cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18357623
title: High-Resolution Global Streamflow Dataset from 1980 - 2020 for 2.94 Million Rivers Using the Physics-Embedded δHBV2–δMC2 Model

description: This dataset provides global-scale streamflow simulations generated with the physics-embedded, high-resolution δHBV2–δMC2 model, developed by the Multi-scale Hydrology, Processes, and Intelligence (MHPI) team at The Pennsylvania State University, led by Dr. Chaopeng Shen’s Hydrologic Deep Learning and Modeling group. This data is provided as is, and we do not assume any responsibility as a result of the use of this data.

This dataset is a direct outcome of Ji et al. (2025), as described below, and has been continuously maintained and updated by Haoyu Ji. In this version, the training period is extended beyond the original 1980–2000 period to include 2001–2020, resulting in improved performance for datasets itself. This Zenodo repository provides continent-based simulation files organized by different MERIT zones. 

Please cite these two papers if you find the data to be of use and refer to the Zenodo main DOI (10.5281/zenodo.17042358) rather than the HydroShare link when acknowledging the data source:

Ji, Haoyu, Yalan Song, Tadd Bindas, Chaopeng Shen*, Yuan Yang, Ming Pan, Jiangtao Liu, Farshid Rahmani, Ather Abbas, Hylke Beck, Kathryn Lawson and Yoshihide Wada (2025). Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning. Nature Communications, doi.org/10.1038/s41467-025-64367-1

Song, Yalan, Tadd Bindas, Chaopeng Shen*, Haoyu Ji, Wouter J. M. Knoben, Leo Lonzarich, Martyn P. Clark, Jiangtao Liu, Katie van Werkhoven, Sam Lemont, Matthew Denno, Ming Pan, Yuan Yang, Jeremy Rapp, Mukesh Kumar, Farshid Rahmani, Cyril Thébault, Richard Adkins, James Halgren, Trupesh Patel, Arpita Patel, Kamlesh Sawadekar, and Kathryn Lawson (2025). High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning. Water Resources Research, doi: 10.1029/2024WR038928

More information on our research group and publications can be found at www.mhpi.info, and our publicly-available codes are also available on Github at www.github.com/mhpi. A wiki with summaries, benchmarks, and more information is available at https://mhpi.github.io.

To facilitate data access, the global river network has been partitioned into zones based on the MERIT Flowlines framework. Users can download subsets corresponding to their regions of interest. The MERIT basin delineations are available at: https://www.reachhydro.org/home/params/merit-basins.Note: The model simulates other variables like ET, soil moisture and snow water equivalent, etc., but are not evaluated. If you are interested, please let us know. we can also provide the near-real-time simulation (from 2021 - now) upon request. See the README file for more information.
DOI: 10.15468/dl.zgxkrd
title: Occurrence Download

description: A dataset containing 2937789 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((21.55131 64.87225,21.92272 64.70841,21.46807 64.13593,20.06625 63.47254,19.04329 62.9948,18.4371 61.77145,17.32574 61.49544,17.90668 60.79458,18.917 60.39153,19.2201 59.71701,18.81597 59.64688,18.66442 59.44846,18.63916 59.21657,18.29818 59.06109,18.03297 58.81347,17.07316 58.57725,16.88373 57.43993,16.12598 56.13264,14.53472 55.31474,13.62543 55.29317,12.6975 55.24178,12.81116 55.76391,12.45754 56.15977,12.08109 56.25827,12.46207 56.49631,10.65591 58.87681,7.40483 57.73827,4.63538 58.40687,3.25066 51.77635,4.15374 52.77095,5.05682 53.3139,8.42832 53.95643,8.12729 55.21232,7.70585 56.09542,8.06505 56.98049,9.53833 57.52386,10.72141 57.84605,10.47587 56.61378,11.1009 56.55231,10.94464 56.13167,10.43122 56.04449,10.34193 55.83195,9.69458 55.44137,9.76155 55.1873,10.27497 55.00849,10.0071 54.82888,10.83303 54.54501,11.5027 54.16777,12.84204 54.50614,13.95817 54.25915,15.40912 54.33731,17.23956 54.89312,20.3647 55.04688,20.83347 55.50463,20.61025 56.1814,21.23527 57.46388,22.44068 57.91726,23.86932 57.1867,24.22648 58.16538,23.3559 58.2242,23.37823 59.09493,23.17732 59.19796,22.19514 58.93404,22.52997 58.67969,21.97191 58.44684,21.61475 58.18892,21.77101 58.38839,21.57011 58.5052,22.21746 58.63324,22.28443 58.78397,21.79333 58.95707,24.18939 59.50187,25.54473 59.74663,26.1982 59.64894,27.74717 59.53869,27.65035 59.74663,28.95729 60.11045,28.32803 60.47029,26.90008 60.27887,23.05187 59.69782,22.15637 59.91691,22.05956 60.30286,20.99465 60.6843,21.21247 61.2481,20.65581 62.76985,21.11566 63.38353,21.84174 63.43769,22.97926 63.99497,24.14098 64.80021,25.06068 64.96458,25.01228 65.44165,23.72954 65.73174,23.07607 65.63207,22.76144 65.7715,22.64042 65.61209,22.15637 65.3913,21.55131 64.87225))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2023-2023"
 ]
}

 The dataset includes 2937789 records from 165 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008691-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e33gdp
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anguloa uniflora",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008746-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ry7kxa
title: Occurrence Download

description: A dataset containing 114877 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pittosporum tobira",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 114877 records from 367 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008689-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/5sf-5k6-xq6
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/31t-xjk-1xx
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/g8t-4kv-k1t
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ep0-r73-45p
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/m3s-l4f-cm1
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/07t-kfd-43x
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/a59-6wa-xvv
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/6g0-vnf-fnw
title: Henrica ramulosa B. de Lesd.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/vns-2j4-gxn
title: Henrica theleodes (Sommerf.) Savic, Tibell & Navarro-Rosinés

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ymh-6es-r5g
title: Henrica theleodes (Sommerf.) Savic, Tibell & Navarro-Rosinés

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/9kq-x5r-kqz
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/m52-hew-432
title: Caloplaca aurea (Schaer.) Zahlbr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/rr9-1w6-mr3
title: Caloplaca aurea (Schaer.) Zahlbr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/trt-w55-z18
title: Caloplaca aurea (Schaer.) Zahlbr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/tvh-gqj-tsd
title: Caloplaca aurea (Schaer.) Zahlbr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/4zp-bf5-yyd
title: Bacidina phacodes (Körb.) Vězda

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/v70-2gw-jh3
title: Bacidina phacodes (Körb.) Vězda

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/2x5-w3k-2ea
title: Psoroma tenue var. boreale Henssen

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/835-n60-rf1
title: Henrica theleodes (Sommerf.) Savic, Tibell & Navarro-Rosinés

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.a3e36b
title: Occurrence Download

description: A dataset containing 226 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Botswana, Zambia, Namibia, Congo, Democratic Republic of the, Angola, Zimbabwe, Mozambique, Malawi)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Occurrence status inferred from individual count, Type status invalid, Recorded date mismatch, Recorded date invalid, Continent derived from coordinates, Country derived from coordinates, Coordinate rounded, Geodetic datum assumed WGS84)",
 "TaxonKey is Galago moholi",
 "Year 2015-2025"
 ]
}

 The dataset includes 226 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008736-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ww94t2
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Conium maculatum",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008731-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fcu8tb
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ovis aries",
 "HasCoordinate is true",
 "Country is Cuba"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008732-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pgzg8g
title: Occurrence Download

description: A dataset containing 1876 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Notoscopelus resplendens (Richardson, 1845)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1876 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004679-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fp39bd
title: Occurrence Download

description: A dataset containing 199 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hieracium glaucinum subsp. baliophyllum (Hyl.) Greuter, Hieracium homophyllum Wiinst., Hieracium longidens Ohlsén, Hieracium murorum subsp. contaminatum (Wiinst.) Gottschl., Hieracium oligostictum Ohlsén)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 199 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004680-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15300752
title: Toko Santi Kosmetik
DOI: 10.5281/zenodo.15300753
title: Toko Santi Kosmetik
DOI: 10.15468/dl.cwchch
title: Occurrence Download

description: A dataset containing 964 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Platyrrhinus lineatus (E.Geoffroy, 1810)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 964 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004682-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wh8vy6
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Desmognathus aureatus (Martof, 1956))"
 ]
}

 The dataset includes 50 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004683-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a5trak
title: Occurrence Download

description: A dataset containing 7395 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((4.24467 50.75007,4.51014 50.75007,4.51014 50.94464,4.24467 50.94464,4.24467 50.75007))",
 "HasGeospatialIssue is false",
 "TaxonKey is Vespa velutina Lepeletier, 1836"
 ]
}

 The dataset includes 7395 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004681-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15303028
title: Salivary CgA and B actine in periodontal health and disease/ data set

description: This dataset includes qPCR cycle threshold (Ct) values for Chromogranin A and β-actin genes obtained from saliva samples of individuals with periodontal health and disease.
DOI: 10.5281/zenodo.15303029
title: Salivary CgA and B actine in periodontal health and disease/ data set

description: This dataset includes qPCR cycle threshold (Ct) values for Chromogranin A and β-actin genes obtained from saliva samples of individuals with periodontal health and disease.
DOI: 10.5281/zenodo.15302578
title: Complementary material to Gone with the wind: urbanization and deurbanization processes in the medieval Muslim Horn of Africa: the case of Handoga (Djibouti)

description: Complementary data of the syntax analysis carried out of the domestic structures of Handoga (Djibouti) and Fardowsa (Somaliland).
DOI: 10.5281/zenodo.15302579
title: Complementary material to Gone with the wind: urbanization and deurbanization processes in the medieval Muslim Horn of Africa: the case of Handoga (Djibouti)

description: Complementary data of the syntax analysis carried out of the domestic structures of Handoga (Djibouti) and Fardowsa (Somaliland).
DOI: 10.15468/dl.349ntp
title: Occurrence Download

description: A dataset containing 5860 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Plethodon vehiculum (Cooper, 1860))"
 ]
}

 The dataset includes 5860 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004686-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8dcn89
title: Occurrence Download

description: A dataset containing 1934 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Desmostachya bipinnata (L.) Stapf"
 ]
}

 The dataset includes 1934 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004687-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5hsm8n
title: Occurrence Download

description: A dataset containing 2592699 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Pl@ntNet observations"
 ]
}

 The dataset includes 2592699 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004673-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cds473
title: Occurrence Download

description: A dataset containing 660 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dichanthium caricosum (L.) A.Camus"
 ]
}

 The dataset includes 660 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004688-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ep8z2a
title: Occurrence Download

description: A dataset containing 4557 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dichroa febrifuga Lour."
 ]
}

 The dataset includes 4557 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004689-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15300664
title: The First Report on Machine Learning-Driven q-RASAR Modeling for Predicting α7nACh Receptor Agonists: A Computational Approach for Anti-Alzheimer's Drug Discovery

description: In the present research, we have explored an advanced computational technique known as the quantitative Read-Across Structure-Activity Relationship (q-RASAR) framework, leveraging machine learning (ML) to enhance predictive precision. Our primary objective was to develop a statistically robust ML-based q-RASAR model for predicting the agonistic activity of compounds targeting the α7 nicotinic acetylcholine (α7nACh) receptor, a critical target in Alzheimer's disease (AD) therapy due to its role in cognitive function and neuroprotection. To achieve this, we have developed a well-validated univariate ML-based q-RASAR MLR regression model using a large dataset of 1,727 structurally diverse heterocyclic and aromatic hydrocarbon compounds, sourced from the freely accessible Binding Database (www.bindingdb.org). The developed q-RASAR model was further applied to assess the applicability domain of the Mcule database (accessible at https://mcule.com/database/), which comprises 1,91,94,405 chemical compounds, facilitating the identification of structurally relevant candidates with potential α7nACh receptor agonistic activity. A graded set of candidate molecules was proposed for further experimental validation to evaluate their potential as anti-Alzheimer’s agents. To further enhance predictability, we have developed various other ML-based q-RASAR models. Furthermore, molecular docking analysis and Molecular dynamic simulation for 100ns were conducted to investigate interactions between the target protein and ligand. The insights gained from this study underscore the crucial roles of hydrophobicity, electronic effects, degree of ionization, and steric factors as key determinants for potential anti-Alzheimer’s agents. This research not only advances our understanding of the molecular determinants influencing CNS drug permeability but also provides a valuable framework for designing next-generation anti-Alzheimer's agent. This approach integrates computational efficiency with predictive accuracy, offering a valuable framework for accelerating the discovery of novel therapeutic leads for AD.
DOI: 10.5281/zenodo.15300665
title: The First Report on Machine Learning-Driven q-RASAR Modeling for Predicting α7nACh Receptor Agonists: A Computational Approach for Anti-Alzheimer's Drug Discovery

description: In the present research, we have explored an advanced computational technique known as the quantitative Read-Across Structure-Activity Relationship (q-RASAR) framework, leveraging machine learning (ML) to enhance predictive precision. Our primary objective was to develop a statistically robust ML-based q-RASAR model for predicting the agonistic activity of compounds targeting the α7 nicotinic acetylcholine (α7nACh) receptor, a critical target in Alzheimer's disease (AD) therapy due to its role in cognitive function and neuroprotection. To achieve this, we have developed a well-validated univariate ML-based q-RASAR MLR regression model using a large dataset of 1,727 structurally diverse heterocyclic and aromatic hydrocarbon compounds, sourced from the freely accessible Binding Database (www.bindingdb.org). The developed q-RASAR model was further applied to assess the applicability domain of the Mcule database (accessible at https://mcule.com/database/), which comprises 1,91,94,405 chemical compounds, facilitating the identification of structurally relevant candidates with potential α7nACh receptor agonistic activity. A graded set of candidate molecules was proposed for further experimental validation to evaluate their potential as anti-Alzheimer’s agents. To further enhance predictability, we have developed various other ML-based q-RASAR models. Furthermore, molecular docking analysis and Molecular dynamic simulation for 100ns were conducted to investigate interactions between the target protein and ligand. The insights gained from this study underscore the crucial roles of hydrophobicity, electronic effects, degree of ionization, and steric factors as key determinants for potential anti-Alzheimer’s agents. This research not only advances our understanding of the molecular determinants influencing CNS drug permeability but also provides a valuable framework for designing next-generation anti-Alzheimer's agent. This approach integrates computational efficiency with predictive accuracy, offering a valuable framework for accelerating the discovery of novel therapeutic leads for AD.
DOI: 10.15468/dl.upzspe
title: Occurrence Download

description: A dataset containing 3784169 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Russian Federation",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2014-2024"
 ]
}

 The dataset includes 3784169 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004642-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.un8sek
title: Occurrence Download

description: A dataset containing 882 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diaphus brachycephalus Tåning, 1928",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 882 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004690-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4xb6zw
title: Occurrence Download

description: A dataset listing the 2380299 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Animalia"
 ]
}

 The dataset's 2380299 records were derived from 53655 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004663-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.