After grapefruit-sized hail hit Missouri, more images may help improve severe storm forecasting. A CoCoRaHS volunteer submitted this photo that displays a hand holding three large and uniquely shaped hailstones from June 14, 2023. Breakthroughs, discoveries, and DIY tips sent six days a week. Tuesday March 10th was a particularly punishing day of bad weather for the residents of Kansas City, Missouri. That evening, hailstones as large as grapefruits bombarded homes, businesses, and vehicles in the area, causing widespread damage to the community.

Science Archaeology Wisconsin'ghost ship' uncovered after 139 years It took citizen scientists only two hours to find the F.J. King's final resting place. Breakthroughs, discoveries, and DIY tips sent every weekday. A group of Wisconsin maritime historians and citizen scientists uncovered a Lake Michigan shipwreck "hidden in plain sight" for nearly 140 years. The team uncovered the waterlogged wreckage of the three-masted wooden schooner in the waters off Bailey's Harbor, Wisconsin. On September 15, 1886, the 144-foot left Escanaba, Michigan, bound for Chicago with 600 tons of iron ore onboard.

Breakthroughs, discoveries, and DIY tips sent every weekday. Two years ago, a team of astronomers requested help from citizen scientists around the world for the Kilonova Seekers Project. Launched in July 2023, the endeavor tasks volunteers with parsing through all-sky survey images captured daily by telescopes on opposite sides of the planet known as the Gravitational-wave Optical Transient Observer (GOTO). Within six months, Kilonova Seekers' over 2,000 volunteers contributed more than 600,000 classifications to researchers, resulting in a total of 20 new discoveries. Now, astronomers have announced the project's first major published find in Astronomy & Astrophysics: a brilliant exploding star observed in near real-time.

We present the progress of work to streamline and simplify the process of exoplanet observation by citizen scientists. International collaborations such as ExoClock and Exoplanet Watch enable citizen scientists to use small telescopes to carry out transit observations. These studies provide essential supports for space missions such as JWST and ARIEL. Contributions include maintenance or recovery of ephemerides, follow up confirmation and transit time variations. Ongoing observation programs benefit from a large pool of observers, with a wide variety of experience levels. Our projects work closely with these communities to streamline their observation pipelines and enable wider participation. Two complementary approaches are taken: Star Guide applies human-centric design and community consultation to identify points of friction within existing systems and provide complementary online tools and resources to reduce barriers to entry to the observing community. Machine Learning is used to accelerate data processing and automate steps which are currently manual, providing a streamlined tool for citizen science and a scalable solution for large-scale archival research.

Citizen Science (CS) leverages the collective efforts of non-specialist/ordinary volunteers in different research tasks, such as collecting, analyzing, and classifying data to solve technical and scientific challenges. CS applications have attracted the attention of academic researchers due to the abundance of data created with high quality at low cost. According to an article in CERN Courier Magazine,3 CS is beneficial for the scientific community, the volunteers involved in the projects, and society as a whole. On the researcher's side, CS helps to achieve scientific data/metadata quickly, obtaining large amounts of valuable information that can contribute to advancing research.3 On the other hand, volunteers become aware of a scientific methodology, are recognized for their contributions, and feel satisfied for being part of a project with scientific and social relevance.2

We propose to design and build an algorithm that will use a Convolutional Neural Network (CNN) and observations from the Unistellar network to reliably detect asteroid occultations. The Unistellar Network, made of more than 10,000 digital telescopes owned by citizen scientists, and is regularly used to record asteroid occultations. In order to process the increasing amount of observational produced by this network, we need a quick and reliable way to analyze occultations. In an effort to solve this problem, we trained a CNN with artificial images of stars with twenty different types of photometric signals. Inputs to the network consists of two stacks of snippet images of stars, one around the star that is supposed to be occulted and a reference star used for comparison. We need the reference star to distinguish between a true occultation and artefacts introduced by poor atmospheric condition. Our Occultation Detection Neural Network (ODNet), can analyze three sequence of stars per second with 91\% of precision and 87\% of recall. The algorithm is sufficiently fast and robust so we can envision incorporating onboard the eVscopes to deliver real-time results. We conclude that citizen science represents an important opportunity for the future studies and discoveries in the occultations, and that application of artificial intelligence will permit us to to take better advantage of the ever-growing quantity of data to categorize asteroids.

When you picture a scientist, do you see a white coat-clad PhD-holder pipetting away at a lab bench? Or maybe a skygazer with a different day job who goes out on clear nights for a good view of the stars? Historically speaking, both of those examples fit the bill. German-British astronomer William Herschel was originally an amateur who observed the night sky using homemade telescopes. He discovered Uranus in 1781, working alongside his sister, Caroline Herschel, who made multiple discoveries herself.

In recent years, the monarch butterfly's iconic migration patterns have come under threat from a number of factors, from climate change to pesticide use. To track trends in their populations, scientists as well as citizen scientists must identify individuals accurately. This is uniquely key for the study of monarch butterflies because there exist other species of butterfly, such as viceroy butterflies, that are "look-alikes" (coined by the Convention on International Trade in Endangered Species of Wild Fauna and Flora), having similar phenotypes. To tackle this problem and to aid in more efficient identification, we present MonarchNet, the first comprehensive dataset consisting of butterfly imagery for monarchs and five look-alike species. We train a baseline deep-learning classification model to serve as a tool for differentiating monarch butterflies and its various look-alikes. We seek to contribute to the study of biodiversity and butterfly ecology by providing a novel method for computational classification of these particular butterfly species. The ultimate aim is to help scientists track monarch butterfly population and migration trends in the most precise and efficient manner possible.

Citizen scientists have helped researchers discover new types of galaxies, design drugs to fight COVID-19, and map the bird world. The term describes a range of ways that the public can meaningfully contribute to scientific and engineering research, as well as environmental monitoring. As members of the Computing Community Consortium (CCC) recently argued in a Quadrennial Paper, "Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research," non-scientists can help advance science by "providing or analyzing data at spatial and temporal resolutions or scales and speeds that otherwise would be impossible given limited staff and resources." Recently, citizen scientists' efforts have found a new purpose: helping researchers develop machine learning models, using labeled data and algorithms, to train a computer to solve a specific task. This approach was pioneered by the crowdsourced astronomy project Galaxy Zoo, which started leveraging citizen scientists in 2007.

Citizen scientists have helped researchers discover new types of galaxies, design drugs to fight COVID-19, and map the bird world. The term describes a range of ways that the public can meaningfully contribute to scientific and engineering research, as well as environmental monitoring. As members of the Computing Community Consortium (CCC) recently argued in a Quadrennial Paper, "Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research," non-scientists can help advance science by "providing or analyzing data at spatial and temporal resolutions or scales and speeds that otherwise would be impossible given limited staff and resources." Recently, citizen scientists' efforts have found a new purpose: helping researchers develop machine learning models, using labeled data and algorithms, to train a computer to solve a specific task. This approach was pioneered by the crowdsourced astronomy project Galaxy Zoo, which started leveraging citizen scientists in 2007.