The idea of a operating a quantum computer with a quantum bit — or qubit — based on a phosphorous atom harks back to a vision articulated by Australian Bruce Kane in research published in Nature in 1998. “The realization of such a computer is dependent on future refinements of conventional silicon electronics,” Kane explained in the abstract to his paper. Researchers in Australia have been striving to put Kane’s concept into practice for more than 10 years, the MIT Technology Review article notes, and their latest step is to get information from an agitated electron:

These guys implanted a single phosphorous atom in a silicon nanostructure and placed it in a powerful magnetic field at a temperature close to absolute zero. They were then able to flip the state of an electron orbiting the phosphorous atom by zapping it with microwaves.

The final step, a significant challenge in itself, was to read out the state of the electron using a process known as spin-to-change conversion.

The end result is a device that can store and manipulate a qubit and has the potential to perform two-qubit logic operations with atoms nearby; in other words the fundamental building block of a scalable quantum computer.

Still, the Australians have work to do, according to their paper, which they submitted on Monday:

Future experiments will focus on the coupling of two donor electron spin qubits through the exchange interaction, a key requirement in proposals for scalable quantum computing architectures in this system. Taken together with the single-atom doping technologies now demonstrated in silicon, the advances reported here open the way for a spin-based quantum computer utilising single atoms, as first envisaged by Kane more than a decade ago.

Meanwhile, researchers in England have done work of their own on quantum entanglement involving phosphorus atoms.

Quantum computers from Canada have seen some commercial adoption, with Lockheed Martin and a Google-initiated lab signing up for D-Wave Systems quantum computers. If competitors from England and Australia come onto the scene, further innovation could follow and cause prices to fall.

Feature image courtesy of Shutterstock user isoga.

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In the eight years since John Ioannidis dropped the bomb that “most published research findings are false,” pockets of activist scientists from both academia and industry have been forming to address this problem, and it seems this year that some of those efforts are finally bearing fruit.

The research auditors

One interesting development is that a group of scientists is threatening to topple the impact factor, which ranks studies based on the journals in which they appear. This filter for quality research is based on journal prestige, but some scientists and startups are beginning to use alternative metrics in an effort to refocus on the science itself (rather than the publishing journal).

Taking a cue from the internet, they are citing the number of clicks, downloads, and page views that the research gets as better measures of “impact.” One group leading that charge is the Reproducibility Initiative, an alliance that includes an open-access journal (the Public Library of Science’s PLOS ONE) and three startups (data repository Figshare, experiment marketplace Science Exchange, and reference manager Mendeley). The Initiative isn’t trying to solve fraud, says Mendeley’s head of academic outreach William Gunn. Rather, it wants to address the rest of the dodgy data iceberg: the selective reporting of data, the vague methods for performing experiments, and the culture that contributes to so many scientific studies being irreproducible.

The Initiative will leverage Science Exchange’s network of outside labs and contract research organizations to do what its name says: try to reproduce published scientific studies. They have 50 studies lined up for their first batch. The authors of these studies have opted in for the additional scrutiny, so there is a good chance much of their research will turn out to be solid.

Whatever the outcome, though, the Initiative wants to use this first test batch to show the scientific community and funders that this kind of exercise is value-adding despite the costs, which are estimated to be $20,000 per study (about 10% of the original research price tag, depending on the study).

Gunn likens the process to a tax audit: not all studies can or should be tested for reproducibility, but the likely offenders may be among those that have high “impact factors,” much like high-income earners with many deductions warrant suspicion.

A stumbling block may be the researchers themselves, who like many successful people have egos to protect; no one wants to be branded “irreproducible.” The Initiative stresses that the replication effort is about setting a standard for what counts as a good method, and finding predictors of research quality that supersede journal, institution or individual.

The plumbers and librarians of big data

While the Reproducibility Initiative is trying to accelerate science’s natural self-correction process, another nascent group is working on improving the plumbing that serves data. The Research Data Alliance (RDA), which is partially funded by the National Science Foundation, is barely a few months old, but it is already uniting global researchers who are passionate about improving infrastructure for data-driven innovation. “The superwoman of supercomputing” Francine Berman, a professor at Rensselaer Polytechnic Institute, heads up the U.S. division of RDA.

The RDA is structured like the World Wide Web Consortium, with working groups that produce code, policies for data interoperability, and data infrastructure solutions. As of yet there is no working group for data integrity, but it is within RDA’s scope, says Berman. While the effort is still in its infancy, the broad goals would be to come up with a way to make sure that the data contained in a study is more accessible to more people, and also that it doesn’t simply disappear at a certain point because of, say, storage issues.  She says with data it’s like we’re back in the  Industrial Revolution, when we had to create a new social contract to guide how we do research and commerce.

The men who stare at data

You can build places for data to live and spot-check it once it’s published, but there are also things researchers can do earlier, while they’re “interrogating” the data. After all, says Berman, you’re careful around strangers in real life, so why jump into bed with your data before you’re familiar with it?

Visualization is one of the most effective ways of inspecting the quality of your data, and getting different views of its potential. Automated processing is fast, but it can also produce spurious results if you don’t sanity-check your data first with visual and statistical techniques.

Stanford University computer scientist Jeff Heer, who also co-founded the data munging startup Trifacta, says visualization can help spot errors or extreme values. It can also test the user’s domain expertise (do you know what you’re doing and can you tell what a complete or faulty data set looks like?) and prior hypotheses about the data. “Skilled people are at the heart of the process of making sense of data,” says Heer. Someone with domain expertise who brings their memories and skills to the data can spot new insights, and in this way combat the determinism of blindly collected and reported data sets. Context, in the form of metadata, is rich and omni-present, Heer argues, as long as we’ve collected the right data the right way. Context can aid in interpretation and combat the determinism of blindly collected and reported data sets.

The three-pronged approach — better auditing, preservation and visualization — will help steer science away from the iceberg of unreliable data.

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But thanks to smarter connected devices, crowdsourcing trends and better medical data analytics and algorithms such clinical trials may become a thing of the past — or at least less of a burden. The launch this week of a crowdfunding campaign for the Scout, a home monitoring device that tracks, pulse, respiratory rate, blood pressure, temperature and other vitals, offers a perfect example of how clinical trials may change. As part of the company’s Indiegogo campaign for the Scout it’s inviting participants to opt into a what will become the usability study it submits to the U.S. Food and drug Administration for approval.

From the campaign web site:

Before Scanadu Scout™ can become a medical device it will have to go through the FDA (Food and Drug Administration) approval process and this is where your help comes in. With the Scanadu Scout™ you will help us by Scouting yourself and giving us feedback to refine the Scanadu Scout™.

This will happen in the framework of official clinical studies in which you will be invited to partake, ONLY IF YOU OPT-IN. For each study, some of you will be contacted and will have to sign an Informed Consent form. With your help we can put Scanadu Scout™ through FDA to become an over-the-counter consumer-grade diagnostic tool.

Scanadu, the company behind the Scout, isn’t the first or only company to recognize the power of connected devices, crowds and data, Transparency Life Sciences, a New York company started by a refugee from the pharmaceutical industry is also tackling the problem of slow, large and expensive clinical trials with crowdsourced data. TLS has built an online tool to collect information from researchers, physicians and patients that will then take their input to design an FDA-approved protocol for a drug study.

The FDA in December approved the first TLS protocol for a study on the effectiveness of a drug for Multiple Sclerosis patients. An industry consultant wrote that TLS took a process that takes 6 months and completed it in 6 weeks thanks to its ability to gather data from multiple sources into its tool that then formats the data properly. The study relies on remote patient monitoring to cut down on office visits, something that the Scout may one day be able to help with.

Remote monitoring cuts costs but also increases compliance and participation in the study, because it reduces doctor visits. Patients in the study still have access to a nurse or doctor and regular check ins, but they no longer have to spend a chunk of their day traveling to an office and waiting. While TLS and Scanadu are using the web to help speed up the FDA trial process, other doctors are thinking about using crowdsourced data to eliminate some of them, instead relying on crowdsourced data to monitor the efficacy of certain drugs and medical devices after their initial approval. And there are tons of startups out there thinking about finding and cataloguing patient data outside of formal trials, such as PatientsLikeMe, Medify and others in this story.

Much as mobile connectivity has changed the way people hail cabs, book tables at restaurants and share photos, the internet of things and the resulting data from consumer devices may soon change the way we test the efficacy and safety of our drugs. Figuring out the right balance of oversight and self-reporting in this new paradigm will be crucial, but it’s something that should happen.

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So what makes Dresden-based AoTerra such hot property? The fact that it does just that: heats properties. And these are no ordinary heaters. These are heating systems with servers inside them.

AoTerra’s system comprises a server unit connected to a heat exchanger or heat pump, which is in turn connected to the property’s ventilation system, and a hot water tank. It’s intended for new-build and renovated properties that meet modern energy efficiency standards and, according to the company, efficiency is nearly 100 percent (the company also only uses “green” energy for its devices).

Each system has a broadband connection and forms part of a distributed, OpenStack-based data center. The result is AoCloud, which offers compute, block storage and object storage (all are currently in beta). Customers can be pretty sure their cloud is as green as it gets, but there are other benefits too – the distributed nature of the cloud could mean low latency, and AoTerra is touting security as a plus, too.

AoTerra is also involved with a couple of Europe-funded projects, namely LEADS (trying to create a “data-as-a-service” model on top of geographically distributed micro-clouds) and ParaDIME (trying to making computing more energy-efficient).

This isn’t the first time we’ve seen the idea of using waste heat from data centers to heat homes — London’s Telehouse West data center was going to do that, although the local council never set up the distribution network and the housing development never got built due to the recession. Telus is planning something similar in Vancouver. But those were about data centers heating nearby developments; what AoTerra has come up with is a step beyond.

Property owners or developers pay €12,000 for the system (about the same as a normal heating system), but they don’t have any ongoing operating costs – from that point on, they get free heating and hot water. And AoTerra gets out of having to pay for air conditioning, which is a pretty major chunk of the cost of running a traditional data center.

Overall, AoTerra claims, its distributed data center costs the company about a tenth of the normal set-up costs for a data center, with its running costs being less than half and CO2 emissions around a third. The company has only been going for a year, and it already has 20 AoHeat devices with over 200 servers installed. It had a turnover last year of €100,000, and has already signed contracts worth €400,000 this year.

AoTerra says it’s negotiating €1.6 million worth of contracts at the moment, and has another €3.1 million worth in the pipeline. This year it wants to sell 100 AoHeat devices, and next year 500 – at that point, it would be one of Germany’s biggest cloud providers. They need the crowdfunding investment to grow the team to match demand, they say.

I’ve yet to see one of these units in action, but the pitch is intriguing to say the least.

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OpenStack Foundation COO Mark Collier (L) and Executive Director Jonathan Bryce (R)

“There’s more tolerance for early adoption around this technology in the U.S. We’ve seen that not just in Europe, but in Asia as well. In the last few months we’ve definitely seen people picking it up in other countries, and in some cases it means they’re getting the benefit of all those early adopters.”

How about numbers? Well, that’s tricky – because OpenStack is open-source, there’s no way of nailing down precisely how many organizations and providers are using it. However, some big hitters are certainly getting publicly behind it.

Marquee adopters

CERN, the European nuclear research organization that runs the Large Hadron Collider, is one of them. Although it’s also involved with sort-of-OpenStack-rival (although less so these days) OpenNebula, CERN has been toying with OpenStack for a while and is now in the process of rolling out a 150,000-virtual machine private cloud using the platform.

Meanwhile, Deutsche Telekom has been involved with OpenStack for over a year now and has been using it to deliver a security service (along with fellow OpenStacker ClearPath) since March this year. This summer it will move more applications onto its OpenStack-based cloud, Kurt Garloff, head of cloud services engineering at the telco, said at Friday’s event.

And then we have the grand French clouds, Cloudwatt and Numergy, both of which are based on OpenStack.

Why now?

According to OpenStack Foundation COO Mark Collier, the technological requirements of European users are the same as those of U.S. users, but the drivers for adoption are often different, “particularly around data sovereignty.”

“For example, in France there are a lot of companies and policies that create an incentive to have local clouds where the data resides,” he told me, pointing out that the open-source nature of the technology and its resulting widespread take-up by variously-sized outfits meant there were “hundreds of cities where you can get OpenStack.”

Florian Haas is the co-founder of Hastexo, a professional services company that isn’t aligned with any vendor, but has found itself working a lot with OpenStack (it’s a heavy contributor on the high availability front). He reckons Europe has been a slow cloud adopter due to a combination of legal and privacy concerns and a general “degree of conservatism” but, now that cloud adoption is happening, it’s happening on OpenStack:

“Of the big four [stacks] we’re not seeing any OpenNebula, although interestingly there are a few German companies here [at LinuxTag] pushing it hard. We’re not seeing any Eucalyptus. We’re seeing a bit of CloudStack and a massive amount of OpenStack.

“Europe is late to the cloud party, but that creates an interesting situation, which is that much of Europe didn’t go through the AWS uptake cycle. Strangely enough, OpenStack is filling a void, rather than displacing something else.

“A lot of the people we talk to are actually using OpenStack to essentially reorganize their data center. They might have old-style iron-and-wires data centers, or they might be running on proprietary virtual solutions. They’re now considering public and private cloud, and OpenStack is the default.”

With this pace of change — everyone keeps talking about the last 6 months — it will be interesting to see how much further things have gone by our Structure:Europe conference in London on 18-19 September, where we will of course be discussing issues such as stack choice.

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News of the Facebook bid emerged earlier this month via reports in the Israeli media, which said that the social network had offered up to $1 billion to acquire Waze, which adds a real-time social element to traffic maps and claims to have close to 50 million active users. More recently, however, the Facebook talks have apparently bogged down over issues involving whether Waze will have transfer most of its staff from Israel to Facebook’s home base in California.

As with many such reports, the Bloomberg news is couched in all sorts of qualified terms, quoting anonymous sources “familiar with the matter” who say the company “may be considering” an offer, and it’s not uncommon for companies to float such rumors when they are looking for more money — or when they want to convince their acquirer to drop certain conditions, such as the requirement that Waze move its operations to San Francisco.

Some Israeli news outlets have also reported that Facebook has locked down its offer with a clause that prevents Waze from negotiating with other companies. And Google may have decided that making a bid for Waze simply isn’t worth the hassle that it might generate from antitrust authorities, who are already said to be looking at the company on other matters.

Google needs Waze more than Facebook does

All that said, however, there are some compelling reasons for Google to make a bid for Waze, as I tried to outline in a recent post. While it would make sense for Facebook to acquire the company — if only because it would help cement the social network’s move into mobile, and broaden the range of behavior and location data it could use to target users — it makes far more sense for Google.

Google Maps is one of the core offerings the company has when it comes to mobile, arguably almost as important as search and email. And it’s clear that Google cares about evolving the service, since it continues to pour resources into redesigns and added features like the ones that Om wrote about recently. There’s also no question that the social data provided by users is a crucial element of maps — and that is what Waze specializes in, and has managed to build right under Google’s nose.

I would argue that Google can’t afford to let Facebook (or Apple, although it reportedly isn’t part of the current negotiations) get its hands on Waze — in much the same way that Facebook CEO Mark Zuckerberg came to the swift conclusion that he couldn’t afford to let Twitter acquire Instagram, and made a surprise $1-billion offer without even consulting his board of directors. If Google hasn’t already made a bid for Waze, I think it needs to get busy working on one.

Post and thumbnail photos courtesy of Flickr user Dunechaser and Eva Blue

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The news came out Friday as part of a flurry of SAP announcements. Another of those also relates to a departure – that of human resources chief Luisa Delgado, whose responsibilities will be taken on by CFO Werner Brandt – but the big non-quitting-related news is that SAP is consolidating its business to better reflect its newfound cloudiness.

SAP’s cloud “go-to-market” strategy will now all be under the purview of Bob Calderoni, CEO of Ariba (alongside SuccessFactors, one of SAP’s major cloud buys of the last two years). And development will all be under the control of technology chief Vishal Sikka.

SAP is pitching this new structure as an innovation accelerator, but does it finally signal a streamlining of the company’s sprawling and often confusing portfolio (a condition I like to call IBMitis)? Yes! And no.

As Sikka said on a conference call today:

“We see an opportunity to not only consolidate and streamline the portfolio, but bring incredible efforts… to transform that in the power of the cloud. We will get into areas that are truly unprecedented – applications for new industries that weren’t possible before [such as] healthcare, banking, oil and energy.”

Which is nice, but – as co-CEO Jim Hagemann Snabe chipped in – SAP has “a lot of commitments” to its existing customers too, and “we’re a company that stands by our commitments.” This may mean we should expect some redundancy within the portfolio to continue for a while yet, in order to keep those with more old-school SAP systems in place happy.

As for SAP’s ongoing cloud strategy, co-CEO Bill McDermott promised that Dalgaard’s exit would lead to “zero business disruption”:

“Our cloud DNA is now embedded across 65,000 minds and hearts and it’s become the soul of SAP. While it’s nice to have one evangelist for the cloud, it’s even better to have 65,000.

“Lars took us from $20 million in terms of revenue to a $1 billion run rate in the cloud. Now it’s about scale because everything is cloud. No other company has gone through this transition so fast – it literally happened in 12-15 months under his leadership.”

McDermott added that Dalgaard had been having “open conversations” with him and Hagemann Snabe for some time about his plans to downgrade his role to that of advisor. “This is the nicest balance he could find in his personal life and we were happy to accommodate him because we think the world of the guy,” he said.

Speaking of SAP’s thorough cloudiness, the company also announced on Thursday that it would deliver its products – including, of course, those on the in-memory HANA platform — on VMware’s newly-re-announced vCloud Hybrid Service IaaS platform, as well as vCloud Suite. This will allow for fully managed services on-premise, in the cloud and in hybrid deployments.

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As a recent PhD, I can attest to the fact that many researchers first experience the iceberg as no more than an ice cube-sized annoyance impeding their work towards a Nobel Prize. Maybe the experimental instructions from a rival lab don’t quite seem to work. Maybe the data you’re trying to replicate for your homework assignment don’t add up, as UMass graduate student Thomas Herndon found. If a spreadsheet error that underpins sweeping global economic policy doesn’t convince you, here is some more evidence that we are already scraping the iceberg of a research and data reliability crisis.

Newton, we have a problem

All those life-saving cancer drugs? Drug makers Amgen and Bayer found that the majority of research behind them couldn’t be reproduced.

Amgen only able to reproduce 11% of landmark papers bit.ly/Hkk2MM ; Bayer had similar findings in NRDD last year bit.ly/Hkklav—
Nature Rev Drug Disc (@NatRevDrugDisc) March 29, 2012

What about neutral reporting of scientific results? A number of pervasive sources of bias in the scientific literature have been discovered, such as selective reporting of positive results, data suppression, or academic competition and pressure to publish. The hunger for ever more novel and high-impact results that could lead to that coveted paper in a top-tier journal like Nature or Science is not dissimilar to the clickbait headlines and obsession with pageviews we see in modern journalism. (The long-form investigative story is experiencing a bit of a digital renaissance, so maybe that bodes well for the appreciation of “slow science.”)

Readers of the New York Times Magazine will recall last month’s profile of Diederik Stapel, prolific data falsifier and holder of the number-two spot in the list of authors with the most scientific paper retractions. ScienceIsBroken.org is a frank compendium of first-hand anecdotes from the lab frontlines (the tags #MyLabIsSoPoor and #OverlyHonestMethods reflect the dire straits of research funding and the corner-cutting that can result) and factoids that drive home just how tenuous many published scientific results are (“Only 0.1% of published exploratory research is estimated to be reproducible”). The provocative title of a 2005 essay by Stanford professor John Ioannidis sums it up: “Why Most Published Research Findings Are False.”

Interestingly, unlike in science where many results are interdependent and on-the-record, in big data accuracy is not as much of an issue. As my colleague Derrick Harris points out, for big data scientists the abilty to churn through huge amounts of data very quickly is actually more important than complete accuracy. One reason for this is that they’re not dealing with, say, life-saving drug treatments, but with things like targeted advertising, where you don’t have to be 100 percent accurate. Big data scientists would rather be pointed in the right general direction faster — and course-correct as they go – than have to wait to be pointed in the exact right direction. This kind of error-tolerance has insidiously crept into science, too.

Lies, damn lies, and statistics

Fraud (the principal cause of retractions, which are up roughly tenfold since 1975)  is not a new phenomenon, but digital manipulation and distribution tools have increased the spread and impact of science, both faulty and legitimate, beyond the confines of the ivory tower. Patients now look for new clinical trial data online in search of cures, and studies that are ultimately retracted (with a delay of 12 years, in the case of the infamous MMR vaccine study published in The Lancet) can persist in their effects on public health, and public opinion.

Even in fields that don’t end up in the news or have direct medical impact, the effects of a retraction can be broad and disconcerting. When a lab at the Scripps Research Institute had to retract five papers because of a software error, hundreds of other researchers who based their work on those findings, and who used the same software, were affected. When the proverbial giants on whose shoulders scientists stand turn out to be a house of cards, years of effort can go down the drain.

For those providing the funding, like foundations and the federal government, events like this present a further justification to tighten the purse strings, and for the general public, they serve to deepen the distrust of science and increase the reluctance to support audacious (and economically and medically important) projects. This is not a PR crisis borne out of the actions of greedy charlatans or “nutty professors” – they are but a highly visible minority. The real problems for research reproducibility have to do with how we handle data, and are much more benign – and controllable.

Openness to the rescue?

As I reported last week, blind trust in black box scientific software is one part of the problem. Users of such software may not fully understand how the scientific sausage they end up with is made. Om has written about the deterministic influence big social data can have on people and businesses. As the velocity and volume of data have increased, the practices of science have struggled to keep pace. (I will discuss some potential solutions to the data-credibility problem in a separate post tomorrow.)

The question of whether the retraction and irreproducibility epidemic is spreading is akin to the debate over whether spiking autism or ADHD rates, for example, are real or the result of better diagnoses. The recent insight that retractions are correlated with impact factor (a measure of journal prestige derived from the number of citations papers in journals receive) seems to suggest that much of the most valued and publicized science could be less than trustworthy. (Retractions can result from innocuous omissions or malfunctioning equipment as well as more severe and willful acts like plagiarism or fraud.)

Some see the phenomenon not as an epidemic but as a rash, a sign that the research ecosystem is getting healthier and more transparent. Openness is indeed a much-touted solution to the woes of science, with even the White House mandating an open data policy for government agencies; the National Institutes of Health (the major biomedical research funder in the U.S.) and many foundations already require research they fund to be publicly accessible.

Efforts to combat seedy science are popping up at an almost viral pace. A new Center for Open Science has launched at the University of Virginia, and the twitterati of the ScienceOnline un-conference spearhead a number of initiatives to improve the practice, publishing and communication of science.

Motivation through tastier carrots

Culling the bird flu epidemic. Getty Images.

Many in these communities point to a broken incentive structure as the source of compromised science. Novel, “breakthrough” results are rewarded, though the foundation of science rests in the power of real physical phenomena to be experimentally replicated. If the best research – the real McCoys in a sea of trendy one-hit wonders – can be identified and rewarded, science, industry and the public stand to benefit. The challenge is finding predictors of high-quality research, beyond the traditional impact factor metric.

Much like scientists can rapidly sequence the genetic code of new influenza strains like H7N9, they are also starting to identify systemic frailties, attitudes and entrenched practices, and take measures to inoculate science against them. Stay tuned for tomorrow’s post, which will cover three remedies to tame the epidemic and revitalize reproducible research.

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Yet that’s exactly what a group of researchers at the University of Illinois have created — a cradle that wraps around an iPhone and turns it into a biosensor that can detect, according to a university press release, “toxins, proteins, bacteria, viruses and other molecules.” Inside that cradle are about $200 worth of mirrors, lenses and a photonic crystal that the researchers claim can identify these substances as accurately as a $50,000 spectrophotometer in the lab.

The cradle is essentially there for support, though, while the phone’s camera and processor do the real work. With everything firmly aligned in front of the camera, a scientist would simply snaps a photo and the CPU processes the result. What it’s processing is the difference in wavelength that the photonic crystal, primed to react to a specific molecule, reflects. The team demonstrates the device and app in the video embedded below.

And if you’re into this type of mobile data collection, another group of University of Illinois researchers actually created a smartphone-powered water-pollution device called MoboSens

Like all things mobile or sensor, though — from SkinScan (now SkinVision) to health care apps like Ginger.io — the biggest value might come from data that has nothing to do with what the app is primarily measuring. Rather, when data about a certain condition, air quality or what have you is tagged with time and geodata, for example, it becomes the basis for mapping how situations are spreading or where there might be safe haven.

Imagine a team of scientists with iPhones dispersed throughout a city after a disaster, painting a real-time picture of what areas are most affected by a particular toxin (or maybe radiation). Taking a longer term approach, researchers could track how situations are evolving over time. Throw in even more data that smartphones are capable of detecting — temperature, ambient noise, vibration, etc. — and we might unlock entirely new ways to think about how diseases spread through the air or what conditions tend to favor the spread of foodborne bacteria.

In some ways, though, this is more than another cool thing you can do with a smartphone. It’s the furtherance of something we’ll discussing in depth at our Structure conference next month, which is how we rethink IT when computation and data are no longer bound within a single server or even the corporate network somewhere. The biological data this app will collect isn’t much use locked inside the phone; it needs a way to reliably and securely connect with other datasets and other services, likely distributed across the country or even the world. That’s where the real opportunity lies.

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Here are my highly subjective highlights:

1: David Ortiz.

A (very fuzzy) David Ortiz at TechStars Demo Day.

There was a bona fide Big Papi moment on stage as the Red Sox superstar and overall Boston-superhero David Ortiz strode on stage to greet Fancred CEO Kash Razzaghi. This startup is building a “social platform” to connect sports aficionados with like-minded fans. Ortiz demanded that Rassaghi “get the Yankees off my cell phone.” (I dropped my phone but recovered in time to get one sub-par shot at left.)

Is there really room for a sports fan platform? Doubtful. But, hey, I’ve been wrong before. And did I mention DAVID ORTIZ???

2: A platform for sustainable, local food

I love the idea behind Freight Farms, which takes shipping crates and retrofits them with water, electricity,  internet access and LED lighting to convert them into compact hydroponic gardens.

The elegant idea is to “take the very structure that makes the global food supply chain possible and make it into a platform for producing local food,” said Brad McNamara, Freight Farms CEO.

Freight Farms CEO Brad McNamara.

They are remotely controlled and stackable which means they take up less real estate. Freight Farms has signed several customers including Katsiroubas Brothers,  a 100-year old Boston produce wholesaler, which is looking for ways to cut transport costs and offer more local product.

“There is nothing better than fresh local food, but the reality is food distribution is a long complicated supply chain — most goods travel 1,500 miles on average to get to your table,” said McNamara.

Freight Farm-grown crops require less water, no pesticides or herbicides. My question: Will their tomatoes taste like other hot-house tomatoes (i.e., like cardboard) or like an actual tomato? If it’s the latter, I’m totally sold.

3: DIY clothing design

In a nod to the burgeoning “maker movement” or do-it-yourself crowd, Mary Huang was to hand to talk up Constrvct, her startup that’s building service that lets you design 3-D clothing onscreen, tweak the size and styling with easy slidebar controls, preview your design on an onscreen mannequin and then make your clothes to order.

“Makers are underserved in the do-it-yourself market — they’re stuck at the same starting point as their grandmothers,” Huang said. Interest in home-designed clothes is rising thanks to Pinterest and Project Runway, she said, quoting a surprising stat: 3 million sewing machines sold last year, double the number from ten years ago.

4: Fixing manufacturing

LinkCycle says it can use its own data science — and existing data — to help manufacturing plants drastically cut their energy costs.

These facilities — many of them rust belt relics — are notorious for wasting energy and to remedy that many spend millions installing meters and hiring auditors to help. Most of that spending is also a waste, according to LinkCycle CEO Sahil Sahni.

“Why spend so much time gathering data when companies are already sitting on heaps of it?” he asked.

LinkCycle instead takes two existing data streams from the ERP systems already running these companies — electricity consumption and total production output. “We developed our own algorithms to take that data and use math — not meters — to save money without having to set foot in the plant,” he said.

Wow, that sounds so easy it makes you wonder why someone else hasn’t done it. Well except for that algorithm part anyway.

So, the new venue was fab but it suffered the same woe as past Techstars events — a lack of reliable connectivity. We soldiered through with personal hotspots and (finally) some intermittent Wifi connections but can’t one of these deep-pocketed sponsors finally figure out how to get reliable broadband into these events? (I’m  looking at you  Microsoft, Rackspace, Verizon and Softlayer.)

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