Galaxy Zoo Volunteers Share Pain and Glory of Research

Clery, Daniel

doi:10.1126/science.333.6039.173

Cited by 92 publications

(64 citation statements)

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“…Attempts to build automated classification systems for galaxy morphologies have historically had difficulties in reaching the levels of reliability required for scientific analysis (Clery 2011). The Galaxy Zoo project 2 was conceived to accelerate this task through the method of crowdsourcing.…”

Section: Introductionmentioning

confidence: 99%

Rotation-invariant convolutional neural networks for galaxy morphology prediction

Dieleman¹,

Willett²,

Dambre³

2015

Monthly Notices of the Royal Astronomical Society

730

604

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Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological analysis has traditionally been carried out mostly via visual inspection by trained experts, which is time-consuming and does not scale to large ( 10 4 ) numbers of images.Although attempts have been made to build automated classification systems, these have not been able to achieve the desired level of accuracy. The Galaxy Zoo project successfully applied a crowdsourcing strategy, inviting online users to classify images by answering a series of questions. Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images.We present a deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry. It was developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project.For images with high agreement among the Galaxy Zoo participants, our model is able to reproduce their consensus with near-perfect accuracy (> 99%) for most questions. Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation. This approach greatly reduces the experts' workload without affecting accuracy. The application of these algorithms to larger sets of training data will be critical for analysing results from future surveys such as the LSST.

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Section: Introductionmentioning

confidence: 99%

Rotation-invariant convolutional neural networks for galaxy morphology prediction

Dieleman¹,

Willett²,

Dambre³

2015

Monthly Notices of the Royal Astronomical Society

730

604

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“…33,34 For certain tasks, human creativity and judgment can outperform automated classication and search algorithms. Recent years have seen exciting mergers between interactive molecular simulation and ideas within crowdsourced human-computer interaction.…”

Section: Introductionmentioning

confidence: 99%

A GPU-accelerated immersive audio-visual framework for interaction with molecular dynamics using consumer depth sensors

et al. 2014

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A GPU-accelerated immersive audiovisual framework for interaction with molecular dynamics using consumer depth sensors. Faraday Discussions, 169. pp. 63-87. ISSN 1359-6640 Available from: http://eprints.uwe.ac.uk/23115We recommend you cite the published version. The publisher's URL is: http://dx.doi.org/10.1039/C4FD00008K Refereed: Yes First published online 19 Mar 2014Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited. UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights.UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. PLEASE SCROLL DOWN FOR TEXT.A GPU-accelerated immersive audio-visual framework for interaction with molecular dynamics using consumer depth sensors † With advances in computational power, the rapidly growing role of computational/ simulation methodologies in the physical sciences, and the development of new human-computer interaction technologies, the field of interactive molecular dynamics seems destined to expand. In this paper, we describe and benchmark the software algorithms and hardware setup for carrying out interactive molecular dynamics utilizing an array of consumer depth sensors. The system works by interpreting the human form as an energy landscape, and superimposing this landscape on a molecular dynamics simulation to chaperone the motion of the simulated atoms, affecting both graphics and sonified simulation data. GPU acceleration has been key to achieving our target of 60 frames per second (FPS), giving an extremely fluid interactive experience. GPU acceleration has also allowed us to scale the system for use in immersive 360 spaces with an array of up to ten depth sensors, allowing several users to simultaneously chaperone the dynamics. The flexibility of our platform for carrying out molecular dynamics simulations has been considerably enhanced by wrappers that facilitate fast communication with a portable selection of GPU-accelerated molecular force evaluation routines. In this paper, we describe a 360 atmospheric molecular dynamics simulation we have run in a chemistry/physics education context. We also describe initial tests in which users have been able to chaperone the dynamics of 10-alanine peptide embedded in an explicit water solvent. Using this system, both expert and novice users have been able to accelerate peptide rare event dynamics by 3-4 orders of magnitude.

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“…the involvement of citizens in scientific research (Bonney et al, 2009). Broader than VGI, citizen science is widespread in areas such as biodiversity monitoring (Hyvoenen et al, 2013;Clavero and Revilla, 2014) and astronomy (Clery, 2011). Although citizen science is not specifically geographic in nature, there are lessons valuable to VGI that have been learned from numerous citizen science projects, some of which are presented below.…”

Section: Best Practices In Volunteer Recruitment Motivation and Retementioning

confidence: 99%

Motivating and Sustaining Participation in VGI

Fritz¹,

See²,

Brovelli³

2017

Mapping and the Citizen Sensor

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Volunteers are the key component in the collection of Volunteered Geographic Information (VGI), so what motivates their participation, what strategies work in recruitment and how sustainability of participation can be achieved are key questions that need to be answered to inform VGI system design and implementation. This chapter reviews studies that have examined these questions and presents the main motivational factors that drive volunteer participation, as determined from empirical research. Some best practices from broader citizen science applications are also presented that may have relevance for VGI initiatives. Finally, a set of case studies from our experiences are used to illustrate how volunteers have been motivated to collect VGI through mapping parties, gamification and working with schools.

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Galaxy Zoo Volunteers Share Pain and Glory of Research

Abstract: A project to “crowdsource” galactic classifications has paid off in ways the astronomers who started it never expected.

Cited by 92 publications

References 0 publications

Rotation-invariant convolutional neural networks for galaxy morphology prediction

Rotation-invariant convolutional neural networks for galaxy morphology prediction

A GPU-accelerated immersive audio-visual framework for interaction with molecular dynamics using consumer depth sensors

Motivating and Sustaining Participation in VGI

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