1,015 research outputs found
Galaxy Zoo: Morphological Classification and Citizen Science
We provide a brief overview of the Galaxy Zoo and Zooniverse projects,
including a short discussion of the history of, and motivation for, these
projects as well as reviewing the science these innovative internet-based
citizen science projects have produced so far. We briefly describe the method
of applying en-masse human pattern recognition capabilities to complex data in
data-intensive research. We also provide a discussion of the lessons learned
from developing and running these community--based projects including thoughts
on future applications of this methodology. This review is intended to give the
reader a quick and simple introduction to the Zooniverse.Comment: 11 pages, 1 figure; to be published in Advances in Machine Learning
and Data Mining for Astronom
Recommended from our members
Trade-offs in motivating volunteer effort: Experimental evidence on voluntary contributions to science.
Digitization has facilitated the proliferation of crowd science by lowering the cost of finding individuals with the willingness to participate in science without pay. However, the factors that influence participation and the outcomes of voluntary participation are unclear. We report two findings from a field experiment on the world's largest crowd science platform that tests how voluntary contributions to science are affected by providing clarifying information on either the desired outcome of a scientific task or the labor requirements for completing the task. First, there is significant heterogeneity in the motivations and ability of contributors to crowd science. Second, both of the information interventions lead to significant decreases in the quantity and increases in the quality of contributions. Combined, our findings are consistent with the information interventions improving match quality between the task and the volunteer. Our findings suggest that science can be democratized by engaging individuals with varying skill levels and motivations with small changes in the information provided to participants
The First Brown Dwarf Discovered by the Backyard Worlds: Planet 9 Citizen Science Project
The Wide-field Infrared Survey Explorer (WISE) is a powerful tool for finding
nearby brown dwarfs and searching for new planets in the outer solar system,
especially with the incorporation of NEOWISE and NEOWISE-Reactivation data. So
far, searches for brown dwarfs in WISE data have yet to take advantage of the
full depth of the WISE images. To efficiently search this unexplored space via
visual inspection, we have launched a new citizen science project, called
"Backyard Worlds: Planet 9," which asks volunteers to examine short animations
composed of difference images constructed from time-resolved WISE coadds. We
report the discovery of the first new substellar object found by this project,
WISEA J110125.95+540052.8, a T5.5 brown dwarf located approximately 34 pc from
the Sun with a total proper motion of 0.7 as yr. WISEA
J110125.95+540052.8 has a WISE magnitude of , this
discovery demonstrates the ability of citizen scientists to identify moving
objects via visual inspection that are 0.9 magnitudes fainter than the
single-exposure sensitivity, a threshold that has limited prior motion-based
brown dwarf searches with WISE.Comment: 9 pages, 4 figures, 1 table. Accepted for publication in the
Astrophysical Journal Letter
Mosquito detection with low-cost smartphones: data acquisition for malaria research
Mosquitoes are a major vector for malaria, causing hundreds of thousands of
deaths in the developing world each year. Not only is the prevention of
mosquito bites of paramount importance to the reduction of malaria transmission
cases, but understanding in more forensic detail the interplay between malaria,
mosquito vectors, vegetation, standing water and human populations is crucial
to the deployment of more effective interventions. Typically the presence and
detection of malaria-vectoring mosquitoes is only quantified by hand-operated
insect traps or signified by the diagnosis of malaria. If we are to gather
timely, large-scale data to improve this situation, we need to automate the
process of mosquito detection and classification as much as possible. In this
paper, we present a candidate mobile sensing system that acts as both a
portable early warning device and an automatic acoustic data acquisition
pipeline to help fuel scientific inquiry and policy. The machine learning
algorithm that powers the mobile system achieves excellent off-line
multi-species detection performance while remaining computationally efficient.
Further, we have conducted preliminary live mosquito detection tests using
low-cost mobile phones and achieved promising results. The deployment of this
system for field usage in Southeast Asia and Africa is planned in the near
future. In order to accelerate processing of field recordings and labelling of
collected data, we employ a citizen science platform in conjunction with
automated methods, the former implemented using the Zooniverse platform,
allowing crowdsourcing on a grand scale.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the
Developing Worl
Recommended from our members
Collaborative yet independent: Information practices in the physical sciences
In many ways, the physical sciences are at the forefront of using digital tools and methods to work with information and data. However, the fields and disciplines that make up the physical sciences are by no means uniform, and physical scientists find, use, and disseminate information in a variety of ways. This report examines information practices in the physical sciences across seven cases, and demonstrates the richly varied ways in which physical scientists work, collaborate, and share information and data.
This report details seven case studies in the physical sciences. For each case, qualitative interviews and focus groups were used to understand the domain. Quantitative data gathered from a survey of participants highlights different information strategies employed across the cases, and identifies important software used for research.
Finally, conclusions from across the cases are drawn, and recommendations are made. This report is the third in a series commissioned by the Research Information Network (RIN), each looking at information practices in a specific domain (life sciences, humanities, and physical sciences). The aim is to understand how researchers within a range of disciplines find and use information, and in particular how that has changed with the introduction of new technologies
Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
(abridged for arXiv) With the first direct detection of gravitational waves,
the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has
initiated a new field of astronomy by providing an alternate means of sensing
the universe. The extreme sensitivity required to make such detections is
achieved through exquisite isolation of all sensitive components of LIGO from
non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to
a variety of instrumental and environmental sources of noise that contaminate
the data. Of particular concern are noise features known as glitches, which are
transient and non-Gaussian in their nature, and occur at a high enough rate so
that accidental coincidence between the two LIGO detectors is non-negligible.
In this paper we describe an innovative project that combines crowdsourcing
with machine learning to aid in the challenging task of categorizing all of the
glitches recorded by the LIGO detectors. Through the Zooniverse platform, we
engage and recruit volunteers from the public to categorize images of glitches
into pre-identified morphological classes and to discover new classes that
appear as the detectors evolve. In addition, machine learning algorithms are
used to categorize images after being trained on human-classified examples of
the morphological classes. Leveraging the strengths of both classification
methods, we create a combined method with the aim of improving the efficiency
and accuracy of each individual classifier. The resulting classification and
characterization should help LIGO scientists to identify causes of glitches and
subsequently eliminate them from the data or the detector entirely, thereby
improving the rate and accuracy of gravitational-wave observations. We
demonstrate these methods using a small subset of data from LIGO's first
observing run.Comment: 27 pages, 8 figures, 1 tabl
A citizen-science approach to muon events in imaging atmospheric Cherenkov telescope data: the Muon Hunter
Event classification is a common task in gamma-ray astrophysics. It can be
treated with rapidly-advancing machine learning algorithms, which have the
potential to outperform traditional analysis methods. However, a major
challenge for machine learning models is extracting reliably labelled training
examples from real data. Citizen science offers a promising approach to tackle
this challenge.
We present "Muon Hunter", a citizen science project hosted on the Zooniverse
platform, where VERITAS data are classified multiple times by individual users
in order to select and parameterize muon events, a product from cosmic ray
induced showers. We use this dataset to train and validate a convolutional
neural-network model to identify muon events for use in monitoring and
calibration. The results of this work and our experience of using the
Zooniverse are presented.Comment: 8 pages, 3 figures, in Proceedings of the 35th International Cosmic
Ray Conference (ICRC 2017), Busan, South Kore
- …