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The Revolution in Astronomy Education: Data Science for the Masses

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Abstract

As our capacity to study ever-expanding domains of our science has increased (including the time domain, non-electromagnetic phenomena, magnetized plasmas, and numerous sky surveys in multiple wavebands with broad spatial coverage and unprecedented depths), so have the horizons of our understanding of the Universe been similarly expanding. This expansion is coupled to the exponential data deluge from multiple sky surveys, which have grown from gigabytes into terabytes during the past decade, and will grow from terabytes into Petabytes (even hundreds of Petabytes) in the next decade. With this increased vastness of information, there is a growing gap between our awareness of that information and our understanding of it. Training the next generation in the fine art of deriving intelligent understanding from data is needed for the success of sciences, communities, projects, agencies, businesses, and economies. This is true for both specialists (scientists) and non-specialists (everyone else: the public, educators and students, workforce). Specialists must learn and apply new data science research techniques in order to advance our understanding of the Universe. Non-specialists require information literacy skills as productive members of the 21st century workforce, integrating foundational skills for lifelong learning in a world increasingly dominated by data. We address the impact of the emerging discipline of data science on astronomy education within two contexts: formal education and lifelong learners. Comment: 12 pages total: 1 cover page, 1 page of co-signers, plus 10 pages, State of the Profession Position Paper submitted to the Astro2010 Decadal Survey (March 2009)
State of the Profession Position Paper submitted to the Astro2010 Decadal Survey (March 2009)
The Revolution in Astronomy Education: Data Science for the Masses
Kirk D. Borne (George Mason University)
Suzanne Jacoby (LSST Corporation)
Karen Carney (Adler Planetarium)
Andy Connolly (University of Washington)
Timothy Eastman (Wyle Information Systems)
M. Jordan Raddick (JHU/SDSS)
J. A. Tyson (UC Davis)
John Wallin (GMU)
Abstract:
As our capacity to study ever-expanding domains of our science has increased (including the
time domain, non-electromagnetic phenomena, magnetized plasmas, and numerous sky surveys
in multiple wavebands with broad spatial coverage and unprecedented depths), so have the
horizons of our understanding of the Universe been similarly expanding. This expansion is
coupled to the exponential data deluge from multiple sky surveys, which have grown from
gigabytes into terabytes during the past decade, and will grow from terabytes into Petabytes
(even hundreds of Petabytes) in the next decade. With this increased vastness of information,
there is a growing gap between our awareness of that information and our understanding of it.
Training the next generation in the fine art of deriving intelligent understanding from data is
needed for the success of sciences, communities, projects, agencies, businesses, and economies.
This is true for both specialists (scientists) and non-specialists (everyone else: the public,
educators and students, workforce). Specialists must learn and apply new data science research
techniques in order to advance our understanding of the Universe. Non-specialists require
information literacy skills as productive members of the 21st century workforce, integrating
foundational skills for lifelong learning in a world increasingly dominated by data. We address
the impact of the emerging discipline of data science on astronomy education within two
contexts: formal education and lifelong learners.
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Preamble
Got data? Of course you do, and there is much more on the way! The exponential growth of
data volumes in astronomy is offering new opportunities for actively involving large numbers of
people in the excitement of discovery as well as posing challenges to our profession to
effectively bridge the gap from data to knowledge. Since earliest human history, astronomers
have been engaged in the study of dramatic and dynamic phenomena, including supernovae,
eclipses, planetary motions, and more. As our capacity to study ever-expanding domains of our
science has increased (including the time domain, non-electromagnetic phenomena, magnetized
plasmas, and numerous sky surveys in multiple wavebands with broad spatial coverage and
unprecedented depths), so have the horizons of our understanding of the Universe been similarly
expanding. This expansion is coupled to the exponential data deluge from multiple sky surveys,
which have grown from gigabytes into terabytes during the past decade, and will grow from
terabytes into Petabytes (even hundreds of Petabytes) in the next decade. With this increased
vastness of information, there is a growing gap between our awareness of that information and
our understanding of it. Training the next generation in the fine art of deriving intelligent
understanding from data is needed for the success of sciences, communities, projects, agencies,
businesses, and economies. This is true for both specialists (scientists) and non-specialists
(everyone else: the public, educators and students, workforce). Specialists must learn and apply
new data science research techniques in order to advance further our understanding of the
Universe. Non-specialists require information literacy skills as productive members of the 21st
century workforce, integrating foundational skills for lifelong learning in a world increasingly
dominated by data. We address the impact of the emerging discipline of data science on
astronomy education within two contexts: formal education and lifelong learners.
Recommendations are presented for:
Training the next generation of specialists to realize the full potential of cyber-enabled science;
Engaging students in authentic learning experiences through the use of astronomical data in
secondary and undergraduate classrooms; and
Actively involving the public in the exploration and discovery of our dynamic Universe
through Citizen Science research opportunities.
As we move toward the national goal of scientific literacy for all, computational literacy must be
considered a core component of that goal, with data science as an essential competency.
The Revolution in Astronomy and Other Sciences
The development of models to describe and understand scientific phenomena has historically
proceeded at a pace driven by new data. The more we know, the more we are driven to enhance
or to change our models, thereby advancing scientific
understanding. This data-driven modeling and discovery linkage
has entered a new paradigm [1], as illustrated in the
accompanying graphic [2]. The emerging confluence of new
technologies and approaches to science has produced a new
Data-Sensor-Computing-Model synergism. This has been driven
by numerous developments, including the information
explosion, the development of dynamic intelligent sensor
networks [http://www.thinkingtelescopes.lanl.gov/], the
acceleration in high performance computing (HPC) power, and
advances in algorithms, models, and theories. Among these, the
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most extreme is the growth in new data. The acquisition of data in all scientific disciplines is
rapidly accelerating and causing a nearly insurmountable data avalanche [3]. Computing power
doubles every 18 months (Moore‘s Law), corresponding to a factor of 100 in ten years. The I/O
bandwidth (into and out of our systems, including data systems) increases by 10% each year a
factor 3 in ten years. By comparison, data volumes appear to double every year (a factor of 1,000
in ten years). Consequently, as growth in data volume accelerates, especially in the natural
sciences (where funding certainly does not grow commensurate with data volumes), we will fall
further and further behind in our ability to access, analyze, assimilate, and assemble knowledge
from our data collections unless we develop and apply increasingly more powerful algorithms,
methodologies, and approaches. This requires a new generation of scientists and technologists
trained in the discipline of data science [4].
In astronomy in particular, rapid advances in three technology areas (telescopes, detectors, and
computation) have continued unabated [5], all leading to more data [6]. With this accelerating
advance in data generation capabilities over the coming years, we will require an increasingly
skilled workforce in the areas of computational and data sciences in order to confront these
challenges. Such skills are more critical than ever since modern science, which has always been
data-driven, will become even more data-intensive in the coming decade [6, 7]. Increasingly
sophisticated computational and data science approaches will be required to discover the wealth
of new scientific knowledge hidden within these new massive scientific data collections [8, 9].
The growth of data volumes in nearly all scientific disciplines, business sectors, and federal
agencies is reaching historic proportions. It has been said that “while data doubles every year,
useful information seems to be decreasing” [10], and “there is a growing gap between the
generation of data and our understanding of it” [11]. In an information society with an
increasingly knowledge-based economy, it is imperative that the workforce of today and
especially tomorrow be equipped to understand data and to apply methods for effective data
usage. Required understandings include knowing how to access, retrieve, interpret, analyze,
mine, and integrate data from disparate sources. In the sciences, the scale of data-capturing
capabilities grows at least as fast as the underlying microprocessor-based measurement system
[12]. For example, in astronomy, the fast growth in CCD detector size and sensitivity has seen
the average dataset size of a typical large astronomy sky survey project grow from hundreds of
gigabytes 10 years ago (e.g., the MACHO survey), to tens of terabytes today (e.g., 2MASS and
Sloan Digital Sky Survey [5]), up to a projected size of tens of petabytes 10 years from now
(e.g., LSST, the Large Synoptic Survey Telescope [6]). In survey astronomy, LSST will produce
one 56Kx56K (3-Gigapixel) image of the sky every 20 seconds, generating nearly 30 TB of data
daily for 10 years. In solar physics, NASA announced in 2008 a science data center specifically
for the Solar Dynamics Observatory, which will obtain one 4Kx4K image every 10 seconds,
generating one TB of data per day. NASA recognizes that previous approaches to scientific data
management and analysis will simply not work. We see the data flood in all sciences (e.g.,
numerical simulations, high-energy physics, bioinformatics, geosciences, climate monitoring and
modeling) and outside of the sciences (e.g., banking, healthcare, homeland security, drug
discovery, medical research, retail marketing, e-mail). The application of data mining,
knowledge discovery, and e-discovery tools to these growing data repositories is essential to the
success of our social, financial, medical, government, and scientific enterprises. An informatics
approach is required. What is informatics? Informatics has recently been defined as “the use of
digital data, information, and related services for research and knowledge generation” [13],
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which complements the usual definition: informatics is the discipline of organizing, accessing,
integrating, and mining data from multiple sources for discovery and decision support [14].
A National Imperative
Our science education programs have always included the principles of evidence-based
reasoning, fact-based induction, and data-oriented science [15]. In this age of the data flood,
greater emphasis on and enhancement of such data science competencies is now imperative. In
particular, we must muster educational resources to train a skilled data-savvy workforce: one that
knows how to find facts (i.e., data, or evidence), access them, assess them, organize them,
synthesize them, look at them critically, mine them, and analyze them.
The Nature article “Agencies Join Forces to Share Data” calls for more training in data skills
[16]. This article describes a new Interagency Working Group on Digital Data representing 22
federal agencies in the U.S., including the NSF, NASA, DOE, and more. The group plans to set
up a robust public infrastructure so that all researchers have a permanent home for their data.
One option is to create a national network of online data repositories funded by the government
and staffed by dedicated computing and data science professionals with science discipline
expertise. Who will these computing and archiving professionals be? They will be a professional
workforce trained in the disciplines of computational and data sciences and who collaborate with
computer science and statistics professionals in these areas, including machine learning,
visualization, statistics, algorithm design, efficient data structures, scalable architectures,
effective programming techniques, information retrieval methods, and data query languages.
Within the scientific domain, data science is becoming a recognized academic discipline. F. J.
Smith argues that now is the time for data science curricula in undergraduate education [17].
Others promote data science as a rigorous academic discipline [18]. Another states that without
the productivity of new disciplines based on data, we cannot solve important problems of the
world” [19]. The 2007 NSF workshop on data repositories included a track on data-centric
scholarship the workshop report explicitly states our key message: “Data-driven science is
becoming a new scientific paradigm ranking with theory, experimentation, and computational
science” [20]. Consequently, astronomy and other scientific disciplines are developing sub-
disciplines that are information-rich and data-intensive to such an extent that these are now
becoming (or have already become) recognized stand-alone research disciplines and full-fledged
academic programs on their own merits. The latter include bioinformatics and geoinformatics,
but will soon include astroinformatics, health informatics, and data science.
National Study Groups Face the Data Flood
Several national study groups have issued reports on the urgency of establishing scientific and
educational programs to face the data flood challenges:
1. NAS report: “Bits of Power: Issues in Global Access to Scientific Data” (1997) [21];
2. NSF report: “Knowledge Lost in Information: Report of the NSF Workshop on Research
Directions for Digital Libraries” (2003) [22];
3. NSB (National Science Board) report:“Long-lived Digital Data Collections: Enabling
Research and Education in the 21st Century” (2005);
4. NSF report with the Computing Research Association:“Cyberinfrastructure for
Education and Learning for the Future: A Vision and Research Agenda” (2005);
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5. NSF ―Atkins Report‖ : “Revolutionizing Science and Engineering Through
Cyberinfrastructure: Report of the National Science Foundation Blue-Ribbon Advisory
Panel on Cyberinfrastructure” (2005) [23];
6. NSF report: The Role of Academic Libraries in the Digital Data Universe (2006) [24];
7. NSF report: “Cyberinfrastructure Vision for 21st Century Discovery” (2007) [25];
8. JISC/NSF Workshop on Data-Driven Science & Repositories (2007) [20].
Each of these reports has issued a call to action in response to the data avalanche in science,
engineering, and the global scholarly environment. For example, the NAS “Bits of Power”
report lists five major recommendations, one of which includes: “Improve science education in
the area of scientific data management” [21]. The Atkins NSF Report stated that skills in digital
libraries, metadata standards, digital classification, and data mining are critical [23]. In
particular, that report states: “The importance of data in science and engineering continues on a
path of exponential growth; some even assert that the leading science driver of high-end
computing will soon be data rather than processing cycles. Thus it is crucial to provide major
new resources for handling and understanding data.” [23] The core and most basic resource is
the human expert, trained in key data science skills. As stated in the 2003 NSF ―Knowledge
Lost in Information‖ report, human cognition and human capabilities are fundamental to
successful leveraging of cyberinfrastructure, digital libraries, and national data resources [22].
Cyberinfrastructure, Human Computation, and Computational Thinking
New modes of discovery have been enabled by the growth of computational infrastructure in the
sciences. This cyberinfrastructure includes massive databases, virtual observatories (distributed
data), high-performance computing (clusters and petascale machines), distributed computing (the
Grid, the cloud, and peer-to-peer networks), intelligent search and discovery algorithms,
innovative visualization methods, collaborative research environments, Web 2.0 tools, and more.
To cope with the data flood, a paradigm shift is required we must go beyond cyber-enabled
discovery to human-powered discovery. By that we mean ―human computation‖ [26] and
―computational thinking‖ [27]. Human computation refers to Tasks like image recognition that
are trivial for humans, but which continue to challenge even the most sophisticated computer
programs” [26]. Computational thinking refers to a new kind of literacy, akin to math or
cultural literacy. It is an epistemological orientation (a way of thinking and knowing) that is
consistent with computational methods of organizing data and processes. Computational thinking
addresses the paradox of machine intelligence: knowing which tasks are best assigned to
computers, and which are best assigned to humans. A writer addressed the application of these
concepts to the astronomical data flood by stating the absolute necessity of public involvement
with astronomical data archives: Though these "virtual observatories" are used primarily by
professionals, they can also be welcoming to educators, students and amateur astronomers, who
get instant access to the best telescopes in the world. And why not open the doors wide? It's hard
to imagine that this data will ever get "used up" that all the good discoveries will one day be
wrung out of it so the more minds working away at it, the better.’ [28] Of course, human
factors research related to human-computer interaction is absolutely essential for this to succeed.
Target Audiences
We address the impact of these data science issues on astronomy education within two contexts:
Formal Education, including K-12 and undergraduate STEM (Science, Technology, Engineering,
and Mathematics) courses, and life-long learners. The latter include citizen scientists, who are
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trained volunteers who work on authentic science projects with scientific researchers to answer
real-world questions. It is well known that among the sciences, two topics seem to grab the most
public devotion and attention: dinosaurs and Space! For example, the release of Google Sky
generated millions of users within the first few days, and the announcement of Galaxy Zoo
(described later) attracted 80,000 volunteers within a few weeks. Much of the reason for this is
intangible: emotion and affective motivations [29].
Modes of Interaction
We consider large sky surveys, with their open data policies (―data for everyone‖) and their
uniformly calibrated massive databases, as key cyberinfrastructure and the major content
providers in the education of astronomical data scientists and in astronomy-based data science
curricula. For different audiences and in different learning settings, we envision three broad
modes of interaction with sky survey data (i.e., database catalogs and image archives), which
represent a progression from information-gathering to active engagement to discovery:
a) Data Discovery What was observed, when, and by whom? Retrieve observation parameters
from an astronomical sky survey catalog database. Retrieve parameters for interesting objects.
b) Data Browse Retrieve images from a sky survey image archive. View thumbnails. Select
data format (JPEG, Google Sky KML, FITS). Pan the sky and examine catalog-provided tags
(Google Sky, World Wide Telescope).
c) Data Immersion Perform data analysis, mining, and visualization (via unified software tools
for astronomy education). Report discoveries. Comment on observations. Contribute followup
observations. Engage in social networking, annotation, and tagging. Provide classifications
of complex images, data correlations, data clusters, or novel (outlying) detections.
Formal Education: K-12 and Undergraduate
Astronomy provides an innately engaging scientific context within which teachers can engage
students in research investigations that make use of publicly accessible databases. These
engaging experiences support both the learning of science and the development of 21st century
workforce skills. Astronomical sky survey data can become a key part of projects emphasizing
student-centered research in middle school, high school, and undergraduate settings.
Professional development, including the preparation and retention of highly qualified teachers,
plays a critical role. The importance of teachers cannot be underestimated. The most direct route
to improving mathematics and science achievement for all students is better mathematics and
science teaching [30]. In fact, teacher effectiveness is the single biggest factor influencing
gains in achievement, an influence bigger than race, poverty, parent’s education, or any of the
other factors that are often thought to doom children to failure.[31] The next-generation
astronomy-based STEM education program will involve student research projects in conjunction
with teacher professional development programs. Taught in an exemplary fashion, using
technology to its best advantage, students can participate in cutting-edge discovery with
authentic classroom research opportunities developed through such efforts.
As described in ―How People Learn‖ [32], the goal of education is to help students develop
needed intellectual tools and learning strategies, including how to frame and ask meaningful
questions about various subject areas. This ability contributes to individuals becoming self-
sustaining, lifelong learners. Engaging students by using real data to address scientific questions
in formal education settings is known to be an effective instructional approach toward this goal.
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Specifically, the National Science Education Standards [33] emphasize that students should
learn science through inquiry (Science Content Standard A: Science as Inquiry) and should
understand the concepts and processes that shape our natural world (Science Content Standard
D: Earth and Space Science). Students learn best if they are not passive recipients of factual
information but rather are engaged in the learning process. The Socratic method tells us that
teaching is not telling; students must be involved in more than listening to learn.”
One goal of having teachers and their students engage in data analysis and data mining is to help
them develop a sense of the methods scientists employ, as well as a familiarity with the tools
they use to ―do science.‖ This is a critical component in understanding the process of science,
including procedures for data collection, analysis of bias, and scientific interpretation. The
common lecture-textbook-recitation method of teaching, still prevalent in today‘s high schools,
tends to discourage students from applying important scientific, mathematical, and technological
skills in a meaningful context. Robert Yager states that this model of teaching science is akin to
teaching all the rules of a sport, like softball, to a childhow to bat, catch, throw, slide, and wear
the uniformbut never letting the child actually play in a game! [34] Recent research has in fact
shown that students benefit more from depth rather than breadth in their high school science
courses [35]. One study examined over 8000 college students in biology, chemistry, and physics.
We believe that the teaching of data science methodologies as a means of increasing the depth of
study in core science courses will have lasting benefit for a lifetime, as well as immediate
tangible benefit in a student‘s college education. Additional research has demonstrated the
efficacy of using data in the classroom for improving student learning in science the report
―Using Data in Undergraduate Classrooms‖ is especially relevant, with valuable suggestions,
education research results, pedagogical insights, teaching scenarios, and resource listings [36].
A particularly acute problem is in undergraduate Physics programs (presumably still the source
of most astronomy Ph.D. students). These programs rarely require much in the way of
computational or statistical (data) sciences training, though they may require a computer
language programming course. Courses in computational science, scientific computing,
numerical modeling and simulation, scientific data and databases, and scientific data mining
should be developed and offered as electives, if not requirements. The new CDS (Computational
and Data Sciences) B.S. degree program at GMU is one of the first to require such courses [37].
Citizen Science: Life-Long Learners
The formal education system does not exist in a vacuum; students, teachers, and families are part
of a broader context for learning. Rich opportunities for learning outside the classroom include
Informal Science Education, Out of School Time (OST), and the world of Citizen Science, where
non-specialist volunteers assist scientists‘ research efforts by collecting, organizing, or analyzing
data. More than a decade of research shows that sustained participation in well-executed OST
experiences can lead to increases in academic achievement and positive impact on a range of
social and developmental outcomes [38]. “Experiences in informal settings can significantly
improve science learning outcomes for individuals from groups which are historically
underrepresented in science, such as women and minorities. Evaluations of museum-based and
after-school programs suggest that these programs may also support academic gains for
children and youth in these groups." [39] Engagement in informal education, visits to museums
and planetaria, and now Citizen Science can help to create an environment that encourages
young people to pursue challenging courses (and maybe careers) in STEM disciplines.
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Citizen Science is one approach to engaging the public in authentic scientific research. Citizen
science is a term used for projects or ongoing programs of scientific work in which individual
volunteers or networks of volunteers, many of whom may have no specific scientific training,
perform or manage research-related tasks such as observation, measurement, or computation. A
recent and highly successful astronomy Citizen Science project, Galaxy Zoo, has involved more
than 185,000 armchair astronomers from all over the world in classifying the morphology of
galaxies from SDSS, resulting in four papers published in peer-reviewed journals (e.g., [40, 41]).
In the 18 months prior to February 2009, 80 million classifications of galaxies were submitted on
one million objects at galaxyzoo.org. The Stardust@home project [42], where volunteers must
pass a test to qualify to participate in the search for grains of
dust in aerosol gels from the NASA Stardust Mission, has
attracted a smaller number of citizen scientists (24,000),
perhaps because of the more sophisticated training and
analysis required by participants, or perhaps because images
of galaxies are inherently more interesting to the larger
public than cracks in an aerosol gel. The accompanying
figure illustrates Design Considerations for Citizen Science
Projects showing three overlapping circles: Research the
Public Can Do, Research that is Interesting to the Public,
and Research Scientists Care About. Finding out exactly
why a particular project occupies one portion of the chart
over another is a key part of the research agenda for Citizen
Science. In all cases, citizen scientists work with real data
and perform duties of value to the advancement of science.
The human is still better at pattern recognition (Galaxy Zoo)
and novelty (outlier) detection (Stardust@home) tasks than a computer, making the galaxy
classification activity and others like it good candidates for successful Citizen Science projects.
Several popular pathfinders exist for Citizen Science projects, which include: (a) passive
engagement projects (e.g., SETI@home [43]) in which the participant sets up a screensaver on
their computer and the rest is done automatically, with very little human contribution or
expertise; and (b) active involvement activities (e.g., the AAVSO [44], and the Audubon Society
Christmas Bird Count [45]) in which the participant becomes an active contributing scientist,
lending their human cognitive abilities to the acquisition of knowledge from large complex
natural systems. We strongly support the deployment of more of the latter experiences, which
contribute significantly to lifelong learning and science education among a broader audience.
An exciting prospect for Citizen Science research projects enters with the advent of time domain
astronomy. For example, starting near the middle of the next decade, the LSST will obtain one
10-square degree image of the sky every 20 seconds, will march along systematically to image
the entire visible sky within a 3-night period, and will repeat this for 10 years. Within each
image, LSST will identify every object that has moved, changed brightness, or (dis)appeared.
LSST‘s key science theme is Mapping the Dynamic Universe. Within this context, predicted
counts of known astronomical transients, solar system objects, supernovae, other dynamic
phenomena, and extrapolations to all types of ―unknown unknowns‖ lead to a combined estimate
of 100,000 events to be detected each night by LSST. The LSST Science Collaboration teams
8
have generated ideas for Citizen Science research projects that engage the public in monitoring,
classifying, and annotating these events for the advancement of astronomical research [46].
Recommendations
We promote the teaching of astronomy (at any grade level and in all settings) as:
a) a forensic science evidence-based inquiry from data collections, learning from data;
b) a dynamic science the changing universe, time domain astronomy;
c) a platform for improving STEM education using the appealing forensic nature of
astronomy as a hook (a detective story to be solved) to draw students into learning,
developing critical thinking skills, using fun real-life data experiences to learn key data
science skills (visualization, analysis, synthesis, deduction, inference, interpretation; and
d) a rich source of life-long learning in which Citizen Science and science centers draw the
interested masses into the joy, excitement, and wonder of learning about our Universe.
We specifically make the following recommendations to the State of the Profession Group on
Astronomy Education:
1. To high schools, colleges, and universities: Provide data-centric research experiences in
STEM courses. Promote the use of real scientific data in the classroom through mini-grants,
curriculum mandates, and/or a faculty reward system. Integrate education and research in
science. Teach critical data science skills to all students as part of their science graduation
requirements. Transform education using databases and information resources in all
disciplines. Establish mentorship programs for minority and underrepresented groups to
seed, support, and sustain their participation in science for a lifetime. Grow curricula
(programs and courses) in data science (in general) and in astronomical data science (i.e.,
astroinformatics, specifically). Promote computational literacy across the curriculum. Hire
faculty with data science training and research experience.
2. To national labs, research centers, and science data archive facilities: Permit science staff to
spend a few percent of their time in Citizen Science outreach projects. Provide professional
development opportunities for educators and their students to work in your research groups
using real scientific data. Develop resource centers for publicly accessible and usable
science data products and services. Create digital libraries via educator forums that archive
specifically data science curricula materials for different core science courses (including
physics and astronomy) as a mechanism for easy transfer of knowledge, lesson plans, and
projects between informal and formal education venues. Promote diversity in the workplace
through stimulus and reward programs in the computational and data sciences.
3. To researchers and project teams: Develop citizen science projects to engage the public and
to take advantage of their cognitive capabilities (human computation and computational
thinking). Reach out to minority and underrepresented communities to broaden participation
in science and discovery. Work with informal environments which already have knowledge
about motivation and engagement in free-choice learning environments (e.g., [47]).
4. To funding agencies: Mandate open data policies for large projects; encourage this for all
projects. Mandate an outreach component in all major projects and missions reward
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innovative public uses of mission/project data. Through the NSF MREFC funding pipeline,
support construction of infrastructure that facilitates sharing of data products with a broad
public audience, not just professional researchers. Support the development and integration
of emerging transformative computational technologies into the science funding pipelines.
Develop programs that fund the development of curricula and educational programs at the
intersection of astronomical and data sciences. Provide viable and sustained funding for: (a)
cross-disciplinary programs that promote cyber-enabled discovery in the sciences; (b) data
science research; (c) early career grants aimed at data-intensive science research; (d)
informal education and human computation initiatives that extend the discovery potential of
large science data sets (Citizen Science); (e) education research in the science of learning
from large data sets; (f) outreach involving large sky surveys and large astronomy databases
and archives; (g) development of unified data tools for education (including data analysis,
mining, visualization, and manipulation); (h) teacher training workshops focused on ―using
data in the classroom‖; and (i) development of astroinformatics as a stand-alone research
and education sub-discipline of astronomy.
5. To professional societies: Motivate and promote the connection between data-intensive
science and classroom learning. Identify the connections between astronomical science and
national education standards in all disciplines (not just math and science). Support
partnerships with major online providers of information (e.g., Google, Microsoft, Yahoo!) to
get the data out to a wider audience. Endorse and promote career paths for professionals
with data science skills, in the same manner in which instrument-builders are afforded
tenure rank and professional standing in academic departments.
Our position is that public support, through Citizen Science and human computing, is essential to
the continued health of the astronomy profession. We advocate increased support of the public
and educational aspects of astronomy outreach, which are easily facilitated through large sky
survey projects with open data policies. We promote the use of astronomical databases both as an
effective mechanism in formal STEM education (through data-centric research experiences in
the classroom) and as a way to teach data science skills relevant to all disciplines and citizens.
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11
Position Paper Contributors and Co-Signers:
Primary Contributors
Kirk D. Borne (George Mason University)
Suzanne Jacoby (LSST Corporation)
Karen Carney (Adler Planetarium)
Andy Connolly (University of Washington)
Timothy Eastman (Wyle Information Systems)
M. Jordan Raddick (JHU/SDSS)
J. A. Tyson (UC Davis)
John Wallin (GMU)
Co-Signers:
Jacek Becla (SLAC)
Michael Castelaz (PARI)
Alanna Connors (Eureka Scientific)
Tim Hamilton (Shawnee State U.)
Chris Lintott (Oxford)
Bruce McCollum (Caltech)
Peter Fox (RPI)
Ashish Mahabal (Caltech)
Julia Olsen (U. Arizona)
Misha Pesenson (Caltech/IPAC)
Andrew Ptak (JHU)
Nic Ross (Penn State U.)
Andrea Schweitzer (Little Thompson Observatory)
Terry Teays (JHU/MD Space Grant Consortium)
Michael Way (NASA/Goddard Institute for Space Studies)
Michael Wood-Vasey (University of Pittsburgh)
... In Borne et al. [Borne et al. 2010], the authors highlight the need to train the next generation of specialists and non-specialists to derive intelligent understanding from the increased vastness of data from the Universe, "with data science as an essential competency" in astronomy education "within two contexts: formal education and lifelong learners". The aim is to manage "a growing gap between our awareness of that information and our understanding of it." ...
... Data science is warmly embraced by more and more disciplines and domains in which it was traditionally irrelevant, such as law, history and even nursing [Clancy et al. 2014]. Its core driving forces come from data-intensive and data-rich areas such as astronomy [Borne et al. 2010 [Siart et al. 2015], media and entertainment [Gold et al. 2013], supply chain management (SCM) [Hazena et al. 2014] and SCM predictive analytics [Schoenherr and Speier-Pero 2015], advanced hierarchical/multiscale materials [Kalidindi 2015; Gupta et al. 2015], and cyberinfrastructure [NSF 2007]. The era of data science presents significant interdisciplinary opportunities [Rudin 2014], as evidenced by the transformation from traditional statistics and computing-independent research to cross-disciplinary data-driven discovery combining statistics, mathematics, computing, informatics, sociology and management. ...
Preprint
The twenty-first century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This paper provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons and thinking about data science and analytics.
... Collaboration on such a large scale requires cooperation and respect amongst scientists from a diverse group of ages, genders, and cultures. In addition to the potential for groundbreaking science, this next generation of astronomy collaborations also comes with a wealth of innovative material and experience that can be used to capture the public's interest in science (Borne et al., 2009). ...
... In addition to more conventional avenues of astronomy EPO such as public lectures, science festivals, and planetarium shows, several innovative avenues for connecting science, and scientists, with the public have emerged. For example, citizen science, whereby expert scientists collaborate with members of the public to complete a science project, is growing in popularity year-after-year (Borne et al., 2009;Haywood & Besley, 2014). ...
Article
We present a case study of the online education and public outreach (EPO) program of The Dark Energy Survey (DES). We believe DES EPO is unique at this scale in astronomy, as it evolved organically from scientists' volunteerism. We find that DES EPO online products reach 2,500 social media users on average per post; 94% of these users are predisposed to science-related topics. We find projects which require scientist participation and collaboration support are most successful when they capitalize on participating scientists' hobbies. Throughout the article, we present recommendations for others interested in facilitating EPO for large science collaborations.
... In Borne et al. [2010], the authors highlight the need to train the next generation of specialists and nonspecialists to derive intelligent understanding from the increased vastness of data from the Universe, "with data science as an essential competency" in astronomy education "within two contexts: formal education and lifelong learners." The aim is to manage "a growing gap between our awareness of that information and our understanding of it." ...
... Data science is warmly embraced by more and more disciplines and domains in which it was traditionally irrelevant, such as law, history, and even nursing [Clancy et al. 2014]. Its core driving forces come from data-intensive and data-rich areas such as astronomy [Borne et al. 2010 [Siart et al. 2015], media and entertainment [Gold et al. 2013], Supply Chain Management (SCM) [Hazena et al. 2014] and SCM predictive analytics [Schoenherr and Speier-Pero 2015], advanced hierarchical/multiscale materials Gupta et al. 2015], and cyberinfrastructure [NSF 2007]. The era of data science presents significant interdisciplinary opportunities [Rudin et al. 2014], as evidenced by the transformation from traditional statistics and computing-independent research to cross-disciplinary data-driven discovery combining statistics, mathematics, computing, informatics, sociology, and management. ...
Article
The 21st century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.
... Adopting open data practices 88 (such as FAIR: findability, accessibility, interoperability and reusability 107 ) wherever possible is also likely to greatly aid collaboration and comparison of datasets, although this will not be possible for all researchers in this area. The importance of these approaches is already well understood by researchers in other fields, such as high-energy physics 108 and astronomy 109 ; we foresee that a similar level of data curation will soon be necessary in HEDP. ...
Preprint
The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems are strongly non-linear and very difficult to understand theoretically or optimize experimentally. Here, we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proven far too non-linear for human researchers. From a fundamental perspective, our understanding can be helped by the way in which machine learning models can rapidly discover complex interactions in large data sets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to ~daily), moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we advance proposals for the community in terms of research design, training, best practices, and support for synthetic diagnostics and data analysis.
... Adopting open data practices 88 (such as FAIR: findability, accessibility, interoperability and reusability 107 ) wherever possible is also likely to greatly aid collaboration and comparison of datasets, although this will not be possible for all researchers in this area. The importance of these approaches is already well understood by researchers in other fields, such as high-energy physics 108 and astronomy 109 ; we foresee that a similar level of data curation will soon be necessary in HEDP. ...
Article
High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis. This Perspective discusses how high-energy-density physics could tap the potential of AI-inspired algorithms for extracting relevant information and how data-driven automatic control routines may be used for optimizing high-repetition-rate experiments.
... Space is an ideal gateway into other STEM fields as it is both intrinsically multi-disciplinary and highly inspirational [13]. The space industry has been successful in inspiring entire nations for decades, and capitalising on that inspiration by maintaining education and public engagement programmes targeted at a large variety of young audiences [14][15][16][17][18][19][20]. ...
Article
Full-text available
Involving students in state-of-the-art research from an early age eliminates the idea that science is only for the scientists and empowers young people to explore STEM (Science, Technology, Engineering and Maths) subjects. It is also a great opportunity to dispel harmful stereotypes about who is suitable for STEM careers, while leaving students feeling engaged in modern science and the scientific method. As part of the Twinkle Space Mission's educational programme, EduTwinkle, students between the ages of 15 and 18 have been performing original research associated with the exploration of space since January 2016. The student groups have each been led by junior researchers?PhD and post-doctoral scientists?who themselves benefit substantially from the opportunity to supervise and manage a research project. This research aims to meet a standard for publication in peer-reviewed journals. At present the research of two ORBYTS teams have been published, one in the Astrophysical Journal Supplement Series and another in JQSRT; we expect more papers to follow. Here we outline the necessary steps for a productive scientific collaboration with school children, generalising from the successes and downfalls of the pilot ORBYTS projects
... Space is an ideal gateway into other STEM fields as it is both intrinsically multi-disciplinary and highly inspirational [13]. The space industry has been successful in inspiring entire nations for decades, and capitalising on that inspiration by maintaining education and public engagement programmes targeted at a large variety of young audiences [14][15][16][17][18][19][20]. ...
Article
Full-text available
Involving students in state-of-the-art research from an early age eliminates the idea that science is only for the scientists and empowers young people to explore STEM (Science, Technology, Engineering and Maths) subjects. It is also a great opportunity to dispel harmful stereotypes about who is suitable for STEM careers, while leaving students feeling engaged in modern science and the scientific method. As part of the Twinkle Space Mission’s educational programme, EduTwinkle, students between the ages of 15 and 18 have been performing original research associated with the exploration of space since January 2016. The student groups have each been led by junior researchers—PhD and post-doctoral scientists—who themselves benefit substantially from the opportunity to supervise and manage a research project. This research aims to meet a standard for publication in peer-reviewed journals. At present the research of two ORBYTS teams have been published, one in the Astrophysical Journal Supplement Series and another in JQSRT; we expect more papers to follow.
Chapter
An increasing number of data science courses are available from research institutions and professional course providers. However, most such courses may look like “old wine in new bottles”, i.e., they are a re-labeling and combination of existing subjects in statistics, business and IT.
Article
Full-text available
Background: We report the results of an undergraduate course in astrophotography designed to engage non-STEM majors in the natural sciences to train future amateur astronomers and citizen scientists. Over 200,000 students enroll in introductory astronomy elective classes every year in the US alone, which will possibly be their only encounter with a natural science. The course relies on constructivist educational methods to teach data reduction and image processing methods while addressing mathematical anxiety. The goal of the course is to offer a positive experience in the natural sciences which has been linked to the education of potential citizen scientists and amateur astronomers, groups which historically have analyzed a great amount of data and have provided numerous discoveries. Results: Studentsenrolledinthecoursereportedahigherunderstandingofdatareduction,imageprocessing, telescope and camera use. Most students were eager to take up astrophotography as a hobby, opening the path to become future citizen scientists and amateur astronomers. We found that the methods required to practice astrophotography create a natural constructivist teaching environment. Conclusions: Thecoursecanbereproducedelsewheretoteachnon-sciencemajorstudentstechniquesindata reduction and image processing as positive experiences to introduce them to STEM fields in the future.
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Full-text available
Affective computing facilitates more intuitive, natural computer interfaces by enabling the communication of the user's emotional state. Despite rapid growth in recent years, affective computing is still an under-explored field, which holds promise to be a valuable direction for future software development. Human-computer interaction has traditionally been dominated by the information processing metaphor and as a result, interaction between the computer and the user is generally unidirectional and asymmetric. The next generation of computer interfaces aim to address this gap in communication and create interaction environments that support the motivational and affective goals of the user. This chapter will introduce and elaborate on the field of affective computing. First the background and origins of the field will be discussed. Next the elements of affective computing and affective human-computer interaction will be discussed along with associated concerns and issues. Next, examples of the diverse range of affective computing applications in current and recent development will be provided. Finally, the chapter will present a discussion of future directions for this promising technology, followed by some concluding remarks.
Article
Full-text available
We thank James E. Faller for his comment and this opportunity to emphasize one of the main points of our article. Our use of the title “science” on the lintel in Figure 2 was deliberate and pivotal. “Information science” is, of course, not excluded. We are trying to convey the view that information is changing fundamentally the conduct of science research, including observational science.
Article
Full-text available
The study aims to characterize contextual learning during class visits to science and natural history museums. Based on previous studies, we assumed that “outdoor” learning is different from classroom-based learning, and free choice learning in the museums enhances the expression of learning in personal context. We studied about 750 students participating in class visits at four museums, focusing on the levels of choice provided through the activity. The museums were of different sizes, locations, visitor number, and foci. A descriptive-interpretative approach was adopted, with data sources comprising observations, semistructured interviews with students, and museum worksheets. Analysis of the museum activities has yielded four levels of choice that affect learning from no choice to free choice activities. The effectiveness of learning was examined as well by looking at task behavior, linkage to the students' prior knowledge and their school's science curriculum, and linkage to the students' life and experience. Our findings indicate that activities of limited choice offered scaffolding, allowed the students to control their learning, and enhanced deeper engagement in the learning process. Within all the choice opportunities, the students connected the visit to their own life experiences and to their prior knowledge, even when the guided activity scarcely addressed it. Critical responses were obtained mainly when the museum environment allows a variety of learning opportunities without directing the students. © 2006 Wiley Periodicals, Inc. Sci Ed91:75–95, 2007
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It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.
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The largest organization of variable star observers in the world, with over 1000 members in more than 40 countries. Its database contains over 9 million observations of variable stars. The organization's headquarters are in Cambridge, Massachusetts....
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This paper is a summary of the keynote talk delivered at the conference. I have tried to keep the sense of the spoken presentation. Some of the ideas are my own, some are borrowed from colleagues. The biases, and whines are my own.
Article
This volume was conceived as a review of basic research in science education and as a discussion of what the research findings mean for K-12 science teachers. The eight reports presented represent different dimensions of science education. Each provides a review of a given dimension and/or a goal of science teaching and suggests ways that current knowledge might affect practice. Reports focus on: (1) a review of some major studies in instruction, with suggestions for applications to science/mathematics curricula (J. Stallings); (2) information-processing psychology and a brief description of a science project using its methodology (J. Larkin); (3) role of instruction in the development of problem-solving skills in science (R. Ronning and D. McCurdy); (4) developing creativity as a result of science instruction (J. Penick); (5) deriving classroom applications from Piaget's model of intellectual development (D. Phillips); (6) the development of an attentive public for science: implications for science teaching (A. Voelker); (7) factors affecting minority participation and success in science (J. Kahle); and (8) status of graduate science education: implications for science teachers (J. Gallagher and R. Yager). Brief summaries of each report and background information are provided in an introduction. A list of six actions by educators that would serve to implement the research findings and set new directions for science education is presented in an epilogue. (Author/JN)
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Future space missions and science programs will be massive data producers. The technology to produce large data volumes must be matched by technologies to process, analyze, and make use of the data flood, in order to reap the maximum engineering benefit and scientific return from those technology investments. In particular, the integration of data from multiple sources will be standard practice, both for operational decision-making and for scientific decision-making. We describe the application of the emerging e-Science paradigm and its related technologies to data-driven discovery in space missions of the future.
Article
This study relates the performance of college students in introductory science courses to the amount of content covered in their high school science courses. The sample includes 8310 students in introductory biology, chemistry, or physics courses in 55 randomly chosen U.S. colleges and universities. Students who reported covering at least 1 major topic in depth, for a month or longer, in high school were found to earn higher grades in college science than did students who reported no coverage in depth. Students reporting breadth in their high school course, covering all major topics, did not appear to have any advantage in chemistry or physics and a significant disadvantage in biology. Care was taken to account for significant covariates: socioeconomic variables, English and mathematics proficiency, and rigor of their preparatory high science course. Alternative operationalizations of depth and breadth variables result in very similar findings. We conclude that teachers should use their judgment to reduce coverage in high school science courses and aim for mastery by extending at least 1 topic in depth over an extended period of time. © 2008 Wiley Periodicals, Inc. Sci Ed93:798–826, 2009