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1. Data and Code For: Shifts in phenology and species ranges synergistically alter the timing and species composition of the flowering season:Shifts in Distributions, Flowering Phenology, and Community-level Flowering Patterns in the United States

   https://doi.org/10.5281/zenodo.17429435  

   Ramirez-Parada, Tadeo Hernan (November 2025, Zenodo)

   {"Abstract":[" \n\nThis submission provides all the data and R code necessary to 1) generate the species distribution estimates and phenological predictions underlying all analyses, and 2) all results presented in the main text of the manuscript titled "Shifts in phenology and species ranges synergistically alter the timing and species composition of the flowering season" (Ramirez-Parada, Park, Peng, Nishino, Kartesz, Record, Davis, and Mazer, 2025). A preprint of this work can be accessed at: https://doi.org/10.32942/X28W5Z . This work was published in Global Change Biology (DOI: https://doi.org/10.1002/ecs2.70070).\n\nIn order for the code to work, users need to preserve the folder structure of the code and data, as the R Markdown files in the "Code" folder have relative file paths that read and write files within the "Data" folder and subfolders therein. These two folders are placed at the same level (i.e., they should both be stored within the same folder). Moving either will require re-writing the filepaths across Rmds.\n\nFor detailed descriptions of all materials (code and data) and instructions for using them, please refer to the "READ ME.md" file within "Project folder" and the main text of the manuscript.Note: This version corrects an error in  the .csv files containing the performance metrics for the three types of SDM fit for each species in the data (described in the main text). The previous version erroneously contained the performance metrics for the same model type across all three .csv files.\n\n "]} 

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2. Simulation and Analysis Code for "Adversarial Obstacle Placement with Spatial Point Processes for Optimal Path Disruption"

   https://doi.org/10.5281/zenodo.17074761  

   Ceyhan, Elvan; Zhou, Li; Charyyev, Polat; Ceyhan, Elvan; Zhou, Li; Charyyev, Polat (January 2025, Zenodo)

   Abstract: Certainly. Here's a suggested "Description" field for your Zenodo submission, tailored for the International Journal of Geo-Information (IJGI) context: This archive contains simulation and analysis code used in the study "Adversarial Obstacle Placement with Spatial Point Processes forOptimal Path Disruption" submitted to International Journal of Geo-Information (IJGI). The research investigates pathfinding through stochastic obstacle environments using fully synthetic data generated via spatial point processes and parameterized simulation models. The simulation/ folder includes R scripts to generate the traversal cost data under various configurations, while the analysis/ folder provides code for statistical modeling (robust regression, random forest, and zero-inflated negative binomial regression) and visualization of the results and code for generating illustrative figures. The results/ folder contains sample simulation outputs and figures used in the manuscript. All data used in the paper can be exactly reproduced using the provided code. For inquiries or reproducibility questions, please contact the corresponding author. 

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3. Data supporting the paper Influence of Biofilms and Extracellular Polymeric Substances on High-Frequency Acoustic Backscatter

   https://doi.org/10.13020/a6fv-pz04  

   Assis, Willian; Yang, Judy Q (January 2025, Data Repository for the University of Minnesota (DRUM))

   Abstract: This dataset contains the data collected and presented in the manuscript "Influence of Biofilms and Extracellular Polymeric Substances on High-Frequency Acoustic Backscatter". It includes acoustic signals (voltage and time) for each tested case (and replicates), hydrophone tests, and acoustic calibration using two transducers. Additionally, it contains the MATLAB codes used to process the data. TechnicalInfo: The file named " AgingNaturalBiofilms.zip" contains 91 folders with 210 .mat files. The file named " EPS.zip" contains 91 folders with 210 .mat files. The file named " Replicates.zip" contains 52 folders with 120 .mat files. The file named " Hydrophone.zip" contains 2 folders with 2 .mat files. The file named "2Transducers.zip" contains 2 folders with 2 .mat files. The file "MatLabCodes.zip" contains the matlab code used to process the data. 

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4. Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

   https://doi.org/10.5061/dryad.r4xgxd2tm  

   Cross, James; Mallick, Kanishka; Aslan-Sungur, Guler; VanLoocke, Andy; Drewry, Darren (June 2026, Dryad)

   Abstract: Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations. TechnicalInfo: # Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing https://doi.org/10.5061/dryad.r4xgxd2tm Authors: James F. Cross; Kanishka Mallick; Guler Aslan-Sungur; Andy VanLoocke; Darren T. Drewry Corresponding author: Darren Drewry ([drewry.19@osu.edu](mailto:drewry.19@osu.edu)) ### Repository Structure The archive consists of the following main components: │ CODE.zip ├─ DATA_OUTPUT │ └── PARAMETERS │ └── SHAP_DATA │ └── STIC_OUT_DATA ├─ HELPER_FUNCTIONS ├─ FIGURES_CODE │ └── Figure [2-9] │ └── Figure [S1-S5] │ A.DEFINE_STIC_parameters.m │ A.DEFINE_TOWER_parameters.m │ B.MAIN_STIC.m │ C.OUTPUT_SHAP_INPUTS_csv.m │ D.RUN_SHAP_COHORT.pynb │ E.GATHER_SHAP_importance.m │ DATA.zip │ PAPER_FIGURES.zip │ └── Figure [1-9] │ └── Figure [S1-S5] │ SOFTWARE.zip ├─ COHORT_SHAP │ └── cohort_shapley-main ### Code Execution * To reproduce results from the paper, the four zip files should be extracted as in the above repository structure. (i.e. with the parent directory consisting of the folders *"CODE","DATA","PAPER_FIGURES","SOFTWARE"*) * The primary findings of the paper are reproduced by running the six files enumerated in the order (A-E) in the section "Code — Main Code Files". * Figure files can be reproduced after running the main files by executing the respective script (*.m) in *"CODE/FIGURES_CODE/Figure X/"*. ### Code **CODE/DATA_OUTPUT/PARAMETERS/** * NET_energy_models/ * Contains pretrained models used by STIC_{ML,Rn} to estimate the net energy (net radiation - soil heat flux). * SHF_models/ * Contains pretrained models used by STIC_{ML,G} to estimate the soil heat flux. * STIC_PARAMS.mat * Output from "A__DEFINE_STIC_parameters.m" * TOWER_PARAMS.mat * Output from "A__DEFINE_TOWER_parameters.m" **CODE/DATA_OUTPUT/SHAP_DATA/** * Stores intermediary data products for SHAP analysis. * .csv outputs are stored from "C__OUTPUT_SHAP_INPUTS.csv" and "D__SHAP_Processing.pynb". * Additionally stores the compiled output from "E__GATHER_SHAP_importance.m" as "SHAP_output.m" **CODE/DATA_OUTPUT/STIC_OUT_DATA/** * Contains the per crop data files output from "B__MAIN_STIC.m". Each .mat file contains a struct including the pre- and post-processed results for each of the STIC formulations. **CODE/HELPER_FUNCTIONS/** * A repository of ancillary functions developed during the project. **CODE/FIGURES_CODE/** * Figure [2-9, S1-S5] * Folders for reproducing each of the individual figures. Each folder contains one ".m" file, which will produce the figure files "STIC_FigX.jpg" and "STIC_FigX.fig" in the local folder. * PRINT_SHAP_STATS.m * Tabulates the SHAP explained variance values, which are depicted in Figure 7. Results are output to the console. * PRODUCE_STIC_TABLES.m * Tabulates the RMSE and R2 performances of the differing STIC formulations, which is depicted in Table 1. **CODE/** * **Main Code Files** * A__DEFINE_STIC_parameters.m * Defines unit conventions and naming conventions used in preprocessing of "B__MAIN_STIC.m". * A__DEFINE_TOWER_parameters.m * Defines tower date scheme and planting rotation used in preprocessing of "B__MAIN_STIC.m". * B__MAIN_STIC.m * The main code file for defining and running the different STIC formulations in use. Per-model (STIC Variations) parameters are assigned to the structures ["gen_params","soil_model_params","net_model_params","obs_model_params"] for STIC_{BP}, STIC_{ML,G}, STIC_{ML,Rn}, and STIC_{Ref} respectively. * Additional pre- and post-processing parameters can be set here in ["processing_parameters", "post_parameters"] * data_folder = './../DATA/'; * output_folder = './DATA_OUTPUT/STIC_OUT_DATA/'; * Data Pre-Processing Options (processing_parameters.xxxx) %below metrics are all less restrictive than their post-processing counterparts * units_file = input_vars_file; % file which specifies mapping of input variable fieldnames to STIC expected fieldnames * stic_params = stic_params_file; % file which specifies STIC expected units and ranges * valid_year_range = [2021,2026]; * valid_hour_range = [-1,25]; %model estimates become unreliable when the sun is down * valid_doy_range = [130,290]; %data outside of the growth season can skew metrics * valid_ndvi_range = [0.3,0.3]; %start of season NDVI cutoff, end of season NDVI cutoff * ndvi_variable = 'daytime_ndvi'; %target fieldname for ndvi reference value * Data Post-Processing Options (post_parameters.xxxx) * valid_year_range = [2021,2022]; %data range to consider * valid_hour_range = [-1,25]; %[min,max] hour of day data to include (0-24) * valid_doy_range = [160,260]; %[min,max] day of year data to include (0-365) * valid_ndvi_range = [0.7,0.7]; %[start of season NDVI cutoff, end of season NDVI cutoff] ndvi minimum value threshold * ndvi_variable = 'daytime_ndvi'; %target fieldname for ndvi reference value * Data is read in from "DATA/" and exported to "DATA_OUTPUT/STIC_OUT_DATA". * C__OUTPUT_SHAP_INPUTS.csv * Features for SHAP analysis are specified here. * Data is read from "DATA_OUTPUT/STIC_OUT_DATA", selected, and output to "DATA__OUTPUT/SHAP_DATA" as .csv files. * D__SHAP_Processing.pynb * Jupyter Notebook for calculating Cohort SHAP attributions from the prior .csv files. Imports the python module from "CODE/COHORT_SHAP". * Additional parameters for the number of bins used for the Variance SHAP calculation, as well as the similarity cutoff and ratio used for Cohort SHAP, can be set here. * infolder = "./DATA_OUTPUT/SHAP_DATA/" * infiles = ["Corn.csv","Miscanthus.csv","Sorghum.csv","Soybean.csv"] # Input files for * variance_bin_count = 10 # Number of bins to use for global variance shap importance calculation * cohort_similarity_prctile_cutoff = 2.5 # Normalizes to the prctile range [2.5,97.5] to reduce outlier sensitivity * cohort_similarity_ratio_threshold = 0.2 # Maximum allowed ratio of "sample distance"/"population distance" to be considered "similar" * cpu_parallel_cores = 64 # Number of cpu cores to use when computing Cohort SHAP * Generates additional files in the "DATA_OUTPUT/SHAP_DATA" directory. * E__GATHER_SHAP_importance.m * Compiles the files in "DATA_OUTPUT/SHAP_DATA" for ease of access and use in figure generation. ### Data ├─ DATA.zip │ └── Corn_Tower.mat\ │ └── Miscanthus_Tower.mat\ │ └── Sorghum_Tower.mat\ │ └── Soybean_Tower.mat\ │ └── PrecipData_2019_2024.mat\ │ └── README.txt **DATA/** * Stores eddy covariance, meteorological, and remote sensing data for each of the four: corn, miscanthus, sorghum, and soybean towers. * README.txt: Includes additional details on the data contained in each file. * Variables (in *_Tower.mat) * DateTime — Timestamp of each measurement; datetime * H_mdsgf — Sensible heat flux; W m⁻² * LE_mdsgf — Latent heat flux; W m⁻² * NETRAD_1_1_1 — Net radiation; W m⁻² * SHF_average — Soil heat flux (averaged); W m⁻² * PPT — Precipitation; mm * SW_IN_1_1_1 — Incoming shortwave radiation; W m⁻² * SW_OUT_1_1_1 — Outgoing shortwave radiation; W m⁻² * LW_IN_1_1_1 — Incoming longwave radiation; W m⁻² * LW_OUT_1_1_1 — Outgoing longwave radiation; W m⁻² * TA_average_filled — Air temperature (gap‑filled); °C * RH_average_filled — Relative humidity (gap‑filled); % * PA_merge_filled — Air pressure (merged & filled); Pa * WS_filled — Wind speed (gap‑filled); m s⁻¹ * IRT_SH1 — Infrared surface temperature measurement; °C * IRT_431 — Infrared surface temperature measurement; °C * NDVI — Normalized difference vegetation index, midday averages interpolated aross season; °C * PRI_filled — Photochemical reflectance index, midday averages interpolated aross season; °C * VPD_merge_filled — Near-surface vapor pressure deficit; Pa * Variables (in PrecipData_2019_2024.mat) * DateTime — Timestamp of each measurement; datetime * decdoy — Decimal day of year * decyear — Decimal year * PPT — Half-hourly precipitation from Iowa Mesonet system; mm ### PAPER_FIGURES │ PAPER_FIGURES.zip │ └── Figure [2-9] │ └── Figure [S1-S5] **PAPER_FIGURES/** * Figure [1-9, S1-S5] * Each folder contains the figure files "STIC_FigX.jpg" and "STIC_FigX.fig" as referenced in the publication. ### SOFTWARE │ SOFTWARE.zip ├─ COHORT_SHAP │ └── cohort_shapley-main **SOFTWARE/COHORT_SHAP/** * Contains version v0.1.0 of the Cohort SHAP module released on March 10th, 2021. Code was developed by Masayoshi Mase, and is included here for reproducibility under the MIT License. * [https://github.com/cohortshapley/cohortshapley?tab=readme-ov-file](https://github.com/cohortshapley/cohortshapley?tab=readme-ov-file) * Mase, M., Owen, A. B., & Seiler, B. (2019). Explaining black box decisions by Shapley cohort refinement. arXiv preprint arXiv:1911.00467. 

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5. Data from: Ligand-to-metal charge transfer control of ⁵¹V NMR thermal sensitivity

   https://doi.org/10.5061/dryad.3ffbg79zn  

   Zadrozny, Joseph; Crans, Debbie (January 2026, Dryad)

   Abstract: Understanding thermal sensitivity of magnetic nuclei is an important step toward noninvasive thermometers in magnetic resonance imaging applications. This data set is associated with the exploration of a specific nucleus, 51V, and its associated temperature dependent spectroscopic properties in a series of four V(V) complexes: [VO(3-OEtHshed)(tbad)] (1, tbad = 5-(adamantan-1-yl)-3-(tert-butyl)benzene-1,2-diol, 3-OEtHshed = (E)-2-ethoxy-6-(((2-((2-hydroxyethyl)amino)ethyl)imino)methyl)phenol), [VO(3-OEtHshed)(cat)] (2, cat = catechol), [VO(3-OEtHshed)(2OHP)] (3, 2OHP = 2-(hydroxymethyl)phenol) and [VO2(3-OEtHshed)] (4). These data demonstrate an increasing thermal sensitivity for the 51V nuclear magnetic resonance signal when the supporting ligand enables a low-energy ligand-to-metal-charge-transfer transition. This is the first demonstration of any sort of design strategy to increase the thermal sensitivity of the 51V nucleus. Methods: Methods for analysis are described in detail in the manuscript and accompanying supplementary information available from the Royal Society of Chemistry at: http://dx.doi.org/10.1039/D6CC02350A Citation for manuscript: A. C. Bates, J. V. Grundy, J. R. Stapf, Ö. Üngör, D. C. Crans, J. M. Zadrozny; Ligand-to-Metal Charge Transfer Control of 51V NMR Thermal Sensitivity Chemical Communications, 2026, DOI: 10.1039/D6CC02350A TechnicalInfo: # Data from: Ligand-to-metal charge transfer control of ^51^V NMR thermal sensitivity Dataset DOI: [10.5061/dryad.3ffbg79zn](https://doi.org/10.5061/dryad.3ffbg79zn) ## Description of the data and file structure All contained data are either the raw data directly from the instruments used to acquire the data, or aggregated collections of the data in a format sufficient for plotting and analysis in external programs. The data that are attached are compressed, zip folders of the raw data and other data used to make figures for the manuscript. Unzip these folders to access the raw data. All contained data are either the raw data directly from the instruments used to acquire the data, or aggregated collections of the data in a format sufficient for plotting and analysis in external programs. Most of the data was processed using Excel and MestreNova, with OriginPro allowing for final production of manuscript figures. ### Files and variables ### File: UV-Vis_Spectra.zip This compressed folder has five different files in it. The UV-Vis summary Excel file "UV-Vis summary.xlsx" contains the final tabulated electronic absorption spectroscopy data for compounds 1-4 from the main manuscript. The data is saved as an Excel file. Data are organized in columns: wavelength (nm), absorbance (A), and molar absorptivity (epsilon) for each of the complexes, respectively. An additional column that converts the wavelength to wavenumbers is provided for convenience. The main manuscript contains the plot of epsilon versus wavenumber as Figure 3. See main manuscript for measurement conditions, used instrument, and other relevant experimental points. The other four files, labeled as "compound name 0.1mM.csv" are the raw instrumental outputs saved in comma-delimited files that can be opened via any text editor and Excel. These data were all collected at 0.1 mM concentration. These .csv files have all experimental data. Briefly, the block of text under "summary" contains instrumental summary information (spectrometer, model, software version, sample name); "parameter" has explicit instrumental collection conditions, e.g., the wavelength window, the slit width, the scanning speed; and below that are the actual data in two columns, one of wavelength, and one of absorbance. ### **File: Low-field_NMR.zip** This compressed file contains two folders that constitute the worked up ("processed") and raw ("unprocessed") low-field NMR spectra. In the unprocessed data folder, there are four subfolders labeled with the compound #s from the manuscript. Dragging one of these folders into MestreNova (see NMR program details below) will immediately open the results of the experiment. There are subfolders with instrumental parameters and a .jdx file, which is a standardized spectral formula (see NMR notes below). The processed data folder contains the worked-up data for each compound. They are saved as tab-delimited .csv files, each with two columns, ppm on the left and spectral intensity on the right. These can be opened in any graphing program. The folder also contains all of them collected into a single MNova file as well, which can be opened with MestreNova.  ### **File: RT_51V_NMR_spectra.zip** This compressed data set has the room-temperature ^51^V NMR spectra for complexes 1-4, which are depicted in Figure 3 in the main manuscript. This folder contains ten different files. There are four tab-delimited .csv files, one for each complex, which are the xy coordinates for the NMR spectra as depicted in Figure 3. These data are separated into two columns, left is chemical shift in ppm and the right one is the 51V NMR signal intensity. These data can be opened with any standard text edit/spreadsheet software. There are also five folders entitled, e.g., "Compound 1 25 dC". These folders contain the raw instrumental output from the NMR for each of the four compounds, all collected at 25 degrees Celsius. These folders can be dragged, in their entirety, into the program MestreNova (see notes below) and it will give an initial process of the corresponding NMR spectrum. The final file in this folder is "All compounds in MeCN at 25dC.mnova". This is the aggregate data set from the room temperature ^51^V NMR spectra, all collected in acetonitrile (MeCN) in a format that can be opened by MestreNova by simply dragging the file into the opened program window (see notes below about MestreNova and NMR processing).  ### File: 2D_NMRs.zip This compressed folder contains all of the 2-dimensional NMR spectra used to assign structure for complexes 1-4. In this folder are four compressed folders that contain relevant NMR spectra for the labeled compound. The folder for compound 1 contains the one-dimensional proton NMR spectra, labeled as 1H and 1H D2O spike for this complex. The files including COSY and NOESY in this folder correspond to the 2D spectra depicted in the manuscript SI file. The folder for compound 2 contains the one-dimensional 1H NMR spectrum (labeled as "1H") and the two-dimensional spectra are labeled with "COSY" and "NOESY". The folder for compound 3 contains the one-dimensional proton ("1H") and COSY and NOESY spectra. Compound 4 only contains the COSY and NOESY 1H spectra. For all folders, data is supplied as folders that can be opened with MestreNova (or other appropriate NMR software, see below) or the MestreNova files themselves. ### File: VT_NMR_data.zip The compressed folder with this data contains variable-temperature NMR spectra for all complexes and tabulated variable-temperature NMR linewidths discussed in the manuscript. All temperatures are in degrees C. There are four subfolders associated with this data set, which are labeled according to the complexes (1-4) which they correspond to. Within these subfolders, there are a set of subfolders labeled with the name of the complex, e.g., "VO(3OEtHSHED)(TBAD)" for complex 1 and then a number, ranging from 25 to 50. These folders are the solution phase NMR spectra for the complexes, and the number corresponds to the temperature of the experiment. Like with other NMR spectra collected here, these folders can be dragged into the MNova (or other) software for analysis. We have also collected the worked up variable temperature data for the compound into a single MNova file in each of these folders. Finally, there is an Excel spreadsheet that collects the temperature dependence of peak positions and their linewidths. This spreadsheet can be opened in Excel or other spreadsheet software. The data are organized in this spreadsheet by compound (1 to 4, from left to right). The solvent of measurement is listed under the name of the compound. The top rows of variable temperature data contain the chemical shift information. Below that, in the data sets marked, e.g., "Peak 4 (SW)" we have the signal linewidths in Hz and then the same values in ppm even further down. There is a "Dd/DT" row which is the thermal sensitivity of the chemical shift for the variable temperature data sets and then a "Resolution" row even further down which contains values of thermal sensitivity divided by the linewidth. ## Code/software Data that is recorded as .csv, .txt, or in other common spreadsheet extensions can be opened in Excel or other standard plotting software. The majority of our NMR data were analysed and processed using [MNova](https://mestrelab.com/main-product/mnova). MestreNova (or MNova) is a common NMR-processing software. Many universities have access to it by purchasing a license, and analysis of the data can be performed simply by dragging the FID files into the program. There are alternatives for analysis, e.g. [Topspin](https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html). There are also free software packages available for analysis. MNova has [NMR Lite](https://mestrelab.com/download-nmr-lite), which is a cheaper version and can be used free for an extended period. The program [NUTS](https://www.aiinmr.com/NUTS-Program-Download) is also a free software package that is available from Anasazi Instruments. In some cases, NMR spectra are saved in the .jdx file type, which can be opened with many different spectral software, including the free [jspecview](https://jspecview.sourceforge.net/). See [here](https://opg.optica.org/as/abstract.cfm?uri=as-47-8-1093) for more details. ## Access information Other publicly accessible locations of the data: * Figures and some tabulated data are available in the SI of the manuscript at DOI: 10.1039/D6CC02350A Data was derived from the following sources: * N/A 

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