Throughout the 20th century, most scientific research funding has come from one of two sources: government grants or private corporations. Government funding is often a function of the political and economic climate, so researchers who rely on it risk having to deal with funding cuts and delays. Those who are studying something truly innovative or risky often find it difficult to get funded at all. Corporate research is most often undertaken with an eye toward profit, so projects that are unlikely to produce a return on investment are often ignored or discarded.

If one looks to history, however, scientific research was originally funded by individual inventors and wealthy patrons. These patrons were frequently rewarded with effusive acknowledgements of their contributions; Galileo, for example, named the moons of Jupiter after the Medicis (though the names he chose ultimately did not stick).

There has been a resurgence of that model — though perhaps more democratic — in the modern concept of crowdfunding. Kickstarter, the most well-known of the crowdfunding startups, enables inventors, artists, and makers to source the funds they need for their projects by connecting to patrons on the platform. Contributors donate money to a project and are kept updated on its progress. Eventually, they may receive some sort of reward — a sticker acknowledging their participation or an example of the completed work. Scientists have begun to use the site, in many cases, to supplement their funding. Anyone can be a micro-patron!

“Deceiving the Superorganism: Ant-Exploiting Beetles” met its goal through Petridish, a funding site.

Science-specific platforms have also appeared on the scene. Petridish is currently showcasing projects looking for funding to study everything from rare butterflies to mass-fatality events. On Microryza, you can fund investigations into cannibalism in T-Rex or viral causes of lung cancer. RocketHub also has a science-specific project roster and recently had a researcher raise funds to study the psycopharmacology of amphetamines. Widely covered as “Help scientist build a meth lab,” the researcher’s write-up of his proposal, including his reasons for crowdfunding it, is excellent and worth a read. And newcomer Iamscientist is combining fundraising help with a community, KnowledgeXchange, which helps researchers to recruit team members and find mentors. While these sites show great promise, several similar platforms founded a few years ago have failed. The extent to which this new crop is popularly adopted remains to be seen, though the excitement around crowdfunding may indicate the time is now right.

While anyone can submit a project to the sites above, there are also hybrid models that enable individuals to “top up” more traditionally funded research. In the UK, MyProjects enables individuals to fund research targeting specific types of cancer; the underlying projects have already been pre-approved and funded by Cancer Research UK. The process is more specific than traditional charitable giving, so contributors feel that they’re making a difference in a specific area that matters to them. The American Association for the Advancement of Science has begun to teach its members about crowdfunding.

For more expensive research, of course, micro-patronage falls short. Breakout Labs, run by the Thiel Foundation, has begun awarding grants of up to $350,000 to “to fill the funding gap that exists for innovative research outside the confines of an academic institution, large corporation, or government.” In exchange, the company retains the rights to its IP, and Breakout Labs is given a percentage of future revenue and an option to invest in an equity round.

Under the traditional grant model, the average researcher spends up to 40% of his or her time chasing funding, and 80% of grant applications are rejected. In addition, the necessity of ties to an academic or industrial organization means that researchers don’t retain control of their IP. The new models of funding can speed up the process, while enabling scientists to keep 100% of their research and results. They also enable citizen scientists to publicize their projects and built communities of involvement.

If you’ve participated in funding scientific research via one of these platforms, or are a scientist who has run a campaign on a crowdfunding site, we’d love to hear your thoughts on the experience in the comments below.

Related:

• Science as a service
• Making the web work for science

So, what if you were to apply the principles of SaaS to science? Perhaps we can facilitate scientific progress by streamlining the process. Science as a service (SciAAS?) will enable researchers to save time and money without compromising quality. Making specialized resources and institutional expertise available for hire gives researchers more flexibility. Core facilities that own equipment can rent it out during down time, helping to reduce their own costs. The promise of science as a service is a future in which research is more efficient, creative, and collaborative.

Outsourcing isn’t a new idea. Contract research organizations (CROs) appeared on the scene in the early 1980s, conducting experiments on a contract basis. Industrial science, especially pharmaceutical research, has been increasingly reliant on CROs; spending on CRO-run research increased from $1.6 billion in 1994 to $7.6 billion in 2004, and is projected to hit $20 billion in 2017. Alongside that trend is a corresponding decrease in the percentage of clinical trials run at academic centers — 63% to 23%. In big pharma, there has been a “strategic push away from the traditional strategies of Mergers & Acquisitions and licensing, toward partnering and outsourcing to acquire new drug candidates.”

Despite the steadily increasing involvement of CROs in industrial research, many academics and smaller researchers have found using outside labs to be cost prohibitive and opaque. For those researchers, the process of outsourcing a study involves googling to find service providers with specific expertise, contacting the provider to determine suitability and cost, and then going through a time-consuming reference check and quality verification process. Some simply don’t know what’s out there; they aren’t sure where to start the googling. For many university scientists, there’s an added layer of complexity in the form of purchase approvals for each facility. This process frustrates the scientist. It also results in many core facilities remaining underused.

Frustration has led a recent crop of enterprising startup founders — many of them scientists themselves — to apply IT “best practices” to science. Their goal is to disrupt the slow-moving pace and high cost of research. To do this, they’re applying innovative business models traditionally used by B2B and B2C startups — everything from the principles of collaborative consumption to decoupling service workers from their traditional places of employment.

One of these startups is Science Exchange, a marketplace that aims to increase transparency around experimental service provider cost and availability. Founded by a biologist, Science Exchange helps researchers source facilities or expertise that is unavailable in their own labs. The providers on the site offer everything from microarray analysis to microgravity experiments aboard the International Space Station. Customers search for a service, request an estimate, and pick a provider from the quotes that come in. Science Exchange handles purchase orders and payment transfers, and provides a project-management dashboard. Through the structure of the site, researchers become aware of new facilities, and providers may suggest new technologies. The relationship has the potential to be more collaborative than a typical provider-client relationship.

Science Exchange is the glue in a unique and developing ecosystem. Some of the providers on the site are themselves startups offering scientific experiments as a service. 3Scan, for example, offers a cutting-edge form of 3D microscopic scanning that produces high-resolution images in a fraction of the time of other methods. Researchers in need of this technique needn’t buy their own knife-edge scanning microscope; they can simply reserve the service.

Some SciAAS startups aim to disrupt CROs. Transcriptic, which describes itself as a “meticulously optimized, technology-enabled remote lab,” is working to change traditional wet lab biology by getting rid of infrastructure overhead. They’ve started with molecular cloning and are focused on reducing the time cost and error rate associated with running protocols by hand. Assay Depot has been called the “Home Depot for biology and medicine.” A researcher specifies the experiment he or she would like to see done, and labs submit bids to perform it.

The promise of applying big data technologies to biological research has led to SaaS data analysis tools built specifically with scientists in mind. SolveBio, a computational biology platform, enables researchers to have access to the latest in data-processing technology without having to maintain computing infrastructure or learn cumbersome tools. Collaborative Drug Discovery (CDD), which spun out of Eli Lilly, is a data platform that was built because the founder believed that the future of drug discovery would involve collaboration across specialized channels. Researchers can store and analyze their data with sophisticated tools, and can also open parts of their repository to others. The Gates Foundation and Novartis have been users. Benchling, a platform for life science data management, is also incorporating IT best practices via version control, aiming to create a “GitHub for biology.”

E-commerce principles underlie new marketplaces for scientific equipment. P212121 is helping SMB suppliers of chemical and laboratory reagents to bring their wares online. Their platform uses software to search and curate tens of thousands of products, and focuses on transparent pricing. Enabling labs to bypass behemoths such as Sigma and Fisher allows them to save money.

Startups are also tackling the problem of expertise by facilitating collaboration. Zombal is a job marketplace for contractors who need experts to meet freelance scientists. By outsourcing areas that are not core competencies, more resources are freed up to focus on what’s needed.

These facets of science as a service are just some of the ways that IT principles are being applied to the realm of research. There’s also exciting activity happening around crowdsourced science, open science, and crowdfunding for scientific research. If you’re a scientist, lab head, or SciAAS startup founder who’s reading this, we’d love to hear your thoughts on the changing face of scientific research in the comments below.

Related:

• Making the web work for science

There’s widespread public sentiment that technology in finance just screws the “little guy.” Some of that sentiment is due to concern about a few extremely high-profile errors. A lot of it is rooted in generalized mistrust of the entire financial industry. Part of the problem is that media coverage on the issue is depressingly simplistic. Hyperbolic articles about the “rogue robots of Wall Street” insinuate that high-frequency trading (HFT) is evil without saying much else. Very few of those articles explain that HFT is a catchall term that describes a host of different strategies, some of which are extremely beneficial to the public market.

I spent about six years as a trader, using automated systems to make markets and execute arbitrage strategies. From 2004-2011, as our algorithms and technology became more sophisticated, it was increasingly rare for a trader to have to enter a manual order. Even in 2004, “manual” meant instructing an assistant to type the order into a terminal; it was still routed to the exchange by a computer. Automating orders reduced the frequency of human “fat finger” errors. It meant that we could adjust our bids and offers in a stock immediately if the broader market moved, which enabled us to post tighter markets. It allowed us to manage risk more efficiently. More subtly, algorithms also reduced the impact of human biases — especially useful when liquidating a position that had turned out badly. Technology made trading firms like us more profitable, but it also benefited the people on the other sides of those trades. They got tighter spreads and deeper liquidity.

Many HFT strategies have been around for decades. A common one is exchange arbitrage, which Time magazine recently described in an article entitled “High Frequency Trading: Wall Street’s Doomsday Machine?”:

A high-frequency trader might try to take advantage of minuscule differences in prices between securities offered on different exchanges: ABC stock could be offered for one price in New York and for a slightly higher price in London. With a high-powered computer and an ‘algorithm,’ a trader could buy the cheap stock and sell the expensive one almost simultaneously, making an almost risk-free profit for himself.

It’s a little bit more difficult than that paragraph makes it sound, but the premise is true — computers are great for trades like that. As technology improved, exchange arb went from being largely manual to being run almost entirely via computer, and the market in the same stock across exchanges became substantially more efficient. (And as a result of competition, the strategy is now substantially less profitable for the firms that run it.)

Market making — posting both a bid and an offer in a security and profiting from the bid-ask spread — is presumably what Knight Capital was doing when it experienced “technical difficulties.” The strategy dates from the time when exchanges were organized around physical trading pits. Those were the bad old days, when there was little transparency and automation, and specialists and brokers could make money ripping off clients who didn’t have access to technology. Market makers act as liquidity providers, and they are an important part of a well-functioning market. Automated trading enables them to manage their orders efficiently and quickly, and helps to reduce risk.

So how do those high-profile screw-ups happen? They begin with human error (or, at least, poor judgment). Computerized trading systems can amplify these errors; it would be difficult for a person sending manual orders to simultaneously botch their markets in 148 different companies, as Knight did. But it’s nonsense to make the leap from one brokerage experiencing severe technical difficulties to claiming that automated market-making creates some sort of systemic risk. The way the market handled the Knight fiasco is how markets are supposed to function — stupidly priced orders came in, the market absorbed them, the U.S. Securities and Exchange Commission (SEC) and the exchanges adhered to their rules regarding which trades could be busted (ultimately letting most of the trades stand and resulting in a $440 million loss for Knight).

There are some aspects of HFT that are cause for concern. Certain strategies have exacerbated unfortunate feedback loops. The Flash Crash illustrated that an increase in volume doesn’t necessarily mean an increase in real liquidity. Nanex recently put together a graph (or a “horrifying GIF“) showing the sharply increasing number of quotes transmitted via automated systems across various exchanges. What it shows isn’t actual trades, but it does call attention to a problem called “quote spam.” Algorithms that employ this strategy generate a large number of buy and sell orders that are placed in the market and then are canceled almost instantly. They aren’t real liquidity; the machine placing them has no intention of getting a fill — it’s flooding the market with orders that competitor systems have to process. This activity leads to an increase in short-term volatility and higher trading costs.

The New York Times just ran an interesting article on HFT that included data on the average cost of trading one share of stock. From 2000 to 2010, it dropped from $.076 to $.035. Then it appears to have leveled off, and even increased slightly, to $.038 in 2012. If (as that data suggests) we’ve arrived at the point where the “market efficiency” benefit of HFT is outweighed by the risk of increased volatility or occasional instability, then regulators need to step in. The challenge is determining how to disincentivize destabilizing behavior without negatively impacting genuine liquidity providers. One possibility is to impose a financial transaction tax, possibly based on how long the order remains in the market or on the number of orders sent per second.

Rethinking regulation and market safeguards in light of new technology is absolutely appropriate. But the state of discourse in the mainstream press — mostly comprised of scare articles about “Wall Street’s terrifying robot invasion” — is unfortunate. Maligning computerized strategies because they are computerized is the wrong way to think about the future of our financial markets.

Photo: ABOVE by Lyfetime, on Flickr

Related:

• Why the finance world should care about big data and data science