cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/spatialdata-glhymps
title: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity
DOI: 10.4211/his-5653
title: CUAHI HIS
DOI: 10.4211/datacenterspec.20120611
title: Draft Specifications for the CUAHSI Water Data Center
DOI: 10.4211/technical.20161019
title: National Water Center Innovators Program Summer Institute Report 2016
DOI: 10.4211/techrpts.200208.tr3
title: Facilities for Transforming Hydrological Science - CUAHSI Technical Report Number 3
DOI: 10.4211/techrpts.20100616.tr9
title: Towards a Framework for Community Modeling in Hydrologic Science
DOI: 10.4211/52abfd80ae794c0588d133897dcfafa3
title: Hydrologic and hydrogeochemical data for alluvial aquifers adjacent to Mekong River Thailand
DOI: 10.5281/zenodo.17916459
title: Subtropical estuarine ecosystems display both long-term stability and change

description: Subtropical estuaries frequently support abundant and diverse faunal assemblages, but it is often unclear how these productive ecosystems are responding to climate change. In the eastern Gulf of Mexico, estuarine faunal assemblages have been sampled for decades as part of a long-term, fisheries-independent monitoring program. We assessed trends in water temperature, submerged aquatic vegetation, and the abundance, richness, and compositional structure of faunal assemblages over more than two decades in four estuarine systems using this dataset. We used both univariate and multivariate analyses to quantify and describe the dynamics of these habitat and faunal variables. Further, we separated our analyses for summer and winter seasons since the latter has been observed to experience stronger responses to climate change in other systems. Water temperature increased in all systems during summer and winter, although the rate of increase in winter was three times greater than in summer. Submerged aquatic vegetation was generally stable over time, but we observed both decreasing and increasing cover in different systems. Overall, faunal abundance and richness was generally stable over time; however, most systems exhibited slow, but significant, changes in assemblage structure, consistent with ecological drift. Further, some faunal changes were indicative of tropicalization, where nearly 30% of the species that increased in abundance had distributions centered at lower latitudes. Moreover, there was a reduction in abundance of a temperate-centered taxon across all study systems in the winter. These results reflected a combination of both stability and change in habitat and the supported faunal assemblages over the past two decades. These ecosystems should continue to be monitored as climate change has the potential to induce profound ecological shifts.
DOI: 10.4211/techrpts.200311.tr5
title: A National Center for Hydrologic Synthesis: Scientific Objectives, Structure, and Implementation - CUAHSI Technical Report Number 5
DOI: 10.5281/zenodo.17916458
title: Subtropical estuarine ecosystems display both long-term stability and change

description: Subtropical estuaries frequently support abundant and diverse faunal assemblages, but it is often unclear how these productive ecosystems are responding to climate change. In the eastern Gulf of Mexico, estuarine faunal assemblages have been sampled for decades as part of a long-term, fisheries-independent monitoring program. We assessed trends in water temperature, submerged aquatic vegetation, and the abundance, richness, and compositional structure of faunal assemblages over more than two decades in four estuarine systems using this dataset. We used both univariate and multivariate analyses to quantify and describe the dynamics of these habitat and faunal variables. Further, we separated our analyses for summer and winter seasons since the latter has been observed to experience stronger responses to climate change in other systems. Water temperature increased in all systems during summer and winter, although the rate of increase in winter was three times greater than in summer. Submerged aquatic vegetation was generally stable over time, but we observed both decreasing and increasing cover in different systems. Overall, faunal abundance and richness was generally stable over time; however, most systems exhibited slow, but significant, changes in assemblage structure, consistent with ecological drift. Further, some faunal changes were indicative of tropicalization, where nearly 30% of the species that increased in abundance had distributions centered at lower latitudes. Moreover, there was a reduction in abundance of a temperate-centered taxon across all study systems in the winter. These results reflected a combination of both stability and change in habitat and the supported faunal assemblages over the past two decades. These ecosystems should continue to be monitored as climate change has the potential to induce profound ecological shifts.
DOI: 10.4211/technical.20171009
title: National Water Center Innovators Program Summer Institute Report 2017
DOI: 10.4211/techrpts.200911.tr8
title: Rationale and Strategy for a Community Modeling Platform in the Hydrologic Sciences
DOI: 10.4211/annual.201111
title: CUAHSI Annual Report 2011
DOI: 10.4211/his-5650
title: CUAHI HIS
DOI: 10.4211/techrpts.200501.tr7
title: Designing Hydrologic Observatories as a Community Service - CUAHSI Technical Report Number 7
DOI: 10.4211/spatialdata-johnstonc01
title: Using Beaver Works To Estimate Colony Activity In Boreal Landscapes
DOI: 10.4211/his-data-shalenetwork
title: Shale Network Data
DOI: 10.4211/his-data-cedarriverforestsnow
title: Cedar River Watershed Forest Snow Observations
DOI: 10.4211/techrpts.200208.tr1
title: A Vision for Hydrologic Science Research - CUAHSI Technical Report Number 1
DOI: 10.4211/stratplan.201012
title: Water in a Dynamic Planet: A Five-year Strategic Plan for Water Science
DOI: 10.15468/dl.9wpw8j
title: Occurrence Download

description: A dataset containing 2102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-4.117549 40.157918, -4.119090 40.198493, -4.172537 40.198493, -4.170976 40.157918, -4.117549 40.157918))",
 "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 2102 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050036-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25574/26576
title: Chandra X-ray Observatory ObsId 26576

description: A Test to Establish that SS 433 is an Ultraluminous X-ray Source
DOI: 10.25574/26577
title: Chandra X-ray Observatory ObsId 26577

description: A Test to Establish that SS 433 is an Ultraluminous X-ray Source
DOI: 10.17632/zrkyb7n3m7
title: Bioinformatics tools for identification and comparison of protein sequences and structures: Supplementary Information
DOI: 10.17632/zrkyb7n3m7.1
title: Bioinformatics tools for identification and comparison of protein sequences and structures: Supplementary Information
DOI: 10.17632/gfjt3m2r6n.3
title: Who Wants to 'Japa' and why? Dataset on Migration Decisions of Nigerian Youth

description: This dataset presents data collected through two surveys: the first among staff and students of 3 universities in Osun State, Nigeria; and the second among intending and serving Corps members as well as those who recently concluded their NYSC (National Youth Service Corps) service year. The first survey was administered in July 2024, and the second one between November 2024 and October 2025. The surveys were collected with the use of online questionnaires. The questionnaires are similar, the second survey containing a few additional questions than the first survey. The cleaned and merged dataset contains 835 observations. 

The surveys sought to understand migration aspirations and perspectives, as well as correlates of migration decisions among Nigerian youth. The surveys were also intended to determine the relationship between developmental idealism and migration within the context of the gripping novel intensity, urgency and desperation by which Nigerian youth move to other countries - a phenomenon that has come to be known colloquially as 'japa'. The dataset captures self-reported migration intentions and related socio-demographic, economic and attitudinal covariates among young Nigerians. Researchers studying migration decision-making, labour market aspirations, views about development of Nigeria in comparison with other countries, and the drivers of irregular and regular migration will find the data useful.
DOI: 10.15468/dl.wwk69h
title: Occurrence Download

description: A dataset listing the 3697 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "Geometry POLYGON((4.39571 44.35216,4.47717 44.35216,4.47717 44.40169,4.39571 44.40169,4.39571 44.35216))",
 "HasGeospatialIssue is false",
 "PublishingOrg is 1928bdf0-f5d2-11dc-8c12-b8a03c50a862"
 ]
}

 The dataset's 3697 records were derived from 171 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050031-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sc9ryhn2ww
title: Accelerating Lax-Wendroff SGFD schemes for acoustic wave equation simulation

description: This repository provides the complete source code and datasets to reproduce figures and numerical results from the manuscript "Accelerating the Lax-Wendroff time discretization method for the first-order acoustic wave equation simulation by designing proper SGFD schemes" published in Computers and Mathematics with Applications.

The implementation includes:
- Optimized Lax-Wendroff time discretization scheme
- Custom-designed SGFD (Spatial Gradient Finite Difference) schemes
- First-order acoustic wave equation solvers
- Numerical examples and benchmark cases

To execute the code and regenerate all figures from the paper, please refer to the README.md file for detailed installation instructions, dependency requirements, and step-by-step execution guidelines. All parameters match those used in the published manuscript to ensure reproducible results.
DOI: 10.17632/sc9ryhn2ww.1
title: Accelerating Lax-Wendroff SGFD schemes for acoustic wave equation simulation

description: This repository provides the complete source code and datasets to reproduce figures and numerical results from the manuscript "Accelerating the Lax-Wendroff time discretization method for the first-order acoustic wave equation simulation by designing proper SGFD schemes" published in Computers and Mathematics with Applications.

The implementation includes:
- Optimized Lax-Wendroff time discretization scheme
- Custom-designed SGFD (Spatial Gradient Finite Difference) schemes
- First-order acoustic wave equation solvers
- Numerical examples and benchmark cases

To execute the code and regenerate all figures from the paper, please refer to the README.md file for detailed installation instructions, dependency requirements, and step-by-step execution guidelines. All parameters match those used in the published manuscript to ensure reproducible results.
DOI: 10.5281/zenodo.17871748
title: YOLOv8-Detected Gravity Wave Event Catalog from VIIRS Day-Night Band Imagery (Suomi-NPP, 2012–2024)

description: This dataset contains gravity wave event lists detected by a YOLOv8 machine-learning model applied to VIIRS Day/Night Band (DNB) imagery acquired by the Suomi NPP satellite from 2012 to 2024.

 

The detailed methodology is described in a manuscript being prepared for submission to Journal of Geophysical Research: Machine Learning and Computation:

Yuta Hozumi, Jia Yue, Seraj Al Mahmud Mostafa, Chenxi Wang, Jianwu Wang, Sanjay Purushotham, and Steven D. Miller,

“Localization and Classification of Gravity Wave Events from VIIRS Day/Night Band Satellite Imagery Using Machine Learning Techniques.”

 

Dataset 1: wave_event_VNP02DNB.csv

This file provides the catalog of detected wave events in CSV format. The event list is the result of a survey of moonless Suomi-NPP VIIRS/DNB images from 2012 to 2024 using the trained YOLOv8 model.

 

The columns are:

1. Satellite name.

2–3. Intensity and geolocation data file names, respectively. The original data files can be downloaded from https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5200/VNP02DNB/ and https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5200/VNP03DNB/.

4. Observation time of the granule in UTC. The format is “YYYY-MM-DD hh:mm:ss,” where YYYY, MM, DD, hh, mm, and ss denote year, month, day, hour, minute, and second, respectively.

5. Total number of detected objects in the granule.

6. Object index within the granule.

7–8. Class ID and class name of the detected wave event, respectively. The class IDs are: 0 for concentric gravity waves (cgw), 1 for frontal waves, 2 for ripples, and 3 for other gravity waves (gw).

9. Detection confidence score, ranging from 0.3 to 1.0.

10–13. Normalized coordinates of the center of the bounding box (x_center, y_center), followed by the normalized width and height of the bounding box.

14–15. Latitude and longitude of the center of the bounding box.

16–23. Latitudes and longitudes of the four corners of the bounding box.

 

Dataset 2: YYYY.zip

Zip archives of DNB images with detected wave events annotated by bounding boxes. 

 

Dataset 3: map_figs.zip

Monthly global maps of detected wave events for each wave class.
DOI: 10.5281/zenodo.17871749
title: YOLOv8-Detected Gravity Wave Event Catalog from VIIRS Day-Night Band Imagery (Suomi-NPP, 2012–2024)

description: This dataset contains gravity wave event lists detected by a YOLOv8 machine-learning model applied to VIIRS Day/Night Band (DNB) imagery acquired by the Suomi NPP satellite from 2012 to 2024.

 

The detailed methodology is described in a manuscript being prepared for submission to Journal of Geophysical Research: Machine Learning and Computation:

Yuta Hozumi, Jia Yue, Seraj Al Mahmud Mostafa, Chenxi Wang, Jianwu Wang, Sanjay Purushotham, and Steven D. Miller,

“Localization and Classification of Gravity Wave Events from VIIRS Day/Night Band Satellite Imagery Using Machine Learning Techniques.”

 

Dataset 1: wave_event_VNP02DNB.csv

This file provides the catalog of detected wave events in CSV format. The event list is the result of a survey of moonless Suomi-NPP VIIRS/DNB images from 2012 to 2024 using the trained YOLOv8 model.

 

The columns are:

1. Satellite name.

2–3. Intensity and geolocation data file names, respectively. The original data files can be downloaded from https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5200/VNP02DNB/ and https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5200/VNP03DNB/.

4. Observation time of the granule in UTC. The format is “YYYY-MM-DD hh:mm:ss,” where YYYY, MM, DD, hh, mm, and ss denote year, month, day, hour, minute, and second, respectively.

5. Total number of detected objects in the granule.

6. Object index within the granule.

7–8. Class ID and class name of the detected wave event, respectively. The class IDs are: 0 for concentric gravity waves (cgw), 1 for frontal waves, 2 for ripples, and 3 for other gravity waves (gw).

9. Detection confidence score, ranging from 0.3 to 1.0.

10–13. Normalized coordinates of the center of the bounding box (x_center, y_center), followed by the normalized width and height of the bounding box.

14–15. Latitude and longitude of the center of the bounding box.

16–23. Latitudes and longitudes of the four corners of the bounding box.

 

Dataset 2: YYYY.zip

Zip archives of DNB images with detected wave events annotated by bounding boxes. 

 

Dataset 3: map_figs.zip

Monthly global maps of detected wave events for each wave class.
DOI: 10.5281/zenodo.17872014
title: YOLOv8-Detected Gravity Wave Event Catalog from VIIRS Day-Night Band Imagery (NOAA-20, 2018–2024)

description: This dataset contains gravity wave event lists detected by a YOLOv8 machine-learning model applied to VIIRS Day/Night Band (DNB) imagery acquired by the NOAA-20 satellite from 2018 to 2024.

 

The detailed methodology is described in a manuscript being prepared for submission to Journal of Geophysical Research: Machine Learning and Computation:

Yuta Hozumi, Jia Yue, Seraj Al Mahmud Mostafa, Chenxi Wang, Jianwu Wang, Sanjay Purushotham, and Steven D. Miller,

“Localization and Classification of Gravity Wave Events from VIIRS Day/Night Band Satellite Imagery Using Machine Learning Techniques.”

 

Dataset 1: wave_event_VJ102DNB.csv

This file provides the catalog of detected wave events in CSV format. The event list is the result of a survey of moonless NOAA-20 VIIRS/DNB images from 2018 to 2024 using the trained YOLOv8 model.

 

The columns are:

1. Satellite name.

2–3. Intensity and geolocation data file names, respectively. The original data files can be downloaded from https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5201/VJ102DNB/ and https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5201/VJ103DNB/.

4. Observation time of the granule in UTC. The format is “YYYY-MM-DD hh:mm:ss,” where YYYY, MM, DD, hh, mm, and ss denote year, month, day, hour, minute, and second, respectively.

5. Total number of detected objects in the granule.

6. Object index within the granule.

7–8. Class ID and class name of the detected wave event, respectively. The class IDs are: 0 for concentric gravity waves (cgw), 1 for frontal waves, 2 for ripples, and 3 for other gravity waves (gw).

9. Detection confidence score, ranging from 0.3 to 1.0.

10–13. Normalized coordinates of the center of the bounding box (x_center, y_center), followed by the normalized width and height of the bounding box.

14–15. Latitude and longitude of the center of the bounding box.

16–23. Latitudes and longitudes of the four corners of the bounding box.

 

Dataset 2: YYYYMM.zip

Monthly zip archives of DNB images with detected wave events annotated by bounding boxes. 

 

Dataset 3: map_figs.zip

Monthly global maps of detected wave events for each wave class.
DOI: 10.5281/zenodo.17872015
title: YOLOv8-Detected Gravity Wave Event Catalog from VIIRS Day-Night Band Imagery (NOAA-20, 2018–2024)

description: This dataset contains gravity wave event lists detected by a YOLOv8 machine-learning model applied to VIIRS Day/Night Band (DNB) imagery acquired by the NOAA-20 satellite from 2018 to 2024.

 

The detailed methodology is described in a manuscript being prepared for submission to Journal of Geophysical Research: Machine Learning and Computation:

Yuta Hozumi, Jia Yue, Seraj Al Mahmud Mostafa, Chenxi Wang, Jianwu Wang, Sanjay Purushotham, and Steven D. Miller,

“Localization and Classification of Gravity Wave Events from VIIRS Day/Night Band Satellite Imagery Using Machine Learning Techniques.”

 

Dataset 1: wave_event_VJ102DNB.csv

This file provides the catalog of detected wave events in CSV format. The event list is the result of a survey of moonless NOAA-20 VIIRS/DNB images from 2018 to 2024 using the trained YOLOv8 model.

 

The columns are:

1. Satellite name.

2–3. Intensity and geolocation data file names, respectively. The original data files can be downloaded from https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5201/VJ102DNB/ and https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5201/VJ103DNB/.

4. Observation time of the granule in UTC. The format is “YYYY-MM-DD hh:mm:ss,” where YYYY, MM, DD, hh, mm, and ss denote year, month, day, hour, minute, and second, respectively.

5. Total number of detected objects in the granule.

6. Object index within the granule.

7–8. Class ID and class name of the detected wave event, respectively. The class IDs are: 0 for concentric gravity waves (cgw), 1 for frontal waves, 2 for ripples, and 3 for other gravity waves (gw).

9. Detection confidence score, ranging from 0.3 to 1.0.

10–13. Normalized coordinates of the center of the bounding box (x_center, y_center), followed by the normalized width and height of the bounding box.

14–15. Latitude and longitude of the center of the bounding box.

16–23. Latitudes and longitudes of the four corners of the bounding box.

 

Dataset 2: YYYYMM.zip

Monthly zip archives of DNB images with detected wave events annotated by bounding boxes. 

 

Dataset 3: map_figs.zip

Monthly global maps of detected wave events for each wave class.
DOI: 10.4211/hs.9940a14c34fa4d85b9bfdd9eabccf9b5
title: ysfgsdg
DOI: 10.4211/hs.3405512bea104d8da620d39a4aee66f5
title: What is Hydrology and Why It Matters
DOI: 10.4211/hs.8839e9415fb94204a4aed6aa1e652c0e
title: WorldWater
DOI: 10.4211/hs.8bfac173675a4fc9a0a57530d82df70b
title: BLANCO RIVER
DOI: 10.4211/hs.fe85a680d0144e79b39e8c483dc1e5aa
title: NOAA NWC - Harvey NWM-HAND Flood Extents
DOI: 10.4211/hs.6fc7688a5cac4c19ae2d3973544c6ae9
title: Accessing discharge data from the United States Geological Survey (USGS) for the Mississippi River at Belle Chasse, LA via web services using python.
DOI: 10.4211/hs.85ab212b18f543b0bd3c7b749cfeef66crossrefsubmissionstatus.pending
title: Long-Term Monthly Baseflow Index Signatures

description: This resource presents data used in the study by van der Heijden et al. (under review, 2025). The data contains monthly mean baseflow indices (BFI) for 1,432 United Stated Geological Survey (USGS) streamflow gages across the Continental United States (CONUS).

The streamflow gages used are those considered of reference-quality by the Geospatial Attributes of Gages for Evaluating Streamflow, version II (GAGES-II) dataset (Falcone, 2011) that contain at least 20 years of continuous daily streamflow measurements.

Baseflow separation was conducted using a single parameter recursive digital low-pass filter based on Lyne and Hollick, (1979) for baseflow separation. The filter, implemented in the R package EcoHydRology (Fuka et al., 2014), was converted to Python (Hamshaw et al., 2020) and as part of this research is now available through the Baseflow Python package developed by Brigham Young University Hydroinformatics group (https://github.com/BYU-Hydroinformatics). The filter parameter was tuned to the long-term flow history of each gage individually using the 10th non-exceedance percentile of streamflow.

References:
Ryan van der Heijden, Ali Dadkhah, Scott Hamshaw, Mandar Dewoolkar, Ehsan Ghazanfari, Norm Jones, Gustavious Williams, Prabhakar Clement, Donna M. Rizzo. “Identifying and interpreting physical processes and NWM prediction bias associated with baseflow regimes across the CONUS.” Water Resources Research (under review, 2025).

Falcone, James A. 2011. “GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow.” Report. Reston, VA. USGS Publications Warehouse. https://doi.org/10.3133/70046617.

Lyne, V. D. and Hollick, M.: Stochastic time-variable rainfall runoff modelling, Hydrology and Water Resources Symposium, Institution of Engineers, Australia, Perth, 10–12 September 1979, 89–92, 1979.

Hamshaw, Scott D., Donna M. Rizzo, Ali Javed, and Linh Nguyen. "Watershed data science at the event scale: Revealing insights in watershed function through analysis of concentration-discharge relationships." In AGU Fall Meeting Abstracts, vol. 2020, pp. H077-08. 2020.

Fuka DR, Walter MT, Archibald JA, Steenhuis, TS, and Easton ZM. “EcoHydRology, a community modeling foundation for Eco-Hydrology.” 2014.
DOI: 10.4211/hs.a3552bea9df243be822a61349a200ece
title: This is terrain,soil,forcing files prepared using HydroTops for  MillCreekIL2010