cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17702563
title: Beta Decay of 64Ga, TAS raw data files

description: Root files containing TAS raw data (TTree format) for the beta decay of 64Ga measured at ISOLDE. Files named bckg_### contain only background measurements whereas the onea named 64Ga_### contain the data corresponding to the radioactive samples (see the names).

The file analyzer.zip contains fully functional code to analyze the data. It is written in C++ to be compiled against Root libraries.

For any questions or comments please refer to enrique.nacher@csic.es.
DOI: 10.5281/zenodo.17702564
title: Beta Decay of 64Ga, TAS raw data files

description: Root files containing TAS raw data (TTree format) for the beta decay of 64Ga measured at ISOLDE. Files named bckg_### contain only background measurements whereas the onea named 64Ga_### contain the data corresponding to the radioactive samples (see the names).

The file analyzer.zip contains fully functional code to analyze the data. It is written in C++ to be compiled against Root libraries.

For any questions or comments please refer to enrique.nacher@csic.es.
DOI: 10.15468/dl.zszhnb
title: Occurrence Download

description: A dataset containing 34228 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dendrosicyos socotrana, Prunus amygdalus, Citrus aurantium, Murraya paniculata, Astragalus mongholicus, Avena strigosa, Erythroxylum sinense)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 34228 records from 899 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007126-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rpskfa
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rhabdophis confusus)",
 "Country is one of (China, India, Indonesia, Pakistan, Bangladesh, Japan, Philippines, Viet Nam, Türkiye, Iran (Islamic Republic of), Thailand, Myanmar, Korea, Republic of, Korea (Democratic People’s Republic of), Iraq, Afghanistan, Saudi Arabia, Uzbekistan, Malaysia, Yemen, Nepal, Sri Lanka, Kazakhstan, Syrian Arab Republic, Cambodia, Jordan, Azerbaijan, United Arab Emirates, Tajikistan, Israel, Lao People’s Democratic Republic, Lebanon, Kyrgyzstan, Turkmenistan, Singapore, Oman, Palestine, State of, Kuwait, Georgia, Mongolia, Armenia, Qatar, Bahrain, Timor-Leste, Cyprus, Bhutan, Maldives, Brunei Darussalam)",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007127-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30704297
title: DEM simulations of oscillatory shear for granular materials: Heaviside Cases with Random System Variables

description: This dataset is a supplement to the repository available at https://github.com/hanxuhku/PINNSR-DA, and it provides supplementary data for the following work:Xu Han, Lu Jing, Chung-Yee Kwok, Gengchao Yang & Yuri Dumaresq Sobral. (2025). <i>White-box machine learning for uncovering physically interpretable dimensionless governing equations for granular materials</i>. arXiv preprint arXiv:2509.14518.
DOI: 10.6084/m9.figshare.30704297.v1
title: DEM simulations of oscillatory shear for granular materials: Heaviside Cases with Random System Variables

description: This dataset is a supplement to the repository available at https://github.com/hanxuhku/PINNSR-DA, and it provides supplementary data for the following work:Xu Han, Lu Jing, Chung-Yee Kwok, Gengchao Yang & Yuri Dumaresq Sobral. (2025). <i>White-box machine learning for uncovering physically interpretable dimensionless governing equations for granular materials</i>. arXiv preprint arXiv:2509.14518.
DOI: 10.15468/dl.cmcrv2
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((103.86187 21.22858,116.18618 21.22858,116.18618 28.61238,103.86187 28.61238,103.86187 21.22858))",
 "HasGeospatialIssue is false",
 "TaxonKey is Lithobates catesbeianus"
 ]
}

 The dataset includes 22 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007128-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m7uw4s
title: Occurrence Download

description: A dataset containing 62716 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Procyon Storr, 1780",
 "Year 2000-2024"
 ]
}

 The dataset includes 62716 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063036-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.71796/wits-figshare.30665075
title: GRT-INSPIRED Baseline data

description: <b>GRT-Inspired HDSS</b>The GRT-INSPIRED (Gauteng Research Triangle) GRT-I) Health and Demographic Surveillance Site (HDSS), established in 2020, monitors the health and wellbeing of an <b>intended cohort of approximately 100 000 people</b> across three urban sites: <b>Hillbrow</b>, <b>Atteridgeville West</b>, and <b>Melusi</b>. Data are collected <b>three times a year</b>—one in-person household interview and two telephonic follow-ups. Each site is managed by a partner institution: the University of Johannesburg (Hillbrow South), Wits RHI (Hillbrow North), and the University of Pretoria (Atteridgeville and Melusi). All data are harmonised and contributed to the national <b>SAPRIN</b> longitudinal database.<b>Baseline enumeration was conducted between 2021 and 2022</b>, during which teams completed the initial count of <b>dwellings, households, and individuals</b> across the three sites. This period established the foundation of the cohort, confirmed residence patterns, mapped the study area, and provided the first comprehensive snapshot of demographic and socio-economic indicators. This baseline now serves as the reference point for all subsequent longitudinal follow-up.The platform aims to track <b>population health</b>, <b>social wellbeing</b>, and <b>economic conditions</b> over time. Core objectives include documenting births, deaths, and migration; monitoring household composition; tracking education outcomes; measuring socio-economic status; and assessing labour participation and the uptake of social protection measures.Data collection includes a household survey component and a verbal autopsy system for all deaths. The three study areas reflect diverse urban realities—from Hillbrow’s dense high-rise environment to the mixed formal and informal dwellings of Atteridgeville, to the rapidly growing informal settlement of Melusi—providing a broad picture of urban demographic and health dynamics.
DOI: 10.71796/wits-figshare.30665075.v1
title: GRT-INSPIRED Baseline data

description: <b>GRT-Inspired HDSS</b>The GRT-INSPIRED (Gauteng Research Triangle) GRT-I) Health and Demographic Surveillance Site (HDSS), established in 2020, monitors the health and wellbeing of an <b>intended cohort of approximately 100 000 people</b> across three urban sites: <b>Hillbrow</b>, <b>Atteridgeville West</b>, and <b>Melusi</b>. Data are collected <b>three times a year</b>—one in-person household interview and two telephonic follow-ups. Each site is managed by a partner institution: the University of Johannesburg (Hillbrow South), Wits RHI (Hillbrow North), and the University of Pretoria (Atteridgeville and Melusi). All data are harmonised and contributed to the national <b>SAPRIN</b> longitudinal database.<b>Baseline enumeration was conducted between 2021 and 2022</b>, during which teams completed the initial count of <b>dwellings, households, and individuals</b> across the three sites. This period established the foundation of the cohort, confirmed residence patterns, mapped the study area, and provided the first comprehensive snapshot of demographic and socio-economic indicators. This baseline now serves as the reference point for all subsequent longitudinal follow-up.The platform aims to track <b>population health</b>, <b>social wellbeing</b>, and <b>economic conditions</b> over time. Core objectives include documenting births, deaths, and migration; monitoring household composition; tracking education outcomes; measuring socio-economic status; and assessing labour participation and the uptake of social protection measures.Data collection includes a household survey component and a verbal autopsy system for all deaths. The three study areas reflect diverse urban realities—from Hillbrow’s dense high-rise environment to the mixed formal and informal dwellings of Atteridgeville, to the rapidly growing informal settlement of Melusi—providing a broad picture of urban demographic and health dynamics.
DOI: 10.15468/dl.wqxuz5
title: Occurrence Download

description: A dataset containing 337653 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bivalvia",
 "HasCoordinate is true",
 "TaxonKey is Mytilus"
 ]
}

 The dataset includes 337653 records from 730 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007087-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f4uz58
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rhabdophis guangdongensis)",
 "Country is one of (China, India, Indonesia, Pakistan, Bangladesh, Japan, Philippines, Viet Nam, Türkiye, Iran (Islamic Republic of), Thailand, Myanmar, Korea, Republic of, Korea (Democratic People’s Republic of), Iraq, Afghanistan, Saudi Arabia, Uzbekistan, Malaysia, Yemen, Nepal, Sri Lanka, Kazakhstan, Syrian Arab Republic, Cambodia, Jordan, Azerbaijan, United Arab Emirates, Tajikistan, Israel, Lao People’s Democratic Republic, Lebanon, Kyrgyzstan, Turkmenistan, Singapore, Oman, Palestine, State of, Kuwait, Georgia, Mongolia, Armenia, Qatar, Bahrain, Timor-Leste, Cyprus, Bhutan, Maldives, Brunei Darussalam)",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007129-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2wmk5e
title: Occurrence Download

description: A dataset containing 8517 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is a41250f0-7c3e-11d8-a19c-b8a03c50a862",
 "TaxonKey is Unionidae"
 ]
}

 The dataset includes 8517 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007212-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kyfmqf
title: Occurrence Download

description: A dataset containing 10565 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-72.67719 4.23996,-72.88573 4.00163,-73.02476 3.77322,-72.90559 3.6044,-72.73677 3.38593,-72.5183 3.40579,-72.12107 3.39586,-71.68413 3.46537,-71.06843 3.52496,-70.84995 3.68385,-70.73078 3.90232,-70.75065 4.3492,-71.25711 4.68684,-71.95225 4.5776,-72.67719 4.23996))",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 10565 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007211-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14006550
title: Dataset for "Origin of Phobos and Deimos : Orbital evolution shortly after formation from a potential dislocation"

description: Repository Documentation

This repository and the accompanying archive contain the input data for the simulations conducted in the research paper (DOI: 10.3847/PSJ/adcab2). Each folder in the archive corresponds to a specific type of simulation discussed in the paper, structured as follows:

Folder Structure and Contents

1. Mars Inclination



Description: Contains input data for simulations associated with Figure 5 of the paper.

Contents:



Multiple subfolders, each corresponding to a different inclination angle of Mars considered in the study.

Each subfolder includes input files for 1,000 unique simulations.




2. multi_fragments



Description: Holds input data for simulations related to Figure 6 of the paper.

Contents:



Several subfolders, each representing a different number of post-collisional fragments analyzed in the simulations.

Each subfolder contains input files for 1,000 unique simulations.




3. old_OE and new_OE



Description: These folders provide input data for simulations associated with Figures 1, 3, and 4 of the paper.

Contents:



old_OE:



Contains input files for simulations replicating the distribution models of Hyodo et al. (2022).



new_OE:



Includes input files for simulations based on the new distribution model described in Section 2.3.1 of the paper.



Each folder contains three subdirectories, each corresponding to a distinct semi-major axis value for Phobos used in the simulations. Each subdirectory holds input data for 600 different simulations.




4. Tides



Description: This folder contains the data for simulations associated with Figure 7 of the paper

Contents:



Each folder contains three subdirectories, each corresponding to a distinct semi-major axis value for Phobos used in the simulations. Each subdirectory holds input data for 600 different simulations.




For a more complete description of the input files, please refer to the README.txt file.

These files are structured to facilitate the reproduction of results presented in the paper and to support further research or verification studies related to the simulations described.
DOI: 10.5281/zenodo.14006551
title: Dataset for "Origin of Phobos and Deimos : Orbital evolution shortly after formation from a potential dislocation"

description: Repository Documentation

This repository and the accompanying archive contain the input data for the simulations conducted in the research paper (DOI: 10.3847/PSJ/adcab2). Each folder in the archive corresponds to a specific type of simulation discussed in the paper, structured as follows:

Folder Structure and Contents

1. Mars Inclination



Description: Contains input data for simulations associated with Figure 5 of the paper.

Contents:



Multiple subfolders, each corresponding to a different inclination angle of Mars considered in the study.

Each subfolder includes input files for 1,000 unique simulations.




2. multi_fragments



Description: Holds input data for simulations related to Figure 6 of the paper.

Contents:



Several subfolders, each representing a different number of post-collisional fragments analyzed in the simulations.

Each subfolder contains input files for 1,000 unique simulations.




3. old_OE and new_OE



Description: These folders provide input data for simulations associated with Figures 1, 3, and 4 of the paper.

Contents:



old_OE:



Contains input files for simulations replicating the distribution models of Hyodo et al. (2022).



new_OE:



Includes input files for simulations based on the new distribution model described in Section 2.3.1 of the paper.



Each folder contains three subdirectories, each corresponding to a distinct semi-major axis value for Phobos used in the simulations. Each subdirectory holds input data for 600 different simulations.




4. Tides



Description: This folder contains the data for simulations associated with Figure 7 of the paper

Contents:



Each folder contains three subdirectories, each corresponding to a distinct semi-major axis value for Phobos used in the simulations. Each subdirectory holds input data for 600 different simulations.




For a more complete description of the input files, please refer to the README.txt file.

These files are structured to facilitate the reproduction of results presented in the paper and to support further research or verification studies related to the simulations described.
DOI: 10.15468/dl.hsdjfn
title: Occurrence Download

description: A dataset containing 2519 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((21.30964 43.46878,67.82312 43.46878,67.82312 62.06621,21.30964 62.06621,21.30964 43.46878))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bromus riparius Rehmann"
 ]
}

 The dataset includes 2519 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009590-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14586093
title: Berita UNNES
DOI: 10.15468/dl.9umtm8
title: Occurrence Download

description: A dataset containing 60321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Uruguay",
 "Geometry POLYGON((-72.64723 -40.75579,-36.35277 -40.75579,-36.35277 -26.24421,-72.64723 -26.24421,-72.64723 -40.75579))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 60321 records from 181 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005348-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.22747.43043
title: 2026 logic calendar by the group Lógica MX
DOI: 10.15485/3013822
title: Sap Velocity Data for Urban Trees in Chicago, Illinois (2024-2025)

description: This dataset contains uncorrected sap velocity measurements using the heat ratio method (HRM) collected using ICT International SFM1x sensors at five urban sites in Chicago, Illinois, as part of the DOE CROCUS project. The data includes continuous monitoring of sap velocity from various tree species, including Maples (Acer spp.): Sugar Maple (Acer saccharum), Silver Maple (Acer saccharinum), and Red Maple (Acer rubrum); Oaks (Quercus spp.): Swamp White Oak (Quercus bicolor); American Elm (Ulmus americana); Honey Locust (Gleditsia triacanthos); Cottonwood (Populus deltoides); and Tree of Heaven (Ailanthus altissima) across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), and West Woodlawn "Blacks in Green" (BIG). These include both street trees and those in urban park locations. Measurements were collected at 15-20 minute intervals, depending on the sensor, and transmitted via Long Range Wide Area Network (LoRaWAN) protocols. The wireless data was collected by Sage Network (https://sagecontinuum.org/) nodes. The dataset includes sensor ID, Global Positioning System (GPS) coordinates, tree species (common and scientific names), tree identification number, diameter at breast height (DBH in cm), uncorrected sap velocity measurements (cm/hr) from both inner and outer probes, and Sage Node identifiers so the data can be mapped to related variables such as air quality and wind speed that were collected on the Sage nodes. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 3-bit binary system indicating physical range violations (< -10 or > 60 cm/hr), step spikes (absolute difference > 36 cm/hr), and stuck sensor conditions (> 10 consecutive identical values). These are raw data, not corrected for wood anatomy or species-specific characteristics. Data is provided in comma separated (CSV) format. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Multi-Function Research LoRaWAN (MFR) Nodes. DOIs for the supporting data are provided as part of this data package.
DOI: 10.15485/2589828
title: Manuscript model outputs, model source code, and figure scripts: The role of geomorphology in mediating biomass allocation impacts on salt-marsh resilience and carbon accumulation

description: This data package provides model source code (C++), model outputs, and figure generation scripts (R) needed to reproduce the following manuscript:
Bruns, Nicholas E., Genevieve L. Noyce, and Matthew L. Kirwan. "The Role of Geomorphology in Mediating Biomass Allocation Impacts on Salt-Marsh Resilience and Carbon Accumulation." Estuarine, Coastal and Shelf Science 327 (December 2025): 109549. https://doi.org/10.1016/j.ecss.2025.109549.
This manuscript investigates how geomorphology mediates the impact of biomass allocation shifts on salt marsh persistence and carbon (C) sequestration under sea level rise. We use a 1-D soil-column model (Kirwan and Mudd 2012) to perform experiments across a range of static root:shoot ratios (RSR = 1-4) spanning observed values. The model explicitly simulates interactions between tidal inundation, productivity, inorganic sediment deposition, and organic matter accumulation. Experiments determine whether geomorphic feedbacks amplify, dampen, or leave unchanged the ecosystem response to biomass allocation shifts.
A first experiment uses constant sea level rise (2.5 mm/yr) to examine equilibrium dynamics and their influence on carbon accumulation rates across different suspended sediment concentrations (SSC). A second set of experiments calculates threshold sea level rise rates for marsh drowning across RSR and SSC combinations. Final experiments apply accelerating sea level rise scenarios (2000-2200) derived from NOAA projections (Sweet et al. 2022) to generate an envelope of expected responses, quantifying the importance of biomass allocation shifts on marsh carbon accumulation and persistence. All experiments are repeated across SSC ranging from 5-50 mg/L to investigate how these interactions vary in micro-tidal marshes with different sediment supplies.
Package contents:
* README.txt with detailed description of package contents and instructions for reproducing manuscript figures and model outputs
* R scripts for generating all manuscript figures
* C++ baseline model code from Kirwan and Mudd (2012)
* C++ source code for 4 experimental model variants used in the manuscript, extending above baseline code
* Model input files (.csv, .txt) including sea level rise scenarios
* Model output files (.txt) used in manuscript analyses
Temporal coverage: Model simulations span years 1900-2200, with accelerating sea level rise scenarios for 2000-2200.
Key variables: Root:shoot ratio, suspended sediment concentration, carbon accumulation rate, vertical accretion rate, marsh elevation, inundation depth, threshold sea level rise rate.