cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.a8f2870ee437416c9d458a49eefb189b
title: Impact of Greywater Reuse on Soil Quality in Santa Clara County, CA: A Comprehensive Soil Sample Dataset

description: This dataset supports the study on the effects of greywater reuse systems on soil quality in Santa Clara County, California, particularly within the context of drought-induced water conservation efforts. The data includes soil characteristics collected from 30 households with operational Laundry to Landscape (L2L) greywater systems. Parameters measured include pH, electrical conductivity (EC), soil organic matter, sodium adsorption ratio (SAR), cation exchange capacity (CEC), and various nutrient and metal concentrations.

Soil samples were collected at three distances from the greywater outlet (12 cm, 1.5 m, and 3 m) and analyzed to evaluate the impact of system age and compliance with California’s plumbing codes on soil properties. The dataset provides critical insights into the potential for greywater reuse to alter soil chemistry, particularly in relation to salinity, nutrient content, and metal accumulation. These findings are crucial for developing sustainable greywater management practices and ensuring long-term soil health in drought-prone residential areas.
DOI: 10.4211/hs.f0f661457b5e42288378b13ad0951d90
title: Groundwater droughts data for 6626 wells in Germany

description: This resource provides data on groundwater droughts across 6626 wells in Germany, drought characteristics and spatial characteristics of the wells. Groundwater droughts are analyzed by anomaly time series in form of the Standardized Groundwater Index (SGI, Bloomfield et al. 2013). The drought characteristics are calculated from the SGI time series at each well. The spatial characteristics are derived from several different geodata sets providing information on the land cover, hydrogeology, topography, and climate. 

This data set was created for and analyzed in the corresponding paper accepted in HESS with the title "Groundwater head responses to droughts across Germany" by Ebeling et al..  The basis for this data set is the set of groundwater times series provided in the repository "Monatliche Grundwasserstände in Deutschland 1990-2021" by CORRECTIV (Donheiser et al. 2022, https://github.com/correctiv/grundwasser-data).  Thus, for the original groundwater head time series please refer to the CORRECTIV-repository (Donheiser, 2022).

This resource includes:
1. SGI time series data
2. Derived drought and spatial characteristics, together with minimal metadata (id and coordinates) extracted from the CORRECTIV-repository.


References:

Bloomfield, J. P., & Marchant, B. P. (2013). Analysis of groundwater drought building on the standardised precipitation index approach. Hydrol. Earth Syst. Sci., 17(12), 4769-4787. https://hess.copernicus.org/articles/17/4769/2013/

Donheiser, M. (2022). Monatliche Grundwasserstände in Deutschland 1990-2021. Retrieved from: https://github.com/correctiv/grundwasser-data. Access date: 2022-11-30.

Ebeling, P., Musolff, A., Kumar, R., Hartmann, A., and Fleckenstein, J. H. (2024). Groundwater head responses to droughts across Germany, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2761
DOI: 10.4211/hs.a339c49566c04c9092f04d5895a1ef09
title: Stream and rainwater geochemical (dissolved P, K, and Ca), stream discharge, meteorologic and aerosol concentration datasets for Cascadilla Creek (Central New York, USA)

description: This resource contains accompanying stream water and rainwater chemistry data (dissolved P, K, and Ca) reported in  the manuscript "Rapid response of stream dissolved phosphorus concentrations to wildfire smoke" by Fernandez et al. The dataset spans an ~ 1.5 year period from April 2022 to October 2023 and captures an unprecedented smoke period that took place in the region during the summer of 2023. 

File is provided in ".xlsx" format. The excel file tabs are organized with a summary page ("Contents") followed by supplementary tables (S1, S2, S3, and S4) that correspond to dataset type (Streamwaters, Rainwaters, AirQuality, and Meteorology). Goechemical data were generated by this study whereas stream discharge, PM2.5 aerosol concentrations, and meteorlogical data were obtained from the USGS National Water Information Service (NWIS, https://waterdata.usgs.gov/usa/nwis), PurpleAir (https://api.purpleair.com/), and Northeast Regional Climate Center (NRCC) CLIMOD-2 (http://climod2.nrcc.cornell.edu/). 

Additional information for how stream discharge was determined for ungauged Cascadilla Creek can be found in the manuscript in  "Online Methods"  under sub-heading "Stream discharge".
DOI: 10.4211/hs.a01b56b5e05c44cd95088e9a9f7b25d8
title: Hydroclimate Over the Great Salt Lake Subbasins

description: This resource holds data and codes for the hydroclimate analyses reported in in the MS Thesis:
Ghimire, B., (2025), "Investigating Changes in Hydroclimate, Land Cover, and Evapotranspiration across The Great Salt Lake Basin and its Major Subbasins," Civil and Environmental Engineering, Utah State University.

It contains the data and Python codes used to extract hydroclimate variables for the subbasins of the Great Salt Lake Basin. Precipitation, air temperature and evapotranspiration were extracted from ClimateEngine, while streamflow was from the USGS. These hydroclimatic variables were averaged over the water years for the period of 2004 to 2021.  This data was used to analyze the water balance and hydroclimatic trends over the river basins that drain to GSL to investigate causes for changes in lake level.
DOI: 10.4211/hs.964aa2c8c6ba4a3c81b19a303643ac17
title: Land cover change in the Great Salt Lake basin

description: This resource holds land cover data used in MS Thesis:
Ghimire, B., (2025), "Investigating Changes in Hydroclimate, Land Cover, and Evapotranspiration across The Great Salt Lake Basin and its Major Subbasins," Civil and Environmental Engineering, Utah State University.


It contains the raster map, data, and Python codes used to estimate land cover class changes within the subbasins of the Great Salt Lake Basin. Publicly available remotely sensed data (raster maps) on land cover from the Land Change Monitoring, Assessment, and Projection (LCMAP) (https://eros.usgs.gov/lcmap/apps/data-downloads) was used to estimate changes in land cover. LCMAP is a collection of raster maps produced by the USGS, available annually from 1985 to 2021, covering the contiguous United States. The land cover change study covered the calendar years 2003 to 2021 to fully align with the water years 2004 to 2021, matching the time period used in other studies in the MS thesis. This data was used to analyze the land cover change and transformation within the Great Salt Lake basins to help understand and estimate the impact of land cover change on the water balance in the basin.
DOI: 10.4211/hs.65d49afe742247a9a3dd5533672ed37a
title: Water budget over land cover classes in the Great Salt Lake basin.

description: This resource holds water budget data over land cover classes used in the MS Thesis:
Ghimire, B., (2025), "Investigating Changes in Hydroclimate, Land Cover, and Evapotranspiration across The Great Salt Lake Basin and its Major Subbasins," Civil and Environmental Engineering, Utah State University.

It contains Python code and coordinates of representative points for each land cover class, that were obtained by sampling grid points to sufficiently represents each class. These representative points were used as inputs into the ClimateEngine API to retrieve precipitation, evapotranspiration, daily mean air temperature, and potential evapotranspiration for each land cover class across the Great Salt Lake subbasins. The data for these variables were then averaged over the water years from 2004 to 2021 and are shared in the resource. These data were used to analyze water yield, defined as the difference between precipitation and evapotranspiration, which indicates the amount of water available for streamflow or storage in the basin. Additionally, total evapotranspiration, considered as a surrogate for water use from different land cover classes, was estimated. This analysis helps to understand how various land cover types influence water availability and usage within the basin.
DOI: 10.4211/hs.5d88721054a5458d93ad837192f38abb
title: High Water Marks July and December 2023 Vermont Floods - UVM Floodplains

description: This dataset represents high-water mark observations made following widespread flooding in Vermont on July 10-11 and December 18, 2023. Sites visited following flood recession are part of a long-term floodplain monitoring network maintained by the University of Vermont. Immediately after the floods, field teams flagged high water marks, took photos, and marked the locations with smart phone GPS. Subsequently a survey team revisited the sites with an RTK-GPS to more precisely capture the location and elevation and post-processed through NOAA's OPUS. 

Data quality is noted based on the following:
Field Rating: Based on initial site visits. Field personnel noted their confidence level in the indicators of the high water marks. High confidence are noted as "Good", and lower confidence points are noted as "Fair" or "Poor"
GPS Quality: Based on RTK-GPS survey quality. More information can be found in accompanying txt file.
DOI: 10.4211/hs.ac0c4f31e8a64c52bae41e5719bd3c14
title: AIMS_SOP_Surface Water Chemistry Field Sampling

description: The following standard operating procedure (SOP) was created for the the Aquatic Intermittency effects on Microbiomes in Streams (AIMS), an NSF EPSCoR funded project (OIA 2019603) seeking to explore the impacts of stream drying on downstream water quality across Kansas, Oklahoma, Alabama, Idaho, and Mississippi. AIMS integrates datasets on hydrology, microbiomes, macroinvertebrates, and biogeochemistry in three regions (Mountain West, Great Plains, and Southeast Forests) to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams. An overview of the AIMS project can be found here: https://youtu.be/HDKIBNEnwdM
This protocol will describe the process of collecting samples for water chemistry in the field. This protocol was used for all Approaches in AIMS wherein field water samples were collected.
DOI: 10.4211/hs.fcfd37788e9a4d5c96fa4006d7178ee2
title: Chlorophyll and carbohydrate content of sediment core tops from San Pablo Bay and Grizzly Bay, California, 2019-2020

description: Sediment core tops collected during summer of 2019 and winter of 2020 in the shallows of San Pablo and Grizzly Bays were analyzed for chlorophyll and carbohydrate content as part of a study to investigate how hydrodynamic forcing and sediment properties influence sediment erodibility. This data is presented here.
DOI: 10.4211/hs.a8b243957f7946e388d10ab206990675
title: TempEst 2 Development Data: Observed Stream Temperature, Covariates, Performance Data, and Analysis Notebooks

description: This resource contains code and data related to the development of the TempEst 2 stream temperature remote sensing model (manuscript in review with Journal of Hydrology).  The code includes the model implementation (model.R), some utility functions (valfn.R), data retrieval scripts for Google Earth Engine (eeretrieval.py and datapts.py), and a reproducible validation notebook (validation.Rmd), along with the knitted PDF of the latter (validation.pdf).  The main data include stream temperature daily mean/max observations retrieved from the USGS NWIS as well as remotely-sensed and gridded observations retrieved using Google Earth Engine from NLDAS, ESA WorldCover, MODIS, ERA5, and EPA Ecoregions (using eeretrieval.py).  These are contained in three files.  AllData.csv includes all observations for mean temperature.  ExtData.csv ("extended data") adds maximum temperature, at the expense of fewer total observations being included.  Ecoregions.csv is not central to the analysis, but includes EPA Level I ecoregion classifications for convenience.

Model performance tests can be reproduced using validation.Rmd.  To run validation.Rmd in full, there must be a Data directory with subdirectories Density and TSLen, a Figures directory, at least one of the main data files (AllData.csv, ExtData.csv) or equivalent, and Ecoregions.csv.  A knitted version of the Notebook is included in this resource.  The error map plots also use an EPA Level I Ecoregions (https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/cec_na/na_cec_eco_l1.zip) shapefile, which is assumed to be in an Ecoregions subdirectory of the *parent* directory.  This dependency can be removed by replacing the `plot.eco` function with ordinary ggplot plotting.

The two rda (RData) files contain different versions of a pre-trained model.  model.rda contains a regular, pre-trained model function that can be used directly to generate predictions.  krigs.rda contains a list of the actual fitted kriging models, which can be used for investigating model components (see demo.pdf).

These data and related items of information have not been formally disseminated by NOAA, and do not represent any agency determination, view, or policy.
DOI: 10.15468/dl.tdmpnn
title: Occurrence Download

description: A dataset containing 1994 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-3.488156 40.676297, -3.489211 40.708497, -3.531707 40.708497, -3.530639 40.676297, -3.488156 40.676297))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 1994 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049997-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.213156b03c964f3db49d11947b0ab9d5
title: Data for: "Surplus nutrient exports differ between irrigated and high-rainfall agricultural catchments: a tale of two catchments in South East Australia"

description: The resources provided here were used to calculate catchment-scale nutrient budgets conducted for two catchments in south east Australia, the high-rainfall Moe Drain catchment, and the predominantly irrigated Lake Wellington Main Drain catchment. The resources provided here include farm-gate nutrient budgets for a collection of dairy farms (annually (July-June) between financial years 2015-16 and 2020-20), and livestock farms (financial years 2019-20 and 2020-21). Farm-gate budgets were calculated using publicly available farm survey information for the Gippsland region of Victoria (collected by the Victorian Government Dairy Farm and Livestock Farm Monitor Projects), or previously published data pertaining relating to vegetable crops.  Flow, total nitrogen and total phosphorus concentrations, as well as estimated monthly nitrogen and phosphorus fluxes are also provided, in additional to data summarising the distribution of land use classes in each catchment. Each folder contains a README file with additional metadata specific to each record. This dataset forms the basis of the following publication: Sargent, R., Wong, W. W., Western, A. W., Cook, P., & Lintern, A. (2025). Surplus nutrient exports differ between irrigated and high-rainfall agricultural catchments: A tale of two catchments in south east Australia. Journal of Hydrology, 133959. https://doi.org/10.1016/j.jhydrol.2025.133959
DOI: 10.4211/hs.8af32fd732c34f078118d9cf0d3fd76d
title: Data used in "Understanding spatiotemporal patterns and drivers of urban flooding using municipal reports"

description: This supports the article entitled "Understanding spatiotemporal patterns and drivers of urban flooding using municipal reports". This resource includes the R codes and data (imperviousness, rainfall, flood reports, topography, census data) used for the analysis.

This resource contains information from the “City of Denver Open Data Catalog” (http://data.denvergov.org).
DOI: 10.17632/smj6ntt8jh
title: Bidirectional Silent Human Communication System Using Subvocal EMG, Secure NFMI Mesh Networking, and Transcutaneous Perceptual Stimulation

description: This whitepaper presents a novel, integrated system for bidirectional, silent, and secure human-to-human communication. The architecture synergistically combines three core technologies:

1. Subvocal Electromyography (sEMG): For capturing and classifying silent speech intentions with ≥85% accuracy using a Temporal Convolutional Network (TCN).
2. Secure NFMI Mesh Networking: A Near-Field Magnetic Induction protocol operating at 13.56 MHz with TDMA-FHSS scheduling, AES-128 encryption, and ECDH key exchange, providing inherent Low Probability of Intercept/Detection (LPI/LPD) within a 2-meter range.
3. Transcutaneous Non-Acoustic Perceptual Stimulation: Utilizing bistable electromagnetic actuators and piezoelectric bone conductors to deliver silent, tactile feedback to the user.

The system is characterized by its ultra-low power consumption (~41 mW average), enabling over 24 hours of operation, and full compliance with ICNIRP 2020 and IEC 60601 safety standards. It is designed for reliability in electromagnetically contested environments.

This publication aims to establish prior art and foster open innovation in wearable communication, with direct applications in critical care (e.g., post-laryngectomy communication), tactical operations, and high-noise industrial settings. By openly sharing this architecture, we intend to prevent patent exclusivity on the core concept and encourage the global research community to advance this technology for beneficial use cases.
DOI: 10.17632/smj6ntt8jh.1
title: Bidirectional Silent Human Communication System Using Subvocal EMG, Secure NFMI Mesh Networking, and Transcutaneous Perceptual Stimulation

description: This whitepaper presents a novel, integrated system for bidirectional, silent, and secure human-to-human communication. The architecture synergistically combines three core technologies:

1. Subvocal Electromyography (sEMG): For capturing and classifying silent speech intentions with ≥85% accuracy using a Temporal Convolutional Network (TCN).
2. Secure NFMI Mesh Networking: A Near-Field Magnetic Induction protocol operating at 13.56 MHz with TDMA-FHSS scheduling, AES-128 encryption, and ECDH key exchange, providing inherent Low Probability of Intercept/Detection (LPI/LPD) within a 2-meter range.
3. Transcutaneous Non-Acoustic Perceptual Stimulation: Utilizing bistable electromagnetic actuators and piezoelectric bone conductors to deliver silent, tactile feedback to the user.

The system is characterized by its ultra-low power consumption (~41 mW average), enabling over 24 hours of operation, and full compliance with ICNIRP 2020 and IEC 60601 safety standards. It is designed for reliability in electromagnetically contested environments.

This publication aims to establish prior art and foster open innovation in wearable communication, with direct applications in critical care (e.g., post-laryngectomy communication), tactical operations, and high-noise industrial settings. By openly sharing this architecture, we intend to prevent patent exclusivity on the core concept and encourage the global research community to advance this technology for beneficial use cases.
DOI: 10.17632/pfz4tgdh63
title: Ultrasound-Assisted Deep Eutectic Solvent Extraction of Bioactive Compounds from Euodiae fructus: Process Optimization, Mechanism Elucidation and Green Assessment

description: Figure and Table
DOI: 10.4211/hs.60a890eb841c460192c03bb590687145
title: A global open-source dataset of monthly irrigated and rainfed cropped areas (MIRCA-OS) for the 21st century

description: Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development.
DOI: 10.4211/hs.1199c12ae64447bd87d5c005a11e984c
title: The effect of rainfall variability on Nitrogen dynamics in a small agricultural catchment

description: The rainfall variability has been testified to be unprecedentedly remarkable in the context of climate change. Extreme storms and droughts have occurred throughout the globe and caused serious impacts on society and ecosystems. However, little has been done to explore the effect of rainfall variability on water quality. This study is aimed to investigate the effect of rainfall variability on Nitrogen (N)  dynamics that impact water quality and aquatic ecosystems. The transport of flow and nitrate was simulated for a small agricultural catchment in central Germany, using the fully coupled surface-subsurface model HydroGeoSphere. Rainfall time-series with the designative parameter characteristics were generated using a stochastic rainfall generator. Three representative years (with high, normal, and low annual precipitations, respectively) were chosen from the past two decades in central Germany as target scenarios. Based on the scenario of low annual precipitation, a fourth scenario with reduced plant uptake was considered to represent the situation that vegetation is partially destroyed by extreme droughts. Simulation of each scenario was conducted to identify the impact of rainfall variability on N dynamics. The results suggest that higher annual precipitation can enhance the transformation and transport of nitrogen. Lower annual precipitation is conducive to the nitrogen retention capacity. Nonetheless, when vegetation suffers from drought stress, the capacity will decline significantly, suggesting that vegetation plays a vital role in N dynamics. The sensitivity analyses of selected parameters of the rainfall generator show that longer interstorm periods inhibit the transformation of N and prompt the accumulation of SON. However, alterative longer interstorm periods and storm durations enhance extensive transformation during a certain time. High mean rainfall intensities contribute to the transformation from SON to SIN in the season of vegetation growth and the season with high temperatures. Overall, the study clarifies the effect of rainfall variability on N dynamics in a small agricultural catchment, which provides theoretical support for us to protect aquatic ecosystems under climate change in the future.
DOI: 10.17632/pfz4tgdh63.1
title: Ultrasound-Assisted Deep Eutectic Solvent Extraction of Bioactive Compounds from Euodiae fructus: Process Optimization, Mechanism Elucidation and Green Assessment

description: Figure and Table
DOI: 10.17632/d6wj3jynp9
title: Smart AI-Driven Logistics Optimization and Sentiment Analysis - Data

description: Smart AI-Driven Logistics Optimization and Sentiment Analysis: The Role of Technology Trust and Organizational Readiness in Enhancing Business Performance Data