Sense-it: A Smartphone Toolkit for Citizen Inquiry Learning

We describe a toolkit for Android smartphones and tablets that enables a user to access all the sensors available on the device. Data from individual sensors can be viewed as dynamic graphs. Output from one or more sensors can be recorded to a spreadsheet, with the sampling rate set by the learner. As a tool for inquiry learning, the sensors can be linked to ‘missions’ on the nQuire-it website, allowing learners to sample and share data for collaborative crowd-sourced investigations. Four nQuire-it missions have employed the sensor toolkit for investigating environmental noise, sunlight levels, air pressure and rainfall, and the speed of lifts (elevators). These four investigations represent a variety of methods to initiate, orchestrate and conclude inquiry science learning. Two of the missions are in the context of a study to develop a community of inquiry around weather and meteorology. The others are intended to engage members of the public in practical science activities. Analysis of the missions and the associated online discussions reveals that the Sense-it toolkit can be adopted for practical and engaging science investigations, though the issue of calibrating sensors on personal devices needs to be addressed

And the winner is: galaxy mass

The environment is known to affect the formation and evolution of galaxies considerably best visible through the well-known morphology-density relationship. We study the effect of environment on the evolution of early-type galaxies for a sample of 3,360 galaxies morphologically selected by visual inspection from the SDSS in the redshift range 0.05<z<0.06, and analyse luminosity-weighted age, metallicity, and alpha/Fe ratio as function of environment and galaxy mass. We find that on average 10 per cent of early-type galaxies are rejuvenated through minor recent star formation. This fraction increases with both decreasing galaxy mass and decreasing environmental density. However, the bulk of the population obeys a well-defined scaling of age, metallicity, and alpha/Fe ratio with galaxy mass that is independent of environment. Our results contribute to the growing evidence in the recent literature that galaxy mass is the major driver of galaxy formation. Even the morphology-density relationship may actually be mass-driven, as the consequence of an environment dependent characteristic galaxy mass coupled with the fact that late-type galaxy morphologies are more prevalent in low-mass galaxies.Comment: 5 pages, proceedings of JENAM 2010, Symposium 2: "Environment and the formation of galaxies: 30 years later

Detecting wildlife in unmanned aerial systems imagery using convolutional neural networks trained with an automated feedback loop

Using automated processes to detect wildlife in uncontrolled outdoor imagery in the field of wildlife ecology is a challenging task. This is especially true in imagery provided by an Unmanned Aerial System (UAS), where the relative size of wildlife is small and visually similar to its background. This work presents an automated feedback loop which can be used to train convolutional neural networks with extremely unbalanced class sizes, which alleviates some of these challenges. This work utilizes UAS imagery collected by the Wildlife@Home project, which has employed citizen scientists and trained experts to go through collected UAS imagery and classify it. Classified data is used as inputs to convolutional neural networks (CNNs) which seek to automatically mark which areas of the imagery contain wildlife. The output of the CNN is then passed to a blob counter which returns a population estimate for the image. The feedback loop was developed to help train the CNNs to better differentiate between the wildlife and the visually similar background and deal with the disparate amount of wildlife training images versus background training images. Utilizing the feedback loop dramatically reduced population count error rates from previously published work, from +150% to −3.93% on citizen scientist data and +88% to +5.24% on expert data

Unveiling the nature of the "Green Pea" galaxies

We review recent results on the oxygen and nitrogen chemical abundances in extremely compact, low-mass starburst galaxies at redshifts between 0.1-0.3 recently named to as "Green Pea" galaxies. These galaxies are genuine metal-poor galaxies ($\sim$ one fifth solar) with N/O ratios unusually high for galaxies of the same metallicity. In combination with their known general properties, i.e., size, stellar mass and star-formation rate, these findings suggest that these objects could be experiencing a short and extreme phase in their evolution. The possible action of both recent and massive inflow of gas, as well as stellar feedback mechanisms are discussed here as main drivers of the starburst activity and their oxygen and nitrogen abundances.Comment: To appear in JENAM Symposium "Dwarf Galaxies: Keys to Galaxy Formation and Evolution", P. Papaderos, G. Hensler, S. Recchi (eds.). Lisbon, September 2010, Springer Verlag, in pres

Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project

Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp.) breeding colonies across the Antarctic Peninsula, South Shetland Islands and South Georgia (03/2012 to 01/2014). Citizen science provides a means by which large and otherwise intractable photographic data sets can be processed, and here we describe the methodology associated with the Zooniverse project Penguin Watch, and provide validation of the method. We present anonymised volunteer classifications for the 73,802 images, alongside the associated metadata (including date/time and temperature information). In addition to the benefits for ecological monitoring, such as easy detection of animal attendance patterns, this type of annotated time-lapse imagery can be employed as a training tool for machine learning algorithms to automate data extraction, and we encourage the use of this data set for computer vision development

Citizen science for observing and understanding the Earth

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges

The Cognitive Ecology of the Internet

In this chapter, we analyze the relationships between the Internet and its users in terms of situated cognition theory. We first argue that the Internet is a new kind of cognitive ecology, providing almost constant access to a vast amount of digital information that is increasingly more integrated into our cognitive routines. We then briefly introduce situated cognition theory and its species of embedded, embodied, extended, distributed and collective cognition. Having thus set the stage, we begin by taking an embedded cognition view and analyze how the Internet aids certain cognitive tasks. After that, we conceptualize how the Internet enables new kinds of embodied interaction, extends certain aspects of our embodiment, and examine how wearable technologies that monitor physiological, behavioral and contextual states transform the embodied self. On the basis of the degree of cognitive integration between a user and Internet resource, we then look at how and when the Internet extends our cognitive processes. We end this chapter with a discussion of distributed and collective cognition as facilitated by the Internet

Crowdsourced science: sociotechnical epistemology in the e-research paradigm

Recent years have seen a surge in online collaboration between experts and amateurs on scientific research. In this article, we analyse the epistemological implications of these crowdsourced projects, with a focus on Zooniverse, the world’s largest citizen science web portal. We use quantitative methods to evaluate the platform’s success in producing large volumes of observation statements and high impact scientific discoveries relative to more conventional means of data processing. Through empirical evidence, Bayesian reasoning, and conceptual analysis, we show how information and communication technologies enhance the reliability, scalability, and connectivity of crowdsourced e-research, giving online citizen science projects powerful epistemic advantages over more traditional modes of scientific investigation. These results highlight the essential role played by technologically mediated social interaction in contemporary knowledge production. We conclude by calling for an explicitly sociotechnical turn in the philosophy of science that combines insights from statistics and logic to analyse the latest developments in scientific research

Harnessing citizen science through mobile phone technology to screen for immunohistochemical biomarkers in bladder cancer

Background: Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. Methods: We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app ‘Reverse the Odds’. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. Results: Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. Conclusions: Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery