cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.f43aa2d7542349219886276bd6358c1d
title: Data for: Bioretention cells provide a tenfold reduction in 6PPD-quinone mass loadings to receiving waters: Evidence from a field experiment and modeling

description: This resource contains 1) measurements from a spike and recovery and subsequent flushing experiment that were performed on a system at the corner of Pine and 8th streets in Vancouver, BC, Canada (see metadata) to investigate the fate and transport of 6PPD-Quinone through stormwater bioretention systems and 2) the version of the "BioretentionBlues" model that was used to estimate fate of 6PPD-quinone. The current version of the model is available from https://github.com/tfmrodge/SubsurfaceSinks
DOI: 10.4211/hs.a876020b85d6413f8486c57dc0b0e3bf
title: Water Chemistry, Nitrogen and CDOM/FDOM Data from the Santa Fe River Watershed, North-Central Florida

description: This resource includes hydrogeochemical data (August 2018 - March 2022) for groundwaters and surface waters across the Santa Fe River watershed in North-Central FL, USA. This data includes physicochemical data (discharge, temperature, dissolved oxygen, specific conductance, pH, oxidation-reduction potential, salinity), major cations (sodium, potassium, magnesium, calcium) and anion (chloride, sulfate) concentrations, dissolved nitrogen concentrations (total dissolved nitrogen, nitrate, ammonium, organic nitrogen), organic carbon concentrations and quality parameters derived from spectroscopic analyses and dissolved nitrous oxide concentrations. Groundwater samples include spring waters of the Ichetucknee Springs system and well waters of the Santa Fe River Sink-Rise system in O'leno State Park.  Surface water samples include Santa Fe River waters across the main river channel from headwaters to confluence with the Suwannee River.
DOI: 10.4211/hs.909aaa5edf1040b6a6244a0ca7f58890
title: Isotope compositions in precipitation across Mexico: 2018-2021

description: Here we report on the spatial and temporal isotope variations of 21 precipitation monitoring stations across Mexico. Our database includes 608 monthly samples collected from 2018 to 2021 over four main regions (between 5 and 2,365 m asl): the Pacific coast, the Gulf of Mexico/Caribbean Sea region, and the Central and Northern Plateaus.
DOI: 10.15468/dl.x9nehm
title: Occurrence Download

description: A dataset containing 614 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Taxus globosa"
 ]
}

 The dataset includes 614 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049982-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.c3615c46ac044b4899293c5cf8782be3
title: The data for “Submarine groundwater discharge strengthens acidification in the coastal semi-closed bays”

description: This is the data in the manuscript "Submarine groundwater discharge strengthens acidification in the coastal semi-closed bays". It includes rainfall, river discharge, evapotranspiration, atmospheric CO2 concentration, marine water quality and tidal fluctuation data. In addition, the data collected by the author is also attached.
DOI: 10.4211/hs.53c0e116d528415fa70ba3f31dbd8714
title: experimental data for "Colloid aggregation and dynamics of fluid structures during two-phase flow in porous media"

description: This resources contains data for the paper on colloid aggregation and dynamics of fluid structures during two-phase flow in porous media. The files are in Excel format and include data for colloid cluster size distribution, volume of wetting fluid clusters, and relative permeabilities.
DOI: 10.4211/hs.74cd4448a2234a47b11078b098325200
title: Sub-monthly time scale prediction of harmful algal bloom cell count in Lake Erie using remote sensing and machine learning

description: This resource contains raw data and results for the manuscript "Sub-monthly time scale prediction of harmful algal bloom cell count in Lake Erie using Remote Sensing and Machine Learning" by Abhinav Gupta, Mohamed M. Hantush, and Rao S. Govindaraju.
DOI: 10.4211/hs.ea239c1052644e989e5440923d90a74a
title: experimental data for "Displacement patterns of a Newtonian fluid by a shear-thinning fluid in a rough fracture"

description: This resource contains the experimental data for the paper "Displacement patterns of a Newtonian fluid by a shear-thinning fluid in a rough fracture". The files include a MATLAB data file for the aperture field and three Excel files for the data of front tip location and velocities at different times.
DOI: 10.4211/hs.74a7fedb4f694ab6aafa3e6d6f47d129
title: Hydrologic (discharge, level, pressure, voltage, electrical conductivity, and temperature) and atmospheric (pressure and temperature) data from injection experiments and a rain event at Bear Spring, MN, USA – June 10-12, 2016

description: We conducted water injection experiments at Bear Spring in southeastern Minnesota, USA, and we monitored responses from the experiments and a rain event (Bilek et al., 2023; Luhmann et al., in press). We injected water from a pool into the overflow spring and monitored hydrologic responses at the perennial spring. The experiments were conducted to monitor for seismic signals as water flows through the karst aquifer, and all corresponding seismic data are provided in Bilek (2016). The experiments confirmed that the overflow spring and perennial spring were connected by at least one conduit in the karst aquifer. The overflow spring was dry for the first two experiments, but it started flowing after a large rainfall event. Because the overflow spring was flowing during the third experiment, the water from the pool simply flowed across the overflow spring run instead of entering the conduit. The hydrologic data are provided that illustrate the response to the three injection experiments and the rain event. Information about the field site and the methods are described below.
DOI: 10.4211/hs.49b0f3b0f6924b2d917b3659fb03926b
title: Data for "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing"

description: This archive includes data used in Zhang et al.'s paper "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing", which has been published in Water Resources Research (WRR) (https://doi.org/10.1029/2022WR034092). The archive contains 1) raw data (daily-scale CAMELS streamflow data and watershed attributes) and 2) MATLAB scripts used to perform data-driven sparse sensing and generate sample figures. The streamflow data used in this study was retrieved from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (https://ral.ucar.edu/solutions/products/camels). The MATLAB code used for data-driven sparse sensing was retrieved from the Github repository by Krithika Manohar (https://github.com/kmanohar/SSPOR_pub) and customized for this study.
DOI: 10.5281/zenodo.17916379
title: THE IMPORTANCE OF ARTIFICIAL INTELLIGENCE  IN NEUROSCIENCE

description: This article highlights the importance and areas of application of artificial intelligence (SI) technologies in neuroscience. Modern SI Systems are increasing efficiency in early detection, analysis, and treatment of brain disorders. In particular, in the diagnosis of diseases such as neurodegenerative diseases (Alzheimer's, Parkinson's), stroke, epilepsy, medical indicators at high resolution are being analyzed through SI algorithms.ation This article highlights the importance and areas of application of artificial intelligence (SI) technologies in neuroscience. Modern SI Systems are increasing efficiency in early detection, analysis, and treatment of brain disorders. In particular, in the diagnosis of diseases such as neurodegenerative diseases (Alzheimer's, Parkinson's), stroke, epilepsy, medical indicators at high resolution are being analyzed through SI algorithms. The article analyzes the advantages, current challenges, and future prospects of SI technologies.
DOI: 10.5281/zenodo.17916380
title: THE IMPORTANCE OF ARTIFICIAL INTELLIGENCE  IN NEUROSCIENCE

description: This article highlights the importance and areas of application of artificial intelligence (SI) technologies in neuroscience. Modern SI Systems are increasing efficiency in early detection, analysis, and treatment of brain disorders. In particular, in the diagnosis of diseases such as neurodegenerative diseases (Alzheimer's, Parkinson's), stroke, epilepsy, medical indicators at high resolution are being analyzed through SI algorithms.ation This article highlights the importance and areas of application of artificial intelligence (SI) technologies in neuroscience. Modern SI Systems are increasing efficiency in early detection, analysis, and treatment of brain disorders. In particular, in the diagnosis of diseases such as neurodegenerative diseases (Alzheimer's, Parkinson's), stroke, epilepsy, medical indicators at high resolution are being analyzed through SI algorithms. The article analyzes the advantages, current challenges, and future prospects of SI technologies.
DOI: 10.15468/dl.tsxe43
title: Occurrence Download

description: A dataset containing 4406 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Russian Federation, China, Japan, Korea (Democratic People’s Republic of), Korea, Republic of, Mongolia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Salix pentandra"
 ]
}

 The dataset includes 4406 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049979-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.e961202cb1db41e7b7ef9258b2873761
title: Extending Height Above Nearest Drainage to Model Multiple Fluvial Sources in Flood Inundation Mapping Applications for the U.S. National Water Model

description: Flood inundation mapping and evaluation software configured to work with U.S. National Water Model.

Please see noaa-owp/inundation-mapping on Github for the latest official version.

Height Above Nearest Drainage (HAND), a drainage normalizing terrain index, is a means able of producing flood inundation maps (FIMs) from the National Water Model (NWM) at large scales and high resolutions using reach-averaged synthetic rating curves. 
We highlight here that HAND is limited to producing inundation only when sourced from its nearest flowpath, thus lacks the ability to source inundation from multiple fluvial sources.
A version of HAND, known as Generalized Mainstems (GMS), is proposed that discretizes a target stream network into segments of unit Horton-Strahler stream order known as level paths (LP).
The FIMs associated with each independent LP are then mosaiced together, effectively turning the stream network into discrete groups of homogeneous unit stream order by removing the influence of neighboring tributaries.
Improvement in mapping skill is observed by significantly reducing false negatives at river junctions when the inundation extents are compared to FIMs from that of benchmarks.
A more marginal reduction in the false alarm rate is also observed due to a shift introduced in the stage-discharge relationship by increasing the size of the catchments.
We observe that the improvement of this method applied at 4-5% of the entire stream network to 100% of the network is about the same magnitude improvement as going from no drainage order reduction to 4-5% of the network.
This novel contribution is framed in a new open-source implementation that utilizes the latest combination of hydro-conditioning techniques to enforce drainage and counter limitations in the input data.
DOI: 10.4211/hs.a5f2049820db480fa3e03c0ea7216b56
title: Supporting data: "Landscape pollution source dynamics highlight priority locations for basin-scale interventions to protect water quality under hydroclimatic variability"

description: This resource contains supporting data for the publication "Landscape pollution source dynamics highlight priority locations for basin-scale interventions to protect water quality under hydroclimatic variability". 

A Cape Fear River Basin Water Quantity and Quality Soil and Water Assessment Tool Model (CFRB WQQ SWAT Model) was developed to examine risks to water quality from point and non-point sources under current land use and weather conditions from 1982-2019, and to facilitate the evaluation of scenarios, including watershed-scale restoration of forests and wetlands. This work built on a Cape Fear River Basin Water Quantity Model developed by USGS for a Coastal Carolinas Focus Area Study of water availability and water use. 

Datasets contained in this resource include:
-Spatial data delineating the basin, the CFRB WQQ SWAT Model subbasins, and the CFRB SWAT Model reach network. 
-Predicted daily streamflow and loads of sediment, total nitrogen and total phosphorus based on the SWAT simulation reach and reservoir outputs.
-Predicted runoff and loads of sediment, total nitrogen and total phosphorus based on the SWAT simulation subbasin output.
DOI: 10.4211/hs.fa0007d553ae4c8682dd9c464e91913d
title: Material for 'Statistical-Topographical Mapping of Rainfall Over Mountainous Terrain Using Beta Scaling'

description: This resource belongs to the manuscript "Statistical-Topographical Mapping of Rainfall Over Mountainous Terrain Using Beta Scaling". It collects the R code and the data needed to reproduce the analyses and generate the figures.

We present a robust approach for quantitative precipitation estimation (QPE) for water resources management in mountainous catchments, where rainfall sums and variability are correlated with orographic elevation, but density of rain gauges does not allow for advanced geostatistical interpolation of rainfall fields. 
Key of the method is modelling rainfall at unobserved locations by their elevation-dependent expected daily mean, and a daily fluctuation which is determined by spatial interpolation of the residuals of neighbouring rain gauges, which are scaled according to the elevation difference. The scaling factor is defined as the ratio of covariance and variance, in analogy to the "beta" used in economics.
The approach is illustrated for the Chirilu catchments (Chillón, Rímac, Lurín) in the Andes near Lima, Peru. The results are compared to conventional IDW interpolation and a merged national rainfall product. The method results in QPE that are better matching with observed discharges. The β-IDW approach thus provides a robust and flexible means to estimate rainfall input to mesoscale mountainous catchments.
DOI: 10.4211/hs.db187b7e328c4158879926d8f9a6dccd
title: Global Land Subsidence Mapping Reveals Widespread Loss of Aquifer Storage Capacity Datasets

description: Groundwater overdraft gives rise to multiple adverse impacts including land subsidence and permanent groundwater storage loss. Existing methods have been unable to characterize groundwater storage loss at the global scale with sufficient resolution to be relevant for local studies. Here we explore the interrelation between groundwater stress, aquifer depletion, and land subsidence using remote sensing and model-based datasets with a machine learning approach. The developed model predicts global land subsidence magnitude at high spatial resolution (~2 km) and provides a first-order estimate of aquifer storage loss due to consolidation of ~17 km3/year globally. China, the United States, and Iran account for the majority of groundwater storage loss due to consolidation. The model quantifies key drivers of subsidence and has high predictive accuracy, with an F1-score of 0.83 on the validation set. Roughly 73% of the mapped subsidence occurs over cropland and urban areas, highlighting the need for sustainable groundwater management practices over these areas. The results of this study aid in assessing the spatial extents of subsidence in known subsiding areas, and in locating unknown groundwater stressed regions.
DOI: 10.4211/hs.a6afe1e912fa4953a6d36ea34971cd58
title: WK Kellogg Experimental Forest Emerald Ash Borer Plot Obs

description: These files contain the hydrometeorological (Wind Speed, Wind Gusts, Wind Direction; Air Temperature, Relative Humidity, Barometric Pressure; Solar Radiation (PYR); Soil Temperature, Soil Moisture) and shallow groundwater level observations (Barometrically Compensated) from two 10m plots located in the the riparian zone of Augusta Creek, Michigan on the WK Kellogg Experimental Forest Property (MSU).  The plots (gap, forest) represent a high (> 85%) and low (< 20%) EAB-induced mortality of overstory trees.  Data were collected from 9/2018 through 9/2023.
DOI: 10.5281/zenodo.17880202
title: Mechanism-Aware GWAS Causal Graph Inference: Complete Dataset v5.0

description: Complete GWAS Dataset for Mechanism-Aware Causal Inference

Version 5.0 - Complete Data Release (December 2025)

This release contains the complete raw and processed datasets (24.74 GB) for mechanism-aware causal graph inference from GWAS summary statistics.

Raw GWAS Summary Statistics (20.9 GB)



Cardiometabolic Traits (8 traits): CARDIOGRAM 2022 (CAD), DIAGRAM 2022 (T2D), GLGC 2021 (HDL, LDL, TC, TG), ICBP (Systolic BP)

Breast Cancer (3 cohorts): FinnGen R12, iCOGS/ONCO overall, iCOGS/ONCO subtypes

Inflammatory Bowel Disease (3 cohorts): de Lange 2017 (IBD, Crohn's disease, Ulcerative colitis)

Total: 313,867,093 variants across 14 GWAS files

Genome-wide significant: 642,451 variants (p < 5e-8)


Regulatory Annotations (3.8 GB)



Activity-by-Contact (ABC) model predictions for enhancer-gene links

Promoter Capture Hi-C (PCHi-C) chromatin interactions


Processed Analysis Results (0.22 MB)



comprehensive_gwas_analysis.json: Summary statistics for all 14 GWAS datasets (variant counts, top hits, quality metrics)

calibration_metrics.tsv: Expected Calibration Error (ECE) for all framework modules

replication_summary.yaml: eQTL Catalogue replication analysis (847 gene-tissue pairs, 78% replication rate)

benchmark/: Gold standard genes (Tier 1) and drug target validation (Tier 2)

mechanism_graphs/: Causal graph examples (SORT1, APOE, TCF7L2)


Key Results



Performance: Recall@20: 76% [71-81%], Precision@0.8: 81% [75-87%]

Calibration: All framework modules ECE < 0.05

Replication: 78% eQTL replication (661/847), r=0.89 effect size correlation

CRISPR Validation: AUPRC = 0.71 [0.67, 0.75]


Methods

This dataset supports a path-probability framework integrating:



SuSiE fine-mapping: Posterior inclusion probabilities for causal variants

coloc.susie colocalization: Gene-tissue associations via eQTL colocalization

ABC & PCHi-C: Enhancer-gene linking via regulatory annotations

Path probability: Joint inference across variant→cCRE→gene→tissue layers


Citation

@dataset{gwas_mechanism_v5,
  author = {Ashuraliyev, Abduxoliq},
  title = {Mechanism-Aware GWAS Causal Graph Inference: Complete Dataset v5.0},
  year = {2025},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.17877601},
  url = {https://doi.org/10.5281/zenodo.17877601}
}


Related Publication

Ashuraliyev A. (2025). Path-probability framework for mechanism-aware GWAS gene prioritization. In preparation for Nature Genetics.

Data Usage

This dataset is released under CC BY 4.0. Raw GWAS data retains original study licenses (see individual files for details).
DOI: 10.4211/hs.b5c65e50679f48dfaf0d5e86dafd9815
title: Bugs Pay for Days of Steady Reservoir Releases to Reduce Costs to Hydropower Customers and Sustain Funds to Maintain Infrastructure

description: Steady low reservoir releases increase downstream primary and aquatic invertebrate (bugs) production. These releases also reduce hydropeaking value, raise costs for hydropower customers, and reduce funds to maintain infrastructure and repay loans. This study quantifies the win-lose tradeoff between hydropeaking value and days per month of steady low releases at Glen Canyon Dam, Arizona. We estimate win-lose tradeoffs for monthly release volumes of 0.71 to 0.95 million acre-feet from March to October 2018 and 0 to 31 days. Conservative estimates indicate that steady low releases on eight weekend days per summer month in 2018 reduced monthly hydropeaking value by $430,000 to $850,000. We used results to design a financial instrument that gives ecosystem managers a budget to choose days of steady low releases and compensate hydropower producers for lost value. One option to reduce costs is shifting days of steady low releases to spring/fall months. Next steps include discussing the proposed instrument with more U.S. Federal agencies, conducting more flow experiments, and monitoring how timing and more steady low flow days per month affect bug production. Managers may extend to other experimental releases that mobilize sediment, build sand bars, or disadvantage non-native fish.

##This resource contains the following items:
+ README.md - Markdown file with documentation for this resource including directions to reproduce results in the manuscript
+ GCD_BugFlowExperiment-main - Folder with sub-folders that contain the data, models, and code to reproduce figures, tables, and results in the manuscript.

+ RindRosenberg-BugsPayForDaysOfSteadyReservoirReleases.docx -- Word document with manuscript for work.
+ Rosenberg-BugsPayForSteadyFlows-AprilAMP.pptx - Power point presentation with overview of work presented at April 12/13, 2023 meeting of the Technical Work Group (TWG) of Glen Canyon Dam Adaptive Management Program (GCD-AMP).
+ KeyFeedbackFromTechnicalWorkGroup-April12-2023.docx - Key feedback from presentation to GCD-AMP Technical Work Group on April 12, 2023.
DOI: 10.4211/hs.a91f25d51038463294711ffe23119ef2
title: PyLoTemp Basic: Frequency Domain Temperature as a Groundwater Tracer solver

description: PyLoTemp is a 1D Vertical Fluid Flux solver that leverages a Most Likely Estimation Technique in combination with Temperature Time series using Temperature as a Groundwater Tracer. PyLoTemp was made with user ease of access in mind, and it is an open source program that was developed in Python; and it can be used on most windows based devices. This manual will instruct users how to use PyLoTemp and covers the "Simple" version of the program, which is separate from the advanced version.
DOI: 10.4211/hs.3b1cc0e03cba4497b8d178f8ce52f3e8
title: Taku River Basin Spatial Stream Network Strontium Isoscape

description: This study presents the application of a dendritic network model, specifically Spatial Stream Network Models, for predicting variations in strontium concentrations [Sr] and isotope ratios (87Sr/86Sr) in large river systems. We applied the model to the Taku River, a significant river system located within the accreted volcanic arc terranes of the northern Cordillera of North America. The model strongly fits the observed data with RMSE=0.05: r2=0.67 for [Sr] and RMSE=0.0003: r2=0.87 for 87Sr/86Sr. Our multidisciplinary data product offers a comprehensive tool with applications across the biosphere, hydrosphere, and geosphere. This model can address diverse research questions ranging from assessing the ecology of wild salmon fisheries to calculating Sr ocean budgets and evaluating silicate weathering feedback.
DOI: 10.4211/hs.9322e1ef25e04822a759c515795642e1
title: Supplement materials for publication: Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models - case studies from Germany and South Korea.

description: Input radar and climate projection data for five sites in Germany and 5 sites in S.Korea
Simulations for validation and future period for LetItRain and WayDown models for each station
GIS data with locations of the study sites
R-scripts to reproduce figures and tables for the manuscript
Parameter description and calibration results for LetItRain model