cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15485/2448016
title: Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)

description: This data release provides all data and code used in the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)" to model stream temperature, evaluate, and assess results. The associated manuscript explores current open questions in prediction in ungauged and unmonitored basins concerning top-down versus bottom-up approaches, tradeoffs between data available and input requirements, and the appropriate representation of catchment attributes as inputs to deep learning models. Modeling was done primarily with long short-term memory (LSTM) models, and stream site coverage spans 1362 locations across the conterminous United States. The data is organized into these items items:Code repository and data for the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)".Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code: - data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- error_analysis_attribute_and_groundwater_dir.zip - workflows for the extended error analysis by stream attribute and groundwater influenceData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2024streamdata, author = {Jared Willard and Fabio Ciulla and Helen Weierbach and Vipin Kumar and Charuleka Varadharajan}, title = {Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models"}, year = {2024}, doi = {10.15485/2448016}, publisher = {ESS-DIVE Repository}, url = {https://doi.org/10.15485/2448016}}MLA: Willard, Jared, et al. Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models". 2024. ESS-DIVE Repository, doi:10.15485/2448016.
DOI: 10.15485/2500238
title: Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)

description: This dataset contains gap-filled measurements of methane flux (FCH4), net ecosystem CO2 exchange (NEE) partitioned into gross primary productivity (GPP) and ecosystem respiration (RE), as well as latent heat flux (LE) from a Great Lakes coastal freshwater wetland at the US-OWC AmeriFlux site. The dataset covers the peak growing seasons (June−September) of 2015−2016, dominated by Typha spp., and 2020−2022, characterized by floating-leaved species (lotus and water lily). These data were generated to investigate how rising water levels and vegetation shifts influence CH4 and CO2 fluxes across two distinct ecosystem states in this wetland.The dataset, provided in CSV format, includes half-hourly gap-filled flux data from June to September for 2015, 2016, 2020, 2021, and 2022. The gap-filled data refers to measurements where missing values due to instrument issues or quality control were filled using artificial neural networks (ANNs).
DOI: 10.15485/2502101
title: Integrated Hourly Meteorological Database of 20 Meteorological Stations (1981-2022) for Watershed Function SFA Hydrological Modeling

description: This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset.The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.
DOI: 10.15485/2507189
title: Shrub Expansion Simulations at Trail Valley Creek Tundra site using E3SM Land Model (ELM) Arctic-focused Version

description: The warming of the Arctic is causing substantial compositional, structural, and functional changes in tundra vegetation including shrub and densification in parts of the Arctic. Assessing the impact of these changes in vegetation composition on the Arctic’s carbon and energy budgets is important to constrain projected local and global surface-atmosphere exchanges. We conduct a sensitivity analysis of the projected surface energy fluxes, soil carbon pools, and carbon dioxide fluxes (net ecosystem exchange, gross primary production, and ecosystem respiration) between present day and 2100 to different shrub expansion rates and air temperature increases under future emission scenarios (intermediate – RCP4.5, and high – RCP8.5) using the Arctic-focused version of the Energy Exascale Earth System Model (E3SM) Land Model (ELM). We focus on Trail Valley Creek (TVC), a mineral upland tundra site located in the western Canadian Arctic, which is experiencing tall shrub densification and expansion. In this study, we run TVC under two different warming scenarios RCP4.5 and RCP 8.5 and simulate different shrubification rates projected until year 2100. In this repository, we include all the forcing, input, parameters, and output data corresponding to all the simulations performed. flmd.csv includes a detailed description of the datasets files.
DOI: 10.15485/2511314
title: Observed and Imputed Volumetric Soil Water Content Timeseries for the New Mexico Elevation Gradient

description: Reliable soil water content (SWC) data are essential for understanding dryland ecosystem dynamics, but high-frequency SWC sensors often fail, creating gaps in critical datasets. To address this, we developed a Bayesian mixture model that imputes missing SWC using both linear interpolation and an ecosystem water balance model (SOILWAT2), tested across six AmeriFlux eddy covariance tower sites in the New Mexico Elevation Gradient, demonstrating its effectiveness in reconstructing SWC patterns while providing insights into the factors driving SWC variability. Daily volumetric soil water content (SWC) data are provided as csv-formatted spreadsheets for the six AmeriFlux sites (US-Seg, US-Ses, US-Wjs, US-Mpi, US-Vcp, and US-Vcs). For each site there is an observed SWC file (site_SWC_gapfill.csv) and a file that contains imputed SWC (imputed_SWC_site.csv). The observed SWC files contain temperature corrected sensor values, tower precipitation data, as well as outputs from SOILWAT2 simulations that were used to impute SWC. The imputed files contain the original observed SWC values and the imputed missing SWC values. When SWC was missing from the original data, the missing value was imputed based on the Bayesian imputation mixture model. The posterior mean of all imputed values is reported as "mean_X". When the observed SWC was NOT missing, mean_X = observed SWC value (original data). The standard deviation, 2.5th percentile and the 97.5th percentile for the imputed values are also reported in the imputed files. There are readme text files for each file type explaining the contents of each column.
DOI: 10.15485/2511315
title: Untargeted metabolite data from a root surface in a rhizobox

description: Raw data is provided from samples analyzed using separate reverse phase chromatographic methods on a high performance liquid chromatograph with mass spectrometry. These porewater samples were collected from a microdialysis which generated samples along the surface of a growing A. sative root (all_hc). This data was used to answer questions connecting rhizosphere metabolite (putatively identified metabolites, hc_putative_norm) changes over time (root growth) with changes in the surrounding rhizosphere biogeochemistry (DOC, redox, pH). Rhizosphere biogeochemistry values are provided in the hc_putative_norm file as averages over their respective range of time that they were collected at. The hc_putative_norm file also contains all normalized values over only the intensity values collected for putatively identified metabolites.
DOI: 10.25582/data.2024-02.3024069/2512460
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026109/2512461
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026110/2512462
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026111/2512463
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026112/2512464
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026113/2512465
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026114/2512466
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026116/2512467
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026117/2512468
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026119/2512469
title: Data for EMSL Project 51287 from February 2024
DOI: 10.15468/dl.eh7ckn
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.6_1",
 "TaxonKey is Cuora trifasciata (Bell, 1825)"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021131-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-02.3026121/2512470
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026122/2512471
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026123/2512472
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026124/2512473
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026125/2512474
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026127/2512475
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026128/2512476
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026129/2512477
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026130/2512478
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026131/2512479
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026132/2512480
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026133/2512481
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026134/2512482
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026135/2512483
title: Data for EMSL Project 51287 from February 2024
DOI: 10.15468/dl.kgejqz
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-72.90035 5.18705,-72.8999 5.15029,-72.86337 5.1494,-72.86359 5.18749,-72.90035 5.18705))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 19 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021132-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-02.3026137/2512484
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026138/2512485
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026139/2512486
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026140/2512487
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026141/2512488
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026142/2512489
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026144/2512490
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026149/2512491
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026155/2512492
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026156/2512493
title: Data for EMSL Project 51287 from February 2024
DOI: 10.15468/dl.4dnnqk
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Laticauda schistorhyncha (Günther, 1874)"
 ]
}

 The dataset includes 71 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021133-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-02.3026157/2512494
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026160/2512495
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026161/2512496
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026163/2512497
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026164/2512498
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026165/2512499
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026166/2512500
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026167/2512501
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026171/2512502
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026172/2512503
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026173/2512504
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026174/2512505
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026175/2512506
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026176/2512507
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026177/2512508
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026178/2512509
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026179/2512510
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026181/2512511
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026182/2512512
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026183/2512513
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026184/2512514
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026185/2512515
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026186/2512516
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026187/2512517
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026188/2512518
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026189/2512519
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026190/2512520
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026191/2512521
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026192/2512522
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026195/2512523
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026196/2512524
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026197/2512525
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026198/2512526
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026199/2512527
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026200/2512528
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026203/2512529
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026209/2512530
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026211/2512531
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026216/2512532
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026217/2512533
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026219/2512534
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026223/2512535
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026227/2512536
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026229/2512537
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026230/2512538
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026231/2512539
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026232/2512540
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026233/2512541
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026234/2512542
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026235/2512543
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026236/2512544
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026237/2512545
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026238/2512546
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026239/2512547
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026244/2512548
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026248/2512549
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026262/2512550
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026263/2512551
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026265/2512552
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026269/2512553
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026270/2512554
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026272/2512555
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026273/2512556
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026278/2512557
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026280/2512558
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026281/2512559
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026282/2512560
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026283/2512561
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026284/2512562
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026285/2512563
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026286/2512564
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026287/2512565
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026288/2512566
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026293/2512567
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026295/2512568
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026296/2512569
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026301/2512570
title: Data for EMSL Project 51287 from February 2024
DOI: 10.15468/dl.wwpz9t
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-98.48671 19.55817,-93.87742 17.30796,-90.28435 18.10642,-89.81254 20.10257,-89.88512 23.55046,-89.92142 25.47402,-98.99482 25.36514,-98.48671 19.55817))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bryozoa"
 ]
}

 The dataset includes 240 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021134-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-02.3026302/2512571
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026303/2512572
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026306/2512573
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026308/2512574
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026315/2512575
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026321/2512576
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026322/2512577
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026326/2512578
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026327/2512579
title: Data for EMSL Project 51287 from February 2024
DOI: 10.25582/data.2024-02.3026331/2512580
title: Data for EMSL Project 51287 from February 2024