This resource collects teaching materials that are originally created for the in-person course 'GEOSC/GEOG 497 – Data Mining in Environmental Sciences' at Penn State University (co-taught by Tao Wen, Susan Brantley, and Alan Taylor) and then refined/revised by Tao Wen to be used in the online teaching module 'Data Science in Earth and Environmental Sciences' hosted on the NSF-sponsored HydroLearn platform.

This resource includes both R Notebooks and Python Jupyter Notebooks to teach the basics of R and Python coding, data analysis and data visualization, as well as building machine learning models in both programming languages by using authentic research data and questions. All of these R/Python scripts can be executed either on the CUAHSI JupyterHub or on your local machine.

This resource is shared under the CC-BY license. Please contact the creator Tao Wen at Syracuse University (twen08@syr.edu) for any questions you have about this resource. If you identify any errors in the files, please contact the creator.

Our dataset are transcripts and codebooks for a focus group study. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. EPA cannot release CBI, or data protected by copyright, patent, or otherwise subject to trade secret restrictions. Request for access to CBI data may be directed to the dataset owner by an authorized person by contacting the party listed. It can be accessed through the following means: Contact Katie Williams, williams.kathleen@epa.gov. Format: The data are transcripts and protected by IRB approvals. This dataset is associated with the following publication: Eisenhauer, E., K. Williams, K. Margeson, S. Paczuski, K. Mulvaney, and M.C. Hano. Advancing translational research in environmental science: The role and impact of social science. Environmental Science & Policy. Elsevier Science Ltd, New York, NY, USA, 120: 165-172, (2021).

This is a PBL module on Air Pollution to be used in an introductory environmental science course to motivate students to analyze related environmental justice issues.Original data was from the US EPA data on "State EJScreen Data at the Block Group Level" (EJSCREEN_2023_BG_StatePct_with_AS_CNMI_GU_VI.csv) which was downloaded from https://www.epa.gov/ejscreen/download-ejscreen-data on December 20, 2023. (Note: Access to the EJSCREEN tool was removed during February 2005).This data was processed and cleaned as described in the data provenance document.Lecture Slides, Activity Sheets and Instructor Notes are available here.The following files are included:Data Provenance and Data Dictionary: Data Provenance and Data Dictionary.pdfR Script for Data Processing: EJSCREEN_Data_Curation_NC_Summarized_by_County.RProcessed Dataset for North Carolina: EJScreen_State_BGLevel_NC_13Columns.csvCurated Data used in the Module - Summarized Dataset for North Carolina (summarized by county): EJScreen_State_BGLevel_NC_Summarized_By_County_13Columns.csvData Dictionary: Data_Dictionary_EJSCREEN_2023_BG_Columns.pdfOriginal Dataset from EPA/EJSCREEN from which Data was Extracted for North Carolina: DS4EJ_EJSCREEN_2023_BG_StatePct_with_AS_CNMI_GU_VI.csv

We designed and organized a one-day workshop, where in the context of FAIR the following themes were discussed and practiced: scientific transparency and reproducibility; how to write a README; data and code licenses; spatial data; programming code; examples of published datasets; data reuse; and discipline and motivation. The intended audience were researchers at the Environmental Science Group of Wageningen University and Research. All workshop materials were designed with further development and reuse in mind and are shared through this dataset.

Environmental scientists stand uniquely poised to capitalize on recent advancements in technology, computation, and data management, however, it is unknown the degree to which this is occurring. We analyzed survey responses of 445 graduate students in California to evaluate understanding and use of such advances in the environmental sciences. Of students who had completed their degree, 64.3% had completed the data life cycle, 30.5% had archived research data so that it is available online, and 61.4% had no plans to create metadata for research data sets. Roughly one-third of students used an environmental sensor and collaborated with someone outside their expertise. Results varied by students’ research status and by university type. Doing excellent science in this data-intensive age may necessitate greater emphasis by university programs on data management best practices borrowed from information technology, and skills supplemented by unique training opportunities, courses, counsel fro...

The repository contains the outputs of the notebook "Variational data assimilation with deep prior (CIRC23)" published in The Environmental Data Science Book.

TERN Ecosystem Data Services and Analytics

TERN Research Data Repository

Simplify your research data collection with the help of the research data repository managed by the Terrestrial Ecosystem Research Network. Our collection of ecosystem data includes ecoacustics, bio acoustics, lead area index information and much more.

The TERN research data collection provides analysis-ready environment data that facilitates a wide range of ecological research projects undertaken by established and emerging scientists from Australia and around the world. The resources which we provide support scientific investigation in a wide array of environment and climate research fields along with decision-making initiatives.

Explore Our Ecosystem Data Portals

Open access ecosystem data collections via the TERN Data Discovery Portal and sub-portals:

Access all TERN Environment Data

Discover datasets published by TERN’s observing platforms and collaborators. Search geographically, then browse, query and extract the data via the TERN Data Discovery Portal.

Search EcoPlots data

Search, integrate and access Australia’s plot-based ecology survey data.

Download ausplotsR

Extract, prepare, visualise and analyse TERN Ecosystem Surveillance monitoring data in R.

Search EcoImages

Search and download Leaf Area Index (LAI), Phenocam and Photopoint images.

Explore our data services

Tools that support the discovery, anaylsis and re-use of data:

Visualise the data

We’ve teamed up with ANU to provide 50 landscape and ecosystem datasets presented graphically.

Access CoESRA Virtual Desktop

A virtual desktop environment that enables users to create, execute and share environmental data simulations.

Submit data with SHaRED

Our user friendly tool to upload your data securely to our environment database so you can contribute to Australia’s ecological research.

Other data portals, tools and services

The Soil and Landscape Grid of Australia provides relevant, consistent, comprehensive, nation-wide data in an easily-accessible format. It provides detailed digital maps of the country’s soil and landscape attributes at a finer resolution than ever before in Australia.

The annual Australia’s Environment products summarise a large amount of observations on the trajectory of our natural resources and ecosystems. Use the data explorer to view and download maps, accounts or charts by region and land use type. The website also has national summary reports and report cards for different types of administrative and geographical regions.

TERN’s ausplotsR is an R Studio package for extracting, preparing, visualising and analysing TERN’s Ecosystem Surveillance monitoring data. Users can use the package to directly access plot-based data on vegetation and soils across Australia, with simple function calls to extract the data and merge them into species occurrence matrices for analysis or to calculate things like basal area and fractional cover.

The Australian Cosmic-Ray Neutron Soil Moisture Monitoring Network (CosmOz) delivers soil moisture data for 16 sites over an area of about 30 hectares to depths in the soil of between 10 to 50 cm. In 2020, the CosmOz soil moisture network, which is led by CSIRO, is set to be expanded to 23 sites.

The TERN Mangrove Data Portal provides a diverse range of historical and contemporary remotely-sensed datasets on extent and change of mangrove ecosystems across Australia. It includes multi-scale field measurements of mangrove floristics, structure and biomass, a diverse range of airborne imagery collected since the 1950s, and multispectral and hyperspectral imagery captured by drones, aircraft and satellites.

The TERN Wetlands and Riparian Zones Data Portal provides access to relevant national to local remotely-sensed datasets and also facilitates the collation and collection of on-ground data that support validation.

ecocloud provides easy access to large volumes of curated ecosystem science data and tools, a computing platform and resources and tools for innovative research. ecocloud gives you 10GB of persistent storage to keep your code/notebooks so they are ready to go when you start up a server (R or Python environment). It uses the JupyterLabs interface, which includes connections to GitHub, Google Drive and Dropbox.

Analysis Ready Ecosystem Data

Our research data collection makes it easier for scientists and researchers to investigate and answer their questions by providing them with open data, research and management tools, infrastructure, and site-based research tools.

The TERN data portal provides open access ecosystem data. Our tools support data discovery, analysis, and re-use. The services which we provide facilitate research, education, and management. We maintain a network of monitoring site and sensor data streams for long-term research as part of our research data repository.

The dataset contains the outputs of the notebook "SEVIRI Level 1.5" published in The Environmental Data Science Book.

Contributions

Notebook

• Samuel Jackson (author), Science & Technology Facilities Council, @samueljackson92

• Alejandro Coca-Castro (reviewer), The Alan Turing Institute, @acocac, 18/01/22 (latest revision)

Dataset originator/creator

SEVIRI Level 1.5 Image Data - MSG - 0 degree

• European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)

FRPPIXEL

• Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)

Dataset authors

SEVIRI Level 1.5 Image Data - MSG - 0 degree

• European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)

FRPPIXEL

• Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)

Dataset documentation

• Martin Wooster, Jiangping He, Weidong Xu, and Alessio Lattanzio. Frp - product user manual. URL: https://nextcloud.lsasvcs.ipma.pt/s/pnDEepeq8zqRyrq (visited on 2021-11-18).

• MJ Wooster, G Roberts, PH Freeborn, W Xu, Y Govaerts, R Beeby, J He, A Lattanzio, D Fisher, and R Mullen. Lsa saf meteosat frp products–part 1: algorithms, product contents, and analysis. Atmospheric Chemistry and Physics, 15(22):13217–13239, 2015.

Graph and download economic data for Employed full time: Wage and salary workers: Environmental scientists and geoscientists occupations: 16 years and over (LEU0254481200A) from 2000 to 2019 about geoscientists, environmental, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

The environmental impact on health is an inevitable by-product of human activity. Environmental health sciences is a multidisciplinary field addressing complex issues on how people are exposed to hazardous chemicals that can potentially affect adversely the health of present and future generations. Exposure sciences and environmental epidemiology are becoming increasingly data-driven and their efficiency and effectiveness can significantly improve by implementing the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship. This will enable data integration, interoperability and (re)use while also facilitating the use of new and powerful analytical tools such as artificial intelligence and machine learning in the benefit of public health policy, and research, development and innovation (RDI). Early research planning is critical to ensuring data is FAIR at the outset. This entails a well-informed and planned strategy concerning the identification of appropriate data and metadata to be gathered, along with established procedures for their collection, documentation, and management. Furthermore, suitable approaches must be implemented to evaluate and ensure the quality of the data. Therefore, the ‘Europe Regional Chapter of the International Society of Exposure Science’ (ISES Europe) human biomonitoring working group (ISES Europe HBM WG) proposes the development of a FAIR Environment and health registry (FAIREHR) (hereafter FAIREHR). FAIR Environment and health registry offers preregistration of studies on exposure sciences and environmental epidemiology using HBM (as a starting point) across all areas of environmental and occupational health globally. The registry is proposed to receive a dedicated web-based interface, to be electronically searchable and to be available to all relevant data providers, users and stakeholders. Planned Human biomonitoring studies would ideally be registered before formal recruitment of study participants. The resulting FAIREHR would contain public records of metadata such as study design, data management, an audit trail of major changes to planned methods, details of when the study will be completed, and links to resulting publications and data repositories when provided by the authors. The FAIREHR would function as an integrated platform designed to cater to the needs of scientists, companies, publishers, and policymakers by providing user-friendly features. The implementation of FAIREHR is expected to yield significant benefits in terms of enabling more effective utilization of human biomonitoring (HBM) data.

Data files for the examples in the book Geographic Data Science in R: Visualizing and Analyzing Environmental Change by Michael C. Wimberly.

The dataset contains the outputs of the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery" published in the ocean modelling section of The Environmental Data Science Book.

Contributions

Notebook

• Jamila Mifdal (author), European Space Agency Φ-lab, @jmifdal

• Raquel Carmo (author), European Space Agency Φ-lab, @raquelcarmo

• Alejandro Coca-Castro (reviewer), The Alan Turing Institute, @acocac

Modelling codebase

• Jamila Mifdal (author), European Space Agency Φ-lab, @jmifdal

• Raquel Carmo (author), European Space Agency Φ-lab, @raquelcarmo

• Marc Rußwurm (author), EPFL-ECEO, @marccoru

The two case studies used publicly available environmental data excerpted from the National Human Exposure Assessment Survey (NHEXAS) (https://github.com/USEPA/HEDS). These U.S. Environmental Protection Agency (EPA) Region 5 data were samples collected in 1995 to 1997 from the first visit, which removed temporal between-visit correlation from sites across Ohio, Michigan, Illinois, Indiana, Wisconsin, and Minnesota. The NHEXAS tap water sampling design distinguished original samples from quality control replicates, and all samples were analyzed in the same laboratory following EPA standard method 200.8 (version 4.4). The case studies analyze subsets of NHEXAS arsenic and chromium data that were selected to meet distributional assumptions and cannot be interpreted as NHEXAS analyses. This dataset is associated with the following publication: Furman, M., K. Thomas, and B. George. Separating Measurement Error and Signal in Environmental Data: Use of Replicates to Address Uncertainty. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 57(41): 15356-15365, (2023).

This is a PBL module on Non-Renewable Energy to be used in an introductory environmental science course to motivate students to discuss related climate justice issues. (Please note that this module has not yet been tested in the classroom as of May 22, 2025.)Original data was from the U.S. Energy Information Administration under their State Energy Data System (SEDS): 1960-2022 (complete) under the Key statistics and ranking section.This data was processed and cleaned as described in the data provenance document.Lecture Slides, Activity Sheets and Instructor Notes are available here.The following files are included:Data Provenance: EIA-Non-Renewable-Data-Provenance.pdfR Script for Data Processing: DSEJ_data_cleaning_EIA.RCurated Data used in the Module: EIA_Non_Renewable.csvOriginal Dataset from EIA: use_tot_realgdp.csv

This data release contains two tables with information on seasonal values for temperature, wind, and snow cover collected at the Colville River Delta, Alaska, 2011-2018. Researchers with the U.S. Geological Survey used an on-site weather station to automatically record the temperature and speed and direction of the wind across the duration of their field season. Researchers also established permanent plots that they visited to record the percent cover of snow from their arrival at the site each spring until all snow had melted.

The CO2 Virtual Data Environment is a comprehensive effort at bringing together the models, data, and tools necessary to perform research on atmospheric CO2.This site presents web-based discovery and access resources designed to streamline the process of accessing relevant global and regional carbon dioxide data sets. Furthermore, this site provides tools for conversion, manipulation, and transformation of the data to facilitate research.

This dataset is a collection of marine environmental data layers suitable for use in Southern Ocean species distribution modelling. All environmental layers have been generated at a spatial resolution of 0.1 degrees, covering the Southern Ocean extent (80 degrees S - 45 degrees S, -180 - 180 degrees). The layers include information relating to bathymetry, sea ice, ocean currents, primary production, particulate organic carbon, and other oceanographic data.

An example of reading and using these data layers in R can be found at https://australianantarcticdivision.github.io/blueant/articles/SO_SDM_data.html.

The following layers are provided:

1. Layer name: depth Description: Bathymetry. Downloaded from GEBCO 2014 (0.0083 degrees = 30sec arcmin resolution) and set at resolution 0.1 degrees. Then completed with the bathymetry layer manually corrected and provided in Fabri-Ruiz et al. (2017) Value range: -8038.722 - 0 Units: m

Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Citation: Fabri-Ruiz S, Saucede T, Danis B and David B (2017). Southern Ocean Echinoids database_An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database. ZooKeys, (697), 1.

1. Layer name: geomorphology Description: Last update on biodiversity.aq portal. Derived from O'Brien et al. (2009) seafloor geomorphic feature dataset. Mapping based on GEBCO contours, ETOPO2, seismic lines). 27 categories Value range: 27 categories Units: categorical Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

2. Layer name: sediments Description: Sediment features Value range: 14 categories Units: categorical Source: Griffiths 2014 (unpublished) URL: http://share.biodiversity.aq/GIS/antarctic/

3. Layer name: slope Description: Seafloor slope derived from bathymetry with the terrain function of raster R package. Computation according to Horn (1981), ie option neighbor=8. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Unit set at degrees. Value range: 0.000252378 - 16.94809 Units: degrees Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Citation: Horn, B.K.P., 1981. Hill shading and the reflectance map. Proceedings of the IEEE 69:14-47

4. Layer name: roughness Description: Seafloor roughness derived from bathymetry with the terrain function of raster R package. Roughness is the difference between the maximum and the minimum value of a cell and its 8 surrounding cells. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Value range: 0 - 5171.278 Units: unitless Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/

5. Layer name: mixed layer depth Description: Summer mixed layer depth climatology from ARGOS data. Regridded from 2-degree grid using nearest neighbour interpolation Value range: 13.79615 - 461.5424 Units: m Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

6. Layer name: seasurface_current_speed Description: Current speed near the surface (2.5m depth), derived from the CAISOM model (Galton-Fenzi et al. 2012, based on ROMS model) Value range: 1.50E-04 - 1.7 Units: m/s Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214, https://data.aad.gov.au/metadata/records/polar_environmental_data

7. Layer name: seafloor_current_speed Description: Current speed near the sea floor, derived from the CAISOM model (Galton-Fenzi et al. 2012, based on ROMS) Value range: 3.40E-04 - 0.53 Units: m/s Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214, https://data.aad.gov.au/metadata/records/polar_environmental_data

8. Layer name: distance_antarctica Description: Distance to the nearest part of the Antarctic continent Value range: 0 - 3445 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

9. Layer name: distance_canyon Description: Distance to the axis of the nearest canyon Value range: 0 - 3117 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

10. Layer name: distance_max_ice_edge Description: Distance to the mean maximum winter sea ice extent (derived from daily estimates of sea ice concentration) Value range: -2614.008 - 2314.433 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

11. Layer name: distance_shelf Description: Distance to nearest area of seafloor of depth 500m or shallower Value range: -1296 - 1750 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

12. Layer name: ice_cover_max Description: Ice concentration fraction, maximum on [1957-2017] time period Value range: 0 - 1 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

13. Layer name: ice_cover_mean Description: Ice concentration fraction, mean on [1957-2017] time period Value range: 0 - 0.9708595 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

14. Layer name: ice_cover_min Description: Ice concentration fraction, minimum on [1957-2017] time period Value range: 0 - 0.8536261 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

15. Layer name: ice_cover_range Description: Ice concentration fraction, difference maximum-minimum on [1957-2017] time period Value range: 0 - 1 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

16. Layer name: ice_thickness_max Description: Ice thickness, maximum on [1957-2017] time period Value range: 0 - 3.471811 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

17. Layer name: ice_thickness_mean Description: Ice thickness, mean on [1957-2017] time period Value range: 0 - 1.614133 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

18. Layer name: ice_thickness_min Description: Ice thickness, minimum on [1957-2017] time period Value range: 0 - 0.7602701 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

19. Layer name: ice_thickness_range Description: Ice thickness, difference maximum-minimum on [1957-2017] time period Value range: 0 - 3.471811 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

20. Layer name:

This data includes the data used to make Figures 1 and 2 in the manuscript.

For Figure 1, we downloaded the titles from publications listed on the NEON publication website (https://www.neonscience.org/impact/papers-publications). Then, we imported the .csv (NEON_Word_Cloud_Data.csv) with the publication titles to the wordart.com software program which created the word cloud from this text. Settings such as word choice, spacing, font, and color were chosen manually. Only the most frequently appearing words (top ~⅓ of the list) were included in the figure. In addition to connecting words such as “and”, words part of NEON’s title (“National”, “Ecological”, “Observatory”, “Network”) were excluded from the final figure.

For Figure 2, we obtained the perimeter data (Chimney_Tops_2_fire.shp) for the Chimney Tops 2 fire from MTBS perimeter data (https://www.mtbs.gov/direct-download). We downloaded the NEON AOP flight boundary for the Great Smoky Mountains NEON site (GRSM_NEON_AOP....

Here is the list of research questions and themes that we, as members of the research community, developed to begin to prioritize the future synthesis needs in ecology and environmental science. To develop these priorities, we convened a virtual workshop at the National Center for Ecological Analysis and Synthesis (NCEAS) on Feb 17-18, 2021, with 127 participants across career stages, institutions, backgrounds, and geographies which were selected through an application process (see Halpern et al. 2022, Appendix S1). Participants were drawn from ecology and environmental sciences and largely identified as natural scientists. We asked workshop participants to anonymously identify key synthesis questions in ecology and environmental science, and the challenges and innovations needed to answer those questions. Participants proposed ideas or questions in pre-workshop brainstorming sessions; added and upvoted questions online; and worked in breakout teams during the workshop to refine upvoted questions into lists of top three questions. These final lists were then grouped into themes by the 12-person steering committee and discussed at length by the workshop participants.

This data set provides a record of the half-hourly averages of automated environmental data collected for 12 SPRUCE plots (4, 6, 7, 8, 10, 11, 13, 16, 17, 19, 20, and 21) beginning during deep peat heating (DPH) and continuing throughout the whole ecosystem warming (WEW) manipulations for the SPRUCE Project (Hanson et al. 2017). In August 2015, WEW was initiated at 5 warming levels (+0, +2.25 +4.5, +6.75 and +9 °C) with 2 plots per warming level. DPH measurements were underway before the initiation of WEW heating treatments and both are expected to operate through 2025. This current version includes data from 2014 through 2024. This data set includes 15 data files provided in comma separated (*.csv) format: 12 individual data files for each of the monitored SPRUCE plots, two data files corresponding to figures from Hanson et al. (2017) that support key analyses of the performance of the WEW systems, and one data file containing snow and ice depths.