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1. TopoSolv-6k: Radii of Gyration and Shear-Rate Dependent Viscosities for Topologically and Chemically Diverse Coarse-Grained Polymers

   https://doi.org/10.5281/ZENODO.16696725  

   Jiang, Shengli; Webb, Michael (January 2025, Zenodo)

   Dataset Description This dataset contains 6710 structural configurations and solvophobicity values for topologically and chemically diverse coarse-grained polymer chains. Additionally, 480 polymers include shear-rate dependent viscosity profiles at 2 wt% polymer concentration.The data is provided as serialized objects using the pickle Python module.All files were generated using Python version 3.10. Data There are three pickle files containing serialized Python objects. Key files include: data_aug10.pickle  Contains the coarse-grained polymer dataset with 6710 entries.  Each entry includes: Polymer graph Squared radius of gyration (at lambda = 0). Solvophobicity (lambda). Bead count (N). Chain virial number (Xi). topo_param_visc.pickle   Shear-rate-dependent viscosity profiles of 480 polymer systems. target_curves.pickle  Contains 30 target viscosity profiles used for active learning. Usage To load the dataset stored in data_aug10.pickle, use the following code: import pickle with open("data_aug10.pickle", "rb") as handle:    (        (x_train, y_train, c_train, l_train, graph_train),        (x_valid, y_valid, c_valid, l_valid, graph_valid),        (x_test, y_test, c_test, l_test, graph_test),        NAMES,        SCALER,        SCALER_y,        le    ) = pickle.load(handle) x: node features for each polymer graph y: labels (e.g., predicted properties) c: topological class indices l: topological descriptors graph: NetworkX graphs representing polymer topology NAMES: list of topological class names SCALER: fitted scaler for topological descriptors (l) SCALER_y: fitted scaler for property labels (y) le: label encoder for topological class indices   To load the dataset stored in topo_param_visc.pickle, use the following code: import pickle with open("poly_data_ml.pickle", "rb") as handle:    desc_all, ps_all, curve_all, shear_rate, graph_all = pickle.load(handle) desc_all: topological descriptors for each polymer graph ps_all: fitted Carreau–Yasuda model parameters curve_all: fitted viscosity curves shear_rate: shear rates corresponding to each viscosity curve graph_all: polymer graphs represented as NetworkX objects   First 30: seed dataset Next 150: 5 iterations (30 each) from class-balanced space-filling Following 150: space-filling without class balancing Final 150: active learning samples    To load the dataset stored in target_curves.pickle, use the following code: import pickle with open("target_curves.pickle", "rb") as handle:    data = pickle.load(f) curves = data['curves']params = data['params']shear_rate = data["xx"]   curves: target viscosity curves used as design objectives params: Carreau–Yasuda model parameters fitted to the target curves shear_rate: shear rate values associated with the target curves     Help, Suggestions, Corrections?If you need help, have suggestions, identify issues, or have corrections, please send your comments to Shengli Jiang at sj0161@princeton.edu GitHubAdditional data and code relevant for this study is additionally accessible at https://github.com/webbtheosim/cg-topo-solv 

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2. Gap-Filled Dissolved Oxygen Data from the Ocean Observatories Initiative Endurance Array Inshore Moorings

   https://doi.org/10.5281/zenodo.15742508  

   Cervantes, Brandy (January 2025, Zenodo)

   Introduction The National Science Foundation Ocean Observatories Initiative (OOI) collects continuous in-situ measurements of dissolved oxygen (DO) on the Endurance Array moorings in the inner shelf region of the Oregon and Washington coasts. Aanderaa Optode 4831 oxygen sensors were deployed at 7 meters depth on the near surface instrument frame (NSIF) and on the collocated coastal surface piercing profiler (CSPP) moorings. The sensors suffer from calibration drift due to biofouling, which can cause a dramatic increase in DO during daylight hours and corresponding decrease at night compared to the conditions in the water column (Palevsky et al., 2023). This enhanced diel signal, when present, is much more pronounced on fixed-depth sensors and usually begins to occur 1-2 months after a mooring is deployed. After this biofouling issue was identified, OOI began deploying UV lamps adjacent to the oxygen sensor in spring 2018, after which there was substantial improvement in DO data quality. Each file in this dataset contains the measured near surface DO and the corrected near surface DO at the Oregon and Washington inner shelf surface moorings (ISSM) with gaps from periods of biofouling replaced with the DO measured by the CSPP.  Methods OOI oxygen data Dissolved oxygen sensors on OOI CSPPs and at fixed-depths on moorings are named “DOSTA”, a contraction of DO Stable Response. The DOSTA data are downloaded on a deployment-by-deployment basis for all available data streams (telemetered and recovered for fixed-depth moorings; recovered only for CSPPs) from the OOI Gold Copy THREDDs catalog. Each deployment file additionally contains the practical salinity, seawater temperature, and pressure measured by the collocated CTD. The telemetered and recovered data streams are combined and interpolated to a common timebase with one-minute resolution.  Evaluate fixed-depth oxygen data The NSIF DO data are quality-controlled using both automated and manual methods to create flags that follow the Quality Assurance of Real-Time Oceanographic Data (QARTOD) standards. Endurance array team members perform a visual inspection of oxygen and ancillary data from each deployment to determine instrument failure from biofouling or other issues. Annotations from human-in-the-loop analyses of failed or suspect data generate the QARTOD flags.    Merge profiler oxygen data QARTOD flags are applied to the CSPP data to omit failed data points. CSPP DO data are averaged from 2-7 meters depth then interpolated to the one-minute timebase. The resulting CSPP time series shows good agreement with the NSIF during data overlaps. Finally, the NSIF DO data is replaced with the CSPP DO data during periods of biofouling or instrument failure, flags are generated for the hybrid DO dataset, and separate netCDF files are created for the Oregon and Washington locations. Files Filename: CE01ISSM-NSIF-DOSTA.nc Description Oregon Coastal Endurance Site CE01, Inner Shelf Surface Mooring, Near Surface Instrument Frame, Dissolved Oxygen Stable Response Geographic Range Latitude: 44.6598 to 44.6598 Longitude: -124.095 to -124.095 Time Range Start: 2014-10-10, 18:00:00 UTC End: 2025-06-24, 20:00:00 UTC Variables: "time", ”depth”, "sea_water_practical_salinity", "sea_water_practical_salinity_qartod_results", "sea_water_temperature",  "sea_water_temperature_qartod_results", "measured_dissolved_oxygen", "measured_dissolved_oxygen_qartod_results", "corrected_dissolved_oxygen", "corrected_dissolved_oxygen_qartod_results" Filename: CE06ISSM-NSIF-DOSTA.nc Description Washington Coastal Endurance Site CE06, Inner Shelf Surface Mooring, Near Surface Instrument Frame, Dissolved Oxygen Stable Response Geographic Range Latitude: 47.1336 to 47.1336 Longitude: -124.272 to -124.272 Time Range Start: 2015-04-10, 05:00:00 UTC End: 2025-06-24, 20:00:00 UTC Variables: "time", ”depth”, "sea_water_practical_salinity", "sea_water_practical_salinity_qartod_results", "sea_water_temperature",  "sea_water_temperature_qartod_results", "measured_dissolved_oxygen", "measured_dissolved_oxygen_qartod_results", "corrected_dissolved_oxygen", "corrected_dissolved_oxygen_qartod_results" 

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3. Data complementing the publication: "Does total column ozone change during a solar eclipse?"

   https://doi.org/10.5281/zenodo.14201523  

   Bernhard, Germar H; Bernhard, Germar H; Janson, George T; Simpson, Scott; Cordero, Raúl R; Sepúlveda_Araya, Edgardo I; Jorquera, Jose; Rayas, Juan A; Lind, Randall N (November 2024, Zenodo)

   Data published in this zip file complement the publication "Does total column ozone change during a solar eclipse?" by Germar H. Bernhard, George T. Janson, Scott Simpson, Raúl R. Cordero,  Edgardo I. Sepúlveda Araya, Jose Jorquera, Juan A. Rayas, and Randall N. Lind, which will be published in the journal "Atmospheric Chemistry and Physics". A DOI of the publication will be added to this meta data description when available. The DOI of the publication's pre-print (paper under review) is: https://doi.org/10.5194/egusphere-2024-2659 The contents of the zip file are organized in the following four subdirectories: - Figures: This directory contains the figures of the paper in PDF and PNG format plus the data used for plotting the figures. - GUVis-3511 Data Processor: This directory contains the software for processing the raw data collected during the solar eclipses described in the publication as well as ancillary data used for processing and manuals describing the software. - Limb darkening functions: This directory contains the functions expressing the change in the spectral irradiance during the eclipses discussed in the publication as a function of time and wavelength. - Raw data: This directory contains the raw data measured during the eclipses discussed in the publication. Each subdirectory and subdirectories nested therein contains "readme.txt" (in English) and "léeme_Espanol.txt" (in Spanish) files with further information of the contents of each subdirectory. 

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4. Data from: Inheritance of DNA methylation differences in the mangrove Rhizophora mangle

   https://doi.org/10.5281/zenodo.5102718  

   Mounger, Jeannie; Boquete, M. Teresa; Schmid, Marc W.; Granado, Renan; Robertson, Marta H.; Voors, Sandy A.; Langanke, Kristen L.; Alvarez, Mariano; Wagemaker, Cornelis A.M.; Schrey, Aaron W.; et al (January 2021, Zenodo)

   {"Abstract":["This record contains supplementary information for the article "Inheritance of DNA methylation differences in the mangrove Rhizophora mangle" published in Evolution&Development. It contains the barcodes (barcodes.txt), the reference contigs (contigs.fasta.gz), the annotation of the reference contigs (mergedAnnot.csv.gz), the SNPs (snps.vcf.gz), the methylation data (methylation.txt.gz), and the experimental design (design.txt). All data are unfiltered. Short reads are available on SRA (PRJNA746695). Note that demultiplexing of the pooled reads (SRX11452376) will fail because the barcodes are already removed and the header information is lost during SRA submission. Instead, use the pre-demultiplexed reads that are as well linked to PRJNA746695.<\/p>\n\n\n <\/p>\n\nTable S13 (TableS13_DSSwithGeneAnnotation.offspringFams.csv.gz): <\/strong><\/p>\n\nDifferential cytosine methylation between families using the mother data set. The first three columns fragment number ("chr"), the position within the fragment ("pos"), and the sequence context ("context"). Columns with the pattern FDR_<X>_vs_<Y> contain false discovery rates of a test comparing population X with population Y. Average DNA methylation levels for each population are given in the columns "AC", "FD", "HI", "UTB", "WB", and "WI". The remaining columns contain the annotation of the fragment, for example whether it matches to a gene and if yes, the gene name ID and description are provided.<\/p>"]} 

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5. Source code: Basins modulate signatures of river salinization

   https://doi.org/10.5281/zenodo.11130548  

   Platt, Lindsay; Dugan, Hilary (May 2024, Zenodo)

   This resource contains source code and select data products behind the following Master's Thesis: Platt, L. (2024). Basins modulate signatures of river salinization (Master's thesis). University of Wisconsin-Madison, Freshwater and Marine Sciences. The source code represents an R-based data processing and modeling pipeline written using the R package "targets". Some of the folders in the source code zipfile are intentionally left empty (except for a hidden file ".placeholder") in order for the code repository to be setup with the required folder structure. To execute this code, download the zip folder, unzip, and open the salt-modeling-data.Rproj file. Then, reference the instructions in the README.md file for installing packages, building the pipeline, and examining the results. Newer versions of this repository may be updated in GitHub at github.com/lindsayplatt/salt-modeling-data. In addition to the source code, this resource contains three data files containing intermediate products of the pipeline. The first two represent data prepared for the random forest modeling. Data download and processing were completed in pipeline phases 1 - 5, and the random forest modeling was completed in phase 6 (see source code).  site_attributes.csv which contains the USGS gage site numbers and their associated basin attributes site_classifications.csv which contains the classification of a site for both episodic signatures ("Episodic" or "Not episodic") and baseflow salinization signatures ("positive", "none", "negative", or NA). Note that an NA in the baseflow classification column means that the site did not meet minimum data requirements for calculating a trend and was not used in the random forest model for baseflow salinization. site_attribute_details.csv contains a table of each attribute shorthand used as column names in site_attributes.csv and their names, units, description, and data source. 

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