A new branch of methods of science research has emerged. The video game is being utilized heavily in gathering an abundance of data for various researches. It is the mass exposure that appeals to many scientists for gathering long term data. There are so many video gamers that it allows these research endeavors to acquire the magnitude of participation the study truly needs. The game Foldit is developed by the University of Washington’s Center for Game Science. This scientific game utilizes the puzzle solving skills of humans to search for the best possible protein combinations. This field utilizes computers to predict protein combinations. Foldit offers new prediction algorithms and protein combinations discovered by the players of the game.

Players are given disarranged proteins and they have to fold the various amino acids in the closest to perfect combination to earn a certain score to beat a level. Players are giving various abilities and items to use to solve the puzzle such as shake, wiggle, and rubber bands. Shake shakes the amino acids of the protean to find the best working arrangement. Wiggle moves the backbone to the best working arrangement. Rubber bands can tie together to objects so that when you shake or wiggle the protein those two objects will attract each other. The rubber band is useful when trying to form hydrogen bonds. Players can also play against others online in a competitive way to find the best proteins and create data for the scientists.

For the player, the game simply feels like a three-dimensional puzzle solving game. Just like any other puzzle solving game, Foldit players begin to realize various patterns of techniques in solving puzzles more efficiently. One typical technique is wiggling before shaking, moving the backbone before you move the amino acids. But this is the data that is being collected. It is used to create new algorithms for predicting the best combination, as well as discovering new protein combinations. The prediction algorithms aid computers in predicting effective proteins more efficiently. Players are also able to discover effective protein combinations that can be used to benefit human health.

I found that the game was initially quite interesting. The pay style was straightforward and the competitive aspect of the game via online added another layer to the game beyond the offline mode. However, I did begin to get bored with the game. I found myself gradually disliking to play the game as it slowly felt more like a chore. It would also get irritating how precise you have to be to find a combination to beat the level. A little move of one amino branch would cost -5 points, yet you still need 6 points to beat the level. Overall, the game is enjoyable for short periods. And it is a very effective research technique that I believe many more branches of science should utilize. Video game sites and stores should one day have a ‘citizen science’ section.

EteRNA is a citizen science project where people help to create synthetic RNA designs.  On their website, they state that their purpose is to “help reveal new principles for designing RNA-based switches and nanomachines — new systems for seeking and eventually controlling living cells and disease-causing viruses. By interacting with thousands of players and learning from real experimental feedback, you will be pioneering a completely new way to do science.”[1] The game starts off with the tutorial teaching you about the different RNA bases: Adenine (A), Guanine (G), Uracil (U), and Cytosine (C).  You are given the task to mutate certain bases on a molecule to try to mimic a shape given to you.

In the tutorial, you are taught different characteristics of RNA, and as the player, you use these characteristics to create a desired RNA molecule. For example, in the below screenshot, they will tell you about base pairings and different ways an entire molecule can connect. In this screenshot, the ends connected, forming a circular molecule.

In the below screenshot, circled in blue, this is the structure that needs to be recreated by mutating certain base pairs. Toward the bottom of the screen, there are varying base pairings besides the usual “A-U, C-G” pairs, and the strength of the bonds are given. The initial base structure that is given to you has all adenine bases. Combining and playing around with different pairings and mutations, you would try to create the circled structure.

After the tutorial and you have the basics, you get slightly more difficult structures and are given some restrictions. In the following puzzle, you must have a certain number of G-U pairs and G-C pairs while trying to make the structure in the first icon given.

The idea behind this entire puzzle game is to create a library of all the different patterns of pairings that could be made in order to create a specific RNA. In the above screen shot, the structure is an actual structure of a plant called arabidopsis thaliana, and users came up with a pattern in order to recreate that structure. Perhaps somewhere down the line in the game, a RNA of a virus can be created, which would give scientists different ways a virus could be surviving.

In theory, it sounds really interesting – that people who may or may not be in the field of genetics, are virtually creating synthetic RNA molecules that exist in real life. However, it is difficult to say whether or not this would be considered “real science.” For people who do not know much about genetics, this is a really easy and fun way to introduce the very basics of RNA to them. On the other hand, there is really no reasoning behind the patterns the player is picking. When I was playing the game, I was randomly choosing pairings to link parts together. I do not have the biochemistry/genetics knowledge to justify any pattern I pick.

In the about section of the game, they stated that they want users to create these molecules because for very large molecules, computers would take a very long time to run, and users are likely to find faster and better patterns [1]. However, I doubt users would play up to the point where they can get to really big molecules that would actually contribute to what the scientists need. Rather than say this is a citizen science project, I think this would be better as an educational tool to teach students the mechanisms of RNA pairings and folding.

I played The Cure game for my citizen science assignment. The purpose of the game was to help develop our understanding of different types of cancer. To my understanding, they create games to help do studies for various cancers. Currently, they are focused on further understanding breast cancer. The way the game works, is a bunch of cards labeled with the name of genes were displayed, and a robot and I took turns in picking cards. The one with the higher score ends up winning. The scoring basis is determined based on how the genes picked are related to breast cancer.

The whole game felt very esoteric. They always gave a description of the gene, but I was not knowledgeable enough in breast cancer to make educated choices. It more so felt like I was guessing, due to the fact that I had no knowledge about breast cancer. Through random guessing however, I was able to complete all the levels, and upon its completion a few thing about this game became clear. When you were giving a score, a tree was shown on how many people had a reappearance of cancer with the gene. The more complex this tree became, the higher score you received. Even though this became apparent, it was much more difficult to actually accomplish this. This only occurred when I was very lucky or when the computer made all the right choices. Overall I felt that the experience was pretty pointless for me. On occasion it is better to have humans to a certain job, but since my knowledge about the topic is insufficient, it would be much better to have a computer running every single combination.

The game itself was very interesting due to the fact that all the results were based on actual events that occurred in humans that had breast cancer. I did feel pretty useless to their cause due to my lack of knowledge, however. Since the computer records my results and compares investigates it later it would most definitely be much more effective for them to have computers do all the matching. Since a computer would avoid repeats, it would be much more successful with this task. I would definitely make this game more available to those who have more knowledge about breast cancer. Those people would definitely be able to have greater impact in the research than the average person. Still, I am sure that they are going to make great progress in cancer research with this kind of game.

For most, where one’s energy comes from is of little concern. So long as the lights turn off and on, people can rest easy. However, for the scientists working within Harvard, the MIT Energy Initiative and Goddard Space Center, it is precisely the issue of energy infrastructure and allocation that keeps them up at night. For researchers to optimize the energy production of a particular city, 4 to 5 potential energy sources must be considered along with their associated market costs, availability, carbon profile and power output. These variables are then further complicated with factors like demand, market fluctuation, and the time of day or year. The result of all these considerations are tremendously complex differential equations without analytical solutions. Computer algorithms alone simply cannot tackle the issue.

Enter the Nova Labs Energy Challenge. After registering for an account, users are presented with a goal; optimize the city’s energy needs by providing the most energy for the least total cost and carbon emissions. The difficulty of the challenge ranges from installing solar panels in Tucson, Arizona, to balancing geothermal, wind, solar and biomass together in Los Angeles.

Participants are presented with potential maps that describe the availability of various energy sources across the United States. After the city to be optimized has been selected, its current energy profile is brought up (which usually illustrates a high dependance on coal or natural gas) in conjunction with average monthly demand, peak demand, as well as a projected budget and energy production target.

Energy Profile of Las Vegas–Click to Enlarge

Designing the system consists of moving a set of sliders, each of which corresponds to a different form of energy production. In this stage, not only is the number of solar panels, wind turbines or geothermal facilities considered, but their overall efficiency too. More efficient technologies are often orders of magnitude higher in cost. Increasing the efficiency of solar power from 12% to 13%, for example, put my project $30 million over budget. Also considered here, is the amount of land area available for development. Some cities, especially those in the southwest, have a larger area that can be lent to development, while cities on the eastern and western seaboards typically have less space for large construction projects.

System Design–Click to Enlarge

After your system has been created, the design is put to the test with a simulator that pits your model against the city’s energy consumption patterns for the past week or year. If your design diminishes CO₂ emissions, has the least total cost and produces enough energy, then your score might be placed on the leader board. Presumably, it is the leaderboard profiles that are contribute the most to the research with their en pointe optimization practices.

System Simulation–Click to Enlarge

What is so striking about citizen science initiatives such as these is the way in which both parties, the researchers and the participants, can come away with new information. The Nova Labs Energy Challenge allows users to take a peek into policy issues and see why, exactly,  it is so very difficult to systematically apply clean energy technologies. Surely, the scientist who acquire their data will be using it for societal benefit, however, what might be even more important than the impacts of the results of this experiment is the general public awareness that the process of experimentation begets. With initiatives such as these, the fundamental challenges of energy production become more tangible. The act of budgeting resources and money, as is done on the Nova Labs website, shows us how very difficult it can be to create a cleaner tomorrow. However, with Nova Lab users crafting solutions using today’s technology and today’s market prices, it also shows us how very near a clean energy future is.

For my citizen science project I decided to participate in a project named Cities at Night. Cities at Night is a project created to archive photos taken from the International Space Station so that they can be sorted, and the photos of cities and the light given off by them can be studied and used for future research. As an avid stargazer who owns a few different telescopes, the problem of light pollution has plagued me for a long time. Living in the city during school and close to it the rest of the year causes light pollution to significantly impact the amount of observations I can make using the telescopes I have. Being so affected by light pollution cause me to want to take part in this project and make more aware of the problems connected to light pollution.

Light pollution causes more problems than you may think. The average person seems to boil down light pollution to the simple issue of not being able to see the stars at night. Although this is a huge and quite depressing problem, one that causes us not to enjoy the nature around us, this is not the only dilemma. Light pollution also causes severe complications on the ecosystem and our health making it a pressing issue that must be dealt with.

The premise of this project was very simple, look at a given picture, and then categorize it based on predetermined categories the picture may fall under.

As you can see with the picture below, the image is shown underneath the different categories. The categories you can place the picture in are: Black, City, Stars, Aurora, Astronaut, None of these, No photo, and I don’t know. When selecting City more categories are then shown.

You first are asked to determine how much cloud cover there is with you options being: Clear, Some clouds, and Cloudy.

Prior to this the program asks you if the image is Sharp or Blurry as seen in the next picture.

I took part in this project for the full month of November, trying to archive some pictures at least a few times a week. The project was very easy to take part in. Simply categorizing the pictures does not require that much mental capacity so I was able to participate at random times when I might not have been feeling that intellectual. This project is also extremely accessible, solely requiring the link and a few clicks to help out. In addition to all this the project is very interesting. You are sorting through some of the coolest pictures of space and our home planet and cities from a perspective not usually looked at.

During the duration of when I was participating in the project all the pictures the site contained were successfully archived. You can continue to categorize pictures, which will wind up being the same that some already categorized but they allow this to reduce error. In addition to this there are some further steps that can be taken. The first is an application called Lost at Night where you can take the pictures marked as a city and try to help determine which city is actually being represented in the photo. After this you can access an eve further step with the Georeferencing Cities application where you associate a physical map with spatial locations to the picture. Although most of my time was spent doing the categorizing, the other tasks are just as interesting although a tad more complicated.

All of these tasks help the organization running this project measure and compare large illuminated areas. With the colors of the images the efficiency of lighting in many cities on the planet can be measured, helping us learn a ton of information on light pollution.

Overall participating in this citizen science project was very rewarding. Not only was it intriguing to look at and categorize the pictures, it felt like I was doing the planet a justice. Hopefully this study continues and brings more awareness to the issues of light pollution, but for now I will be taking my telescopes upstate to the mountains to get away from the light.

I will shamefully admit that citizen science was something that I wasn’t aware of until I entered this class. The concept truly intrigues me– the possibility that I could have an impact on something much larger than me without doing anything I can’t really understand– I think it’s amazing. Unfortunately, I chose a project that even if it is doing something, isn’t doing it in a way that reeled me in.

Worm Watch Lab is a citizen science program ran by the Medical Research Council. The objective is to observe thirty second videos of the nematode worm C. Elegans in order to help understanding of how the human brain works. One might ask how we are comparable to worms– they have almost as many genes as us, and they also happen to be closely related to humans. In the videos, worms are tracked to see how frequently they lay eggs, and the viewer contributes by pressing the “Z” key each time the worms lay eggs. The worms don’t lay a lot of eggs. In fact, the worms lay so few eggs that the site feels a need to warn you that you could sit through thirty to fifty videos before seeing an egg. There is also a solid chance that once an egg appears, you won’t even know, because in the videos they’re small gray blobs that are the same shade as the small gray worms.

Needless to say, this got frustrating very quickly. Still, I did learn a few things. The observations were relevant because some of the worms had chemical imbalances that parallel the human brain. The most common imbalances were of dopamine and acetylcholine. Other worms had undetermined imbalances that were said to cause them to lay abnormally large or small amounts of eggs, and some had no imbalances at all. Since the worms didn’t lay many eggs– less than ten across around fifty videos– I cannot really attest to those patterns in relation to their imbalances. However, worms with dopamine or acetylcholine imbalances were noticeably either way more or way less active than normal worms, and if any worms ever did lay eggs, it was usually them.

I think Worm Watch Lab is an interesting concept, but I wish I could have learned more. I don’t see much sense in tracking an excessive amount of worms with unknown imbalances, since that data definitely doesn’t have the potential to be significant. I also think Worm Watch Lab could benefit immensely from color video, if that’s even something that’s possible, because I’m still not sure if all of the eggs I identified were actually there and things could definitely be more distinguishable. Still, I am intrigued by the concept of citizen science and will likely contribute to other efforts in the future.

Cropland Capture as a game consists of only three buttons: your left, down, and right arrow keys. From there, you are shown rectangles of pieces of land taken from Google Earth (a related plug-in is required to play) and you have to press one of your three arrow keys to indicate whether or not it is usable cropland. According to the website’s FAQ, they are using the FAO, Food and Agriculture Organization’s definition of cropland; cropland includes “annual crops annual crops (e.g. maize, wheat) and perennial crops (such as coffee, tea, palm oil, fruit orchards, etc.)” while pastures are for livestock and forest plantation are for timber.

Though it sounds simple as a game, they are addressing a greater issue about the future. In their calculations, by 2050 our population should increase by another 2 billion – which provides a problem in how we are producing food. By making a game where people are practicing science at its simplest (bare observation), everyone can help identify potential locations for crops and even help identify the impact of climate change on Earth’s croplands.

These clips of images from Google Earth are shown to multiple players are their accuracy is based on a majority rule; if the majority of players identify the patch as viable cropland, then it is so. It is not just aerial views from Google Earth being shone, there is an abundance of photographs from specific sites themselves.

It is tricky to resist the urge to hit yes for every slightly green image that appears, but through practice, it progressively becomes quicker and easier to categorize them. Areas that look dry and brown could be categorized cropland while certain areas that are green are not. There are little details that vary from picture to picture that manage to become the deciding factor.

Through a simple task of hitting an arrow key, players are helping to contribute to the prevention of future worldwide hunger as well as advancing the relationship between the general public and science.

After Bio Blitz ended, I never thought I would be spending any more time bird watching. But for my Citizen Science Project I made the spontaneous decision to participate in a Birds Near Me project for Citizen Science. Birds Near Me is essentially a worldwide birding map created by a birder, Gerry Shaw. The map allows for people to zoom in and select any part of the world and view the different species and abundance of birds spotted daily. Birds Near Me is available to the public for free by downloading an app on your iPhone or tablet. The app gives you free access to the abundance and diversity of birds in regions around the world, shows pictures and descriptions of the birds sighted, highlights certain nearby bird “hotspots” for bird lovers to explore, and presents you with notable and unusual bird sightings near you. The Birds Near Me Project app is powered by the website eBird, where birders all around the world actually view and record the bird sightings found on Birds Near Me.

To participate in the Birds Near Me project for Citizen Science I had to create my own account on eBird in order to record my own findings. eBird has a number of projects available for both bird experts and beginners. I chose to participate in a specific project entitled My Yard eBird. This is a year round project where participants daily record the number of bird sightings in their own backyards and log them into the eBird database, for people to look up on Birds Near Me. I myself participated in this project for a week, and each day for half an hour identified and counted the number of birds I spotted my backyard. I created a log where I identified the abundance of birds I saw and the particular species of each bird spotted.

The task itself proved to be tedious at certain points, especially since there are fewer numbers of birds flying around Brooklyn backyards in late Autumn, than in the summer. Also, because of the season the birds I spotted were often of similar, native species. In fact, the majority of birds I spotted in my backyard were either Rock Pigeons or house sparrows, species of birds commonly found in Brooklyn since probably the founding of Brooklyn. However, there were interesting moments in this Citizen Science project. After days of seeing pigeons and sparrows, it was a pleasant surprise for me to spot certain bird species like the Blue Jay or Red Cardinal. I was even able to see a v-formation of geese flying above my garden on one occasion.

Although this Citizen Science project appears simple enough, the information gathered is actually of great importance to scientists. By having a clear understanding of the abundance and diversity of bird populations, scientists can thus determine the impact birds have on the environment, and on public health. For example, with a greater understanding of bird diversity, scientists like those we studied in class, were able to predict the effects of passerine and non-passerine bird species on diseases such as the West Nile Virus. In all, participating in a Citizen Science project showed how even the smallest contributions to a large scientific task can make a difference when unified.

Link to my eBird log: http://ebird.org/ebird/eBirdReports?cmd=subReport

Reverse the Odds is a colorful puzzle adventure game created by Cancer Research UK to aid Dr. Anne Kiltie and her team of researchers. This game is accessible to many people around the world since it is available for both Android and Apple devices. Reverse the Odds is a fairly simple, leisurely game that someone could pick up and play during a boring train ride or while waiting in line. Although it is quite simple and slowly paced, the point of the game is to look for patterns of specific biomarkers in cancer cells from slides of tumors provided by the researchers. The reason why this was done is because it is better to have millions of eyes looking for these patterns rather than the same few people all the time.

Biomarkers are molecules that indicate how a cell might behave or if there are molecules present and working to further allow the cancer to grow. The slides analyzed in game by all the players will help researchers learn more about cancer behavior such as how aggressive the cancer might be and/or how the cancer will respond to different treatments.

The biomarker the researchers are looking for in this game is a protein called MRE11. This protein is present in all the cells in a tumor, but some cells have more of this protein than other cells. The reason why this molecule is so important is because it is used to detect damage to the DNA that could be caused by radiotherapy. Therefore, being able to gauge how much MRE11 is in the cell will help doctors and researchers decide if it is safe to choose radiotherapy for the patient. So for researchers and doctors it is important to be able to find and recognize these molecules in order to make correct treatment decisions for patients with bladder cancer and other types of cancers, radiotherapy or surgery.

The game starts off with a girl who discovered the “Odds” as she was becoming ill. The Odds were sickly and white and she realized that the best way to help them is to give them potions that would reverse their sickly state and help restore their world (hence ‘reversing the odds’).

The game has three main screens. The first one is the map of the Odds’ World where there are different cubes. If you click on a cube you will be taken to the second screen where you analyze tumor slides. If you get the same guesses as the majority of other people in the world who have played the game you receive a potion and in the screen after that you play a puzzle game where you try to get a certain amount of Odds reverted back to their original form while trying to collect gems, etc. If you succeed you reversed the Odds in one cube area.

The game begins with the map of the Odds’ home world where you select the first flashing block and pour a potion on it to give it life again. From there begin analyzing tumor slides for the protein MRE11 in order to get more potions to reverse the Odds’ condition in a puzzle game and to finally get more blocks back to normal (not dull and plain, but filled with life instead). The photos below show how progress in analyzing the slides and winning the puzzles changes how colorful the world of the Odds becomes:

The further you get the more vibrant and colorful the world gets:

The game constantly reminds you of what to look for in each slide to identify MRE11:

Below are images of the puzzle game that follows the analysis of the slides:

The point of this game is to help researchers find and analyze tumor slides using human’s ability to recognize patterns in order to potentially help future cancer patients better choose their treatment. Despite this being a very serious subject and intended to help with cancer research, the game is quite fun and very addictive. All of you should give it a try!

AgeGuess: A citizen science project on human biological and chronological age.

AgeGuess conceptually is very simple: an online game where participants post head shots of themselves and guess the ages of each other based on those photos. It was created by two research scientists, Uli Steiner and Dusan Misevic, at the Max-Planck Odense Center on the Biodemography of Aging in Denmark. The players can upload photos from any time in their life, whether it’s 10 or 50 years ago, as long as the photos are clear, focused, and don’t include other people in the frame of the shot uploaded (the age minimum for photos is 14). Users earn points based on the accuracy of their guesses and are ranked against other users; they earn 10 points for an exact guess, 7 points if they are 1-2 years off, 5 points if they are 3-5 years off, 2 points if they are 6-10 years off, and 1 point if they are more than 10 years off. They also receive 10 points for every acceptable photo they upload. Users are encouraged to upload photos from different time periods or from deceased people to add a genetic component to the resulting data. But, the underlying purpose of the site is to create a data set for research purposes into the study of human aging that is the first of its kind. (AgeGuess)

This type of data set is critical, because aging is obviously a problem that concerns everyone. By definition, aging is the act of getting older. The actual likelihood of dying exponentially increases as you get older—excepting infant mortality—as age is deleterious. Actually, the odds that someone will die after age thirty doubles about every 8 years until much older ages. But, aging is a poorly understood and highly variable process. Some humans seem to naturally die decades before others, and other humans appear decades younger than they actually are. Whether or not this is due to slower rates of aging is still to be determined. Biologically, we can measure age at the cellular level as well as holistically for the organism. But, bio-markers can be used to measure age not directly based on time -— perceived age as an example. (AgeGuess)

This Citizen Science project is interested in determining the difference between perceived age and chronological age, and how that difference can be used as a powerful aging bio-marker. The project researchers would like to see if the fact that every four years we gain one year of life expectancy changes perceived age over the years. Perceived age is also affected by stress or medical conditions, so this data set is also intended to discern whether the disparity between perceived age and chronological age changes over time, is genetically predisposed, or whether this disparity is more common to certain ethnic groups. They are also interested in seeing if the accuracy of perceiving age is heritable, more common to one age group, or more common in one ethnic group—as this would affect how that groups chooses sexual partners.

I really enjoyed participating in this Citizen Science project, as perceiving other people’s age is something we subconsciously do every day in our appraisals of those around us. Over the course of this last month, I did decently well on guessing ages. Although I never made an exact guess (sadly) I only have a standard deviation of 4 years on my guesses. I’m solidly in the middle of the rankings—ranked 1526 out of about 3000 participants so far. Interestingly enough, the guesses made about my age were fairly accurate, with my real age being 19 years, the average guess being 19 years, and only 3 years of variance and 2 years of standard deviation on guesses made about me. I assume that this is due to the photo I uploaded, as I am often told in person that I look at least 21—people are shocked when they find out I’m not even 20.  I think that Citizen Science projects like this one are an incredible way to harness the internet for social good and would absolutely participate in one again.

Before participating in a citizen science project, I had absolutely no idea what it was. I thought it was going to be an extremely time consuming process, something those only really interested in science could enjoy. I was wrong. It turns out; citizen science projects are actually pretty cool. As I was finding a study to participate in, I passed by a lot of really interesting ones, from a study which seeks to understand how people classify TV theme songs to a project that maps the DNA of thousands of people in order to trace a common ancestor. Since these were either over or expensive to participate in ($40 for the DNA kit), I chose Citizen Sort.

Citizen Sort is a project created by students and staff at Syracuse University’s School of Information Studies. It was created in order to help scientists classify different species of animals, plants, and insects. When I participated, I helped classify moths, sharks, and stingrays. I did this through a modified memory game but instead of matching the same picture together, I matched pictures of organisms to the category they fit.

For example, I would have pictures of different moths and I would have to put the moths into whatever category they fit into. This reminded me of when we used to classify rocks in my high school earth science class. It was fun and usually pretty easy. It was actually harder than I thought it would be, originally, since some of the pictures are hard to see the differences between the different organisms or place them into a specific category. I did the best matching with moths at a 90% correct level overall. Sharks, I just couldn’t get. The best score I got was 40%. It was harder to tell the difference between sharks than moths and the categories were much more specific.

I ran into some pretty terrifying looking sharks too. Look at this guy; he’s straight out of Jurassic Park. Damn nature, you scary!

Overall, I had a lot of fun on my project. I spent a couple hours just playing around and trying to beat my previous scores. It’s awesome that a game can actually help scientists in the real world. If the internet is good for something, this is it. It’s awesome that anyone can participate in science experiments just by playing an online game. This is a really interesting approach to studying the sciences and I think this could definitely help younger kids get interested in science and actually learn from playing games. Overall, I was very happy with my citizen science project and I hope to participate more in the future.

The whale song project is an interactive study founded by Scientific American and Zooniverse. The site shows calls from both Orca and Pilot Whales. Orcas are not actually considered whales, they belong to the dolphin family. Whales and dolphins are both considered cetaceans and are closely related, but still differ in bodily structure.

Not only does the site show an image of the call, which you can click on and listen to, but shows where on the map this call was recorded. Sometimes a whale’s call is outside of the normal human hearing range, so the frequency of the calls on the site are slowed down in their frequency in order to allow humans to be able to hear them. However, this can make it difficult to truly hear similarities between the calls of different whales.

The site allows us to see spectrograms, which you can click on to hear. The point of the site is to be able to click on different spectrograms and be able to match calls of the same whale. If it is not possible to find a match, you have the ability to move on to different calls. You can find matches by looking at the spectrogram or by simply listening to the call, the combination of both allowing an easier method of matching.

The study that this is based on is trying to answer the question of when and why animals make specific calls. The communication of killer and pilot whales is still poorly understood, and the larger number of datasets showing their calls makes is difficult to interpret the behavior of the marine mammals. Tracking and interpreting their calls allow us to understand possibilities of what the calls mean as well as their movements.

I enjoyed participating in this Citizen Science Project because I find the behavior of marine mammals to be extremely interesting. I have spent much of my time learning about marine mammals and behavior, but have never gone in depth with my research on their various calls. Though I still feel that I have much to learn about what the whale’s calls actually mean and how I can interpret this data, matching the calls of different whales was an informative experience.

A link to the site can be found below:

http://whale.fm

Phylo is a game project that was developed by two computer scientists: Jerome Waldispuhl and Mathieu Blanchette. Often, video games are referred to as a waste of time or a simple hobby to replace efficiency with fun. However, there is an almost unlimited amount of video game players around the world and through Phylo, these two computer scientists were able to tap into this unlimited resource and advance scientific observations. The simple game requires one to move around left or right the genome of different species and align common colors along a column. The more matches and the less empty spaces there are between blocks of genome, the higher the score.

A collection of these blocks generated by every player slowly benefits the genome study. Through Phylo, they were able to “sequence a single genome for less than a thousand dollars in a day” and thus this game is extremely money and time efficient. Through the masses’ participation with the game, long lists of different genome patters and similarities are catalogued and put into code so that the computer scientists can then program it to benefit the genome research. This information will then lead to possible ways to trouble shoot different diseases and aid in creating proteins that can potentially cure these diseases.

The beginner level starts simple, as there are only two genomes to match. As more and more layers and genes are added to the plate, it becomes much more difficult. There are several choices to consider such as how to minimize empty spaces between genomes, how to efficiently place the genome so that no changes need to be made again, and how to surpass the goal points they initially design. Once one reaches the end, the final score is shown. There is also a list of top scores with anonymous tags because this citizen science project is an anonymous contribution.

The question arises as to whether a computer designed to do these operations is a better option than opening it to the public. The benefits of having the people work on this project is that a computer who is designed to look for these patterns is hard to create and engineer. Computers do not have a natural ability to recognize patterns that humans do, and therefore creating a new program to do that is not cost efficient. Humans have this latent ability to recognize patterns. Harnessing another latent ability of humans, the need for lazy joy and procrastination, this program collects thousands of new genome correlations a day, which further progresses genome research and shows everyone a little bit more about the world.

 Small screenshot of the game in play

 After completion, this is the screen.