What’s in your water?

By Sarah Newman October 3rd, 2017 at 8:05 am | Comment

World Water Monitoring
World Water Monitoring
Our dependency on clean water is something we all have in common.
 
In celebration of the Clean Water Act’s 45th anniversary (October 18), we’ve selected six citizen science opportunities to monitor the vitality of water near you.
Find more citizen science projects on SciStarter’s Project Finder.
Cheers!
The SciStarter Team

North Carolina King Tides Project

North Carolina King Tides Project
Grab your camera and document flood events throughout North Carolina. Your photos help communities understand vulnerabilities to coastal flooding and inform community planning.
 
Location: North Carolina, United States

Creek Freaks: Engaging Youth in Water Quality

IWLA
Middle school students around the globe are the experts on their local streams and creeks. Enlist your kids and monitor the quality of creeks and streams near you.
 
Location: Global

CitClops/Eye on Water

CitClops_EyeonWater
Monitoring the quality of the water in our oceans is a big job, especially when that water is constantly moving and changing. Using the Eye on Water app from the CitClops project, you can do your part by collecting information about water color, clarity and fluorescence of oceans around the world.
Location: Global

Cooperative Lakes Monitoring Program

MICorps
  
Looking for a reason to get out on the lake? Michigan’s Cooperative Lakes Monitoring Program is the second oldest lake monitoring program in the country.  Document changes in lake quality and share your observations with scientists.
 
Location: Michigan, US

Volunteer Stream Monitoring Program

MICorps
 
Live in Michigan? Help keep tabs on the streams in your community. MiCorps’ Volunteer Stream Monitoring Program (VSMP) provides technical assistance, training, and grants to volunteer stream monitors to ensure the collection of reliable, high-quality data.
 
Location: Michigan, US

FLOW Program

Amigos de Bolsas
Head to the beach with the Follow and Learn about the Ocean and Wetland (FLOW) program! You’ll learn about coastal ecology, participate in the collection of scientific data and get involved in environmental quality monitoring. You might also get a little sand in your shoes.
Location: Huntington Beach, CA

Discover more summertime citizen science on the SciStarter calendar. Did you know your SciStarter dashboard helps you track your contributions to projects? Complete your profile to access free tools. Want even more citizen science? Check out SciStarter’s Project Finder! With 1100+ citizen science projects spanning every field of research, task and age group, there’s something for everyone!

Mission: Starlight Uses Space to Spark Curiosity in Chemistry

By Kristin Butler September 26th, 2017 at 3:11 pm | Comment

How can you protect an astronaut from getting a sunburn in space?

Kids learn how chemistry can protect astronauts at England’s National Space Center in Leicester. Credit: National Space Centre, Leicester 2

The Royal Society of Chemistry in London has designed a collection of hands-on chemistry experiments that kids can do to explore this question and discover the answer for themselves.

The project is called Mission: Starlight. It is free and includes downloadable videos, worksheets, and instructions on how to teach four different hands-on, hour-long lessons and experiments designed for elementary and secondary school students. Read the rest of this entry »

DeepMoji: Citizen science to create emotional AI algorithms

By Guest September 22nd, 2017 at 1:24 pm | Comment

Detecting emotional concepts, such as sarcasm, within a text is not an easy task for an artificial intelligence (AI) algorithm. For instance, consider the sentence below. Without the emoji present it’s not clear what the author was feeling. The author could have been sad because of a lack of a special someone in his life or he could have been happy due to a great experience with a close friend.

 

The emoji disambiguates the sentence and makes it clear that the author was in a happy loving mood. Our AI algorithm can make use of this way that authors self-annotate the emotional content of text.

The core idea is that if our AI algorithm can understand what emoji was part of the sentence, then it has a good understanding of the emotional content of the text. We can then use this emotional understanding for many other difficult and important tasks such as detecting hate speech and online bullying.

Applications in industry and for researchers

It can also be relevant for many applications in industry. The classic use case is companies wanting to make sense of what their customers are saying about them. But there are many other use cases now that interaction through language is becoming an important part of many technology products. For instance, all chatbot services (Siri, Alexa and many others) might benefit from having a nuanced understanding of emotional content in text.

I personally experienced the limitations of the former methods for analysing emotions in text when I wanted to examine trends in racism on social media with colleagues at MIT. We quickly found that the existing methods could not capture all of the nuances required to properly detect hate speech, as they mostly focused on marking a text as either negative or positive. Moreover, these existing methods had issues with the sarcasm and slang that often occurs on social media.

Our DeepMoji algorithm, however, does not suffer from this shortcoming. For instance, the algorithm can capture slang such as ‘this is the shit’ being a positive statement. Similarly, it can understand the context of each word, thereby learning that despite a sentence containing the generally positive word ‘love’, it may not be positive. We also make an online demo available here, for those interested in testing the capabilities of our algorithm.

How does the AI algorithm learn?

We extracted a dataset of 55B tweets from Twitter, containing short messages about anything and everything. These tweets were then filtered into a dataset of 1.2B English tweets that contain emojis. For each of these tweets, the algorithm is trained to predict the emoji that was part of the original tweet.

Once the algorithm has learned to map text to emojis, we can then transfer this knowledge to a specific task such as racism detection by adding just a little bit of data on that specific task. With this approach, we beat the best existing algorithms across benchmarks for sentiment, emotion, sarcasm and offensive language detection. Our method also works for texts coming from sources that are very different from social media and never contain a single emoji or hashtag. Hopefully, researchers and practitioners in industry can use it for a lot of other interesting purposes as well. That’s why we make our algorithm freely available for anyone to use.

Understanding emoji usage

Another interesting part of the research project is that it can give us insight into how we use emojis. Our AI algorithm learns to group emojis into overall categories associated with, for example, negativity, positivity or love. Similarly, the algorithm learns to differentiate within these categories, mapping sad emojis in one subcategory of negativity, annoyed in another subcategory and angry in a third one as seen below.

 

 

Future of emotion analysis

This research is only a small step towards sophisticated emotion analysis. In large part, our project builds on the way that people express their emotions, but there might sometimes be a discrepancy between what people say and how they feel. We believe that the next big step is to better understand this discrepancy. For that, we’d like your help.

In collaboration with social scientists and psychologists we have setup a small website to gather the needed data, which we will then share with the research community. You can help us improve the potential impact of our emotional AI algorithm and our understanding of emotions in general by simply telling us how you felt when writing your tweets on Twitter. Our project is also a SciStarter Affiliate so your contributions are tracked and credited within your SciStarter dashboard. You can find more citizen science projects to help researchers on SciStarter’s Project Finder.

Explore DeepMoji here.

About the author:

Bjarke Felbo is a graduate student at the Massachusetts Institute of Technology (MIT) working on problems that lie in the intersection of statistics, machine learning and computational social science. He is one of three Marvin Minsky Fellows supported for their promising research within artificial intelligence. Major media outlets such as BBC and Newsweek have covered his work.

Categories: Citizen Science

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Discovering our Common Humanity through Space Archaeology

By Kristin Butler September 21st, 2017 at 1:57 pm | Comment

Like many people, I was first introduced to the world of archaeology by Indiana Jones, that adventuresome character who lit up the big screen rescuing artifacts from villains by the skin of his teeth.

Indy was awesome and will always have a place in my heart. But while he succeeded in making archaeology seem romantic, I never understood why it was important or believed I could join the adventure until I was introduced (via the small screen) to a real life archaeologist named Sarah Parcak.

Parcak is a space archaeologist. She is one of about 200 archaeologists around the world who use satellite imagery to locate evidence of the civilizations that came before us.

Recently she launched Global XPlorera citizen science project that promises to democratize the field of archaeology while dramatically increasing the number of archaeological sites that can be found in less time. Read the rest of this entry »

Introducing SciStarter 2.0; built with you in mind.

By Darlene Cavalier September 19th, 2017 at 10:03 pm | Comment

You spoke, we listened. So come on over and check out the new SciStarter, your source for real science you can do! We feature more than 1600 current opportunities for you (yup, you!) to advance scientific research, locally or globally.

Help scientists and community leaders monitor the quality of water, air and soil near you. Learn how to report levels of light pollution, a serious issue affecting sleeping and nesting habits of wildlife (not to mention it’s the reason you probably can’t see the Milky Way!). Or help Alzheimer’s researchers analyze real brain blood flow movies and simply click an image to record when blood vessels are stalled.

With support from the National Science Foundation and others, and your feedback, we’ve created new features for participants, projects owners, and researchers. We hope you like your dashboard, for example, where you can bookmark, join, or track your contributions to projects and events of interest to you, connect with scientists, find other participants, and so much more.

Will you kindly fill out your profile then complete this survey to let us know what you think about the new features?

Your feedback will help us understand where we need to put our efforts next in order to support your interests and needs in citizen science.

Cheers!

The SciStarter Team

Want more citizen science? Check out SciStarter’s Project Finder! With 1600+ citizen science projects spanning every field of research, task and age group, there’s something for everyone!

Categories: Citizen Science