Rethinking the Design of Ionic Conductors Using Meyer?Neldel?Conductivity Plot
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Gao, Yirong↗
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Using Median Point in Keeling Plot to Reduce the Uncertainty of the Isotopic Composition of Evapotranspiration
Abstract The isotopic composition of evapotranspirationδ ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculateδ ET , though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested theδ ET uncertainty using the mean point [σ ET (mean)] and median point [σ ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. Theσ ET (mean) would be greater thanσ ET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter ofr 2 > 0.8, around 70% ofσ ET (mean) was greater thanσ ET (median). This phenomenon might be due to the normality of the vapor concentrationC υ producing the asymmetric distribution of 1/C υ . The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentrationC υ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.
Meteorology & Atmospheric Sciences↗
Admittance-Based Stability Analysis: Bode Plots, Nyquist Diagrams or Eigenvalue Analysis?
Not provided.
Engineering↗
SPRUCE Soil Volumetric Water Content in Experimental Plots, Marcell Experimental Forest, Minnesota, 2017-2025
This dataset contains soil volumetric water content (VWC) measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) Experiment. Measurements were made inside SPRUCE experimental plots in the S1 Bog at the US Forest Service Marcell Experimental Forest in northern Minnesota, USA from 2018-2025 (2018-06-14 to 2025-12-31). Observations were made with METER 10HS soil moisture sensors. To reduce bulk density related variability, 10HS sensors were placed inside mesh tubes filled with peat at a standard bulk density. The observations under standard bulk density represent relative differences in water content between hummock and hollow positions, and in response to experimental treatments. Productivity of peatlands, and their keystone species sphagnum, are highly dependent on water availability, which is affected by lateral inputs, precipitation, ground water depth and evapotranspiration. Knowledge of near surface and sphagnum water content is useful to understand peatland function. This dataset contains 8 data files in comma-separate values (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.
Warren, Jeffrey [ORNL] (ORCID:0000000206804697)↗
Plotting XPS Narrow Scans (and Other data) with MATLAB, 1. Using the 'plot' and 'subplot' functions.
As researchers, we often focus more on data collection than data work-up. However, skillfully plotting data so that it can be effectively visualized is often an important step toward making it impactful. Data visualization in academic writing takes the form of plots, figures, graphs, spectra, etc., which are often combined with cartoons and schematics. An important goal of data visualization is to make complex data and concepts more accessible, understandable, and usable, and to do this in a concise manner – ‘a picture is worth a thousand words’ as the old cliché goes. Data visualization is both art and science, where, of course, data visualization depends on the type of data in question. Herein, we discuss ways of representing/plotting data using MATLAB from the MathWorks ® .
97 MATHEMATICS AND COMPUTING↗
Plotting XPS Narrow Scans (and other Data) with MATLAB’s Powerful Plotting Capabilities, 2. An Introduction to Handle Graphics
The graphical representation of numerical data plays a vital role in chemistry, materials science, and every other scientific field. No matter how good your results are, poorly represented data can ultimately make your work confusing. Indeed, with an increase in the amount of data that is being gathered, e.g., by the Materials Genome Initiative, or in X-ray photoelectron spectroscopy (XPS) or time-of-flight secondary ion mass spectrometry (ToF-SIMS) depth profiles, the importance of effectively representing data has increased. Good figures also matter because many researchers will not look at more than the title, abstract, and perhaps a few key figures from a paper.
42 ENGINEERING↗
Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.
59 BASIC BIOLOGICAL SCIENCES↗
Mapping wall-to-wall fractional cover of Arctic tundra plant functional types in Alaska using 20-m spatial resolution satellite imagery and harmonized plot observations
Estimates of fractional cover (fCover) across given land surfaces are used to assess, and often model, vegetation composition and diversity, which are crucial for understanding the health and functioning of terrestrial ecosystems. Remote sensing provides a useful means for scaling local, plot-measured fCover estimates to regional scales. Leveraging a recently synthesized and harmonized plot database, this study generated wall-to-wall maps of fCover for six Alaskan-Arctic plant functional types (PFT), including non-vascular plants, forbs, graminoids, and deciduous and evergreen shrubs, using 20-m satellite data (Sentinel-1, Sentinel-2, ArcticDEM) using a machine learning regression approach, specifically the random forest (RF) algorithm, which is well-suited for handling nonlinear relationships and high-dimensional satellite datasets. This study additionally addressed the spatio-temporal inconsistencies e.g., sampling scale, plot size, and collection year in plot measured fCover by adopting a multivariate outlier detection approach—Cook’s distance—to identify high-quality plots for model training and validation. Our approach achieves high accuracy (R 2 = 0.59–0.93, root mean squared errors = 0.02–0.10 for all PFTs) between plot-observed and satellite-derived fCover when using high-quality plot samples. The mapped fCover characterizes the spatial patterns of different PFTs across the tundra biome at a 20-m resolution, providing key information needed for improved representation of Arctic tundra vegetation in terrestrial biosphere models to better understand climate-vegetation feedback across the Arctic tundra.
Arctic tundra↗
SPRUCE Vertical Profiles of CO2 and H2O Concentrations in Air in Experimental Plots Beginning in 2015
This dataset provides a record of hourly average CO2 and H2O concentrations in air at 4 vertical locations (0.5, 1, 2, and 4 m above ground level (agl)) on the central tower location for each of 13 SPRUCE experimental plots. Ten plots have enclosures (plots 4, 6, 8, 10, 11, 13, 16, 17, 19, and 20) and three plots (5, 7 and 21) are ambients without enclosures. Data are included for measurement periods beginning in 2015 and extend throughout the whole ecosystem warming (WEW) manipulations for the SPRUCE Project (Hanson et al. 2016, 2017). In August 2015, WEW was initiated at 5 warming levels (+0, +2. +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. Elevated CO2 treatments were initiated within 5 experimental plots (Plots 4, 10, 11, 16 and 19) in June of 2016. All measurements were made at the 8.1-ha S1 bog forest site in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF).
54 ENVIRONMENTAL SCIENCES↗
SPRUCE Terrestrial Laser Scanning of Experimental Plots, Beginning in 2015
This data set comprises terrestrial laser scanning (TLS) point clouds from the SPRUCE experimental plots located in the S1-Bog of the Marcell Experimental Forest (MEF) near Grand Rapids, Minnesota. In 2015 and 2020 the 12 experimental SPRUCE plots were scanned near peak foliage ("summer”). In 2016-2019 and 2021-2023 the 12 plots were scanned prior to green up ("spring”) and once near peak foliage (Table 1). Additional data will be added through summer 2025. A RIEGL VZ-1000 unit was used to collect all scans. Four TLS scans in each plot were registered to produce a single point cloud per plot using RiSCAN PRO v2.3 software. Up to six additional scans were registered to account for data occlusion when necessary. Point clouds are saved in LAS (*.las) 1.2 format. Point clouds were geo-registered to surveyed plot locations. There are 192 *.las files provided with this dataset.
54 ENVIRONMENTAL SCIENCES↗
SPRUCE Microtopography of Experimental Plots Derived from Terrestrial Laser Scans Beginning in 2016
This dataset consists of maps and summaries of very high resolution (1 cm2) spatial data derived from terrestrial laser scans (TLS) characterizing the microtopography (hummocks and hollows) in 12 SPRUCE experimental plots from 2016-2018. The SPRUCE experiment is located in the S1-Bog of the Marcell Experimental Forest (MEF) near Grand Rapids, Minnesota. Additional data will be added through Summer 2021.The TLS sampling plan follows the SPRUCE experimental design, with scanning of the ten enclosed treatment plots with five temperature treatments (0, 2.25, 4.5, 6.75, and 9°C) with and without elevated CO2 (~900 ppm) and the two monitored ambient plots without enclosures. Enclosures are octagonal in shape and are 12.8 m in diameter (114.8 m2 in area), 8 m tall, with a belowground corral that penetrates through the peat to glacial material below, which averages 2.5 m deep across the site. In the spring of each year, four TLS scans were taken per plot/enclosure and registered together to produce a single point cloud for each plot (Figure 2A). Digital surface models (DSMs) were generated from the point clouds and clipped to the inside boundary of the boardwalk in each plot resulting in an approximately 9 x 9 m study area. . All other characterization data types were derived from plot DSMs (elevation), including: slope (Figure 3), concavity (Figure 4), index used for microform classification (Hollow Index) (Figure 6), and microforms (Figure 2C; hummocks and hollows) classified using three methodologies.
54 ENVIRONMENTAL SCIENCES↗
SPRUCE Plot-Level Water Table Data Assessments for Absolute Elevations and Height with Respect to Mean Hollows Beginning in 2015
This data set provides a record of the half-hourly automated water table depth data collected for 12 SPRUCE plots (4, 6, 7, 8, 10, 11, 13, 14, 16, 17, 19, 20, and 21) beginning during deep peat heating (DPH) in 2015 and continuing throughout the whole ecosystem warming (WEW) manipulations for the SPRUCE Project (Hanson et al. 2017). The data currently cover 2015-05-12 through 2024-12-31 and will be updated periodically throughout the duration of the SPRUCE project. Also provided are daily plot normalized data for water table depths with respect to mean hollow locations within each plot. The hollow referenced data provide the basis for the intercomparison of peat saturation or aeration across treatment plots. A limited data set for manual water table observations made periodically from 2015-2022 are also provided as a reference to the plot center automated observations. In August 2015, WEW was initiated at 5 warming levels (+0, +2. +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. Elevated CO2 treatments were added in June 2016 (Enclosures 4, 10, 11, 16 and 19). All measurements were made at the 8.1-ha S1 bog forest site in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). This dataset contains three data files in comma separated (*.csv) format.
54 ENVIRONMENTAL SCIENCES↗
Tree damage and mortality measurements across seven ForestGEO plots in the tropics between Oct 2016 and Mar 2023
Annual records (raw data) on tree survival and structural completeness of 36,524 trees (2,467 species) collected across 29 censuses in seven tropical forests distributed across the Neotropics (Amacayacu, Colombia; Barro Colorado Island (BCI), Panamá; Yasuní, Ecuador) and Asia (Fushan, Taiwan; Huai Kha Khaeng (HKK), Thailand; Khao Chong (KC), Thailand; Pasoh, Malaysia). This dataset was used to compare aboveground biomass loss via damage to living trees relative to total AGB loss (mortality + damage). Variable definitions: site: name of the ForestGEO plot stemID.ams: Unique ID for the stem in the annual mortality surveys (ams) treeID.ams: Unique ID for the tree in the ams date.full.census: date of the previous full census of the plot, format: YYYY-MM-DD dbh.full.census: diameter at the breast height (dbh in mm) measured during the previous full census of the plot home: height of measurement of dbh (in m) meanWD: species-level wood density (g cm-3) date.ams: date of the ams, format: YYYY-MM-DD status: survival status of the tree (A: alive; D: dead; NF: not found; "?": unknown) H_considering_damage: living length of the main axis in meters; provides an estimate of the amount of remaining living tissues along the main axis of the stem (e.g., the height of breakage or the height discounting wood decay) b: the remaining proportion of branch volume within the living length (b∈[0,1]). weights.ind: frequency of the [size class x species] bins within the forest plot relative to their frequency in the sample. Necessary to extrapolate estimates to the whole plot.
54 ENVIRONMENTAL SCIENCES↗
SPRUCE Experimental Plot Top View Aerial Images beginning June 2019
This data set provides a record of the aerial images of the SPRUCE experimental plots obtained periodically using an unmanned aerial vehicle (UAV) with attached camera from 2019-05-05- to 2022-08-25. New images of the plots will be added periodically. The initial RGB images may be used for studies of greenness and snow coverage and compared with ground-based measurements and chamber-based tree and shrub Phenocam images (e.g. Schädel et al., 2022). Also included are NDVI (Normalized Difference Vegetation Index) images showing calculated difference between visible near-infrared (NIR) light reflectance from vegetation, as well as infrared (IR) and multispectral images. Files are presented in JPEG (*.jpg) or PDF (*.pdf) format inside of 28 compressed (*.zip) files. Images were collected for the 10 plots with enclosures, the 7 unchambered plots, and the environmental monitoring instrument cluster area. Images are slightly off-plot center at 12 to 15 meters above ground level or approximately 2 meters above the top of an experimental chamber.
54 ENVIRONMENTAL SCIENCES↗
SPRUCE Peat Mercury, Methylmercury and Sulfur Concentrations from Experimental Plot Cores, 2012
This data set reports the results of analyses of peat core samples for total mercury and methylmercury, as well as carbon, nitrogen and sulfur. Samples were collected as part of a larger sampling campaign from the SPRUCE experimental study plots located in the S1-Bog. From 2012-08-13 to 2012-10-15, a team of SPRUCE investigators and collaborators collected core samples of peat in the SPRUCE experimental plots. The goal was to characterize the biological, physical, and chemical characteristics of peat, and how those characteristics changed throughout the depth profile of the bog, prior to the initialization of the SPRUCE experimental warming and CO2 treatments. Cores were collected from 16 experimental plots; peat cores were collected from the hummock and hollow surfaces to depths of 300 cm in 10 cm, 25, and 50 cm increments (Pierce et al, 2022). Three replicate cores were collected from both hummock and hollow locations in each plot. The coring locations within each plot were mapped. This dataset contains one file in comma separated (*.csv) format. User Note: The coring descriptions and some reported data are the same as in the related data set, Iverson et al. (2014). Samples for mercury and carbon, nitrogen and sulfur analyses by the University of Minnesota were collected as part of this sampling campaign.
54 ENVIRONMENTAL SCIENCES↗
Litterfall and Branchfall Mass Flux in Malaysia Lambir Hills GEM plots (2008-2010)
This data package includes two folders: Litterfall and Branchfall. The Litterfall folder includes three files: "GEM_Lambir_Hills_Litterfall_Mass_Flux_Data_2008_to_2010.csv" includes the litterfall data, the "GEM_Lambir_Hills_Litterfall_Metadata.xlx" includes the descripion of the columns in the data file, and the PDF file "GEM_Lambir_Hills_Litterfall_Methods_Description" includes a brief description of the litterfall sampling methods. The Branchfall Folder includes four files: the "GEM_Lambir_Hills_Branchfall_Data_2009_to_2010.csv" file includes the branchfall data, the "GEM_Lambir_Hills_Branchfall_Metadata_2009_to_2010.xlsx" includes the description of the columns in the csv data file, the "GEM_Lambir_Hills_Branchfall_Methods_Description.pdf" includes a brief description of the branchfall collection methods, and the "GEM_Lambir_Hills_Branchfall_Data_Metadata_Methods_TransectSums.xlx" includes data, metadata, methods description, and a 'TransectsSums' tab where the reader can find the sum of the branchfall data in each transect Litterfall and Branchfall data were collected to quantify canopy productivity and net primary productivity allocated to branch turnover, which are components of the total net primary productivity and ecosystem carbon budget. The methods follow the Global Ecosystems Monitoring (GEM) protocols (see gem.tropicalforests.ox.ac.uk). The data collection took place from 2008 to 2010 in two 1-ha GEM plots in Lambir Hills National Park, Sarawak, Malaysia, within the Lambir 52-ha ForestGEO plots. One of the plots was located on clay soil and the other on sandy loam. The site is an old-growth moist tropical forest dominated by Dipterocarpacea. The climate is moist tropical and aseasonal. Litterfall was collected biweekly from 25 litter traps per plot. Branchfall was estimated using four 100-m transects, which were surveyed every two months.
54 ENVIRONMENTAL SCIENCES↗
Implementation of Plot File Testing in the DYNA3D/ParaDyn Software Quality Assurance Suite
Automated testing of DYNA3D/ParaDyn plot files was added to the DYNA3D/ParaDyn software quality assurance (SQA) test suite. The new capability extracts select data from the plot files generated during each verification run and compares it to the same baseline answers used to verify the problem. Deviations between baseline answers and plot file values are reported in the same manner as solution discrepancies, and differences in precision levels between the baseline answers and plot file results are accounted for. The new testing leverages the existing SQA test suite framework and test problems and the Python Mili reader and minimally increases the overall run time (< 5%) of the SQA test suite. This new capability provides incremental end-toend testing of the most common DYNA3D/ParaDyn simulation workflows.
42 ENGINEERING↗
SPRUCE Diurnal and Seasonal Patterns of Water Potential in S1 Bog and SPRUCE Experimental Plot Vegetation beginning in 2010
This data set reports on water potential from excised terminal shoots or leaves (Mainthemum only) of vegetation at the SPRUCE site located in the S1 bog. Pretreatment measurements were collected from outside plots within the S1 bog. Post treatments were collected from inside SPRUCE plots. Water potential was measured on the overstory canopy species, consisting of the evergreen Picea mariana (Mill.) B.S.P. (black spruce) and deciduous Larix laricina (Du Roi) K. Koch (tamarack) trees, and ericaceous shrubs < 1 m tall, consisting of the evergreen Rhododendron groenlandicum (Oeder) Kron & Judd (Labrador tea) and Chamaedaphne calyculata (L.) Moench. (leatherleaf). Some measurement campaigns also included the evergreen Kalmia polifolia, Wangenh. (bog laurel), the deciduous Vaccinium angustifolium (Aiton) (blueberry), and the herbaceous Maianthemum trifolium (L.) Sloboda (three-leaf false Solomon’s seal). This dataset contains one data file in comma-separated (*.csv) format. This is the second release of water potential data from the site with additional data from 2018 and 2019. These data cover 2010-06-22 to 2019-07-16. Further results will be added to this data set and released to the public periodically as quality assurance and publication of results are accomplished. There are 10 experimental plots in SPRUCE: five temperature treatments (+0, +2.25, +4.5, +6.75, +9°C) at ambient CO2, and the same five temperature treatments at elevated CO2 (+500 ppm). These data span the pre- and post-treatment periods when enclosed plots were exposed to warming and elevated carbon dioxide (CO2) within the SPRUCE experiment. The pretreatment samples were collected from south end of the S1 bog prior to SPRUCE boardwalk construction, then throughout the bog once the boardwalks were completed.
54 ENVIRONMENTAL SCIENCES↗