The Future of Citizen Science Is in Apps

Citizen scientists Marine Voskanyan and Jyoti Prajapati say apps, machine learning, and A.I. are the keys to open science communities.

Jyoti Prajapati (center) works on a prototype app to display hotspots of invasive species at the Joint ICTP-IAEA Workshop on Machine Learning in Citizen Science.

Marine Voskanyan and Jyoti Prajapati were among a group of nineteen scientists who participated in the Joint ICTP-IAEA Workshop on Machine Learning in Citizen Science, held in Trieste, Italy, from February 27 to March 4, 2023. Participants learned how to rapidly develop mobile apps to accelerate the process of collecting, storing, and visualizing data for citizen science projects. You can read more about the workshop in this article.

Robert Parks, a curriculum developer, writer, and designer for MIT App Inventor, was one of the instructors for the workshop. Here, Parks talks to Voskanyan and Prajapati about their thoughts on citizen science, app development, and the workshop. 

Bringing People Together to Save Armenian Lakes

Marine Voskanyan is a data analyst, cartographer, and water quality researcher from Armenia. During the citizen science workshop, Voskanyan and her lab partner built a prototype app that sends air pollution readings to the cloud, then updates a heat map of air quality in a region for everyone with the same app. 

Marine Voskanyan (left) and her lab partner hold up the app they developed during the Joint ICTP-IAEA Workshop on Machine Learning.

Robert Parks: What is your experience with citizen science?

Marine Voskanyan: Public participation in data collection is the new normal—and scientists and decision-makers have come to trust citizen science data. Before 2020, I was a volunteer researcher in citizen science projects in Armenia, but they all ended because of COVID-19 and war in the region. Now, I’m starting a citizen science project to monitor surface water quality with my students and the local community.

What was the experience like of creating apps during the workshop?

I had never made an app before, so I was surprised to find that it really wasn’t that difficult. As a data analyst and cartographer, I have often found valuable datasets about water quality that I would not have even imagined existed as a student. I first planned to use GIS tools to distribute and visualize this kind of data on web-based maps. Then I realized—people don’t check websites anymore (habits change, people look for time-saving tools). Thinking about an app-based solution instead got me fully engaged during the workshop.

How could apps help citizen science projects, in general?

App creation is a real boost for citizen science projects. An app can increase the level of organization and transparency in the work of both volunteers and decision-makers. Apps also motivate people by making them feel part of the “big team.”

If some people feel too shy or too busy to participate in a project, an app can function as a way to inform these users about the issue based on the uploaded data of other app users. An app can quickly analyze, categorize, and visualize existing data to show the real state of the environment. Apps can get the various stakeholders in a citizen science project more engaged by making this independent data publicly available. Using apps could contribute to better decision-making for communities.

Apps can also improve traditional education in many countries. Youth are ambitious and get bored very quickly with the old stuff. We should try to find ways to involve them in citizen science projects.

What are the details of the app you’re working on?

I am interested in making an app that monitors surface water quality. Volunteers will measure several water-quality parameters using cost-effective kits: water temp, hardness, acidity (pH), oxygenation conditions (BOD, DO), and nutrients (nitrogen and phosphorus). After measuring the parameters, they’ll upload the data via the app and immediately see a map showing the pollution level of the water. There are five classes of water pollution, indicated by colors on the map. Users can sort by neighborhood, agricultural site, industry, forests, etcetera, and they can try to predict potential sources of pollution.

How did you first get interested in lakes in Armenia?

Since 2018, Lake Sevan in eastern Armenia began “blooming” with algae. No one has been able to determine the source of pollution causing the blooms. The present gap in water quality data is a key challenge for stakeholders to care for the lake. As a water-quality specialist, I could not ignore this problem.

Because of limited human and financial resources, the government alone cannot monitor water quality across the entire 224-kilometer length of Lake Sevan. Volunteers are needed to gather data to understand the reason for blooming and control future blooms in the lake. People living around the lake should be trained to make basic water quality measurements using the kits.

I have been thinking about this in contrast to the many pure water drinking water sources available in the country. Well-managed underground water sources, located on almost every block, come out of the ground untreated like a small fountain. And during scorching summers, we enjoy this cold, tasty, clean water.

Is there anything else you would like to say about the citizen science workshop in Trieste?

Creating the international network is a strong starting point for citizen science project development, and involving app design, A.I., and machine learning is the future of citizen science.

Making Invasive Plant Data Accessible to Everyone

Jyoti Jagdish Prajapati is a Ph.D. student in the Department of Mathematics at the Institute of Chemical Technology in Mumbai, India, and a project fellow at the Indian Statistical Institute in Giridih, India. She is the lead author of the paper “An Occurrence Data Set for Invasive and Naturalized Alien Plants in India” in the journal Ecology. During the citizen science workshop, Prajapati developed a prototype app showing hotspots for invasive plant growth on a map of her community.

Jyoti Prajapati (right) converses with a peer at the Joint ICTP-IAEA Workshop on Machine Learning in Citizen Science.

Robert Parks: Have you made any apps before the workshop?

Jyoti Prajapati: Before the workshop, we had made an app in R. I generally do programming for modeling and calculations. I tried to study JavaScript but couldn't find time for it. After working on App Inventor, I was like, “That's something great where I don't need to study JavaScript from the beginning. I can create an app so easily.” I’m pretty happy with my prototype. Once I start giving my time to it, I will definitely make one for our work.

How do you think apps could help citizen science projects, in general?

We need to gather lots of information—which is hard for one or two people in the project to reach out at a large spatial scale. And today smartphones are not only in the hands of the rich and people living in urban regions—but also in the hands of those in rural regions. So I believe apps will help gather and spread important information to people expeditiously. Apps are a cost-effective way to conduct research on citizen science projects.

Are you interested now in creating an app for your project?

Yes, we’re interested in creating an app for our project. In the first phase, we will use available data and perform a data-cleaning process. In the second phase, using machine learning models on available data, we will display current and future projections in the app for each desired species. Along with the projections, we can show other important information related to the species, such as ways to use the species. In the third phase, we will gather information and simultaneously rerun the model in the background, displaying updated current and future projections in the app.

Is there anything else you would like to say about the citizen science workshop in Trieste?

I am not a hardware person. Working with the microcontrollers was tough for me. But the app connection with sensors and collecting CO2 levels from our surroundings was fantastic. Time was short, so I could not work with the sensors efficiently. I learned a lot and enjoyed the session!