Hurricane Maria Destroys Puerto Rico's Science Programs Then Presents Unusual Research Opportunities?

Source: Union of Concerned Scientists

The devastation caused by Hurricane Maria is still being revealed nearly a year and a half after the storm ripped through the island.  Of course, anyone who has lived through a disaster like this will tell you that the island will probably never recover.  Not to mention that the loss of life can never be replaced.  With that being said, any community (or island) must find the courage to recover and re-establish life as it were if possible.  

Under normal conditions, agencies such as FEMA (Federal Emergency Management Agency) would provide sufficient funds to help the island start the journey toward recovery.  Unfortunately, we do not live in normal conditions at the current moment under the current administration.  Funding agencies are being stressed beyond reach for existing funds and when this occurs, areas like scientific research usually suffer the most. 

How Did Maria Impact Science?

At the very least, the lightest impact (which actually may not be true due to PTSD), the lab members may undergo treatment to make sure that there are no residual medical issues after a storm has hit the island.  Of course, if you have no laboratory staff: graduate students, undergraduate students, postdoc's, professional researchers -- then you have no lab.  Meaning, all the best equipment can occupy the lab, but without scientists to run and monitor the instruments, then there is no lab.



The second critical component of any scientific laboratory are the scientific instruments and infrastructure in which these along with the supplies (beakers, tubing, cell cultures, glove boxes, etc.) needed to conduct good/sound science.  This is sometimes the perceived most critical component of any scientific laboratory.  Although, I would argue that the scientists which occupy any laboratory are the most critical components to any scientific instruments.  I have yet to see any scientific instrument just start collecting data by itself without any scientist's intervention/initiation.

A recent article in 'The Scientist' titled "Science in Puerto Rico Still Recovering After Hurricane Maria" details some of the disastrous consequences to a scientific laboratory after a storm of a magnitude such as Hurricane Maria.  The human damage alone can be irreplaceable not to mention the buildings and local municipal utility grid.  And when the destruction to the infrastructure is considered, parameters such as mold and water damage can set a laboratory recovery back several months to years:

Giray’s lab is among 14 or so in the Julio Garcia Diaz biology building, which was among those severely damaged, particularly as it was already undergoing roof repairs when the storm hit. Water seeped in through the roof and windows, damaging costly research equipment, furniture, and lab materials. Toxic mold thrived in the moist, hot climate, creating hazardous conditions that made the building uninhabitable. Power outages cut off researchers’ freezers and fridges, destroying precious genetic and tissue samples for good. The damages are estimated to range from $250,000 up to $2.5 million dollars per lab in that building, says Giray, a behavioral biologist whose main focus is honeybees.

Even more important are samples which are collected outside of the laboratory or purchased for several thousand dollars which are sensitive to temperature/humidity/vibrational fluctuations:

Some of the casualties from the hurricane are less easy to restore: “Collections take much longer time and may never be replaced,” says Giray’s colleague Riccardo Papa, who lost almost all of his DNA samples documenting the diversity of butterflies across South America when his lab’s –80 °C freezer lost electricity. Papa, an evolutionary biologist, didn’t have a lab again until a week ago, and until recently has been meeting with his students and postdocs at coffee shops or places around campus to discuss research. He has been able to do some experiments and genetic analyses in another building. Repairs are still underway for the damaged insectary, in which his team raises butterflies.

Research must go on.  With or without the infrastructure.  Here in California, after the Northridge Earthquake in 1994, FEMA set up temporary 'mobile homes' to serve as both classrooms and temporary offices along with laboratories in certain circumstances.  To hear that 'group meetings' were still being held at coffee shops is a testament to the pace of recovery.  In a majority of cases after a disaster, classroom recovery comes first, then eventually research laboratories.  Although, it is worth remembering that each research laboratory group is made up of students and research professors who take years (applying for individual grants/writing publications) to acquire the appropriate funding to purchase research scientific instrumentation.  Therefore, to put a price on the total loss in the event of a disaster like Hurricane Maria proves extremely difficult.



The total cost to a researcher is really unknowable for years to come.  Some researchers never recover and decide to shut down their laboratories after such a storm.  Which leaves current graduate students without an end in sight to their degrees (M.A. and PhD).  Additionally, staff (professional researchers) might quickly find themselves out of work and have to leave regions like Puerto Rico and find work elsewhere.  Which means transplanting their families and children's education to a different geographical location.  The cost can be severe not just to the researcher themselves.



More can be written in future articles on this theme of disasters and research laboratories.  Either together or separately.  The total cost to a geographical location from a disaster such as Hurricane Maria can only be estimated at the beginning (a very rough approximation).  The price tag evolves over time with the disbursement of emergency funds by organizations such as FEMA along with other federal organizations or the Congress.  The terrible destruction to a scientific institution is terrible to say the least.  Restoring science should be a high priority among others on the island of Puerto Rico.

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What is a typical day like for a systems engineer at JPL?

Source: Phys.Org

Hollywood gives us a picture (one example) of a typical day in the life of a systems engineer at the Jet Propulsion Laboratory.  What does that picture look like?  An example might be shown below:

Source: RareHistoricPhotos.com

Now compare that with the written description from an interview of a true systems engineer at the Jet Propulsion Laboratory in Pasadena as highlighted on the 'Science & Entertainment Exchange' website shown below:

What is a typical day like for a systems engineer at JPL?

The one thing I love about my job as a systems engineer is that there really is no such thing as a typical day. It changes dramatically over the lifecycle of a project, which goes like this. In the early phases of a project, the scientist community and NASA decide what it is that needs further study. Take Jupiter, for example. How was Jupiter really formed? Related to that question are things like: Does Jupiter have a core? How big is the core? What is the water vapor content of the atmosphere?

Next, a call for proposals is sent out and engineers work with scientists to figure out how to go about finding the answers. Can we use a telescope on Earth? Or do we need to send a spacecraft all the way to Jupiter? Can it just fly by the planet or does it need to go into orbit? Then, we come up with a specific design for the spacecraft and instruments. For the instruments: they are often selected through a parallel proposal process. For the spacecraft side: if a spacecraft is going all the way to Jupiter, we work through big design questions like: Does it need nuclear power? Or can we use solar power? If we use solar power, how big would the arrays need to be? Over time, we mature the design to a very high level of detail, then build parts, and assemble them. There are many points throughout the design process for testing things, performing analyses, etc., to ensure everything is going to come together smoothly and perform the way we expect. Eventually, we launch the spacecraft. Once we are in this operations phase, we are getting the data back from the instruments, but also managing the health of the spacecraft.

So far, I have worked on projects starting from the middle of the design phase through the final assembly, testing, launch, and operations phases. My job focuses a lot on troubleshooting and resolving design disconnects. For example, early in the design phase a telecom engineer might want 100 watts of power to make sure the signal back to Earth is very strong and easy to lock onto, but the power system may be providing only 500 watts for the entire spacecraft. The systems engineer’s job is to work with engineers from both of those areas (and the rest of the spacecraft too) to explore the trade space and figure out the best approach.

The description above implies the images below:

Source:JPL

Laboratories like the one above and below house teams of scientists who work collaboratively to think about all of the considerations for a given mission.  A team which appears like the picture below:

Source: JPL/NASA

The laboratory above (spacecraft factory) is a result of years of work by NASA engineers.  Over the course of decades, space scientists have worked to optimize (perfect) the process of design, construction, testing, and launching/mission.  According to the description above by the systems engineer, a day can take on many different forms.  Which highlights a very important observation which frequently arises when non-scientists visit laboratories.  The scientific process has many components which range from constantly sourcing out funding for various research projects to solving unexpected problems encountered during research and development.


Conclusion...


The traditional (old image) of a scientist or systems engineer is one that is not only outdated but has changed over the last few decades.  What image do I speak of?  The image of men chalking up the boards with equations has been replaced largely by computational methods.  A scientist working alone in his/her laboratory day after day has been replaced by a more collaborative working environment -- diverse with different genders, race, and ethnic backgrounds.  Which spurs different angles of creativity and ideas in solving a project at hand.  Since funding is getting more hard to find, more consideration into each part of the process from planning to finalizing construction of a spacecraft is considered in more detail. The result is a more diverse and inclusive interdisciplinary research and design group of scientists who are more concerned about living in a better world and beyond. 



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The NFL Is Collecting Big Data?

Currently, there is a data revolution occurring in the world.  Recent articles in professional journals often highlight the need for science based data degrees.  The hope is to have data scientists migrate often away from the field of science into more lucrative jobs crunching numbers to determine how to increase the number of "likes" and the sort.  Add to that craziness, and you get big organizations like the National Football league joining the party of 'big data' collection.  The question is the following:



What is the NFL going to do with the 'big data' collected?



In order to find out a few of the possibilities, one must continue to read below.  The possibilities are endless, although, the initial reasons are restrictive.

What Is Big Data In Football?

When I read the title of the article on the website "Scientific Computing" titled "The NFL Joins The Data Revolution in Sports", the first question that came to mind is:



What data are they collecting that is not already being collected?



I was confused by the title of the article, since, one would think that a huge organization like the NFL would already have an enormous amount of data.  Think about the gambling industry across the world and their profits on sports.  One would imagine that big data has played a significant role already in generating an enormous amount of profits from big data.  Evidently not.  Hard to believe.



According to the article mentioned, the NFL is just entering the field of "Big Data":

In some potentially game-changing news for the way we understand professional football, the National Football League began the 2016 preseason by placing tracking sensors in its footballs for the first time. The chips are also in balls used in Thursday night games.

Over the past decade, we’ve seen an explosion in data analytics in sports, particularly on the professional level. Technological advances in cameras and sensors have allowed teams, media and fans to gain insight into a bunch of previously gray areas of sport performance, such as the National Basketball Association’s use of SportVU to track every bit of player and ball movement on the floor.

The concept of integrating numbers and analysis into scouting, training and coaching isn’t new. But access to powerful hardware and software has greatly increased the quality and quantity of available data. A nearly insatiable appetite for data on sports has created a sports analytics market that is set to grow from the millions to the multiple billions of dollars over the next few years.

 The amount of data generated during each game would be enormous.  By keeping the sensor limited to the football and possibly the sidelines, the data generated would be reduced too.  Although, with a reduction of data flowing in from the game, the accuracy of the plays suffer too.  The author mentions that the next step would be to incorporate sensors into the players 'shoulder pads' - which would increase the data stream coming in.



Overall, the practice would be transformative to the entire industry.  I wonder how that would change the challenges that referees face during the game.  Currently, during a challenge, the play is reviewed on a closed circuit screen available to the referee and officials only.  With the rise of sensors, now the game can be analyzed by each team in real time.  Although, the technology is not distributed in real time yet.


Any avenue of improvement that the coaching staff can incorporate into the teams training regimen would be greatly sought after.  Currently, teams are exploring both game simulators and drone coverage of their practices to improve overall flow.  The incorporation of data from the NFL offers two great aspects of improvement:

Ideally, data from ball trackers or shoulder pad trackers could serve two purposes for the NFL. First, it can help teams understand player movement and the flow of play more completely, providing coaches a greater understanding on how players are physically performing during plays, and allowing for input from coaches to players on how to fix their technique to increase efficiency or limit exposure to injury, possibly leading to more efficient training and practice.

Second, the data can be used by the league’s media partners, and perhaps its fans, to further explain the game to audiences, particularly on television. By tracking player movement digitally, clearer representations of what makes individual football plays succeed (or fail) can be provided. These data also allow media to break down individual physical accomplishments, such as extraordinary bursts of speed by wide receivers.

The NFL’s plan to release tracking data within 24 hours of a game’s end points to a future in the league where hard data on player and ball movement are integrated into the daily strategic calculations of each coaching staff. This will likely create a rush to innovation within NFL coaching, as each staff grapples with what will likely be a huge amount of data every week, trying to come up with best practices and analytical methods for evaluating and using that data constructively.

Of course, generating a tremendous amount of data means that the NFL along with individual teams that participate need to have the technological infrastructure (computing power, data scientists, etc.) to make meaningful use of the data coming into the organization.  This requires both technology and scientists to handle that technology in a fruitful manner.



That means scientists will be taken away from professional fields in which they were trained to contribute.  Is this good?

NFL Data Science Improves Science Indirectly

There are a tremendous amount of scientists who are interested in sports.  At least, that is my impression after going through the university system in a science driven field -- through an advanced degree program.  The prospect of losing a scientist to the NFL organization at first sight might seem unethical.  Scientists should stick within their field (discipline) right?



Not necessarily.  There might be many benefits by losing data scientist to the NFL.  First, the scientist working for the NFL will inevitably have a appropriate infrastructure to handle the large amounts of data coming in.  In science, funding is scarce and often sought out among many research groups.


I have always maintained that in order to improve the funding for science, we need the entertainment industry and the sports industry to get involved (financially and technologically) to boost the ability of science.  Why?  Not all great ideas come from working on science problems in science.


What do I mean by this last statement?


A famous story about the world famous physicist Albert Einstein revolves around generating his best ideas while shaving.  Successful people will often tell stories of ideas which have been generated about their business while performing outside work or tasks.  The shower or shaving are just two.


Additionally, while performing a job outside a given field, a scientist may gain insight into the problems within their field.  This methodology is sometimes referred to as "thinking outside the box."  By tackling problems associated with dealing with large data sets like players in a game, other problems might be tackled using different algorithms.  Can you think of any?  I can.



One such problem is tracking people in real time in a city and finding potential threats (WM -- chemical and biological weapons, etc.).  Sifting through the data to find meaningful answers might improve the governments ability to sift through data to find a threat.  Although, the funding opportunities to develop an algorithm or simulation might be too costly on part of the city.  Therefore, having organizations such as the sports organizations tackling the data regarding player movement within a given region (on field inside a stadium) will inevitably improve our ability to detect a threat.


As most of us know, the entertainment industry is rich in funding and not at a loss for funding such interesting projects.  Alternatively, new algorithms will be made (which are proprietary for the NFL) to tackle the issue of analyzing real-time data.  But the inherent thinking or structure of mining the data is what is critical.  After that is known, then an algorithm could be changed to achieve that specific problem.  This prospect offers a great future to science and society in the future.

Conclusion...

The correlations which will arise as a result of data mining real-time player information have yet to be realized.  By the descriptions in the cited article above, we are just at the tip of the iceberg in terms of finding relationships within such data sets.  Additionally, no one knows the benefit or adverse effect the data mining will have on both the gaming (gambling industry) and the NFL organization.



Hopefully, out of such data mining algorithms, safer players (with less injuries, etc.) will result.  Science will inevitably benefit out of the data mining processes that are developed.  I have no doubt about that.  Scientists are interested in sports and already use the industry to approach problems in science.  Even if progress is made on the initial thought process of how to find correlations in the data, I believe that meaningful results will arise from the exercise.  Initial findings suggest that this is the case.  Although, as I mentioned, we are just at the 'tip of the iceberg' in the process.  Stay tuned!



Until next time, Have a great day!