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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19

Stomatal conductance and leaf temperature raw data from Aug 2016-Nov 2017 at the B34 tower in Manaus, Brazil

This data package contains raw data from stomatal conductance and leaf temperature measurements from the B34 tower in Manaus, Brazil. The attached zip file contains two data files and two metadata files. Additional metadata, such as site information and equipment manuals, are included in the data package listed under Dataset Reference(s). Contact israelmdt@gmail.com if you need to use this dataset for additional information.

54 ENVIRONMENTAL SCIENCES↗

Stomatal conductance and leaf temperature raw data from Aug 2016-Nov 2017 at the K34 tower in Manaus, Brazil

These are raw data from stomatal conductance and leaf temperature measurements from the NGEE-Tropics K34 tower in Manaus, Brazil. The attached zip file contains two data files and two metadata files. Related data packages, including additional metadata such as site information and equipment manuals, are included in the data package listed under Dataset Reference(s). Contact israelmdt@gmail.com if you need to use this dataset for additional information.

54 ENVIRONMENTAL SCIENCES↗

Stomatal conductance and leaf temperature raw data from Aug 2016-Nov 2017 at the B34 tower in Manaus, Brazil

This data package contains raw data from stomatal conductance and leaf temperature measurements from the B34 tower in Manaus, Brazil. The attached zip file contains two data files and two metadata files. Additional metadata, such as site information and equipment manuals, are included in the data package listed under Dataset Reference(s). Contact israelmdt@gmail.com if you need to use this dataset for additional information.

54 ENVIRONMENTAL SCIENCES↗

Raw leaf temperature data from May 2017- Aug 2017 at the B34 and K34 towers in Manaus, Brazil

This data package contains raw data from leaf temperature measurements. Additional metadata, such as site information and equipment manuals, are included in the data package listed under Dataset Reference(s) along with all related data packages. Leaf temperature measurements were made using Teflon insulated type T thermocouples (OM-CP-OCTTEMP-A Nomad®, Omega Engineering) directly attached to the abaxial side of the leaf using a breathable white tape and configured to register measurements every 15 s. No data processing or QA/QC was done on the raw datasets. Processed datasets will be uploaded separately. Contact israelmdt@gmail.com if you need to use this dataset for additional information.

54 ENVIRONMENTAL SCIENCES↗

Air temperature and relative humidity raw data from June 2017-Oct 2018 at the B34 tower in Manaus, Brazil

This data package contains raw data from stomatal conductance and leaf temperature measurements from the B34 tower at the NGEE Tropics site in Manaus, Brazil. Related data packages, including additional metadata such as site information and equipment manuals, are included in the data package listed under Dataset Reference(s). Air temperature and relative humidity data were obtained using a thermohygrometer (HC2S3, Campbell Scientific®) installed in tower K34 making a profile of (HR_: 7m, HR_2: 14m, HR_3: 21m). Data files are included in CSV and Excel file formats. No data processing or QA/QC was done on the raw datasets. Processed datasets will be uploaded separately. Contact israelmdt@gmail.com if you need to use this dataset for additional information.

54 ENVIRONMENTAL SCIENCES↗

Open Energy Data Initiative (OEDI) FY22-24 (Final Technical Report)

Final technical report for the Open Energy Data Initiative (OEDI) project covering fiscal years FY22 through FY24. The DOE Open Energy Data Initiative (OEDI) is a partnership between the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and major cloud providers including Amazon, Microsoft, and Google to provide universal access to big data in the cloud. At the heart of OEDI is a centralized repository of high-value energy research datasets aggregated from the U.S. Department of Energy's Program Offices, National Laboratories and other collaborators. It aggregates smaller, domain-specific repositories, allows direct data submissions, and includes support for big data through its energy data lakes. OEDI's data lakes make high-value data universally accessible and help researchers, collaborators and the general public overcome many of the obstacles to accessing and using big data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Vegetation Warming Experiment: Thaw depth and dGPS locations, Utqiagvik, Alaska, 2021

Thaw depth measurements within and around warming chambers, and in ambient plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Measurements were taken at the start and end of chamber deployment, and two intermediate times during the 2021 growth season. dGPS measurements of chamber and ambient plot locations are also included. The files included in this data package are in .csv format, and include 2 data files and 3 metadata files. This data was recorded as part of the Zero Power Warming (ZPW) vegetation warming experiment. Other datasets under the Vegetation Warming Experiment include data for environmental conditions, leaf physiology, leaf traits, and landscape and plot phenocam images. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

A machine learning method of modern urban building energy modeling: A case study of Chicago

Urban-scale building energy modeling is vital for urban planning. However, it can be challenging to assimilate reliable non-geometry building data for urban-scale modeling without extensive investment. Here, this study introduces a novel approach to developing modern urban-scale building energy stock data using geographic information systems and machine learning algorithms without necessarily requiring pre-supplied non-geometric metadata. The proposed framework integrates building footprint and height data to estimate gross floor areas, and matches each building to a pool of candidate records from ComStock or ResStock—filtered to the same county and ranked by geometric similarity—demonstrate a proof-of-concept case study in Chicago for predicting energy use intensity (EUI) using scalable datasets. The model achieved a mean bias error (MBE) of 0.08 kWh/m² and root mean square error (RMSE) of 14.84 kWh/m² under full metadata input for EUI prediction. With only location inputs, the model captured 69.2 % of EUI within predicted ranges. These results demonstrate the model’s potential to support early-stage urban planning, identify candidates for energy-efficient retrofits. By removing the dependency on detailed pre-surveys or extensive building metadata, the approach overcomes a key barrier in traditional urban-scale building energy modeling, illustrating a pathway toward broader and more cost-effective application, though further multi-city validation and improved treatment of pre-1925 buildings are needed.

Energy Use Intensity↗

ESS-DIVE reporting format for leaf-level gas exchange data and metadata

Here we present documentation of the ESS-DIVE reporting format for leaf-level gas exchange data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-leaf-gas-exchange v1.0. The zip contains the reporting format description, guidelines, variable tables and instructions in GitHub markdown language (*.md) and 2 metadata templates as spreadsheets with drop down options (*.xlsx files, also function in GoogleSheets).

54 ENVIRONMENTAL SCIENCES↗

Lab Homes

This dataset includes processed data from the Lab Homes (LH) Test Facility located on the PNNL campus in Richland, WA. This a set of 2 identical homes that allow for the side-by-side comparison/performance evaluation of different technologies under the same weather at any given time. The dataset spans December 6, 2021 to December 27, 2021 and represents a series of tests performed; calibration, set-point excitation, pre-heating, free-floating and warm up. The measurements correspond to whole building electrical power, HVAC energy use, water heating, appliances and lighting, as well as space temperatures, space humidity, window glass surface temperatures, through glass solar radiation, and meterological data from an onsite meteorological weather station. In addition to the measurements, a metadata .json file, a .ttl file to visualize the data as per BRICK schema, and a detailed .pdf description of the dataset are also provided.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Site and endmember spectra of terrestrial vegetation and soils for the Colorado Headwaters Ecological Spectroscopy Study, June-July 2025

This dataset provides site and endmember spectra collected during the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign. The site spectra were collected to help validate airborne hyperspectral data acquired by the National Ecological Observatory Network's aerial observation platform (NEON AOP). Endmember spectra were collected to augment existing spectral libraries with additional samples of bare surfaces and non-photosynthetic vegetation. All measurements were acquired with an Analytical Spectral Devices (ASD) FieldSpec4 Hi-Res NG (Next Generation) spectroradiometer, which records radiance at 1nm (nanometer) intervals from the ultraviolet to the short-wave infrared (350-2500 nm). The dataset includes spectra measured at meadow sites where the CHESS team also collected vegetation samples for trait analyses. The site spectra were collected with the ASD FieldSpec4 palm grip attachment using an 8° field-of-view foreoptic. Site spectra are integrated measurements of the entire surface within the foreoptic’s field of view. For site-level spectra, the sun is the illumination source. A Spectralon panel mounted on a tripod was used for instrument optimization and white reference measurements for all site spectra. Site spectra were acquired within two hours of solar noon and within 48 hours of a NEON AOP overflight. Site spectra are labeled by date, sampling area, and site number according to the naming conventions of the CHESS campaign’s data management plan. The dataset also contains endmember spectra in the following categories: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare (soil/rock), and flowers. Endmember measurements were acquired using either the contact probe or the leaf clip attachments of the ASD FieldSpec4. In these configurations, the bulb inside the spectrometer provides the light source for the measurements. The spectrometer was optimized and white reference measurements were recorded using the circular white pucks attached to the contact probe and leaf clip. Because they do not rely on solar illumination, contact probe and leaf clip measurements were collected during a broader time frame than the palm grip site spectra. Some endmembers were measured at CHESS meadow sites, while others were collected within the larger sampling area or in nearby locations (e.g. Gothic Townsite) with similar characteristics. Radiance, reflectance, and metadata files are split into three subfolders according to measurement type: proximal/palm grip (prx), contact probe (cp), and leaf clip (lc). Radiance spectra are provided in ASD file format (.asd file extension). All ASD files can be opened using the provided scripts. Metadata is provided in two formats: CSV file format (no geolocation) and GEOJSON file format (includes geolocation for each spectra). The dataset includes a set of pre-processed reflectance spectra as CSV files (yyyymmdd_rfl.csv). The python scripts and jupyter notebook used to calculate reflectance spectra from the ASD radiance data is included here and was previously published at: https://doi.org/10.3334/ORNLDAAC/2446. There is also a folder of JPEG photographs corresponding to selected spectra. We include a protocol document with detailed steps for ASD FieldSpec4 assembly and operations. This data additionally contains a file level metadata (flmd.csv) and data dictionary (dd.csv) file. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗

Temporal Study 2021-2022: Sensor-Based Time Series of Surface Water Temperature, Specific Conductance, Total Dissolved Solids, Turbidity, pH, and Dissolved Oxygen from across Multiple Watersheds in the Yakima River Basin in Washington, USA (v2)

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides periodic (weekly or biweekly) in situ hydrological and water chemistry sensor data, handheld sensor water chemistry data, general environmental context photos, and field metadata collected at six sites within multiple rivers in the Yakima River Basin in Washington, USA. In addition to the sensor data, there are plots of continuous in situ sensor data and R scripts used to generate the plots. Related sample-based water chemistry data are published separately at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1898912.The data package was originally published in September 2022. It was updated in June 2025 (v2; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one main data folder containing two sensor-specific subfolders, and one photographs folder. The main data folder includes file-level metadata (flmd), data dictionary (dd), installation methods, field metadata, handheld sensor data, field data collection protocols, international generic sample number (IGSN) mapping file, and a readme file. Each sensor subfolder (BarotrollAtm and MantaRiverData) contains a subfolder containing sensor timeseries data and plots. The BarotrollAtm Data subfolder contains In Situ Rugged BaroTROLL sensor pressure and air temperature data. The MantaRiverData subfolder contains Eureka Manta+ 35B multisonde temperature, specific conductance, and turbidity. The FieldPhotos folder contains environmental context photographs and videos. All files are .csv, .pdf, .R, .jpg, .jpeg, .heic, .mov, or .mp4.

54 ENVIRONMENTAL SCIENCES↗

Continuous soil temperature measurements from 2019-10-4 to 2020-10-4, Teller road Mile 27, Seward Peninsula, Alaska

The dataset contains depth-resolved soil temperature measured at 45 discrete locations in a watershed located along the Nome-Teller road at Mile 27 in Seward Peninsula, Alaska. The dataset was generated to understand the local heterogeneity of soil thermal dynamics and their controls in a discontinuous permafrost region. At each location, temperatures were measured by a distributed temperature profiling probe designed based on Dafflon et al (2022). The dataset includes a description of the probe locations in the "Probe_locations.csv" file and the 45 data files (Soil_temperatures_*.csv). Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in NGA513_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Produced Water DNA Database (PW-DNA): Utilizing KBase to generate an environmental specific curated molecular database

The deep subsurface is estimated to host the majority of Earth’s microbial biomass yet remains one of the most challenging environments to access and study. One common approach to investigate these microbial communities is through the analysis of produced water from subsurface reservoirs, where researchers can assess water and gas chemistry along with molecular (DNA/RNA) sequence data. Advances in high-throughput sequencing have greatly expanded our understanding of these environments and their biotechnological potential. However, further progress requires large-scale, integrative meta-analyses across diverse datasets. To address this need, we developed the Produced Water-DNA (PW-DNA) Database, a curated, publicly available resource that consolidates microbial DNA/RNA sequences, geochemical data, and relevant metadata from in situ hydrocarbon environments such as coal beds, oil reservoirs, and natural gas systems. The PW-DNA database delivers three core benefits to the research community: (1) it improves data sharing by linking environmental microbial datasets with corresponding geochemical parameters, enabling more robust filtering and analysis; (2) it connects with complementary research databases to promote broader dissemination and interoperability; and (3) it supports technological innovation by serving as a resource for identifying microbial trends and exploring genetic potential. While individual studies have highlighted basin-specific microbial communities and functional redundancy in biogeochemical cycling, a comprehensive, system-wide perspective is needed to better understand connectivity and novelty across subsurface ecosystems. By designing the PW-DNA in the KBase platform, we provide a reproducible, visual framework for integrating large-scale genomic and geochemical data, enabling researchers to perform more informed analyses and experimental design. Ultimately, this resource enhances the ability to identify, characterize, and interpret microbial functions across diverse subsurface environments, thereby accelerating discovery in subsurface microbiology and biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗

WHONDRS Surface Water and Sediment Geochemistry and Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon (v2)

This dataset supports a broader study developing conceptual models for river corridor critical zone processes across spatial scales and was generated in collaboration with the HJ Andrews River Corridor Critical Zone Workshop in 2025. The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen) from 48 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Some of the sites have been impacted by the Holiday Farm Fire and the Lookout Fire in 2020 and 2023, respectively. Related data were collected as part of the workshop and will be published separately in collaboration with other workshop attendees and available at http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. Related genomic data can be found on the National Center for Biotechnology Information (NCBI) under BioProject PRJNA1503030 (see critical details section below for more information). Additional related data collected in 2016 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3377027 and http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1-2019 (Ward et al., 2019). This data package was originally published in March 2026. It was updated in August 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos, (2) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data, (3) a data checks report, (4) a folder of sample data, (5) file-level metadata, (6) data dictionary, (7) field metadata, (8) readme, (9) international generic sample number (IGSN) mapping file; and (10) field protocol. The sample data subfolder contains surface water and sediment (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages, (2) total dissolved nitrogen data and averages, (3) methods codes, (4) FTICR-MS methods; and (5) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the CoreMS processed data and seven subfolders, thee containing .xml files for each sample type (sediment, surface water and blank samples), three containing the sediment CoreMS output files for each sample type (sediment, surface water and blank samples), and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, .json, .jpg, or .jpeg.

Biogeochemistry↗

Ontology-Enriched Specifications Enabling Findable, Accessible, Interoperable, and Reusable Marine Metagenomic Datasets in Cyberinfrastructure Systems

Marine microbial ecology requires the systematic comparison of biogeochemical and sequence data to analyze environmental influences on the distribution and variability of microbial communities. With ever-increasing quantities of metagenomic data, there is a growing need to make datasets Findable, Accessible, Interoperable, and Reusable (FAIR) across diverse ecosystems. FAIR data is essential to developing analytical frameworks that integrate microbiological, genomic, ecological, oceanographic, and computational methods. Although community standards defining the minimal metadata required to accompany sequence data exist, they haven’t been consistently used across projects, precluding interoperability. Moreover, these data are not machine-actionable or discoverable by cyberinfrastructure systems. By making ‘omic and physicochemical datasets FAIR to machine systems, we can enable sequence data discovery and reuse based on machine-readable descriptions of environments or physicochemical gradients. In this work, we developed a novel technical specification for dataset encapsulation for the FAIR reuse of marine metagenomic and physicochemical datasets within cyberinfrastructure systems. This includes using Frictionless Data Packages enriched with terminology from environmental and life-science ontologies to annotate measured variables, their units, and the measurement devices used. This approach was implemented in Planet Microbe, a cyberinfrastructure platform and marine metagenomic web-portal. Here, we discuss the data properties built into the specification to make global ocean datasets FAIR within the Planet Microbe portal. We additionally discuss the selection of, and contributions to marine-science ontologies used within the specification. Finally, we use the system to discover data by which to answer various biological questions about environments, physicochemical gradients, and microbial communities in meta-analyses. This work represents a future direction in marine metagenomic research by proposing a specification for FAIR dataset encapsulation that, if adopted within cyberinfrastructure systems, would automate the discovery, exchange, and re-use of data needed to answer broader reaching questions than originally intended.

59 BASIC BIOLOGICAL SCIENCES↗

Leafweb: Dataset in Support of Coupled Modeling of Photophysics, Photochemistry, and Biochemistry of Photosynthesis, December 2022 Release

This data set contains measurements of leaf gas exchange and Pulse-Amplitude Modulated (PAM) fluorometry of light, carbon dioxide (CO2), oxygen (O2), and temperature responses from 26 C3 photosynthetic and four C4 photosynthetic species measured by independent researchers in Canada, China, Finland, Netherlands, and United States in the field, garden, or greenhouse. Data were collected between 1987 and 2021, however data for individual species only provide coverage over a few hours to one year. Species include three lianas, three shrubs, two boreal deciduous trees, two boreal evergreen needle-leaf tree, three temperate deciduous trees, four tropical deciduous trees, three tropical evergreen trees, one C3 grass, three C4 grasses, and six crop varieties. These measurements are conducted according to standard protocols in gas exchange (Long and Bernacchi 2003) and PAM fluorometry (Baker 2008). The Scots pine (Pinus sylvestris) dataset contains one-year continuous fluorometry observations made at intervals of 10 or 30 minutes in the field under natural environments using Walz monitoring PAM. Measurements from all other 29 species include simultaneous PAM fluorometry and gas exchange observations. Among these 29 species, seven species were measured with light response curves only (i.e., light intensity varied systematically with ambient CO2 concentration controlled at a constant level, e.g., 400 ppm). All the other 22 species were measured with both the light response and CO2 response (i.e., ambient CO2 concentration varied systematically with light intensity controlled at a constant level, e.g., 1200 µmolm-2s-1). For most species, measurements were made with temperature controlled at ~ 25 degrees Celsius (C) with the exceptions of Scots pine (natural diurnal and seasonal variations), tomato cultivar Basket Vee (~ 21 degrees C), and cotton which contained temperature stress experiments (9 to 40 degrees C). All measurements were made at ambient O2 concentration except for the tomato cultivar Basket Vee and cotton which used two O2 levels (2 and 21%) and rice and tomato cultivar Growdena which used five O2 levels (2, 10, 21, 35, and 50%). This dataset supports the complete modeling of photosynthesis at the leaf scale, including photophysical, photochemical, and biochemical reactions. It will support research in the applications of remotely sensed solar induced chlorophyll fluorescence and the responses of energy dissipation pathways to environmental variations across species and climates. This dataset contains 260 data files in comma separate (.csv) format contained inside 11 compressed (*.zip) files. Additional metadata are provided: one data dictionary, a species information table, and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

air temperature↗

SPRUCE Photosynthesis and Respiration of Picea mariana and Larix laricina in SPRUCE Experimental Plots, 2016-2017

This dataset contains empirical physiological, morphological, and chemical data collected on two dominant conifer species, Picea mariana and Larix laricina, between May 2016 and August 2017 at the SPRUCE (Spruce and Peatland Responses Under Changing Environments) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Analyses assessed the response of conifers to whole-ecosystem warming and elevated atmospheric carbon dioxide (CO2) concentrations. Measurements include light-saturated net photosynthesis and dark respiration at ambient atmospheric carbon dioxide (CO2) concentration (2016), CO2 response curves of light-saturated net photosynthesis (2017), leaf morphology (leaf mass per unit leaf area) and nitrogen content (on both mass and area basis). Leaf photosynthesis and respiration were measured with portable photosynthesis systems (LI6400XT, LI6800, LI-COR Biosciences, USA). In 2016, photosynthesis and respiration were measured at a common temperature of 25 ˚C to assess thermal acclimation, and at growth temperatures (for each of the five treatment temperatures) to assess field performance. In 2017, CO2 response curves of net photosynthesis (so-called A-Ci curves) were conducted at different leaf temperatures (15, 25, 32.5, 40 and 45 ˚C) for assessment of biochemical parameters and their temperature sensitivity. In addition, dark respiration measurements at different leaf temperatures (10, 15, 20, 25, 30, 35, 40, and 45 ºC) were also taken. This dataset contains one data file in comma-separate values (*.csv) and Microsoft Excel (*.xlsx) format. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗