Digital data collection is becoming the norm in every field, including commercial fishing. While there are high tech vessels with constant satellite connections, that’s a fraction of the 2.8m global fleet, including the 75,000 or so commercial fishing boats in the United States[1]. That means fishing software and hardware needs to work with limited connectivity as well as glare, saltwater, and — ideally — big rubber gloves. Many of these products need to feed into regulatory systems, so you’ll have to account for that on the backend, and while you may be in a position to mandate use of your app regardless of usability ignoring UX/UI altogether generally leads to lackluster adoption or worse unintended consequences. All this in a space where public budgets are tight and private profit margins are thin.

These aren’t new challenges for anyone working in civic tech (except maybe the saltwater corrosion) and we’re immensely grateful for the documentation that other designers and product managers have shared about their experiences. We’re offering these reflections in the interest of adding to that body of knowledge and expanding the chapter on fisheries. We had the chance to support a team at NOAA Fisheries’ Northwest Fisheries Science Center and support them in applying modern product development practices to a new multi-function platform for fishermen[2]. Here are our takeaways:

The benefits of partnership

We came into the project through a public-private partnership, where the core work was done by an internal team with government funding, while our team had philanthropic funding from the Net Gains Alliance to augment their work and fill gaps. We had strong and dedicated agency partners but it was a small team with a long backlog. We offered general PM support and took on tasks that were easier for an external partner to do. For example, as a number of groups have pointed out, the Paperwork Reduction Act limits federal agencies’ ability to do surveys, especially on topics that are considered to change rapidly like mobile device ownership. We looked at publicly available survey data, but, as we quickly learned, general smartphone trends were not indicative of what devices were in the hands of our target fishermen. Our team had the capacity and flexibility to do design interviews, especially before we had anything to test, and we could add a layer of privacy protection in how we synthesized and stripped personal details out of our reports. Also, we could pay for snacks and coffee, which are essential to good conversations and often excluded from government budgets.

Keep the internal users engaged

The lead developers set up bi-weekly check ins with the internal agency teams, including managers and the staff working on compliance and enforcement. If you’re in the private sector reading this we can hear you rolling your eyes at something that might seem blindingly obvious but treating your agency colleagues as customers who should be engaged with and delighted by your products is still a bit of a culture shift in civic tech, especially in agencies that have not been first in the digital transformation queue, like many natural resource agencies. Agency staff are used to frequent, long meetings; they are less familiar with two-week sprints that result in new wireframes based on the feedback they provided last time. We wrote up some guides for the NOAA Fisheries team (drawing heavily on work from 18F and USDS) that mainly served to reinforce the best practices they were already deploying, tie them to other civic tech efforts, and make them fishier. Getting the internal users accustomed to regular check-ins made discussions easier once we had to start making tradeoffs between fishermen’s and managers’ requirements.

Recruiting fishermen testers took more time than we anticipated

We were doing this in 2020, when COVID-19 meant everything took longer than expected, but still it was quickly clear that we had an uphill job working in a community that was unfamiliar with user testing. Kate’s worked in and around West Coast fisheries for over a decade and contacted fishermen she knew, including ones who’d participated in design sessions on other projects. We had agency staff with longstanding industry relationships help with outreach. We offered to run tests on the weekends, early in the morning and late at night. We tried to set up an in-person testing event, but COVID-19 restrictions shut that down. Overall it took more than a month to build our first testing group and we didn’t get the full range of perspectives we wanted (operating systems, vessel configurations, ports). We asked the people who did participate to recruit their friends and family. Once people had participated, we asked them if they’d be willing to do a second session later we could show how we’d incorporated their feedback, which most agreed to.

We paid them

We asked for an hour of their time and tried to wrap up our discussion on time, although some wanted to talk longer. For that, we offered them $100 in compensation, which some declined. We advertised that we were paying testers in our recruitment emails but this did not seem to affect our recruitment. We made the decision to compensate people before COVID-19 cases started spiking in the U.S. and we were glad we did once ports and seafood supply chains started shutting down and fishermen were tied up at the dock. Even without a pandemic, we support compensating participants when you can as a matter of fairness and because it’s common practice in the private sector, sending a signal that fisheries and natural resource tech is as worthy of good design.

It was worth it

Even with all our constraints we got so many of the great outcomes you want out of design testing: original ‘brilliant’ ideas were scrapped and iterated, testers were delighted by things no one thought of as ‘features,’ small changes brought big delights, and bugs were squashed. Maybe most importantly, the testing sessions offered a dialogue opportunity between agency staff and fishermen based on listening and learning, not regulating. The development team had asked to be on the virtual testing sessions to hear feedback firsthand. We gave testers the option to talk just with us or have agency staff on as well and all opted to let staff participate. This led to some real-time ‘a-ha’ moments about the product design, and about the way the platform would operate inside a larger political and management context, one where the developers had limited control. There were things the apps couldn’t change, but for what they could do, and what they needed to do, they’re doing a better job of it because of testing and the input of fishermen.

[1] FAO 2018 Statistics and that’s an estimate.

[2] We’re saying “fishermen” because that’s the term people asked us to use. There’s a whole paper on “Should we call them fishers or fishermen?” if you want to dive into the terms, and we tried out a few other words like “captain” as we went along but here we’re going with what they asked to be called.

We’re using the mapping process to discover common ground and conflicts across the community of data contributors and users. We’re often talking at the program design stage, where you have government agency data needs, service providers that need to satisfy those requirements, and industry partners that create the data, all trying to create a working system of technology and policy.

We want that group to look at the data landscape and articulate:

1. What “data” are we talking about?
2. Who controls that data?
3. Who can access it?
4. Who pays for it

By discussing each of these aspects — even if in describing them you realize you don’t all mean the same thing — you can surface the kind of people and alignment issues that will tank even the most technically amazing data management systems. Especially if you’re in a highly-regulated space and you don’t have infinite time and lawyers. Better to find out now that one person’s “raw data” is another person’s “processed images” and design a program that gets you what you actually need. Here’s what we do.

Who do you need in the discussion?

Like a design sprint or project kick-off, 8–10 people is a good start. You can do it with a larger group (and we have) but you might want to test it out with a few participants first to preview the sticking points. You could also give people a worksheet to fill out in advance and have them share at the meeting, or send it in if they can’t attend.

You could hold this discussion when you think everyone is on the same page so you can document “yep, this is what we think we’re talking about here.” Or you can do it when you’re not sure what everyone thinks. We’ve found real value in bringing different perspectives into the room because — per the old story of blindfolds and elephants — that’s where you discover big gaps and insights. When we’re data mapping for fisheries, these are the kind of perspectives we’re talking about:

• A research scientist, who has very specific data needs and wants to increase data collection (volume and velocity)
• A lawyer, who needs to track and ensure compliance
• A mid-sized vendor, who wants to be able to meet customer needs without making sixteen custom product lines
• A fishing association, who would like to manage their own data system that connects to government services
• A data manager, who needs to decide on APIs and handle the back-end design

What supplies do you need?

• A white board with two colors of pens, and/or
• Two packs of 3” x 5” stickies (great if you have two colors), or
• A virtual display — Mural, Google Slides, the whiteboard in Zoom, Office360, etc
• Someone to take notes
• Someone to run the process (these last two can be the same person but it’s usually easier to have two, especially for a virtual gathering)

How long do you need?

No more than 3 hours. Take a break after 90 minutes. If it’s easy — great! End early and start fixing. If it’s way more complex than you thought, well, now you have a starting point to keep digging. Come back and try another part the next day.

Set up the mapping space

Across the top of our workspace, we’re going to write stages of the ‘data life cycle’ or ‘data value stream.’ The internet has plenty of examples of what constitutes a data life cycle (including one in the NOAA Fisheries Data Management policy). We use:

Plan | Collect | Transmit | Analyze | Share | Store

We keep Transmit in as its own step because the oceans aren’t fully wired (yet), so getting big files from boats to data centers may involve delays of days or weeks as well as postage. Look at a few examples and pick the phases you need to focus on but resist the urge to list more than seven. Cluster where you think things are fairly well agreed on; split out steps where you think there’s confusion.

We often don’t spend a lot of time on the “Plan” phase but we keep it in because YOU HAVE TO HAVE A DATA PLAN. If you work outside Big Tech you have limited resources for data collection and management. You cannot afford to say “collect everything and we’ll figure it out later.” There are also security and privacy risks to having a big pile of data. If you don’t know what you’re collecting the data for, don’t. At least talk about how you will have a data plan and maybe who’s going to create it.

Work from left to right in columns

Below your life cycle stage, start a column on the far left with the four questions:

1. What “data” are you talking about?

Maybe you want to talk about a lot of data bucketed by source (daily satellite imagery) or maybe you want to be very specific (home addresses associated with licenses). As you move through the value chain you may branch off data products, such as a group of water temperature sensors that becomes daily summary reports under “Analyze” and “Share.”

2. Who controls the data?

Essentially, who gets to set the rules and permissions and who’s ultimately responsible for availability and quality. This can be interpreted as “ownership” but for some groups that might trigger “data as property” discussions you want to steer clear of. This can also be the place where you talk about tricky legal issues like who’s liable if the data get hacked or how to to maintain an evidentiary chain of custody for criminal violations.

3. Who can access the data?

In the detailed notes you want to capture who and how. You might be able to fit this in a sticky box if you have a small group (or a virtual display). If you need quick reference on the wall, go with icons, shapes, or colors so at a glance you can see who can get their hands on what data.

4. Who pays?

Mandatory fisheries monitoring tends to have a blend of financial responsibilities; the government picks up some costs and others are passed on to industry. Data can have value beyond just meeting compliance requirements. One reason we’re having more of these conversations is the growing interest in getting more data access by the fishing industry, global food supply chains, and data analysts outside of fisheries. This question lets you see how the payers and the data users line up.

Here’s an example for a generalized monitoring program using on-vessel video to track catch:

From Collect through Analyze you can see that industry would have to pay for all the costs but couldn’t access data until it’s been analyzed by the vendors and Shared. Then, there’s big question mark about if they could ever see the video (and if they might have to pay extra to do so). Maybe industry doesn’t want to see video and vendors only need to secure video data for government recordkeeping. Or, maybe there’s a different suite of data products that could satisfy enforcement and the science teams. After building out this map with the group, we know where we need to start the discussion about program design and we have a common language to talk about the ingredients.

Have you tried similar mapping for alignment exercises in your work on fish (or something else)? Let us know what you’ve discovered.

Seafood is a $390 billion global industry, including everything from seaweed to tuna, both farmed and wild. Per capita fish consumption is growing worldwide and fish remain the main source of high-quality protein for many people. The ocean’s complexity and remoteness can make it seem like a magical food production machine. Fish just appear in your nets until, one day, they don’t. Keeping fish populations healthy requires active work on the part of fishers, seafood buyers, managers, and scientists, who all need better tools and data systems to stay on top of catches, products, and ocean conditions. Let’s break the problem space into four general areas: Management & Enforcement, Traceability & Trust, Research & Forecasting, and Business Operations.

Management & Enforcement

If you work in finance, health care, or gov tech, you’re familiar with working in a (highly) regulated industry. Fisheries tend to be a mix of highly scrutinized and ignored. Since the law has historically treated ocean waters and coastal lands as a public resource, most governments play a strong role in controlling the access and use of their waters out to 200 nautical miles from shore. Aquaculture (fish, seaweed, shellfish) generally requires permits to lease the water and lands below it. Navies and international fisheries organizations want to track ships as they slip across national waters and cross into the high seas. With 50+ years of industrial fishing and fisheries science behind us, there is widespread agreement that good management requires setting and enforcing some type of limit on catch, which managers do with a variety of measures like gear restrictions, closing areas to fishing, and setting fishing seasons. This means scientists and government managers need solutions to track if rules are being followed and fishers need tools to help them comply.

What solutions might look like: On-board cameras that monitor what’s kept or discarded, AI to find pirates, digital forms that display rules & regulations based on your fishing location, sensors on fishing gear, voice-activated data collectors for hands-free use on a wet deck. Bonus for building interoperability to transfer data seamlessly across public and private data systems.

Traceability & Trust

Seafood is one of the most highly traded global food commodities: caught in one country, shipped to another country for processing, and then re-imported or shipped through multiple ports before finally making its way to a restaurant or fish counter. The convoluted supply chain from bait to plate makes it difficult for data to travel with the fish, challenging anyone who wants to verify that the species, catch location, and other details are what it says on the label. This is a particular concern for third-party certification groups, like the Marine Stewardship Council and Fair Trade, and the major buyers committed to purchasing certified products, like Walmart and Whole Foods. Poor data can also hide labor and human rights abuses. Improved traceability has been a goal of many in the seafood space for years but progress is slow. This is your area if you’re looking to use blockchain or if you want to be in Barcelona in June 2018.

What solutions might look like: Better product tracking software that can integrate data from multiple sources, handheld DNA testers for fish buyers or port inspectors, universal product IDs that work in any database anywhere in the world. Check out the winner of the 2016 Fishackathon: Fishazam.

Research & Forecasting

The ocean’s complexity and remoteness can make it seem like a magical food production machine. Fish just appear in your nets until, one day, they don’t. Even processes that are relatively well-studied — the lifecycles of salmon, the dynamics of kelp forests — are changing with warming waters and increased acidity. Every ocean food from seaweed to tuna is affected by these new ocean dynamics; estimating the size and direction of those effects is essential for businesses and for anticipating catastrophic environmental failures. We might be able to move fish farms before a toxic algae bloom hits or change catch limits and fishing areas daily to protect the reefs and living habitats that act as fish nurseries. Today, we have the computing power and data science methods to search for trends across terabytes of historic ocean research or create inferences from non-ocean data sets to fill data gaps. We need to develop and apply those data tools.

What solutions might look like: Robots, deep learning on ocean data to improve predictive models, eDNA testing that lets you detect fish by sampling the water, using internet search history to estimate fishing activity, counting fish with sound.

Business Operations

Anything that addresses problems in one of the other three categories could apply to this category as well, if it’s designed with the right users in mind. That’s not the usual approach for fisheries, where a digital logbook that sends machine-readable data to regulators might only send PDFs to fishers. No one wants to enter the same information on three separate forms, paper or digital. Fishers need to manage their licenses and operating requirements, find the best price, and track profits and losses. Seafood businesses need dashboards and secure data systems, with good data management protocols and partitioned access. Bonus for integrating smart scales, sensors on totes or shipping containers, and interfaces that can handle wet hands and fish slime.

What solutions might look like: Cell phone micropayment systems for fishers, PermitMaster 2.0, one touch reporting for fishers and fish buyers.

The pathways into ocean innovation are growing. Fish 2.0 wrapped its third business competition in October and the Fish Free Feed Challenge is on its second round. There are ocean-focused investment collaborations, like Schmidt Marine Technology Partners, the Salmon Innovation Fund and SIFF Capital. You could apply to the Ocean Solutions Accelerator (deadline Feb 17, 2018) or the TechStars + The Nature Conservancy Sustainability accelerator (deadline Apr 8, 2018). You could also get hands-on experience at the worldwide Fishackathon the weekend of February 10th, where teams at 50+ sites around the will prototype solutions for problem statements from fisheries experts. Wherever you live, whatever you’re working on, take the opportunity to connect to the sea.

Disclaimer: Links are not investment advice. I have no financial stake in any of the companies mentioned here, although I will be a judge at the 2018 San Francisco Fishackathon where I may be given free snacks.

After I left, I got so many questions from colleagues about “How did you do that? No, really, how did you do that?” that I wrote up this guide. And I do not charge for it, so I might as well put it on Medium. This is primarily for people in the “knowledge” sector, where figuring out what you can do as a consultant is part of the challenge; there’s no simple upwork category for your content and process expertise. You might have to try a few different ways of packaging and delivering your work before you get it right. But, if you’re curious and thinking about going solo, here are some tips to get you started.

1. Start saving money now, before you start consulting

You’ll want a cushion to cover you between jobs and keep you from freaking out. You’ll probably run out of money at some point, so save now to push that off as long as possible. Enough cash to cover 3 months of spending is a good rule of thumb, and by that I mean what you spend now on all the things in your life, plus a little extra for your new website, etc. Be realistic about your expenses. Do a little soul-searching about how much you really need to spend on coffee and beer and life-sized unicorn statues. Some people really like being highly leveraged, but I’m more a fan of this quote:

“The key to eternal happiness is low overhead and no debt.” — Lynda Barry

2. Figure out what your skills are & what you can market

I found StrengthsFinder helpful in talking about what I offer clients and I saw a career coach who talked me through what I wanted to do until it sounded like an actual service. Look at other consultants’ websites to see what words they use and borrow liberally. You need to be able to explain what you can do that will help your clients quickly and effectively. Practice this on friends until they look excited and no longer quizzical.

You may have seen this Bud Caddell diagram about finding the sweet spot between what you’re good at, what you like to do, and what you can get paid for. I agree with Les Jones that, in the beginning, you should give a little on the what you’re good at/what you can get paid for intersection. Maybe you can use that project to refine your skills, to “learn and earn.” Maybe you didn’t like that kind of gig in your old job, but you find you do when you’re running it yourself. You might even be able to do a few of these little gig experiments in addition to your full-time job.

3. Brush up your online presence

You should be findable online. You should look like a business. Even if you’re doing all word-of-mouth, no advertising consulting, eventually someone in contract compliance or HR will search for you and they should find business things. At a minimum, update your LinkedIn profile. Buy a domain and set up a website, even just a basic landing page, and create a work email.

4. Get business cards

Yes, even today, you want a physical artifact, especially if you work outside the tech industry. Both Moo & Minted will do the design for you. If you’ve got a design and you’re in the Bay Area I recommend GreenerPrinter. If you want to get crafty, take a letterpress class and print your own.

5. Set your rates

I wrote a separate post about setting your rates if you work for nonprofits, but the short version is:

• Figure out what you need to make to cover your expenses (see #1). Depending on the kind of work you do, you may have an hourly rate, a day rate, and/or a cost for each specific package of services.
• Find out what your competitors charge. If you work somewhere now that hires consultants, look at all the contracts you ethically can to see what people charge. Ask other consultants. Look at freelance sites.
• If your rate is way above the competition, you’ll have to justify it based on the quality of your work and your general awesomeness. Practice explaining this. Or consider dropping your rate a bit.

6. Figure out your legal risk tolerance

This may involve talking with an actual lawyer, which I am not. Seriously, I am not pretending to provide actual legal advice here. Just food for thought.

Contracts. Some people are comfortable working with handshake deals and others would prefer contracts with every detail spelled out. Even if you don’t feel you need signed contracts, some clients will require them so get familiar with the basic terms. If your current employer has contract boilerplate they make everyone sign, see what you can borrow from that language (do not do this if the lawyers will claim their contract language is IP, but seriously, this stuff is pretty standard and you can find it online). Check out Freelance Union’s Contract Creator.

Insurance. There are two main types of insurance to consider, mainly General Liability and Errors & Omissions (aka E&O). Maybe you think you do not need insurance. It’s not cheap and if you’re pretty much working from home, not using contracts, maybe it will never come up. If you’re going to a client’s office and somebody trips over your markers and sues, that client might pass the buck to you, in which case you will really wish you had insurance. Some clients will require it. Going freelance is a brave enough thing to do, so if having insurance will make you feel more confident go get some. Even if you feel invincible, it’s worth looking into insurance rates in case a client requires you to have it and you have three days to lock down a policy. Hypothetically.

Incorporating. You can start consulting with very little structure. Just use your name as your business name and you’re a sole proprietor. You don’t need to set up a business entity (LLC, S-Corp) or even a business name (aka a DBA or Doing Business As)to get started. But you could. You should talk to your accountant and actual lawyers about this. If you’re already legally entangled with anyone else, you might want to see if they have strong feelings about merging your business assets with their tax returns. Whatever you decide, you’ll need to pay state and federal taxes (and maybe city taxes, too) so try not to change your business name too often.

7. Set up business accounts

Start separating business costs from your other costs as soon as you can, because sorting things out from your personal account (especially if you have joint accounts) is a pain. Having clients send electronic payments to your personal account looks sketchy and some businesses won’t do it. Credit card, checking, savings. Done.

8. Pay your taxes the way you want to do your work

It’s your business now and all those quarterly tax payments and deductions are on you.

I hate all of this and I never want to look at a Schedule C. Go find an accountant now. And a bookkeeper.

I like my privacy. Maybe you don’t want your home address on every document you file with the city and every email. Maybe your mailbox isn’t a secure place to get checks or you need to send and receive lots of packages. You could rent an office to cover most of those issues. Co-working spaces can accept your mail and some will agree to be your legal address. UPS Boxes and P.O. boxes give you secure mailing addresses but you might not be able to use them for your operating address for legal documents like business permits. You can also get an EIN from the IRS so you don’t have to use your social security number for billing and W-9s.

I like my pajamas. Does your home have a space where you can sit and work the way to want to? Sweet, you can take a home office deduction. Do you need noise-cancelling headphones to make that happen? That’s an essential work supply. Will no amount of ear-plugging help you work from home? Will your neighbors complain if you have clients coming over for meetings every week? Or do you just work better when people are around? If that’s you, sure, you can live at a coffee-shop, but maybe you want to sublet a desk in a friend’s office or join a co-working space.

Paying estimated taxes is painful. Yep, instead of having tiny payments you probably didn’t notice withdrawn from your paycheck each period, you now actively have to send the government money in one big chunk. I like to give myself a ‘tax holiday’ on quarterly tax payment days, where I hit ‘make payment’ and then go outside and enjoy all the things I just bought with my taxes: sidewalks, parks, air that doesn’t kill me… Maybe you’d rather celebrate by calling your elected officials or writing essays on tax reform. Reward yourself and the get back to work.

9. Find tools to automate parts of your new consulting life

There’s a wealth of digital tools available for small businesses, including virtual assistants, time tracking software, project management, scheduling, and bookkeeping. New tools crop up (and go bust) every day so I can’t list them all here but I will give a shoutout to two of my favorites: Harvest and Calendly. Your time is valuable and you should outsource or automate as much as you can so you can focus on the things robots can’t do, like helping a shelter get its clients into long-term housing. Get your internal system set up the way you like it before you take your first big job. About six months in, get rid of what’s not working and try something new.

10. Tell everyone you‘re available for work and they should hire you

Ask other consultants to join their list of people they bring in for a few hours when a job is bigger than their firm can handle. Socialize. Bid on jobs all wrong and work way more hours than you thought you would. Deliver an awesome product anyways. Learn to bid on jobs better. Make your clients glad they hired you.

11. Pay yourself in time

Go for a hike in the middle of the day. Work at night because you’re productive then and you like cooking your family breakfast. Work from anywhere you want that has an internet connection — the ferry, Lake Tahoe, Panama. Take vacations. That time in between clients and jobs can be stressful but there is a limit to how much you can hustle more work. When you’re freelancing, you’re valuing your time to all your clients. Pay yourself in time, too.

Even if you have a few steady clients, consulting is inherently an erratic job. Learning to ride out that fluctuation is one of the hardest parts of running the business of you. You may try freelancing and find the unpredictability so maddening you head back into an organization ASAP. If you do, being your own boss, even for a short time, will teach you a lot about being a better employee and managing other people.

It’s a 20 degree day in Medford, Massachusetts and yesterday’s snow is gusting across the road as I cross the river. I’ve flown across the country to gather in a small basement office with a small group of engineers, fishery scientists, and friends recruited with the promise of dinner and energy drinks. We’ll spend the next five hours watching New England fishermen scoop fish out of bright orange plastic totes, place them on a measuring strip, quickly take their hands off, and let them go over the side of the boat. As we scan through the digital footage, we mark the time when the fish appears, record the fish species, and measure it on the screen by drawing a line from mouth to tail. It’s like a slow, meticulous game of fruit ninja.

All this work and snacks is in preparation for a competition we plan to run this summer to train computers to identify fish. We’re working towards a future where monitoring what fisherman catch and what they release can be done in real time, making it easier and more affordable for fishermen and managers and anyone who wants to keep track of what’s taken out of the ocean to do just that. It’s a merger of high-tech, sophisticated computer science and one of the oldest professions in the world.

This is the coming revolution in conservation, in knowing and understanding the world around us. The natural world is full of remarkable and often hard-to-observe phenomenon. Scientists studying the rainforest used to camp out for days in hopes of observing a rare glimpse of a jaguar, but now they can set up cameras to record constantly and review that footage from their laptop anywhere in the world. Advances in conservation technology are generating more data than ever before on what lives where, who eats who, and what’s disappearing and how fast, but it still requires human time to watch that video footage. We need to pair our new sensor systems with analytical tools that let us convert that data into information and knowledge.

Which is the kind of problem that big for-profit companies excel at tackling and that people are excited to build tools for because fixing a big corporate problem can get you a big corporate paycheck. If you can become a billionaire writing an algorithm to process banking data would you spend part of your time tweaking that same algorithm to model fish populations? The world of data competitions suggest that, for some people, that answer is yes. Our basement group gives me hope that more and more there are engineers, data scientists, UX designers, and other curious innovators drawn to unusual and challenging problems, with solutions measured in impact beyond profit.

There is profit involved here. In a lot of small coastal communities fishing is the biggest business around. Saltwater fishing — both commercial and sport — in America is a $300 billion dollar business supporting 1.8 million jobs. More and more people want to know where their seafood came from and that they’re not eating the last fish in the sea. That means counting fish accurately becomes all that much more important, and some fishermen are eagerly embracing new technologies to be able to demonstrate just how sustainable their business is. Their goal is to maximize fishing revenue while leaving enough fish in the ocean to breed new fish for next year’s catch. With more effective electronic tools it could be easier for fishermen to track that catch and for managers to monitor the overall impact on fish populations. Our project has funding from the National Fish and Wildlife Foundation to work on this, but our $200,000 budget is barely the annual salary of one Silicon Valley software engineer. So, our team involves people who will work at a deep discount, for the thrill of solving a complicated problem, and for some delicious pulled pork.

One reason people seem to get excited about creating algorithms for fish is that fishing presents a novel set of challenges to a computer. Poor lighting on a boat deck or underwater can distort colors and blur a fish’s edges. In bright sunlight, the glare of a shiny fish reads as no data to a digital camera. If fish are moving around through a net or a chute you have to be clever to make sure you can tell one fish from another and not double-count. Even in the best conditions some fish can be difficult to tell apart. We’re joined this Saturday morning by a guy we refer to as the Fish Boss, a fishery scientist who also spent years as an observer, riding along on fishing trips counting the fish as fishermen hauled them aboard. We hunch over our screens moving frame by frame to annotate the fish in the video, occasionally calling Fish Boss over to make sure we haven’t confused one flounder with another.

If we succeed it it should make counting fish easier for fisherman and managers not just in New England but all over the US and around the world. We’re designing our products to be open source, to make it more likely that other fisheries can adopt and implement what we help develop. Some of those tools, like the video annotation software, are simple enough that anyone can use them, making them contributions to the growing citizen science movement. It’s time to take the internet of things outside and use our growing technological capacity to support the natural world around us, and the businesses and the quality of life it sustains.

Machine learning, meet the ocean was originally published in TDS Archive on Medium, where people are continuing the conversation by highlighting and responding to this story.

When I walk around Oakland, I see the ocean. Not because I’m on a hill, or peering through cranes at the port, but because once you know where to look, the ocean is everywhere. When I watch clouds blow across the sky, I’m watching wind powered by ocean temperatures and currents. Stacks of folded jeans or the bags of beans at my corner market conjure up images of shipping containers stacked end to end on Panamax ships, slowly plowing through the high seas. The oxygen I’m sucking in on my run was once carried around the ocean in the grip of cold, cold water molecules.

I first jumped into marine biology in high school and even as I drifted into other fields, I brought that sea view with me. It’s as if I were a radiologist and I couldn’t stop picturing the bones inside the kid sitting next to me on the train, or a web designer with View Source on all the time. The sea is the steel behind the building façade, the miniature watch gears beneath the face, the details you take for granted so that you can get on with your task list. Infrastructure — built or grown — succeeds when it moves so smoothly that it’s invisible, and by succeeding in that way, it helps you forget it exists. The ocean’s imperturbable surface gives the appearance of a miraculous, perpetual motion machine that needs no maintenance from us.

The downside of invisibility is being ignored.

The ocean does need maintenance. For millennia, we’ve expected it to feed us, take us from one place to another, and keep our weather running along smoothly while we dumped trash and carbon dioxide into it and spent our time with our cooler friend, the moon. I mean, we just discovered glow-in-the-dark sea turtles in July. Almost half of the world’s population lives within 75 miles of the coast now, and rising sea levels are bringing the oceans closer to the other half. It’s time to get to know your neighbor.

To do that, we need give the oceans a little more attention and raise our expectations. Twenty years ago we didn’t expect glass to respond to our touch or that we could track our packages from the warehouse to our door. Now we take these things for granted. Many of our ocean information systems are stuck in the 1980s, or even the 1780s. We hand enter millions of paper receipts so that six months later we know how many fish were caught. We call recreational fishermen on the phone and ask them what they caught last month. We may adopt high-tech tools on one end, like identifying pirate fishing ships with satellites, but the data still flow into outdated systems with limited public accessibility. It should be easier to see if countries are cheating on their fishing reports, what the pH of the ocean is, or how your shrimp were farmed. The lack of ocean transparency helps high seas criminals and unscrupulous dealers who want to pass off one fish as another, and it hurts the scientists, fishermen, and managers who are playing by the rules and planning for an ocean that can sustain us through the next millennium.

You don’t have to make the sea your full time job to help open up the oceans (although if you want to, you’d be in good company and the benefits are pretty great). Whatever you’re doing now, chances are the oceans could use a little bit of your expertise. The oceans deserve good design and user interfaces. They need SQL and R and machine vision. They need data mining to reveal the dynamics at the heart of the ocean’s food system. They need citizen scientists, entering what they find on beach walks and explorations with their $900 underwater robots. Right now, the winners of the U.S. State Department’s 2015 fishackathon are at Chile’s global ocean summit presenting their app to help stop illegal fishing. Which they built as a hobby project. If you’re wondering what to do with your life, you could do much worse than pivoting to ocean ideas and spending time at fisherpoet festivals.

I imagine a world where the ocean is visible to everyone. Where your smartwatch can tell you the tides and the surf and what you took out of the sea that day — oxygen, fish tacos, imported printer paper. Where the back end of your search engine is also compiling and training ocean algorithms. Where your recaptcha is a sea creature you have to name. Where you can see your connection to the common sea, and, if you choose, dive in. It’s time to open up the ocean’s data for the ocean’s sake and for ours.

Based on this estimate, Boston is paying $11.43 to melt a ton of snow. What if they put it in a shipping container instead? You can fit 2,385 cubic feet of snow in a standard 40-foot shipping container, or about 35 tons, saving the city of Boston $408.86 in snow-melting costs. That shipping container now holds about 17,887 gallons of water, or 1/10 of what an average family of four uses in a year. 17,887 gallons will bring you $116.29 at current LA rates.

So, you could conceivably get Boston to pay you to take the snow and sell it to LA to make $525, except for that shipping & handling. And that’s gonna cost you. You could go cheap and ship it via the Panama Canal for $2,071, or you could put it on a train for $2,595. Trucking it is even pricier. None of these estimates include the costs of shoveling that snow into the container in the first place. Or the carbon costs of that shipping, which you might want to add, considering the links between climate change and extreme weather.

I might be underestimating the citizens of Boston and their determination to dig out. They might be willing to pay $1,700 to send their snow to a needy California family. Or maybe a cross-country water pipeline is looking like a bargain investment for future storms and droughts.

Last week there was an awkward date between Science Magazine and Twitter. A fairly thoughtful piece about how scientists can, should and do use Twitter was wrapped around a list of the “Top 50 Science Stars of Twitter.” The internet was not amused:

Science, you can do better than this. As can the big scientific community that is already actively using social media. Let’s break this down and get to some learnings, starting with the criteria for “Top Scientists”.

Twitter follower numbers are not a great metric for anything but number of Twitter followers. Five percent of Twitter accounts are fake and eight percent are autoretweeting robots. That’s 30 million followers that aren’t actually listening. Generally, the more you tweet, the more robots you’ll get following you. So, without analyzing the individual follower lists of each of each scientist, you can’t be sure that, say, P.Z. Myers actually reaches more humans than Steven Pinker. Science points out that follower number is a ‘very crude proxy of influence’ but that they chose this as a criteria because it was the ‘most accessible’. And yet somehow not accessible enough to catch many female scientists, including Dr. Sylvia Earle, whose 34,600 followers would have placed her 20th.

Science – you are one of the top science journals in the world. You do not get a pass on your methods. Especially when you lead your story with the summer’s new metric: The Kardashian Index.

Geneticist Neil Hall was perturbed that scientists seemed to be ‘famous for being famous’ while not contributing to science in the true coin of the realm – academic publications. He created a formula comparing scientific citations against Twitter followers and dubbed it the “K-Index.” I give Dr. Hall credit for coming up with a metric to get at what he saw as a problem: the overexposure of scientists who don’t publish enough. Even in its current rough and cheeky form, the K-Index still provides slightly more information than Science’s choice of straight Twitter followers. If Science did its rankings by K-Index, Neil Tyson drops to the bottom, Daniel Gilbert rockets into third place and two women – Karen James & Amy Mainzer – move into the top 20. Dr. Hall suggests that anyone with a K-Index over five should stop Tweeting and get back to publishing. Everyone on Science Magazine’s list has at least an eight, so they won’t get invited to any of Dr. Hall’s panels.

That gives us two definitions of “Top” — the most followers and the K-Index. Neither of which tell you much about influence. Is a scientist who gets the most retweets more influential than one who cultivates a network of scientists across disciplines? How would you evaluate scientists who use Twitter for conversations, so their high tweet count is more replies than broadcasts? If only there were data scientists and social media companies thinking about this very issue. Oh wait, it’s 2014.

I took the totally free step of checking the 9/21/2014 Tweetreach of Science’s top scientist, Neil Tyson, along with Sylvia Earle, and popular science blogger Dr. Bethany Brookshire. Both Sylvia & Bethany crushed Neil. You would expect Science, a nonprofit media organization, would have some social media protocols and metrics that they use to track their own blogs and excellent writers. Perhaps they could have used those to rank scientists and compared that to the K-Index.

For all the flaws in Science’s approach, they’ve opened up a conversation about what should be measured and how. Dr. Hall initially thought his K-Index would highlight the contributions of women scientists like Rosalind Franklin and Ada Lovelace, who did not get much public attention in their day. You could tweak the K-Index to include other social media, adjust for time in the field (and Twitter start date) and other factors if your goal was to identify scientists who accomplish great things without notoriety. But is that the greatest goal for scientists? Or for the universities and foundations who support them?

If we want scientists to innovate, collaborate,
or work on topics that impact society,
we should reward them
when they use the internet to do so.

There are scientists who use social media very deliberately. They have a strategy, as any good organization or business does. They’re crowdfunding research or recruiting volunteers. They’re having public conversations about their work with colleagues and potential graduate students. They’re actively building networks to discover new research partners. They use Twitter and other social media platforms as “an informal arena for the pre-review of works in progress.” These scientists should be evaluated by their success in hitting their strategic goals, not by follower numbers. I’m not saying every scientist needs to be on Twitter or Instagram or whatever the latest platform is but it wouldn’t hurt scientists to understand social media better — including how their peers & funders will evaluate their performance — so they can make an informed decision to opt out.

A few months ago, I was at dinner with a very social media savvy biologist who’d just finished reviewing grants for the National Science Foundation. NSF asks scientists to explain how a project will have ‘broader impact.’ He said the vast majority of proposals filled in that section by saying “I’ll put the results on the internet” and then, magically, impact will happen. I know other colleagues who’ve put social media metrics into their tenure packages, which are then reviewed by senior members of their department who have little or no experience with social media. Or view it with some of the same skepticism as Dr. Hall. The latest generation of scientists grew up with social media woven into their lives. If the scientific community adopts an index that penalizes scientists for their online presence we run the risk of discriminating against an entire cohort of new researchers.

Until scientists across disciplines get to better agreement on social media metrics – what to measure and why – good science won’t get out to the audience it deserves and scientists won’t be applauded for using social media wisely and well. Which is a waste.

Title photo by Karyn Traphagen from 2013 ScienceOnline Oceans,
where the Twitter traffic shuts the servers down.

Mullainathan and Shafir call slack the extra mental bandwidth you have when you’re not experiencing scarcity. They use the example of packing for a long vacation. If you have a big suitcase, you have slack. You can bring things you might not use, you can toss things in without rolling them up tightly, and you bring that carefree mindset with you to the beach. If you have a very small suitcase, you have to spend a lot of effort figuring out exactly what to take. You’ll plan in advance, maybe purchase new things, and you might spend time on the plane and during your trip wishing you had packed better or brought that other pair of shoes. This is how scarcity begets scarcity; the lack of money isn’t just a financial tax, it’s a tax on your mental capacity.

Nonprofits spend a lot of their mental bandwidth fundraising and reporting to their members and funders. Donors are right to expect transparency for their donations but they’re not always willing to pay for the cost of all that transparency. It takes staff and internet access and rent to track progress and write up reports, which are costs often lumped together as ‘overhead.’ Foundations and charity rating sites have often evaluated organizations by how much they spend on overhead as opposed to what they spend protecting forests or serving meals. Most foundations limit the percent of any grant that can be spent on overhead. The implication is that restricting your charitable dollars for food or medicine or research is more valuable, or more virtuous, than investing in a nonprofit’s management infrastructure.

The problem is that this gives nonprofits no slack to work with. When all your funds are tightly tied to specific deliverables, you’re scrambling to cover the coffee for your volunteer clean-up. Setting aside time for staff training or researching new ideas can seem wildly out of reach. When I was a foundation program officer, I once had a finance advisor recommend against a grant because the grantee regularly used a standing line of credit. She thought it was a sign of weakness. I thought it was pretty impressive that the bank had that much faith in the nonprofit and besides, grants and donations didn’t arrive on regular schedules. The line of credit let them make payroll between grants, which meant they had staff around to actually write the grant proposals and do the program work. It gave them the slack to spend more time thinking about changing the world and less time worrying about layoffs. We made the grant anyways.

Getting out of a scarcity trap takes more than a simple, one-time intervention; you need to step back and see the cumulative stresses on the system. If you’re a funder worried about accountability, ask for breakouts of where unrestricted funds go but don’t limit up front. You could give money to infrastructure like staff training, R&D, and grant development. Or you could take an experimental tip from Mullainathan and Shafir and try and understand where the scarcity bottlenecks occur and target those. Create a ‘scarcity fund’ and track when the organization draws against it. You could find it’s not truly lack of money but a need to change the timing of when grants arrive, something you could improve by collaborating with other foundations.

Scarcity isn’t always bad. Scarcity can focus the mind, like a deadline, and too much slack can make people wasteful. But we’re far from that end of the spectrum in the social sector today. As we look more and more into taking business practices into the independent sector, we should consider importing some of their approaches to slack, like a Google 20 percent time for nonprofits. Finding and retaining good talent and designing and iterating social solutions takes investors and funders who understand the need for flexible funds. Focusing on impact might mean untying the hands of your grantees.

Delta Airlines’ recent announcement that they would be changing their frequent flyer program brought a wave of angry customer tweets. While the old system rewarded passengers for miles flown the new program is pretty much pay to play — the more you spend the more you get to fly free. Some prior loyal flyers are dumping Delta, vowing only to fly them as a last resort. Business wonks are celebrating this as a smart move, one that truly rewards the “best” customers. That’s true if you define “best” as “spending the most money” but it’s clear there are a lot of frustrated fliers out there who took Delta at their word when they said it was about loyalty and frequency. I sat in an airport lounge watching this news play out and the disappointment and anger sounded so familiar. You think you and your partners have the same definition of success and it turns out you just used the same words to mean very different things.

I work in the social economy, where we spend a good deal of time talking about tracking impact and measuring what matters most. When you’re a public company, it’s obvious that you measure success by your profits. You don’t need to hold meetings with your shareholders to explain why you’re tracking your revenue. You’ve got one solid indicator of success everyone understands.

That’s rarely the case in the social economy. When your goals are housing veterans or restoring beaches quarterly profit targets don’t make much sense as metrics. Even if you have profits to show, that’s not enough information to know if you’re really having an impact. That means it’s up to you — your group, your field, your funders, and your clients — to define success and figure out metrics that do make sense. And getting everyone on the same page is a key part that’s too often left out. One group defines success as more college engineering graduates, while another measures new tech start-ups and another tracks elementary science education. A foundation or a donor wants to build the U.S. tech economy over the next five years. Are they all working towards the same goal? How can they see if their individual efforts will add up? You need a shared vision of the system, of how it works and how to make change. You need a nested set of indicators to track progress within a year and over multiple years. You don’t all need to waste time and money measuring things that don’t matter, just because they’re easily measured.

Taking your temperature every day won’t tell you your risk for getting diabetes and tracking the Dow Jones Index won’t give you a full picture of the U.S. Economy.

When you and your partners share an understanding of what you want to change and what success looks like, it’s easier to identify your metrics and trace a throughline from what you see today to what you want to see in the future. Right now the social sector is hungry for data and there’s a risk of making a Delta-like mistake, where one group thinks “best” means more forests and another thinks it’s less CO2. They may both be right, but they’ll be talking past each other. And then everyone is frustrated.