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Harnessing the NEON data revolution to advance open environmental science with diverse and data-capable community

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It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation — across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.
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SPECIAL FEATURE:
HARNESSING THE NEON DATA REVOLUTION
Harnessing the NEON data revolution to advance open
environmental science with a diverse and data-capable community
R. CHELSEA NAGY ,
1,
JENNIFER K. BALCH,
1,2
ERIN K. BISSELL,
3
MEGAN E. CATTAU ,
4
NANCY F. GLENN ,
4,5
BENJAMIN S. HALPERN,
6,7
NAYANI ILANGAKOON,
1
BRIAN JOHNSON,
1
MAXWELL B. JOSEPH ,
1
SERGIO MARCONI ,
8
CATHERINE ORIORDAN,
9
JAMES SANOVIA,
10
TYSON L. SWETNAM,
11
WILLIAM R. TRAVIS,
1,2
LEAH A. WASSER,
1,2
ELIZABETH WOOLNER,
1
PHOEBE ZARNETSKE,
12
MUJAHID ABDULRAHIM,
13
JOHN ADLER,
2,14
GRENVILLE BARNES,
15
KRISTINA J. BARTOWITZ,
16
RACHAEL E. BLAKE,
17
SARA P. BOMBACI,
18
JULIEN BRUN,
6,7
JACOB D. BUCHANAN,
19
K. DANA CHADWICK ,
20,21
MELISSA S. CHAPMAN,
22
STEVEN S. CHONG,
6,7,23
Y. ANNY CHUNG ,
24
JESSICA R. CORMAN ,
25
JANNELLE COURET,
26
ERIKA CRISPO,
27
THOMAS G. DOAK,
28
ALISON DONNELLY,
29
KATHAR YN A. DUFFY ,
30
KELLY H. DUNNING,
31
SANDRA M. DURAN,
32
JENNIFER W. E DMONDS,
33
DAWS ON E. FAIRBANKS,
34
ANDREW J. FELTON ,
35
CHRISTOPHER R. FLORIAN,
36
DANIEL GANN,
37
MARTHA GEBHARDT,
38
NATHAN S. GILL ,
39
WENDY K. GRAM,
40
JESSICA S. GUO,
41
BRIAN J. HARVEY,
42
KATHERINE R. HAYE S ,
43
MATT HE W R. HELMUS ,
44
ROBERT T. HENSLEY,
36
KELLY L. HONDULA ,
17
TAO HUANG,
4,45
WILEY J. HUNDERTMARK ,
46
VIRGINIA IGLESIAS,
1
PIERRE-ANDRE JACINTHE,
47
LARA S. JANSEN,
48
MARTA A. JARZYNA ,
49,50
TIONA M. JOHNSON,
51
KATH ER INE D. JONES,
36
MEGAN A. JONES,
52
MICHAEL G. JUST ,
53
YOUSSEF O. KADDOURA,
15
AURORA K. KAGAWA-VIVANI,
54
ALEYA KAUSHIK,
55
ADRIENNE B. KELLER ,
56
KATE LYN B. S. KING ,
57
JUSTIN KITZES ,
58
MICHAEL J. KOONTZ ,
1
PAIGE V. K OUBA,
59
WAI-YIN KWA N,
60
JALENE M. LAMONTAGNE ,
61
ELIZABETH A. LARUE ,
62
DAIJIANG LI,
63,64
BONAN LI,
65
YANG LIN ,
66
DANIEL LIPTZIN,
67
WILLIAM ALEX LONG,
68
ADAM L. MAHOOD ,
2
SAMUEL S. MALLOY,
69
SPARKL E L. MALONE ,
37
JOSEPH M. MCGLINCHY ,
1
COURTNEY L. MEIER,
36
BRETT A. MELBOURNE,
70
NATHAN MIETKIEWICZ,
71
JEFFERY T. MORISETTE,
72
MOUSSA MOUSTAPHA,
73
CHANCE MUSCARELLA,
34
JOHN MUSINSKY,
36
RANJAN MUTHUKRISHNAN ,
74
KUSUM NAITHANI ,
75
MERRIE NEELY,
76,77
KARI NORMAN ,
22
STEPHANIE M. PARKER,
36
MARIANA PEREZ ROCHA,
78
LA
IS PETRI ,
79
COLETTE A. RAMEY,
3
SYDNE RECORD ,
80
MATTHEW W. ROSSI,
1
MICHAEL SANCLEMENTS,
36
VICTORIA M. SCHOLL,
1,2
ANNA K. SCHWEIGER ,
81
BIJAN SEYEDNASROLLAH ,
30
DEBJANI SIHI,
82
KATHLEEN R. SMITH,
3
ERIC R. SOKOL ,
36,83
SARAH A. SPAUL D IN G,
83
ANNA I. SPIERS,
1,70
LISE A. ST.DENIS,
1
ANIKA P. S TA CC ON E,
84
KAITLIN STA CK WHITNEY,
85
DIANE M. STANIT SK I,
55
EVA STRICKER,
86
THILINA D. SURASINGHE,
87
SARAH K. THOMSEN,
88
PATRISSE M. VASEK,
10
LIXIAOLU,
89
DIYANG,
90
RONG YU,
29
KELSEY M. YULE,
91
AND KAI ZHU
92
1
Earth Lab, CIRES, University of Colorado Boulder, Boulder, Colorado, USA
2
Department of Geography, University of Colorado Boulder, Boulder, Colorado, USA
3
Biology Department, Metropolitan State University of Denver, Denver, Colorado, USA
4
Human-Environment Systems, Boise State University, Boise, Idaho, USA
5
University of New South Wales Sydney, Sydney, New South Wales, Australia
6
National Center for Ecological Analysis and Synthesis (NCEAS), Santa Barbara, California, USA
7
University of California Santa Barbara, Santa Barbara, California, USA
8
School of Natural Resources & Environment, University of Florida, Gainesville, Florida, USA
9
Ecological Society of America, Washington, D.C., USA
10
Department of Math, Science, and Technology, Oglala Lakota College, Kyle, South Dakota, USA
11
BIO5 Institute, University of Arizona, Tucson, Arizona, USA
12
Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
13
Department of Civil and Mechanical Engineering, University of Missouri Kansas City, Kansas City, Missouri, USA
14
CIRES, University of Colorado Boulder, Boulder, Colorado, USA
15
Department of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, Florida, USA
16
Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, Idaho, USA
17
National Socio-Environmental Synthesis Center, University of Maryland, Annapolis, Maryland, USA
18
Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
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19
Department of Biological Sciences, Bowling Green State University, Bowling Green, Ohio, USA
20
Department of Geological Sciences, University of Texas Austin, Austin, Texas, USA
21
Department of Integrative Biology, University of Texas Austin, Austin, Texas, USA
22
Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, California, USA
23
University of California Berkeley Library, University of California Berkeley, Berkeley, California, USA
24
Departments of Plant Biology and Plant Pathology, University of Georgia, Athens, Georgia, USA
25
School of Natural Resources, University of Nebraska Lincoln, Lincoln, Nebraska, USA
26
Department of Biological Sciences, University of Rhode Island, Kingston, Rhode Island, USA
27
Department of Biology, Pace University, New York City, New York, USA
28
Department of Biology, Indiana University, Bloomington, Indiana, USA
29
Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
30
School of Informatics, Computing & Cyber Systems, Northern Arizona University, Flagstaff, Arizona, USA
31
School of Forestry and Wildlife, Auburn University, Auburn, Alabama, USA
32
Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
33
Department of Physical and Life Sciences, Nevada State College, Henderson, Nevada, USA
34
Department of Environmental Science, University of Arizona, Tucson, Arizona, USA
35
Department of Wildland Resources, Utah State University, Logan, Utah, USA
36
Battelle, National Ecological Observatory Network, Boulder, Colorado, USA
37
Department of Biological Sciences, Florida International University, Miami, Florida, USA
38
School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA
39
Department of Natural Resources Management, Texas Tech University, Lubbock, Texas, USA
40
University Corporation for Atmospheric Research, Boulder, Colorado, USA
41
College of Agriculture and Life Sciences, University of Arizona, Tucson, Arizona, USA
42
School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
43
Department of Integrative and Systems Biology, University of Colorado Denver, Denver, Colorado, USA
44
Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
45
Cary Institute of Ecosystem Services, Millbrook, New York, USA
46
Department of Earth and Environment, Boston University, Boston, Massachusetts, USA
47
Department of Earth Sciences, Indiana University Purdue University, Indianapolis, Indiana, USA
48
Department of Environmental Science & Management, Portland State University, Portland, Oregon, USA
49
Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, Ohio, USA
50
Translational Data Analytics Institute, The Ohio State University, Columbus, Ohio, USA
51
Atlanta, Georgia, USA
52
Boulder, Colorado, USA
53
US Army, ERDC CERL, Champaign, Illinois, USA
54
Department of Geography and Environment, University of HawaiʻiatM
anoa, Honolulu, Hawaii, USA
55
National Oceanic and Atmospheric Administration, Boulder, Colorado, USA
56
Department of Ecology, Evolution, and Behavior, University of Minnesota Twin Cities, St. Paul, Minnesota, USA
57
Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, USA
58
Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
59
Department of Plant Sciences, University of California Davis, Davis, California, USA
60
CALeDNA, University of California Los Angeles, Los Angeles, California, USA
61
Department of Biological Sciences, DePaul University, Chicago, Illinois, USA
62
Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA
63
Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, USA
64
Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, USA
65
Department of Biological & Ecological Engineering, Oregon State University, Corvallis, Oregon, USA
66
Soil and Water Sciences Department, University of Florida, Gainesville, Florida, USA
67
Soil Health Institute, Morrisville, North Carolina, USA
68
Science and Technology Innovation Program, Woodrow Wilson International Center for Scholars, Washington, D.C., USA
69
Battelle Center for Science, Engineering and Public Policy in the John Glenn College of Public Affairs, Ohio State University,
Columbus, Ohio, USA
70
Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, USA
71
CoreLogic, Irvine, CA, USA
72
U.S. Department of Agriculture Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, USA
73
Department of Biological Science, University of Ngaoundere, Ngaoundere, Adamawa, Cameroon
74
Environmental Resilience Institute, Indiana University, Bloomington, Illinois, USA
75
Department of Biological Sciences, University of Arkansas-Fayetteville, Fayetteville, Arkansas, USA
76
GEO AquaWatch, Clearwater, Florida, USA
77
Global Science and Technology, Inc, Greenbelt, Maryland, USA
78
Department of Biology, University of Oklahoma, Norman, Oklahoma, USA
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
79
School for Environment and Sustainability, University of Michigan, East Lansing, Michigan, USA
80
Department of Biology, Bryn Mawr College, Bryn Mawr, Pennsylvania, USA
81
Remote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, Switzerland
82
Department of Environmental Sciences, Emory University, Atlanta, Georgia, USA
83
INSTAAR, University of Colorado Boulder, Boulder, Colorado, USA
84
Department of Ecology, Evolution, & Environmental Biology, Columbia University, New York, New York, USA
85
Department of Science, Technology, and Society, Rochester Institute of Technology, Henrietta, New York, USA
86
Department of Biology, University of New Mexico, Albuquerque, New Mexico, USA
87
Department of Biological Sciences, Bridgewater State University, Bridgewater, Massachusetts, USA
88
Department of Integrative Biology, Oregon State University, Corvallis, Oregon, USA
89
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York, USA
90
Wyoming GIS Center, University of Wyoming, Laramie, Wyoming, USA
91
Biodiversity Knowledge Integration Center, Arizona State University, Tempe, Arizona, USA
92
Department of Environmental Studies, University of California, Santa Cruz, Santa Cruz, California, USA
Citation: Nagy, R. C., J. K. Balch, E. K. Bissell, M. E. Cattau, N. F. Glenn, B. S. Halpern, N. Ilangakoon, B. Johnson,
M. B. Joseph, S. Marconi, C. ORiordan, J. Sanovia, T. L. Swetnam, W. R. Travis, L. A. Wasser, E. Woolner, P. Zarnetske,
M. Abdulrahim, J. Adler, G. Barnes, K. J. Bartowitz, R. E. Blake, S. P. Bombaci, J. Brun, J. D. Buchanan, K. D. Chadwick,
M. S. Chapman, S. S. Chong, Y. A. Chung, J. R. Corman, J. Couret, E. Crispo, T. G. Doak, A. Donnelly, K. A. Duffy,
K. H. Dunning, S. M. Duran, J. W. Edmonds, D. E. Fairbanks, A. J. Felton, C. R. Florian, D. Gann, M. Gebhardt, N. S. Gill,
W. K. Gram, J. S. Guo, B. J. Harvey, K. R. Hayes, M. R. Helmus, R. T. Hensley, K. L. Hondula, T. Huang, W. J. Hundertmark,
V. Iglesias, P.-A. Jacinthe, L. S. Jansen, M. A. Jarzyna, T. M. Johnson, K. D. Jones, M. A. Jones, M. G. Just, Y. O. Kaddoura,
A. K. Kagawa-Vivani, A. Kaushik, A. B. Keller, K. B. S. King, J. Kitzes, M. J. Koontz, P. V. Kouba, W.-Y. Kwan,
J. M. LaMontagne, E. A. LaRue, D. Li, B. Li, Y. Lin, D. Liptzin, W. A. Long, A. L. Mahood, S. S. Malloy, S. L. Malone,
J. M. McGlinchy, C. L. Meier, B. A. Melbourne, N. Mietkiewicz, J. T. Morisette, M. Moustapha, C. Muscarella, J. Musinsky,
R. Muthukrishnan, K. Naithani, M. Neely, K. Norman, S. M. Parker, M. Perez Rocha, L. Petri, C. A. Ramey, S. Record,
M. W. Rossi, M. SanClements, V. M. Scholl, A. K. Schweiger, B. Seyednasrollah, D. Sihi, K. R. Smith, E. R. Sokol, S. A.
Spaulding, A. I. Spiers, L. A. St. Denis, A. P. Staccone, K. Stack Whitney, D. M. Stanitski, E. Stricker, T. D. Surasinghe,
S. K. Thomsen, P. M. Vasek, L. Xiaolu, D. Yang, R. Yu, K. M. Yule, and K. Zhu. 2021. Harnessing the NEON data
revolution to advance open environmental science with a diverse and data-capable community. Ecosphere 12(12):e03833.
10.1002/ecs2.3833
Abstract. It is a critical time to reect on the National Ecological Observatory Network (NEON) science to
date as well as envision what research can be done right now with NEON (and other) data and what training
is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted
from planning and construction to operation and maintenance. In this overview, the history of and founda-
tional thinking around NEON are discussed. A framework of open science is described with a discussion of
how NEON can be situated as part of a larger data constellationacross existing networks and different
suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 exist-
ing publications, funded proposal efforts, and emergent science at the very rst NEON Science Summit
(hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that
the ecology community will address with NEON data in the next 10 yr are outlined, from understanding
drivers of biodiversity across spatial and temporal scales to dening complex feedback mechanisms in
humanenvironmental systems. Last, the essential elements needed to engage and support a diverse and
inclusive NEON user community are highlighted: training resources and tools that are openly available,
funding for broad community engagement initiatives, and a mechanism to share and advertise those oppor-
tunities. NEON users require both the skills to work with NEON data and the ecological or environmental
science domain knowledge to understand and interpret them. This paper synthesizes early directions in the
communitys use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent sci-
ence themes, open science best practices, education and training, and community building.
Key words: community; continental-scale ecology; diversity; inclusion; National Ecological Observatory Network;
open data; open science; Special Feature: Harnessing the Neon Data Revolution.
vwww.esajournals.org 3December 2021 vVolume 12(12) vArticle e03833
SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
Received 31 March 2021; revised 4 June 2021; accepted 24 June 2021. Corresponding Editor: Debra P. C. Peters.
Copyright: ©2021 The Authors. Ecosphere published by Wiley Periodicals LLC on behalf of The Ecological Society of
America This is an open access article under the terms of the Creative Commons Attribution License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited.
E-mail: nagyrc@gmail.com
INTRODUCTION
Summary of foundational thinking around NEON
Environmental challenges facing todays soci-
ety require thinking across scales from local to
continental or global, data collected across differ-
ent ecoregions and over decades, multidisciplin-
ary expertise, team science approaches, and
training in skills like computer and data science
(Keller et al. 2008, Schimel 2011). A fundamental
goal of the National Ecological Observatory Net-
work (NEON) is to improve our understanding
of and ability to predict the effects of environ-
mental change (e.g., climate change, land-use
change, biological invasions, altered nutrient
cycling) at continental scales representing both
terrestrial and aquatic ecosystems (Field et al.
2006, Schimel et al. 2011, Kao et al. 2012). The
scientic infrastructure of NEON was designed
to meet this goal by using a standardized, multi-
scale sampling strategy consisting of systemati-
cally deployed aquatic, ground, and tower-based
sensors, eld sampling, and high-resolution air-
borne remote sensing (Schimel et al. 2007,
Kampe et al. 2010, Taylor et al. 2011). NEON
delivers a coordinated and standardized set of
calibrated and documented data on key plant
and animal taxa as well as microbes and algae,
environmental and atmospheric variables, and
remote sensing data across the United States
(Field et al. 2006, Kampe et al. 2010). These data
can help reveal linkages between ecological pat-
terns and processes across scales and identify
drivers of change and the resultant ecological
consequences (Atkins et al. 2018, Read et al.
2018, Hall et al. 2020, Marconi et al. 2021). The
Observatory design (including 81 sites across 20
ecoclimatic domains; NEON 2021a) and sam-
pling protocols (Keller et al. 2008, Kao et al.
2012) will capture temporal scales across local,
regional, and continental spatial extents to enable
meaningful connections to satellite remote sens-
ing, geospatial, and other network data. The
transformational potential of NEON as a highly
integrated scientic observatory was recognized
early on in its deployment (Balch et al. 2020b), as
were the challenges of working with disparate
types of ecological and environmental data. Real-
izing the full potential of NEON will require that
data are easy to access and use by scientic and
educational communities. However, NEON data
will not be able to answer all questions; it will
not replace the need for eld ecology or skills
to conduct hypothesis-driven, experimental
research (e.g., study design, data collection), the
value of an intimate understanding of a particu-
lar organism or ecosystem, or the utility of other
individual sites, networks, and data sources, but
rather can be used in conjunction with all of
these other existing sources of information.
History of NEON
The necessity for a long-term, geographically
widespread ecological observatory network with
consistent data collection had long been recog-
nized by the scientic community. Workshops in
20002005 led to an initial plan for NEON with
an ambitious 30-yr timeline (NEON 2021b). The
following ve years were dedicated to planning
and designing, and in 2011, the National Science
Foundation (NSF) approved funds to build
NEON (NEON 2021b). The construction of
NEON sites was completed in 2019 (NEON
2021b). While some data products at some sites
were available as early as 2010, it was not until
2019 that all sites had each data product avail-
able. Thus, after almost 20 yr of envisioning,
planning, and construction, NEON is now fully
operational. The challenges of running an opera-
tion at this scale are signicant and have been
noted elsewhere (Mervis 2015, Cesare 2016,
Collins and Knapp 2019, Rogers 2019). While
some of the community has been hesitant or
resistant to embrace NEON due to the steep
learning curve and other challenges (Sagoff
2019), some of the community is eager to use
NEON data because of the potential that this
large NSF investment offers.
vwww.esajournals.org 4December 2021 vVolume 12(12) vArticle e03833
SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
Some individual researchers and lab groups
began analyzing components of NEON data
even before the full scope of the Observatory was
complete (Anderegg and Diffenbaugh 2015,
Ghabbour et al. 2015, Read et al. 2018, Scholl
et al. 2020), yet further efforts were needed to
build a cohesive NEON user community. Fur-
thermore, while there is an understanding in
much of the community about the power of the
data being produced by the Observatory, many
potential users may face barriers to utilizing
these data sets (Balch et al. 2020b). Therefore,
Earth Lab at the University of Colorado Boulder
hosted the rst NEON Science Summit in 2019 to
continue to build a robust and sustainable
NEON user community, which is essential for
the Observatory to realize its full potential.
2019 NEON Science Summit
The NEON Science Summit, held at the Univer-
sity of Colorado Boulder in October 2019, was the
rst unconference(a meeting with participant-
driven agenda and working group topics) focused
on building new science from NEON data prod-
ucts. In total, there were 170 participants through
a mix of in-person and remote participation.
Throughout two-and-a-half days, ˜15 breakout
working groups used NEON data to explore
questions such as: What are the environmental
drivers of microbial community composition
across sites (see Qin et al., in this special issue)?
How can ground, uncrewed aerial systems
(UAS), airborne, and satellite data at NEON sites
be linked for applications such as detecting and
segmenting individual trees (see Koontz et al.
and Gann et al., in this special issue)? Does the rela-
tionship between native and non-native species
richness change with spatial scale (see Gill et al.,
in this special issue)? This paper synthesizes the
work from the 2019 NEON Science Summit and
the Grand Challenges that the NEON user com-
munity identied as priority areas to address.
NEON OPEN SCIENCE
Open science principles and methods (e.g.,
making samples, data, workows, software,
publications freely available) are changing the
eld of ecology (Hampton et al. 2015). As a key
tenet of the NEON mission at the outset, this
commitment to open science has the potential to
accelerate ecological research and increase the
diversity of scientists involved by removing bar-
riers to access. The diversity and number of data
products (NEON 2021c), tutorials (NEON
2021d), and analytic tools (neonUtilities (Lunch
et al. 2021) and geoNEON (NEON 2020) pack-
ages in R) that NEON provides are a key
resource for open ecological research. In addi-
tion, NSF requires all scientists funded by Macro-
systems Biology and NEON Enabled Science
grants to archive their data with the Environ-
mental Data Initiative (EDI; EDI 2021) to pro-
mote data discovery and use. An extended
commitment of the scientists using these
resources to make their data, code, and work-
ows open will increase efciency and facilitate
greater coordination across a larger collaborative
community. Key opportunities that are expected
to bring added value to open NEON data include
the following: harmonization with other observa-
tion networks (such as the Long-Term Ecological
Research (LTER), Long-Term Agroecosystem
Research (LTAR), Critical Zone Observatory
(CZO), AmeriFlux, USA-NPN National Phenol-
ogy Network, and others) and data sources, open
science contributions from the NEON user com-
munity, and facilitation, training, and curation
that lead to a robust and popular NEON soft-
ware toolbox.
Data harmonization to answer continental-scale
ecology questions
The why of linking data.Participants of the
2019 NEON Science Summit identied that
network-to-network data collaborations are criti-
cal for continental-scale ecology (Balch et al.
2020b; SanClements et al., in this special issue).
NEON has existing collaborations with Ameri-
Flux, the PhenoCam network, the LTER network,
the LTAR network, the National Phenology Net-
work, and others. These collaborations leverage
multiple sources of spatial and temporal resolu-
tion and thus improve our ability to understand
complex processes, phenomena, and change over
space and time compared to individual net-
works. Because of its spatial and disciplinary
breadth, NEON is well poised to act as a central
hub in a network of networks (SanClements
et al., in this special issue). Expanding these part-
nerships would be benecial to NEON and the
whole community.
vwww.esajournals.org 5December 2021 vVolume 12(12) vArticle e03833
SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
Broadening our networks and multi-scale
analysis.NEON data can be used in conjunction
with data from other networks at co-located
sites, to expand sites in spatial extent, to stan-
dardize data collection protocols, or to synthe-
size complementary data products across
networks. Existing long-term (i.e., decadal) eco-
logical networks (e.g., CZO, LTER) at co-located
sites provide inferential power and historical
context for contemporary patterns observed by
NEON (Hinckley et al. 2016b). For example,
long-term experiments at LTER sites manipulate
some of the drivers observed by NEON, contex-
tualizing patterns observed at NEON sites (Jones
et al. 2021). Other continental-scale monitoring
efforts (e.g., community or citizen science data,
North American Breeding Bird Survey, eBird)
can ll in gaps between NEON sites and expand
the spatial reach of NEON data. NEON also
carries the potential to contribute to global-scale
networks such as GLEON (Global Lakes Ecologi-
cal Observatory Network) and GEO BON
(Group on Earth Observations Biodiversity
Observation Network). NEON collaborated with
the USA National Phenology Network to ensure
that data collection protocols between the two
networks were standardized from NEONs
inception. A recent collaboration between the
Environmental Data Initiative (EDI), LTER, and
NEON resulted in a harmonized data model
(ecocomDP) for community ecology observations
that provides an analysis ready data product for
synthesis of community ecology data sets across
the LTER and NEON (OBrien et al., 2021;
Record et al. 2021; Li et al., in this special issue).
NEON site data and Airborne Observation
Platform (AOP) observations can be used syner-
gistically for calibration and validation and
inform existing and future missions/networks.
The ability to task NEON resources in support of
these studies will continue to expand the utility
of the NEON program and synergies. For exam-
ple, the AOP was tasked in 2018 in support of
the Department of Energys ongoing Watershed
Function Science Focus Area (Chadwick et al.
2020). This project produced publicly available
data sets and functional trait models that are
now being used for a wide range of studies,
including assessment of sensitivity requirements
for NASAs Surface Biology and Geology (SBG)
Designated Observable (Cawse-Nicholson et al.
2021; Thompson et al., unpublished manuscript). A
priority of the SBG program is to understand the
global distribution of vegetation functional types
and traits. Precursor studies like this one will
allow NEON AOP studies to inform SBG archi-
tecture. Over the long term, together, imaging
spectroscopy satellite missions and NEON data
will provide repeat observations of chemical
properties of vegetation, aquatic biomass, and
soils at variable, and complimentary, resolutions
and repeat times.
NEON data can be extended vertically and
horizontally with UAS, airborne, and satellite-
based observations to answer questions requir-
ing multi-scale observations and analysis. For
example, following on Schimel et al. (2019), plot
data and ux tower data can be integrated with
UAS, airborne, and satellite observations to
obtain productivity and carbon measurements
from the eddy covariance footprint to continental
scales. The multi-temporal observations from
NEON data are also primed for trend analyses
by integrating with data from Landsat, Landsat/
Sentinel harmonization, Moderate Resolution
Imaging Spectroradiometer (MODIS), Global
Ecosystem Dynamics Investigation (GEDI), and
long-term monitoring plots from other networks
to understand vegetation dynamics from distur-
bances (e.g., re, beetle kill) and soil dynamics
from experimental manipulations (Wieder et al.
2020). Lastly, there is an opportunity to utilize
UAS data for eld validation and scaling to
observations at NEON sites, as well as non-
NEON sites through NEONs assignable assets
program (NEON 2021e).
Best practices for sharing data, code, software,
and entire workflows
All components of NEON are documented
and intended to be reused as community stan-
dards for data collection and processing. The sci-
ence carried out using NEON data must be open
and reproducible, encouraging the creation of
online space to store, review, and share tools and
software to build upon each others efforts. Yet
successfully building a community of scientists
who share code, software, and data products
built upon NEON data is one of the key chal-
lenges identied during the Summit and requires
community adoption of best practices (Hey 2009,
Bechhofer et al. 2013).
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
Open software, tools, and code to support efcient
open science.In modern science, software under-
lies a majority of science outputs; it is critical that
researchers strive for openness in their work to
the degree possible. This includes making code
accessible with permissive licensing (Dabbish
et al. 2012, Loeliger and McCullough 2012), cit-
able using DOIs, documented and maintained
over time to support reproducibility and re-use.
Although these approaches are historically not
part of traditional science training, organizations
such as rOpenSci (rOpenSci 2021) and pyOpen-
Sci (pyOpenSci 2021) provide community sup-
port and a peer-review process to ensure both
citation credit for software developed and high-
quality scientic tools. The review process is fur-
ther supported by the Journal of Open Source
Software and Methods in Ecology and Evolution
to simplify publication avenues. Tools and work-
ows for working with NEON data can make
use of these open science principles to advance
scientic discovery by building on collaborative
knowledge. An open science approach will also
ensure continued improvements of NEON-
specic analytical tools, expansion of the macroe-
cological knowledge base, and systematically
address knowledge gaps.
Open data and processing pipelines.The volume
and variety of data produced by the NEON user
community are signicant and require the adop-
tion of best practices for sharing, curating, and
archiving data workows and derived products
following FAIR data principles (Wilkinson et al.
2016). To make the greatest use of NEON and
extended data sources, data are given structure
in the form of a schema or index and must have
community established metadata templates, for
example, DublinCore (Weibel et al. 1998), Eco-
logical Metadata Language (EML; Fegraus et al.
2005), or DarwinCore (DwC; Wieczorek et al.
2012). Derived data can be hosted on public
repositories such as Dryad (Isard et al. 2007),
Pangaea (Pangea 2021), the EDI Data Portal (EDI
2021), Environmental Systems Science Data
Infrastructure for a Virtual Ecosystem (ESS-
DIVE; Varadharajan et al. 2019), or CyVerse Data
Commons (CyVerse 2021).
When extended to the regional and continental
scale over many years and decades, the computa-
tional and management specications of NEON
data require analyses on distributed, scalable
cyberinfrastructure. While some researchers pay
for computing and storage services via platforms
like Amazon Web Services, limited access to
computational resources by students or
researchers at small and underserved institutions
can be remedied by using free and open scien-
tic cyberinfrastructure resources. High-
performance computing (HPC), high-throughput
computing (HTC), and cloud services are freely
available to U.S.-based researchers via the
eXtreme Scientic and Engineering Discovery
Environment (XSEDE; Towns et al. 2014) and
CyVerse (Merchant et al. 2016, Swetnam et al.
2016, Bucksch et al. 2017). Additionally, privately
operated CyberGIS platforms, such as Google
Earth Engine (GEE; Gorelick et al. 2017), re-host
some public Earth observation system (EOS) sat-
ellite and aerial remote sensing data and provide
a resource for exploratory data analysis. Work-
ow Management Systems (WMS) enable
researchers to analyze vast quantities of data on
distributed cyberinfrastructure. Exemplar WMS,
such as Pangeo (Eynard-Bontemps et al. 2019),
SnakeMake, Makeow, and WorkQueue
(Albrecht et al. 2012, K
oster and Rahmann 2012,
Zheng and Thain 2015), are used by the Life and
Earth science communities for analyzing massive
corpuses of scientic data and could be adopted
by NEON users. Processing NEONs environ-
mental, soil metagenomic data, or AOP data
across the entire observatory requires scalable
computing which are most easily accomplished
via a WMS (Thessen et al. 2020).
The NEON toolbox will empower an open,
collaborative NEON community
The scientic community has already built
many different tools and products to make work-
ows for processing and analyzing NEON data
more efcient (NEON 2021f); however, these
efforts are widespread, often disconnected, and
the tools developed are not easily discoverable.
Further, the tools are often developed for single
project use and thus not generalized to support
the broader community of NEON data users (but
see Li et al., in this special issue). There are several
potential advantages to sharing resources among
NEON users including (1) reduced redundancy
in efforts as groups independently develop tools
with similar functionality; (2) adoption of
methods or algorithms with novel approaches
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
better suited for tackling questions with the
available volume of data rather than those tradi-
tionally used by a specic community; and (3)
lower investment of skills, resources, and time
for individual groups that do not have pre-
existing tools and workows. Sharing derived
data, tools, and software that users generate to
draw greater meaning from NEON public data
products will signicantly improve the use and
scope of NEON in addressing emerging scientic
challenges across the continent.
Existing efforts could be leveraged to make
NEON tools more discoverable. For
example, rOpenSci (rOpenSci 2021) and pyO-
penSci (pyOpenSci 2021) support software peer
review and community development of citable,
tested, well-documented, and discoverable soft-
ware. Along with software and tools, the user
community will also be adding packages to more
easily visualize, download, and redistribute por-
tions of data products from pipelines that would
require computational architecture too expensive
to run on local computers. Derived data sets can
be stored in open-access repositories (e.g.,
Zenodo, Dryad, Figshare, or EDI) with options
for version control and DOIs. Although NEON
will not directly maintain these software, tools,
or data, it can play a central role in making them
discoverable by the community.
THE INTELLECTUAL MERIT OF NEON SCIENCE:
STATUS AND FUTURE
The ecological community has already taken
advantage of existing NEON data products and
NEON Biorepository samples and specimens. As
of October 2020, 267 publications have described,
referenced, and used NEON data and network
resources. While the range of topics varies
greatly, drawing on the 181 open-access data
products and 63 collections of physical samples,
certain key themes have emerged (Fig. 1). The
large emphasis on data suggests how valuable
these products have been for the ecological com-
munity. Prominent topics include tracking phe-
nology changes, forest structural dynamics and
tree classication, soil organic matter (or carbon)
dynamics, ecological forecasting, and small
mammal biodiversity patterns. For example,
early work has used tree classication and map-
ping techniques, and convolutional neural net-
works with combinations of hyperspectral
imagery, lidar, and RGB, to identify tree species
and individual tree crowns (Dalponte et al. 2019,
Fricker et al. 2019b, Weinstein et al. 2019,
Scholl et al. 2020). Others have examined
changes in plant phenology in deciduous forests
(Seyednasrollah et al. 2020) and alpine systems
(Dorji et al. 2020) in a changing climate.
Fig. 1. Word cloud created from article titles from 180 NEON-related publications from 2017 to 2020.
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
Researchers also evaluated continental to global-
scale dynamics of soil carbon by leveraging
NEON resources (Kramer and Chadwick 2018,
Hall et al. 2020).
The NEON Science Summit Steering Commit-
tee surveyed the work funded through NSF
Macrosystems (and other agencies/programs)
and papers that have used NEON data to inform
our virtual breakout groups preceding the Sci-
ence Summit. These preliminary calls were then
used to distill areas of interest among the 170
participants. While much thought has been given
to what will be possible with NEON data over
the course of decades (e.g., tracking phenological
changes resulting from climate change), the focus
of the 2019 NEON Science Summit was to deter-
mine what can be done with NEON data and
NEON-compatible products right now and in the
next decade. This section synthesizes the main
areas that Summit participants are addressing
through individual efforts and group work that
stemmed from the Summit. Topics range across
fundamental ecology and contemporary prob-
lems such as response to environmental change
among species, communities, and ecosystems.
There were also some emergent themes that may
not have been anticipated in the foundational
thinking around NEON.
Fundamental ecology
Ecology as a discipline aims to understand and
predict how biotic and abiotic features of the
environment interact. Fundamental questions
span multiple scales of biological organization,
ecosystem processes, space, and time. A survey
of ecologists identied 100 fundamental yet
unanswered ecological questions (Sutherland
et al. 2013), covering the basic understanding of
eco-evolutionary feedbacks, processes driving
population, community, and biodiversity pat-
terns, species interactions and invasion dynam-
ics, unique qualities of disease and
microorganisms, ecosystem functioning, and
human interactions. NEON is well poised to
address many of these questions with its spa-
tially nested hierarchical design and systematic
sampling of Earths biotic and abiotic compo-
nents across North America (Schimel et al. 2007,
Keller et al. 2008, Schimel and Keller 2015).
The NEON Science Summit identied several
key fundamental areas in ecology that NEON
data can be used to address now: (1) testing of
our understanding of ecological patterns and
processes across spatial and temporal scales; (2)
determining the drivers of biodiversity patterns
across the United States; and (3) documenting
ecosystem processes and the services that nature
provides. Some examples of basic questions that
arose from the Summit include the following:
Which ecological patterns and dynamics scale up
and/or down in time and space? What controls
decomposition? How generalizable are ecologi-
cal observations from single sites to the continen-
tal scale? What drives trait variation? Which
ecological patterns and processes are more
context-dependent or species-specic, and which
are more generalizable?
Biodiversity across scales
Understanding temporal and spatial scales at
which drivers affect biodiversity is necessary to
inform robust modeling of historical, current,
and future patterns (Delsol et al. 2018, Gonzalez
et al. 2020). Spatial synchrony of populations
and communities is an emerging area of research
that can help determine the degree of resilience
and resistance of biota to environmental change
(Zelnik et al. 2018). Central to quantifying syn-
chrony is identifying the appropriate scales at
which ecological systems function. NEONs hier-
archical designfrom plots to sites to domains
enables investigations of the drivers of biodiver-
sity within and among spatial scales. For exam-
ple, at continental scales, NEON data on small
mammals and intraspecic body size variation
helped reveal the role of biotic interactions and
climate in mediating patterns of community
composition and trait plasticity across NEON
domains (Read et al. 2018). This research has
contributed to greater understanding of the
biotic mechanisms that drive the Latitudinal
Diversity Gradient (Read et al. 2018). When com-
bined with remotely sensed data (e.g., airborne
and satellite imagery) and organismal data
across the United States (e.g., North American
Breeding Bird Survey, USDA Forest Inventory &
Analysis), NEONs organismal and Airborne
Observation Platform (AOP) data will further
advance continental-scale assessment of biodi-
versity patterns. The variety of taxa sampled by
NEON allows investigators to seek generalities
in terms of the spatial and temporal scales of
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
drivers, while also accounting for taxon-specic
life-history characteristics and traits as well as
other biological constraints (e.g., regional species
pools and biogeographic lters) (Kao et al. 2012).
Researchers are already tackling the scales of
biodiversity drivers and responses using NEON
data and asking such questions as: What can
NEON data tell us about the spatial synchrony
of populations and communities at the continen-
tal scale? What are the drivers responsible? How
do temporal dynamics (e.g., shifts in tempera-
ture, precipitation, discharge) affect biodiversity
patterns in aquatic communities across the
United States? How do different metrics of biodi-
versity (e.g., species richness) relate to structural
(LaRue et al. 2019), functional, and spectral
diversity? Future directions could include map-
ping changes in biodiversity in a changing cli-
mate. Where will the greatest losses of
biodiversity occur and how can this inform con-
servation and management?
Evaluating disturbance dynamics with NEON
Disturbance plays an important role in the
structure and function of terrestrial and aquatic
ecosystems (Thom and Seidl 2016, Daam et al.
2019). The impacts of disturbance on an ecosys-
tem are controlled by its intensity, frequency,
size, spatial pattern, and spatial extent. Some dis-
turbances like wildres can occur multiple times
during an ecosystems response period, affecting
the resilience and recovery of the system
(Bartowitz et al. 2019; Mahood & Balch 2019).
NEONs sampling design, including repeat sam-
pling covering the full range of U.S. ecological
and geo-climatic diversity, helps evaluate the
impacts of these frequent disturbance events.
NEONs high spatial and temporal resolution air-
borne remote sensing data (e.g., lidar, hyperspec-
tral, and photogrammetry) allow mapping
patterns of disturbance within and across biomes
(Fig. 2), though the extent to which NEON cap-
tures disturbance history for various distur-
bances has yet to be examined. For example, the
extent of bark beetle attack, forest re, or ood/
hurricane damage can be estimated using
remotely sensed vegetation structure and func-
tion over the disturbed area. Further, the valida-
tion of remotely sensed data with coincident
ground-based data can facilitate more accurate
estimation of disturbance scale. Finally, NEON
recently released a Site management and event
reportingdata product (DP1.10111.001) which
includes on-the-ground documentation of distur-
bance events as well.
The wealth of NEON data includes detailed
eld measurements about plants, animals, soil,
microbes, nutrients, freshwater, and the atmo-
sphere that can be used to advance the under-
standing of ne-scale variation of ecosystems
under variable conditions. The combination of
sites for long-term observation and assignable
assets that move through time further enables the
capture of ecosystem dynamics in response to dis-
turbance. The multi-scalar observations from
NEON can provide a macrosystem view of distur-
bance dynamics and ecological patterns. For the
disturbances that NEON sites do not capture well,
metrics of impact and/or environmental correlates
(e.g., ecohydrological variables that indicate u-
vial disturbances) can be determined by combin-
ing NEON and associated data sets. Furthermore,
NEON data provide a unique opportunity to
improve forecasts of future disturbances (e.g.,
extreme weather events such as droughts, spread
of invasive species) and inform management rec-
ommendations at the continental scale.
Carbon and climate dynamics
A major opportunity for NEON data use is
exploring biogeochemical feedbacks in the climate
system (e.g., carbonclimate feedbacks). Baseline
C storage and uxes (e.g., productivity and
decomposition) across ecoregions can now be
assessed, and processes directly tied to climatic
gradients and vegetation cover across the United
States can be evaluated. Further, it is possible to
examine how other nutrient pools and dynamics
in soils and plants affect C uxes across NEON
sites. Patterns in soil chemistry (e.g., C and nitro-
gen (N) concentrations, net N mineralization and
nitrication rates, C and N isotopes), as well as
changes in soil C stability at individual sites and
across the United States, can be examined in a sys-
tematic and repeated design not previously avail-
able (Hinckley et al. 2016a, Weintraub et al. 2017).
In the long term, the decades of data collected
by NEON on vegetation cover, aboveground bio-
mass, soil physical and chemical properties, lit-
terfall and ne woody debris production, litter
chemical properties, root biomass and chemistry,
soil carbon dioxide concentrations, and climate
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
will offer a site-by-site detailed history to track
climate change and carbonclimate feedbacks.
These data will be extremely valuable for sup-
porting climate action and related decision-
making. Furthermore, the capacity of ecosystems
to sequester C can be better dened to improve
future climate modeling scenarios (Kramer and
Chadwick 2018). The timing of vegetation phe-
nology and the duration of the growing season
play a key role in determining the amount of C
sequestered by different ecosystems. Therefore,
the phenological data being recorded at NEON
sites will be critical in the development of accu-
rate C budgets across ecosystems.
Situating NEON in a coupled human
environment systems framework
The growing human population and associ-
ated activities (e.g., agricultural expansion,
urbanization, transportation) across the globe
inuence nearly all patterns and functions of nat-
ural systems, while changing natural systems
directly impact the socioeconomic and physical
well-being of humans (Fedele et al. 2017). Thus,
human and natural systems must be considered
as a coupled socialenvironmental system (SES;
Liu et al. 2007, Grimm et al. 2017, Balch et al.
2020a). The NEON network was designed to cap-
ture environmental rather than social gradients.
However, the integration of local environmental
observations and multi-temporal remote sensing
data from the NEON observatory, combined
with socioeconomic data, could enable under-
standing of humanenvironment interactions
(Pricope et al. 2019). Research Coordination
Networks, such as the newly funded project,
Landscape Exchange Network for Socio-
environmental systems research (LENS),which
will leverage detailed observations from the
NEON AOP to study SES across the United
States, can improve our capacity to use NEON
toward SES research more broadly. NEON sites
subjected to active land management can
also inform future land-use planning and
Fig. 2. NEON AOP captures impact of disturbances at high spatial resolution. (a) Chimney Tops 2 re bound-
ary (2016) at Great Smoky Mountain, Twin Creek (GRSM) NEON site. (b) Canopy height difference calculated
using NEON lidar canopy height models from the years 2016 (pre-re) and 2018 (post-re). (c) Biomass (2017)
estimated from correlation with NDVI and LAI parameters derived from NEON AOP data.
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
adaptive natural resource management in the
Anthropocene. Additionally, NEONs assignable
assets, or rapidly deployable mobile suites of
NEON instruments, could be used to target pro-
cesses of interest in humanenvironmental sys-
tems. Ultimately, this knowledge can be
integrated into management solutions to help
society to adapt to environmental change. The
ecological forecasting, land-use change, and cli-
mate change science that NEON enables in the
long term involve tight coupling between
humans and the environment and can bridge
natural and social sciences.
Ecological forecasting
Ecology is increasingly turning to ecological
forecasting to aid in preparing for the future
drivers of change that affect ecosystems, species,
and communities. An important role for NEON
data is to establish baselines of biodiversity, bio-
geochemical pools and uxes, ecological struc-
ture, and other indicators to initialize forecasts
and compare against future scenarios. NEON is
taking the pulse of changing U.S. ecosystems and
helping us predict their future health. As the
eld of ecological forecasting, or eco-forecasting,
moves forward, NEON-enabled science can con-
tribute iterative, probabilistic projections (Dietze
and Lynch 2019). A step toward this goal of com-
munity members building iterative, near-term
forecasts with NEON data is the NEON Ecologi-
cal Forecast Challenge, the rst round of which
occurred in 2020, put on in collaboration with
the Ecological Forecasting Initiative (EFI 2021).
Ultimately, the aim is to develop the capability to
anticipate or forecast ecological change to better
prepare for, adapt to, or prevent change.
Emergent themes
NEON-enabled science has tackled some clas-
sic questions in ecology, and the Science Summit
suggested some unique, and perhaps unantici-
pated, possibilities for NEON observations data
(in combination with other data) that have not
been possible until now (Box 1). A basic oppor-
tunity for new insights built into the NEON
design stems from the integrated, co-located
measurements of many processes with NEON
tower data collection. In this way, independent
research can capitalize on the NEON infrastruc-
ture to add additional measurements and
experiments at NEON sites. Other new efforts
include researchers mapping individual trees
and tree species using high-resolution airborne
and satellite data (Weinstein et al. 2019, Scholl
et al. 2020). And several novel questions examine
scale: a multi-scale understanding of the carbon
cycle by integrating data sources from eld plot
data to satellite imagery, better understanding
the changing predictors of canopy height across
scales (Fricker et al. 2019a), and how biodiversity
patterns change across temporal scales.
The potential to address cutting-edge, multidis-
ciplinary science questions with NEON
(and NEON-compatible) data is unprecedented.
Although NEON was envisioned as a stand-alone
Box 1.
The following are the emergent topics that are pos-
sible to address now using NEON (and other) data
products, by category of questions.
Foundational ecology
1. What controls metabolic rates?
2. What drives trait variation?
3. What are the patterns in biodiversity across taxa
from beetles to trees?
4. What controls decomposition or transpiration
rates?
Species from space
1. How can tree species of individual tree crowns be
identied?
2. How do spectral signatures correspond to leaf
traits?
Change detection and forecasting
1. Whats the phenological response to interannual
variability in climate?
2. How is the hydrology of streams changing?
3. What would it take to forecast ecological
processes?
Invasive species
1. How does plasticity of the genome level pre-
dict plant success, across native and invasive
species?
Data harmonization and scaling
1. How can different types and sources of data be
integrated?
2. How does ecological pattern and process scale?
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
observatory, it now sits within a data ecosystem
with many points of leverage. Linking across data
sets and networks of observatories has begun,
including in some ways not anticipated in early
thinking around NEON. While one goal of NEON
is to detect change over time, which will become
possible with decades of data, early NEON and
NEON-linked science is already used for change
detection. For example, using NEON data to
understand vegetation recovery after a re
requires integration of additional data sets (e.g.,
Landsat, MODIS, UAS) since only a couple of the
NEON sites have experienced a re. Other inter-
disciplinary opportunities abound including use
of NEON data for ecohydrological studies like
hierarchical clustering of surface water chemistry
across NEON sites (see Edmonds et al., in this spe-
cial issue) and soil data for pedological research on
biogeochemical uxes. Finally, opportunities exist
to advance NEON technology as innovations in
instrumentation, observing platforms, and data
science surpass what the observatory initially
offered (e.g., unmanned aerial vehicles (UAVs)).
Engagement by a diverse community of investiga-
tors enhances this opportunity for continued sci-
entic advancement.
BUILDING THE NEON COMMUNITY
Diversity, inclusion, and accessibility in STEM
How NEON can serve a diverse and inclusive
community.Building a diverse and inclusive
community expands professional opportunities
for NEON users by encouraging the creation of a
more accessible user experience. There is the
potential to mobilize NEONs vast resources to
grow such a community while also addressing
persistent disparities in STEM participation. While
open NEON infrastructure and data are central
draws (and by themselves remove a central bar-
rier of the resources needed to collect empirical
data that many researchers lack), training oppor-
tunities (e.g., workshops that provide hands-on
experience with the data) and networks of people
are critical for building a diverse and inclusive
community. These resources can be incentives for
the larger community of environmental scientists
who have yet to join the NEON community. For
example, a training exercise might provide early-
career or underrepresented scientists the opportu-
nity to build their skill sets while also becoming a
resident NEON data expert at their home
institution. Similarly, these scientists may nd
new professional linkages and interests within
the NEON user network.
Diversity.Diverse backgrounds are critical to
developing innovative and creative scientic per-
spectives and will expand the visibility of NEON
beyond the current ecological community. Diver-
sity can be measured in many different ways, but
attracting participation from groups that have
smaller representation in science and engineering
elds than their representation in the U.S. popu-
lation will increase diversity among NEON
users. These groups include women, people with
disabilities, and underrepresented racial/ethnic
groups including Black, Indigenous, and People
of Color (BIPOC). Post-event anonymous sur-
veys following the 2019 NEON Science Summit
indicated that 31.3% identied as non-white and
55% identied as female. Furthermore, develop-
ing mechanisms to attract users at different
career stages, including students (K-12, under-
graduate, and graduate); early-, mid-, and late-
career scholars; educators and public outreach
professionals; and professionals outside of acade-
mia, will also diversify and enrich the user com-
munity. During the NEON Science Summit,
participants were asked to consider the question:
Who isnt here and how can they be brought
into the fold?Many of the responses went
beyond identication of racial and ethnic minori-
ties and emphasized the importance of building
a coalition of NEON data users that includes a
wide range of perspectives and members beyond
the academic ecological research community
(Fig. 3).
Inclusion.The NEON user community can
build on previous efforts to increase diversity with
targeted outreach, recruitment, and training to
groups at underrepresented institutions, such as
Historically Black Colleges and Universities
(HBCUs), Hispanic Serving Institutions (HSIs),
and Tribal Colleges and Universities (TCUs). The
Environmental Data Science Inclusion Network
(EDSIN) was created to foster an online commu-
nity to develop training opportunities, shared
resources, and leadership experience for scientists
who are traditionally underrepresented in data
science and environmental science. Inclusive prac-
tices not only increase opportunities for underrep-
resented populations, they also foster diversity in
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
the types of research questions addressed using
NEON open-source data and expand the visibility
of the network to more potential users.
The knowledge generated from NEON prod-
ucts could be deepened through the co-
production of science by members of the NEON
community and stakeholders (i.e., leaders of
underrepresented groups) to determine what
types of ecological research questions are of
interest to a range of communities. Building trust
and meaningful relationships to jointly tackle
research questions requires time and space to
both listen and respond to the issues identied
by all groups. Collaborations stemming from
these efforts will help identify potential research
sites for assignable assets that enable targeted
groups to connect with NEON data.
However, it is not enough to identify and
recruit members of underrepresented groups to
join the NEON community; an inclusive environ-
ment encourages the development of essential
skills and is open to new opportunities. Such an
environment will recognize that the inclusion of
diverse voices in science strengthens the creation
of knowledge. Summit participants identied
three main elements critical to these efforts: (1)
education and training, (2) nancial support, and
(3) accessibility (Fig. 4).
Accessibility in STEM.Continued funding for
infrastructure, both physical and virtual, is
essential for training ecologists and environ-
mental scientists at all career stages, from
undergraduate students to senior scientists, and
from different professional settings, including
Fig. 3. Responses to the question asked of 2019 NEON Science Summit participants about who was not at the
Summit that should have been (i.e., who or what groups were not well represented).
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
non-academic institutions. Creating a broad
network of users will begin to address the chal-
lenge of accessibility to NEON data. The NSF-
funded Quantitative Undergraduate Biology
Education and Synthesis (QUBES) is a consor-
tium of academic institutions, NSF projects, and
professional societies that connects researchers
across disciplines and supports efforts to
improve quantitative literacy and data skills at
the undergraduate level by offering training
and course development opportunities to fac-
ulty (QUBES 2021a). Efforts like these can be
expanded to meet the needs of diverse
populations using NEON data. Other key infra-
structural challenges include access to com-
puters and reliable internet. Each institution
may have unique barriers and the larger com-
munity needs to be mindful of the current
states of physical infrastructure and levels of
training to make NEON data accessible for all.
An online hub could help support individuals
and institutions that serve underrepresented
populations to encourage the use of NEON data
and provide opportunities for learning how to
use them. To kick things off, one opportunity is
to host a series of short webinars to give college
Fig. 4. Ideas from 2019 NEON Science Summit participants about how to recruit and retain underrepresented
groups.
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
instructors and their students an introduction on
how to access NEON data. These webinars could
be followed by a virtual conference that includes
lightning talks by members of target groups to
present on their use of NEON data. Continued
mentorship for faculty that serve underrepre-
sented populations is key and will require time
and commitment to the people at those schools/
institutions. In the long term, for a sustainable
diverse NEON community, members should
explore the mandates and pursue the resources
of NEON, NSF, and others to engage with these
target groups (see Community Engagement Initia-
tives). Lastly, the community needs to spread the
word to national, regional, and local agencies
(e.g., Tribal ofces, land management agencies,
scientic societies, conservation organizations,
and community groups) that opportunities exist
for those from all professional and personal
backgrounds. These outreach efforts can comple-
ment and build on the curriculum development
and teaching resources described below.
NEON presents a need for new data-intensive
curriculum in ecology
NEON data offer an opportunity to teach sci-
entic inquiry and ecological principles relevant
to STEM careers but also transferable data sci-
ence and critical thinking skills. A major chal-
lenge identied at the Summit is the limited
number of people with skills to use NEON data
among both the faculty and the students of the
ecological community.
A different model of teaching and doing ecological
science.The availability of NEON data, while a
tremendous opportunity for understanding envi-
ronmental change, presents some unique chal-
lenges. The data are collected by an external
entity rather than individual scientists and that
collection is driven by NEON-focused mission
science requirements. The scientist then develops
questions that can be answered using NEON and
other data. This model of science requirements
driving data collection is not new: Remote sens-
ing, astronomical telescope, particle collider, and
other agency-driven data collection missions
have implemented this approach to support the
science community for decades (National Acade-
mies of Sciences, Engineering, and Medicine
et al. 2018). However, data like those being col-
lected by NEON at the U.S. continental scale,
with standard measurements and more than 180
data products, are new to the natural resource
sciences.
NEON thus presents a suite of new technical
challenges associated with using data that are
collected externally in formats and structures
that support long-term and large data sets; their
use will be new to many ecologists. These for-
mats include HDF5 (the structure of the hyper-
spectral remote sensing data), and text les with
relational database-like structures, to name just
two. Additional challenges are associated with
reading and interpreting metadata, understand-
ing documentation (e.g., Algorithm Theoretical
Basis Documents; ATBD), describing sampling
designs, data collection methods, calibration pro-
cedures, nested structures in data storage (which
vary by protocol), and uncertainty calculations.
Further, many data sets require specic training
to understand and evaluate quality (e.g., data
from imaging spectroscopy, ux towers); it is not
always appropriate to use products from the
data portal without close consideration and l-
tering, which requires familiarity with domain
literature and expertise. The cross-domain nature
of the data collected by NEON (e.g., remote sens-
ing, ux towers, organismal diversity) further
encourages questions that are best addressed by
large interdisciplinary teams, thus demanding a
new suite of skills in the realm of communication
and collaboration that also are not traditionally
taught in natural resource science curricula.
Addressing the community gap in data science
skills.The skills needed to effectively work with
NEON and other large data sets are not currently
taught in most curricula, which struggle to keep
pace with changes in technology and data pro-
cessing. Education and training opportunities for
students, educators, researchers, and community
partners are key to building a vibrant and
diverse community (Fig. 4). An example of a
workshop that provides such opportunities is the
Critical Skills to Scale Up Ecology: An ESA
SEEDS and NEON Workshop,an intensive
week-long training designed to introduce ecolog-
ical data skills to graduate students with under-
represented backgrounds (originally scheduled
for June 2020 and postponed due to the COVID-
19 pandemic). Another example is the Earth Lab
Earth Data Science Corps (EDSC), funded by the
NSF. The EDSC provides students at Tribal and
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
other schools serving historically underrepre-
sented groups in STEM with data skills training,
mentorship, and a paid summer internship
where they work on a real-world project: One
Tribal student in the 2020 internship used NEON
lidar data to evaluate forest structural diversity
in the western United States. Improved access to
these types of opportunities will also require
nancial support for those such as K-12 teachers,
contingent university faculty, and non-academic
professionals. Providing funding for early-career
scientists and underrepresented populations can
encourage participation at in-person training
opportunities. Furthermore, the development of
hybrid or remote conference and workshop
opportunities make these events accessible to a
broader population, especially for people who
face obstacles to travel, such as families, resource
constraints, and heavy teaching loads. Finally,
the identication and development of tools that
improve accessibility of NEON data, such as
GUIs (graphical user interfaces), and IDEs (inte-
grated development environments), and creation
of teaching modules that help build the skills
necessary to engage with NEON data can be pro-
moted across the NEON user community.
Instructor challenges for teaching with NEON
data.A working group at the 2019 NEON Sci-
ence Summit identied challenges facing instruc-
tors interested in using NEON data in
undergraduate courses. While some guidance
and educational modules and lessons do exist for
instructors who wish to use NEON data, these
are not housed in a centralized location or main-
tained by a single organization. Additionally,
some instructors are lacking the data skills them-
selves to implement the lessons in the classroom
or face other infrastructural and technological
challenges. Some examples are given below of
NEON Data Education faculty mentoring net-
works (see Naithani et al., in this special issue)
and partnerships for instructors wishing to
implement NEON data in the classroom.
Resources for instructors include teaching and
learning modules, and full courses available on
the NEON Learning Hub (NEON 2021g), the
Earth Data Science learning portal (earthdatas-
cience.org; Earth Lab 2021), the QUBES portal
(QUBES 2021b), and the Environmental Data-
Driven Inquiry and Exploration (EDDIE) web-
site (EDDIE 2021). These modules can be added
to existing courses or used for self-paced learn-
ing can further democratize access to these data
skills. However, they lack a centralized platform
and a clearly identied agency responsible for
curating NEON instructional resources which
creates barriers to their use in the classroom.
The instructional level and types of data science
skills required to complete the modules are not
always easily discerned from the current
descriptions. A framework is needed that would
allow instructors to easily identify the intended
audience (e.g., undergraduate course level),
learning goals, and assessment tools included in
these modules. This would also allow instruc-
tors interested in creating new modules to iden-
tify and ll gaps in the current resources.
Sharing educational materials on a maintained
and funded portal like QUBES or CyVerse
would help with their adoption. Additionally,
adding tags and providing links to these
resources on NEONs website would further
facilitate discoverability.
Instructors may face many barriers to imple-
menting NEON data in their classrooms includ-
ing the instructors own lack of data science skills
and familiarity with the data products among
others (e.g., lack of funding for new curriculum,
technology, and time). Sustained and improved
outreach and instructor training is necessary to
overcome this rst barrier. The NEON commu-
nity should identify and target populations and
institutions that are not currently engaged with
or utilizing NEON data, such as primarily under-
graduate institutions (PUIs), minority-serving
institutions, and Tribal colleges. One such exam-
ple is developing a curriculum with PUI ecology
and GIS faculty that is centered around testing
the use of NEON terrestrial and airborne collec-
tions to introduce spatial ecology and macrosys-
tems biology concepts in undergraduate courses
(Styers et al. unpublished manuscript). A lesson
learned from these activities is that it is helpful
for NEON staff to participate and introduce
some of the NEON sampling design and data
portal, and for researchers familiar with working
with NEON data to help PUI faculty quickly get
up-to-speed on how to work with the data. It is
also important to consider the resources available
at these institutions and address technological
challenges related to hardware and Internet con-
nectivity that may exist.
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SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
Several faculty mentoring networks and part-
nerships exist including collaborations with
NEON, Ecological Research Education Network
(EREN), and the Biological and Environmental
Data Education (BEDE) Network and are hosted
by QUBES, which support faculty in both using
and developing new data-intensive curricula for
their courses (BEDE 2021, EREN 2021, NEON
2021h). These faculty mentoring networks work
with faculty members from undergraduate
teaching institutions to develop, implement, and
publish data-driven teaching modules to
empower undergraduate instructors to incorpo-
rate data science skills in their courses. Such part-
nerships could provide a fruitful avenue for
incorporating real-world skills and experiences
using NEON data in undergraduate curricula.
Community engagement initiatives
In order to maximize the benet of NEON, the
community needs activities that support
researchers within, and attract researchers from
beyond, current NEON users. Achievement of this
robust community will require enhanced funding
to support broad community engagement initia-
tives, and a science-forward centralized mecha-
nism to share and advertise those opportunities.
This support should take on at least three syner-
gistic forms: (1) engage and train early-career
researchers, (2) build an inclusive community of
scientists, and (3) prioritize community engage-
ment activities. All three support mechanisms
require nuanced approaches to building trust in
and transparency around data and shared goals
that enable the transition from place-based science
to research that spans across and links ecological
systems. Building an inclusive and expansive
research community involves dialogue with multi-
ple interests and recognition of the emergence of a
new culture of ecology based on team science and
data sharing. Workshops, lecture series, and train-
ing are some activities that require funding. In par-
ticular, outreach and funding are needed to attract
and support underrepresented groups and those
who are not current NEON users. Additionally,
outreach to other disciplines will help increase
awareness of how NEON data can contribute to
the conversation on public policy and applications
to address emerging societal and ecological chal-
lenges. Such an effort can enhance the value of this
revolutionary ecological infrastructure.
WHY NEON MATTERS
It is important to remember how NEON is
similar to and different from other efforts that
came before it. In particular, many of the ques-
tions remain the same; abiding environmental
questions and problems still drive individual PIs
as well as networks such as NEON, LTER, CZO,
AmeriFlux, etc. However, NEON was estab-
lished in the era of open science that aims to
democratize the access to knowledge with
freely available data to all students and in all
classrooms.
The success of NEON matters both to the eco-
logical community and society as a whole.
NEON represents the largest single U.S. invest-
ment in ecology to date, and it will inform future
scientic missions by other agencies and data
networks. The observatory will provide critical
science for key decisions in the management of
ecosystems and habitats. It is up to the commu-
nity of users to increase awareness of how
NEON data can contribute to informed public
policy and socially relevant applications. It offers
new opportunities for tackling big challenges
such as climate change, land-use change, and
ecological transformations that affect all life on
Earth. NEON will not stand alone as a solution
to these complex challenges (see SanClements
et al., in this special issue), but offers opportunities
to build on other approaches, data, and knowl-
edge to support individual scientists and the eco-
logical community to address them.
CONCLUSIONS
NEON is intended to help us observe, under-
stand, and interpret the response of species, com-
munities, and ecosystems to our changing
environment. This revolutionary observatory
network for ecology will enable us to ask and
answer local- to continental-scale questions with
a design that includes sites located across ecore-
gions. NEON aids understanding of ecological
change across space and time and the forecasting
of future conditions with drivers like land-use
change and climate change. The standardization
of collection methods across sites, and open
access to the data can foster a new, open ecology
for the next generation of scientists, including
microbial ecologists, biogeochemists, community
vwww.esajournals.org 18 December 2021 vVolume 12(12) vArticle e03833
SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
ecologists, atmospheric scientists, and more.
NEON became fully operational during the data
and analytics revolutions and now is poised to
contribute to advancing the discipline in conjunc-
tion with other long-term observatories and data
sets. As data science and analytics have become
critical skills for todays scientists, training on
how to access and analyze NEON data is also
critical. There is a need to develop a curriculum
to teach these skills to a diverse, inclusive NEON
user community. Collaborative, hands-on work-
shops like the 2019 NEON Science Summit will
build a broad community, and investing in other
inclusive mechanisms will sustain this engaged
community.
ACKNOWLEDGMENTS
R. C. Nagy and J. K. Balch contributed equally to
the work reported here and were co-lead authors.
Funding for the 2019 NEON Science Summit was pro-
vided by NSF Award #DBI 1906144. Additional fund-
ing was provided by Earth Lab through the University
of Colorado, Boulders Grand Challenge Initiative, the
Cooperative Institute for Research in Environmental
Sciences (CIRES), and the North Central Climate
Adaptation Science Center (NC CASC). AKS was sup-
ported by the University of Zurichs University
Research Priority Programme on Global Change and
Biodiversity. We are grateful to all of those who helped
make the 2019 NEON Science Summit a success: Dawn
Umpleby, Linda Pendergrass, David Zakavec, Rebecca
Stossmeister, Alycia Crall, Kate Thibault, Claire Lunch,
Megan Jones, Katie Weeman, Katy Human, Ally Faller,
Lauren Herwehe, Jenny Palomino, Tim Dunn, Nathan
Campbell, Nan Regnier, Elizabeth Woolner, Katie
Lamb, Troy Burke, Jim Lee, Jay Burghardt, and
Michelle Meighan. Several co-authors on this paper
including Christopher R. Florian, Robert T. Hensley,
Katherine D. Jones, Courtney L. Meier, John Musinsky,
Stephanie M. Parker, Michael SanClements, and Eric
R. Sokol are currently employed by Battelle and the
National Ecological Observatory Network. The nd-
ings and conclusions in this manuscript are those of
the authors and should not be construed to represent
any ofcial U.S. Government determination or policy.
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DATA AVAILABILITY STATEMENT
Data are available from Dryad: https://doi.org/10.5061/dryad.866t1g1rj
vwww.esajournals.org 22 December 2021 vVolume 12(12) vArticle e03833
SPECIAL FEATURE: HARNESSING THE NEON DATA REVOLUTION NAGY ET AL.
... Methodological and epistemological challenges involved in using these data led the authors of this paper to recognize the necessity of having a team of collaborators to validate methods and test results before formally embedding them into a standard algorithmic process. While there is some research on the social and technical factors that allow for effective team science (Rhoten 2003, Oliver et al. 2018, there is room to consider how to best foster collaborations that can synthesize the wide variety of NEON data products to address interdisciplinary problems (e.g., Nagy et al. 2021). Interdisciplinary collaborations have been identified as avenues for fruitful and novel research in ecology and the environment as discussed above, but especially for understanding complex socioenvironmental issues (Palmer et al. 2016). ...
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