and outside academia) express some degree of disappointment over people like me leaving (after 4 years PhD + 2 years Post-doc) but none of them put their money where their mouth is.

To be clear, I can understand that a PhD candidacy justifies a temporary contract and I'm not even asking for a permanent position directly after a PhD (as would be standard in industry), I'm only asking for a reasonably safe perspective towards a permanent position reasonably soon after graduating. Can't exactly start a family if you don't have any kind of job security beyond the next couple months.

The biggest problem scientists in academia face is that they can’t do actual research. They spend most of their time doing stuff that doesn’t matter. So probably the biggest leverage you have is to reduce the time they spend doing stuff that doesn’t matter.

The most effective way to do that is to establish an alternative form of institution that focuses only on research (and teaching if you like - teaching is not the problem). A tough challenge. One line of attack would be to contrast your costs with academia’s full economic coating model.

Failing that, here’s some things you can do:

- develop a paper reference system that actually works well. Mendeley is the best there is and it’s (IMO) rubbish. Poorly designed.

- build a typesetting system that’s an alternative to latex but is WYSIWYG. Particularly important outside of CS.

- build a free conference organising / review tool that works. EasyChair is popular and utter garbage.

- build tools to automate the grant writing process. A step by step system to create a grant proposal, tailored to each grant scheme. Yes, this would potentially damage the grant application process. But it doesn’t make any sense anyway, so at least this would free up a few years where academics could churn the required proposals out fast until you were somehow disallowed.

- provide free slide materials, examples, exercises for all the main CS etc topics. Some kind of “piece it together” kit so lecturers could save time making slides and other materials that tick boxes. Diagrams, for instance, would be very useful. Pseudocode too.

NB you probably won’t make any money.

Slightly smaller-scale than most suggestions here, but for the average nonscientific HNer, the best way to help scientists is to improve their programming tools. In the Python ecosystem, for instance; numpy/scipy, scikit-learn, and matplotlib are widely used across dozens of disciplines, and are open-source projects relatively welcoming of new contributors. Julia is a whole new language for scientific computing, where all the fundamental tools are still being built and refined. Raspberry pis, 3D printers and other “hobbyist maker” tools are appearing in research labs to help develop novel instrumentation, so hardware-oriented people can help by contributing to open-source efforts of that kind.

Come work with us at Plex Research. A huge problem our founder ran into while doing drug discovery research was that there's tons of data in the world, but no one is using it because it's all in thousands of different places. As a programming, it was crazy to me that even the most advanced organizations in industry (Novartis, Sanofi) or academia (Harvard, Stanford, ...) are still keeping many important datasets in Excel.

We're pulling together all of the world's biomedical research data, structuring it as a graph, and allowing research access all of it as easily as Google does the web:

https://www.plexresearch.com/products.html

> indexing all open research with free unlimited access, similar to arxiv-sanity.com but better

This space is pretty crowded, in my opinion.

I don’t know much about biology, but I can tell you that in materials, it’s all about data. The materials design problem and predicting new materials comes down to knowing properties of other materials. A lot of progress has been made by using datasets generated by quantum mechanical calculations by the Materials Project, OQMD, AFLOW and NOMAD, but materials design is tricky because what we want to predict are the outliers that we haven’t seen yet: materials with the highest strength, etc..

There’s value to be created for materials researchers by curating experimental data in a digital, usable form, since so much is locked up in papers, but you really need domain expertise for this and there’s another problem that the experiments are so sparse and have so many features (chemistry, microstructure, thermal history, etc) that people have really only been successful when focusing on particular classes of materials.

Note: The following is an earnest suggestion and not just a recruiting plug, but it is also a recruiting plug. If this is not an appropriate place for this content then comment and I'll remove (I do not frequently HackerNews)!

Personally, I believe the highest leverage thing we can do to promote scientific research is to help connect the dots between related discoveries in the service of bringing new ideas to commercialization/impact as quickly and reliably as possible. I joined Citrine (https://citrine.io/platform/) a few years ago thinking that we would help accelerate research by direct support - building simulation, data, and machine learning tools for scientists. It became clear very quickly that the community was already in a pretty strong position (smart cookies, those research scientists) on that front. The biggest opportunity turned out to be scaling expert knowledge - bridging the no man's land from ideation to scale-up, integration, and manufacturing. At Citrine, we're building infrastructure that helps researchers contribute to an organization-wide knowledge graph capable of supporting inference in the scale-up or manufacturing context based on relationships learned in the R&D phase. This fundamentally changes the ROI calculus for basic research because it can plausibly support the entire product life cycle.

If this sounds exciting to you, then consider joining us! We just raised a Series B and are growing quickly.

If you have an applied math and software eng. background and want to help generalize and scale our property inference infrastructure, then I think you'd enjoy working with me and my team in SSE: https://citrine.io/careers/#scientific-software-engineer.

If you have a backend software eng background and want to build distributed services for scientists and engineers at some of the biggest materials and chemicals companies in the world, join us in engineering: https://citrine.io/careers/#sr-backend-software-engineer.

If you want to help software related AI research, start compiling massive high quality training data sets and giving it away for free. Easier said than done though.

No immediate financial return? I hope you can accept no financial return, period. In general the easiest way for an individual to accelerate general research is through generous funding. But even then it’s not like a slider in a game where you provide more funding and things get done faster. There’s diminishing returns after a point. Not that I’m trying to discourage you, but I hope you’re thinking about it the right way before you waste a lot of time and money.

I suggest talking to actual researchers and asking them what they really need, and give them that. Basically the same as a startup going out and talking to customers. The only research probably being done around here is largely software related, and probably not changing the world much in ways that actually matter.

One of the big challenges for university researchers is trying to find talent and dedication in the enormous pool of undergrads and masters students. Nearly every professor relies heavily on the recommendation system or grabbing from the pool of students in a class. The problem with this is that it's often hit or miss with a net-neutral return on investment.

If I send a PhD student to spend a certain amount of time independently training two students, then I am investing that grad student's time into something that could be spent doing research. If one of those students is flaky and is mostly there to pad their resume, it's largely a lost investment (even more so if their work requires extra effort to fix). If the other is graduated by the following semester, the gained productivity might not exceed the other's loss by the time they leave.

The best situation is when you're working with an undergrad who is destined to continue on to the PhD program because you know they'll be dedicated and possibly have a head-start on their thesis work. If you could figure out how to connect these students with advisors using metrics and private social networking, then you will amplify their productivity significantly. You'll improve the likelihood professors will take on undergrads and potentially push researchers into the field earlier.

How you achieve this, I'm not entirely sure. Perhaps you can make it easy to set up competitions which test the skills you need. The winners will be asked to join the lab to work on some project. If some lab on the other side of the country which does similar research also wants to do the same competition, make it easy for them to share and run it.

I have a request. Somebody needs to make matplotlib, but with a C API and lots of language bindings instead of only a python API. For example when I'm writing rust, my best option is to use a Python interop library that calls matplotlib...

Some interesting companies at the intersection of science/engineering that are hiring software devs:

- https://www.ginkgobioworks.com/ Synthetic biology engineering

- https://www.benchling.com/ Online LIMS

- https://neuralink.com/ Brain Machine Interface

- https://strateos.com/ Programmatic Cloud Lab (disclaimer, I work here)

Sci-Hub removes barriers to accessing scientific information. Their infrastructure is slow and has trouble. If you are looking for real impact, help them scale.

I don’t think this is a comprehensive answer, but if you want a nice summary of how climate change might be impacted by ML/DL applications, this is hot off the press: Tackling Climate Change with Machine Learning, https://arxiv.org/abs/1906.05433

I think there's lot of room to improve tools in bioinformatics. In practice bioinformatics pipelines tend to be bundles of loosely organised python scripts and I've heard files on the order of a few GB described as Big Data because the processing times are so slow (days for stuff that could take milliseconds)

It would help to pair up with practicing scientists and explore what parts of their workflow can be improved

On the intersection of biology and machine learning, there is one of the holy grails of science: protein structure prediction.

I'd recommend starting reading about Google's AlphaFold, since this is currently considered state of the art in the field: https://deepmind.com/blog/alphafold/

> What ideas do you have on how one can accelerate scientific research?

I work in genetics (as a software engineer).

If there was a major flaw in current scientific research (that involves software), it's that most labs care more about getting published than they do about the reproduce-ability and validation of their work. This means most of the software written in research is ad-hoc, write once, and often never looked at again. It was put together for the sole-purpose of producing some output that could be put in a paper and then lost to time.

A current "holy grail" of software in research would be to fix that: empower other labs to validate and reuse the software written and reproduce the work of other labs with different data sets. And it is actively being worked on in a couple places (that I know of, perhaps more):

* https://genepattern-notebook.org/

* https://app.terra.bio/

* https://software.broadinstitute.org/wdl/

Some of these are just about giving the community a common framework to use for their software (CWL, WDL, Jupyter), others are about data storage and making it easily accessible for others to use in the cloud for reproducing results.

If you want to have a impact, joining one of these groups would probably put you in a much closer position to doing that.

If you just wanted to work on something in your spare time that would be incredibly valuable, then might I suggest this:

It's amazing how much work is done in the scientific community using CSV/TSV files (usually gzipped). And most of that work is done via perl, sed, and awk. And often these are huge I'm working with a VCF file (TSV) right now that's 2 TB in size ZIPPED! It's crazy. Researchers often don't have the time, resources, or know-how to put together a simple Spark cluster and use it.

A command line tool that allowed someone to run SQL (or SQL-like) commands on a gzipped CSV file FAST would be invaluable. And if it could JOIN across CSV files ... wow!

This will sound harsh, but it isn’t meant to be.

Scientists code better then you do science.

This is simply a consequence Of a weeding out mechanism for those that have no coding skills. The only ppl who get away with no coding skills are important professor with grad students to do the coding.

This isn’t to say that our skills are great, but a generic programmers (I.e. CS majors) science abilities are approximately zero (common, no thermo in an “eng” undergrad???)

So what can you do?

Since you mentioned science and not engineering, I’d ignore the AI advice. Science needs models based on mechanistic understanding of the underlying phenomena. A model that merely predicts is useful for engineers, not scientists.

“materials simulator (eg how can we get a material having a given set of properties)“

This is already done, but of limited usefulness. First the Mtls simulators are far from perfect. Then there is the problem of actually synthesizing the mtls. These simulations are more typically done to weed out bad candidates.

“No immediate financial return”

Wrong attitude. Only an attitude of “no financial return” helps science. That’s not to say you won’t make money off of it, but that can never be a goal since (true) science advances freely (again see the Gaussian jerk vs. Einstein or Landau - who contr. more?)

Instead, focus on making the programming tools scientists use better, easier to use and GPL. GPL is important because an MIT license by itself allows a scientist to use others work while blocking others (see Gaussian).

For example, making python (or Julia?) better would be one of the most important contributions you could make. The matplotlib guy was deeply mourned in science.

The two cents of a physical sciences researcher who once flirted with the Valley.

I saw this list linked yesterday from another HN article, might give some useful jumping off points: https://github.com/kakoni/awesome-healthcare

One related thing I found from there was a list of projects for magnetic resonance imaging specifically: https://ismrm.github.io/mrhub/

I'd assume trying to contribute to those projects would hopefully give greater ROI than building a new thing (without a very specific idea of what to build and the market for it)?

Find any way to make a lot of money, gain a lot of political power or gain influence over those who have money or power and use it to fund research and make it more appealing culturally.

As a faculty running a lab in human genomics /genetic I would say either join a lab which needs your skills or work for a univeristy in support of their IT and high-performance computing needs. People with computational skills, an interest in doing/enabling research, and willing to accept a sub-market salaery are obviously rare. IT departments at universities are under-staffed and over red-taped, but most researchers depend on them to actually do work.

I would go talk to scientists and try to understand deeply what they’re working on, how they do it, and what systems (social/technical/political) allow them to do it. I’ve done this a few times and it’s always illuminating and inspiring.

My dream would be to have a place I could look at any paper (even if not on a campus internet connection), be able to look at raw data, code, and have a forum to facilitate discussions between researchers.

I know the first won't happen for awhile, but it is a dream (open science).

The second is direly needed but in some cases not practical. But I think there could be a lot to gain from just having a small portion of this. It could help verify results significantly. Also imagine if people could research things without having to have all the fancy instruments. Some of this already happens, but I think it is harder to find and not always easy to sort though. It isn't connected to papers and research. Just having a paper and a link to the data would be tremendous help.

Similarly code. I don't know a researcher that hasn't made an accidental bug in the code that changes results (some slight, some major). I think we need to get over that WE ALL write hacky code. Hacky code is better than a vague description in a paper because you don't have enough room to write an accurate description of your model. Science is supposed to be replicated! Even linking papers with a github account would be tremendous help. Some people don't want to share code, and I think this is a shame and anti-scientific (especially if you are using public money), but that's a rant on its own.

Researchers email one another all the time. Some of these discussions should be public. Papers leave a lot of gaps. An area researchers could add extra notes that couldn't be fit in a page limit, where collaboration can happen, or where people can just ask questions, would be great. Replicating results can be hard and we should be learning from one another's hurdles. That's the point of science after all, for to push the progress of humanity. Lack of ability to communicate should not be a gate.

Along with these things it would be nice to encourage putting up null results. Aside a paper I would love to know what challenges a researcher fought. That's where most of the work is. It is funny, we constantly talk about failure being 80-90% of research. Whole projects failing or just banging your head on the desk because you can't figure out why something isn't working. Let's open this up. Let's help one another. Let's talk about what went wrong and how we fixed things to get to our success. I can't think of anything that would help science more than this.

Learning a lot how science actually works and how flawed it is I came to the conclusion that the biggest boost in scientific progress you could ever achieve is by eliminating science waste and doing more good science.

There are a few tiny steps in the right direction, but it's frustratingly slow. Once you understand a phenomena like publication bias it's hard to swallow that there are still empirical studies published without preregistration. There are so many studies published with such low quality that it's a complete waste, because the likelyhood that they're some statistical fluke is much higher than that they tell the truth.

Though the problems are known since a while, little has changed: https://journals.plos.org/plosmedicine/article?id=10.1371/jo...

So as someone working in application of medicines, I'd agree with the comments that say those of us doing these things have learn to make code work, but are not software engineers (and would not claim to be). This means that if you can improve the tools that are used (and don't care about making a return), the leverage would be massive.

I imagine it isn't trivial (otherwise it would have been done by now), but imagine if R was automatically parallel processing; seldom is the code in my field worth figuring out how to make it parallel, but if it was automatically, then a hell of a lot of time would be saved! I know there are two packages that kind of make it work, but so far I've yet to hit a threshold making it worth it (I just leave thigns running overnight).

Abstract from that to projects like Zotero and you can see how you could have an impact on a lot of people by enabling them to do what they do.

I felt the same, and decided to learn how to do that. Followed this one guy i admired a lot. The way he is doing it is to merge software engineering practices into science (in this case AI and robotics), and reduce the cost to do iteration.

For us it means: 1. 1 click binary deployments 2. Safer iterations that allow for making mistakes, so that they can do 10s at the same time instead of 1 really safe one. 3. Logging visualization and whatnot on a unified infra.

We are not scientists, but we know damn well how to scale software and business. It applies everywhere. Think of tensorflow, before most people couldnt do ai themselves, now it is damn easy and more things will happen as a result.

This way they can concentrate on science while we concentrate on scaling it. We are betting on couple breakthroughs as a result of increased enthropy.

You can get a job at a company doing research. Improve their tooling and this make them research faster. SRI comes to mind in this regard.

Inside the company you can also push them to try and open source the tools. Unlikely that’ll work if they don’t do it already, however this skill set will eventually let you contribute more in the future. After some time on the job, start a personal open source project or start a company directed at some of the issues you saw.

It’s general advice and it’s the long game, but will likely help you have more impact.

The advice above may be more useful for engineers earlier in their career, but you can accomplish that in a handful of years.

Forget science: as an “army of one” you’re unlikely to make a contribution of sufficient magnitude that some needle might be moved in the right direction. Take advantage of the skills you do have: make money by doing what startup entrepreneurs do, then donate all proceeds to scientific research.

There may be the next Zuckerberg hiding in you. Imagine what would be possible if you could reach that level of wealth and use all that money to propel science forward. I’m not talking about some chickenshit foundation; real impact is made by committing all of your financial means to helping science. That’s how a real difference is made.

Side note: I am the co-founder of Aidlab - a device and a platform that is widely used by biomedical scientists and students in their research (https://www.youtube.com/watch?v=wY0YPOKNk88)

In my free time I am working on my next project - an open-sourced platform where everyone can contribute to help fight death. The platform allows uploading anonymized, structured health records - publicly available. Why? Dying is a number #1 problem that should be solved together.

OP, if interested, drop me an email: jdomaszewicz (at) aidlab.com

Regarding the last point: try applying libraries like Quantum espresso [1] or CP2K [2] to real world problems and apply machine learning with the solutions they provide to a given problem. There's a tremendous amount of academic research being done in this direction but try and take these libraries and make them useful for real world applications.

[1] https://github.com/QEF/q-e

[2] https://github.com/cp2k/cp2k

There are a lot of citizen science initiatives you can participate in including folding at home and so on. Your best bet is going to be to find a field you love and find experts in the field to work with and learn from - theres definitely a lot of lack fo software talent in some areas that you'd be able to make a dent into but outside of citizen science initiatives, you need to start with understanding the problems that need to be solved which can be much more technical and difficult to understand than they ultimately could be to solve. Good luck!

Provide funding for high school students to spend a summer working in a research lab? The amount of funding doesn’t need to be high and it may pique early interest for the next generation .

>>> Indexing all open research

I'd also check out analytics platforms such as VIVO. End goal is a universal workflow for all research, discovery and collaboration. Solving this problem will have immediate impact. For example, computational epidemiology and containing Ebola outbreaks in "hot zones" hundreds of miles apart.

Web of Science

https://clarivate.com/products/web-of-science/

If you have the money to work full time on a personal project you have the time to sign up for a conference or three on the kind of scientific topic you like. Ideally, pick one near your home/in the closest metropolis.

Once there you can apply the following no-social-skills-needed guide to making contacts at a conference.

1. Watch the keynote.

2. After the keynote, walk up to pretty much anybody. Ask the following questions: "Do you think (keynote title) is a major area for (conference subject matter)?" and almost regardless of what they answer, you can follow up with "oh, really? Is what you're studying related to (keynote subject)?". Those two questions are enough to make virtually any academic launch in a paragraph-long exposé. Usually by the 6th conversation you have along those lines, you'll have a good summary of which subjects are considered important right now. If the person looks like they like you or are invested in talking about their topic, you can follow up by giving your contact info and saying you're interested in them sending you a paper on the subject they talked about (one of theirs if applicable).

3. There will be designated poster sessions. The posters are giant sheets of papers with young people in front of them. Walk up to a young person standing in front of a poster that doesn't look that slick but where the subject matter interests you (slick posters are from bigger labs where you are less likely to have an impact). Ask the person what they do, how long they've been doing it for, how big is their lab, and what's the most time-consuming step in their research right now.

4. If anyone asks you what you do, say you're an expert in (your computing area, web apps, cloud, data analysis, whatever) and interested in the intersection of (your area) and (conference name). Some of the people will say they think (your area) would be great for (their subfield) because of (super niche stuff you wouldn't have thought of). Grab their contact info so you can have a 1:1 meeting with them later where they find you a research subject.

Follow up by having email conversations with a few of the people. If a grad student looks like they can use your help, ask for an intro to the PI. Eventually you'll walk your way into a (possibly paid) research project. It's that simple. Thank you for caring about your world and its future.

Build tools to make research more efficient and reproducible. The research community is a small market and has little money. Building tools is often not regarded as scientific activity and can lead to dead ends if publication is pursued.

So, if you do not have to rely on income anymore and want to contribute to science, build tools for the community. Or better, come up with a way to help scientists to build their own tools fast and efficiently.

Software tools to help with scientific discovery / hypothesis generation. I'm thinking specifically about Literature Based Discovery[1] (LBD), but there are probably other useful approaches in that domain.

I have a little project[2] related to LBD, which I'm working on here and there. There's still a long way to go, but I'm optimistic there's something to be accomplished in this space that could help.

And then you've got things like Robot Scientist[3] which is crazy interesting.

If AI, in the AGI sense, interests you, I might mention the possibility of contributing to / working with one of the popular "Cognitive Architecture" systems like ACT-R[4], SOAR[5], or OpenCog[6].

[1]: https://en.wikipedia.org/wiki/Literature-based_discovery

[2]: https://github.com/fogbeam/Valmont-F

[3]: https://en.wikipedia.org/wiki/Robot_Scientist

[4]: https://en.wikipedia.org/wiki/ACT-R

[5]: https://en.wikipedia.org/wiki/Soar_(cognitive_architecture)

[6]: https://en.wikipedia.org/wiki/OpenCog

Solve the following problem:

"I want to find all research labs concerned with topic X in this area"

=> Create an (open) crunchbase/patreon for university research groups

Right now, each university has their own website, with their own layout and varying degrees of information. General scraping is impossible. Finding research groups with certain interests without trawling through an endless amount of publications in related journals or randomly meeting people at conferences is difficult. The only comprehensive lists are university ranking sites, but they are not detailed down to the research group level.

- create a crowdsourced, public index of running projects

- help labs find each other and collaborate on work

- help people apply to labs with open positions

- let donors and investors find and support projects more efficiently

- make science journalism easier, by connecting reporters straight to the source

The main difficulty is creating the network effect which is why, I suppose, no one has done it yet.

Contribute to TaxonWorks http://taxonworks.org/. Software from a small endowed group that builds tools that support those who describe Earth's species. Software is completely open source, many opportunities to improve what's going on there.

I've helped one on one by working with someone who had a problem and needed some code a few times (sometimes getting paid, sometimes for free for a friend). I think that's higher impact than most of the remaining low hanging fruit. These where all 1 coder projects.

There are also larger coding projects in the sciences with teams. Running the database (and doing whatever else needs doing in code) for a telescope for instance. I don't have personal experience with them, but search around and you can probably find them.

The only thing I can think of that would have helped across the projects I've worked on would be better (more convenient, I don't care that much about how the output looks as long as it conveys the information) graphing/data visualization tools.

Re: materials simulator, you should totally check Materials Project [1]. I guess that they are open to getting some help in their github repos.

[1] https://materialsproject.org/about

I think the best way to accelerate scientific research is to recreate Bell Labs in a modern setting, free from the desire to succeed.

Get a huge endowment fund (the hard part), hire a bunch of smart and promising people, and tell them you want them to change the world.

I work on a project called OpenReview (https://openreview.net) that aims to improve scientific peer review by encouraging transparency and research on the peer review process itself. Right now most of our activity is within the machine learning academic community, but we have plans to spread into other areas of computer science and more.

We’re looking for developers, if you’re interested: https://codeforscience.org/jobs?job=OpenReview-Developer

Let me know if you want to learn more and we can find a way to connect.

I’m the engineering director at Quartzy (YC S11). We’re focusing on making research more efficient through the laboratory supply room. We help labs through a group buying experience to avoid waste, and offer discounts to their contract prices by working directly with manufacturers.

This isn’t the most obvious way to make an impact, but we’ve effectively saved many labs thousands of dollars on equipment and consumables that can then be reallocated toward their research. Our mission is to increase the efficiency of scientific research — the supply room is just the beginning.

Would be happy to chat - tristan@quartzy.com

A shoutout for asking “what can I do” as opposed to “why is research so slow?”

A lot of journals (including the one I am an editor at) would probably consider leaving the fangs of elsevier and co and go full open access publishing. However, there are a few obstacles to that, such as missing open and reliable editorial and publishing platforms, contacts to libraries, etc.

In my opinion providing these things and actively encouraging journals to make the jump to open access would be a huge service to academia. Also, I wouldn’t be surprised if one could find funding for such a venture.

Donating money toward researchers. All of the following situations below may or may not have a useful outcome but direct investment would be the easiest one and one you can leverage.

The least/most you can do in this space is likely contribute actively in something you believe in.

Industry wide limitations are largely down to policy & regulations

Make programming accessible to scientists.

Today, running any compute/memory intensive experiment requires working through so many tools and terminologies (e.g. AWS, SSH, python, dependencies, virtualenvs). Scientists really need just Jupyter notebook like experience that's collaborative, reproducible, and powerful (i.e. run on any hardware with 1 click).

> how one can accelerate scientific research

Easy peasy. Help (for example) this guy find funds for continuing his research: https://scholar.google.com/citations?hl=en&user=Q0w_e84AAAAJ

Edit: there are many like him.

You mention meta.org, you know you could work with us on it https://chanzuckerberg.com/join-us/openings/?team=engineerin... (search "meta" on that page)

Shameless plug: come work for Primity Bio. My team builds a bioinformatics web application that (among other things) is several orders of magnitude faster than the other tools, which fundamentally changes the scope of analysis that researchers can feasibly do. Email in bio.

to me, the biggest hurdle is the pretentiousness in the research world and the way paper is written. if someone can rewrite papers in plain english, removing all jargons, talking more about intuitions, that will help encourage more people to get involved in scientific research.

Here's a journal for publishing null results: http://www.jasnh.com/

Probably not the answer you would expect, but one very simple action you could do is to sign/share this petition: https://publiccode.eu.

I think you might enjoy this interview with Elon Musk:

Elon Musk - How to Build the Future

https://www.youtube.com/watch?v=tnBQmEqBCY0

The job role for you might be as a "Research software engineer"

Put yourself in survival conditions and use science to overcome it

Run BOINC, Folding@Home, or like distributed computing project. Many of those projects are run by education institutions.

Sign up for a prospective health cohort such as AllOfUs in the US or UK Biobank or equivalents in other countries.

an idea: create quality libraries for working with genome data.

Start a business

I am writing with visibility of only Computer Science research. Not sure whether it will hold in other fields.

I think one of the biggest impediment to acceleration of scientific research is writing research papers the way they are written now. I write this with a perspective gained from roughly reading 1-5 papers thoroughly a month, scan 1-10 papers per week as a part of my work as AI engineer.

I cannot emphasize more on how much garbage I have to ignore (even in good papers) and arduously search for the bottom line statements being made by the authors. No wonder, people (at least ones I know including some professors and researchers) just want to see if the code or related materials were published on GitHub and go directly to it. Sometimes scan through Paper Review website.

One improvement I suggest is -- write two separate papers

1. First one, makes succinct points, explain the mathematics / system design, results following some broad set of formatting guidelines. This paper could only be studied by someone who understands the vocabulary used in the field.

2. Write another paper explaining the background and inference of results etc. Someone with no expert knowledge could make efforts to understand that.

Cochrane reviews.

Wiki (ISH) based.

Does the graph, this alone might be enough to help science and can be used to kick-start to the site (do you want to open source your graph and data questions)

I'd guess you have to have multiple graphs for topics due to disagreements that can't be solved. But this can't be guessed, would need to be worked out. I think people would get reputations and this would push forward topics.

This is mostly for medicine... But might expand.