cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.y358ax
title: Occurrence Download

description: A dataset containing 1512 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Moussonia deppeana",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 1512 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003840-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q6sh7d
title: Occurrence Download

description: A dataset containing 4276 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 51.21395264452012
 AND decimallatitude &lt;= 56.135291543055025
 AND decimallongitude &gt;= -86.34612226022934
 AND decimallongitude &lt;= -73.49581818030033
 AND GBIF_Within('POLYGON ((-73.495818 51.213953, -73.495818 56.135292, -86.346122 56.135292, -86.346122 51.213953, -73.495818 51.213953))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 4276 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003829-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tg2ax9
title: Occurrence Download

description: A dataset containing 317 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Piper lapathifolium",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 317 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003843-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6x54sq
title: Occurrence Download

description: A dataset containing 16317 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trema micranthum",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 16317 records from 502 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003842-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.df24dc
title: Occurrence Download

description: A dataset containing 1567 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lipaugus weberi"
 ]
}

 The dataset includes 1567 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003844-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17173378
title: Massive-scale single-nucleus multi-omics identifies novel noncoding drivers of Parkinson's disease

description: Most genetic variants contributing to complex diseases reside in the noncoding genome. While common variants uncovered by genome-wide association studies fail to explain much of the observed heritability of these diseases, rare variants often have higher effect sizes and cumulatively explain a larger portion of heritability. However, rare variants, particularly rare noncoding variants, have remained under-characterized largely due to the difficulties of accurately predicting variant functionality at scale, given that each individual carries an average of ~10,000 rare variants. Here, we generated multi-omic data from >3.3 million nuclei sampled from five brain regions across a cohort of 80 individuals with Parkinson’s disease (PD) and 21 neurologically normal control individuals with matched 30x whole-genome sequencing. We use this data to identify cell type-specific features of PD, map cell type-specific chromatin accessibility and expression quantitative trait loci, and train machine learning models to predict the effect of variants on gene regulation. We identify rare noncoding variants statistically associated with sporadic PD and extend our approaches to predict drivers of familial PD of unknown genetic origin. Our results underscore the significance of rare noncoding variants in complex diseases and provide a roadmap for applying similar approaches in other disease systems.
DOI: 10.5281/zenodo.17173379
title: Massive-scale single-nucleus multi-omics identifies novel noncoding drivers of Parkinson's disease

description: Most genetic variants contributing to complex diseases reside in the noncoding genome. While common variants uncovered by genome-wide association studies fail to explain much of the observed heritability of these diseases, rare variants often have higher effect sizes and cumulatively explain a larger portion of heritability. However, rare variants, particularly rare noncoding variants, have remained under-characterized largely due to the difficulties of accurately predicting variant functionality at scale, given that each individual carries an average of ~10,000 rare variants. Here, we generated multi-omic data from >3.3 million nuclei sampled from five brain regions across a cohort of 80 individuals with Parkinson’s disease (PD) and 21 neurologically normal control individuals with matched 30x whole-genome sequencing. We use this data to identify cell type-specific features of PD, map cell type-specific chromatin accessibility and expression quantitative trait loci, and train machine learning models to predict the effect of variants on gene regulation. We identify rare noncoding variants statistically associated with sporadic PD and extend our approaches to predict drivers of familial PD of unknown genetic origin. Our results underscore the significance of rare noncoding variants in complex diseases and provide a roadmap for applying similar approaches in other disease systems.
DOI: 10.5281/zenodo.18475194
title: A Conceptual Framework for AI-Driven Autonomous Navigation and Debris Avoidance in Interplanetary Space Missions: Integrating CR3BP Dynamics, Stochastic Modeling, Bayesian Inference, and Physics-Informed Neural Networks

description: This conceptual paper presents a framework for interplanetary spacecraft navigation and debris avoidance, integrating Circular Restricted Three-Body Problem (CR3BP) dynamics with invariant manifolds, stochastic differential equations (SDEs) for perturbation modeling, global sensitivity analysis through Sobol indices, and Hamiltonian-preserving physics-informed neural networks (H-PINNs) with Proximal Policy Optimization (PPO) augmented by entropy regularization and recurrent neural networks (RNN/LSTM) for delayed Markov Decision Processes (MDPs) and interpretable decision-making. It includes Unscented Kalman Filters (UKF) with algorithmic details and Mars-specific applications, Approximate Bayesian Computation (ABC), multi-agent collision avoidance using B-plane parameters with collision probability gradients and game-theoretic coordination, solar radiation pressure modeling, structural uncertainty quantification distinguishing aleatoric and epistemic types using Gaussian Processes (GP) for discrepancy, and Fokker-Planck equations for probability evolution. Applications are tailored to Mars missions, including entry navigation with stochastic atmospheric density modeling, rover autonomy, and sample return; lunar missions with PINNs for trajectory prediction, thermal management with radiative heat transfer constraints, and low-thrust propulsion in CR3BP; asteroid missions for exploration and mining; and deep space missions such as asteroid exploration and outer planet probes. Validation uses NASA SPICE kernels integrated with CR3BP coordinate transformations, 10,000-run Monte Carlo simulations, Pareto optimality for trade-offs in Delta-v, arrival time, and collision risks, ensuring compliance with COSPAR planetary protection guidelines for Mars special regions. The framework is supported by mathematical derivations, including PINN loss functions for CR3BP with symplectic integration, non-spherical harmonics (J2/J3), Poincaré maps for stability, convergence proofs for PINNs under SDE noise, reproducible Python simulations, quantitative statistics, Popperian falsifiability criteria, computational complexity analysis for onboard RAD750 processors, Lyapunov stability for neural controllers, quantization error analysis in fixed-point arithmetic, Fisher information matrix for sensor sensitivity, stress testing for sensor failures, Yarkovsky effect on debris, and debris modeling as stochastic fluid.
DOI: 10.5281/zenodo.18571272
title: A Conceptual Framework for AI-Driven Autonomous Navigation and Debris Avoidance in Interplanetary Space Missions: Integrating CR3BP Dynamics, Stochastic Modeling, Bayesian Inference, and Physics-Informed Neural Networks

description: This conceptual paper presents a framework for interplanetary spacecraft navigation and debris avoidance, integrating Circular Restricted Three-Body Problem (CR3BP) dynamics with invariant manifolds, stochastic differential equations (SDEs) for perturbation modeling, global sensitivity analysis through Sobol indices, and Hamiltonian-preserving physics-informed neural networks (H-PINNs) with Proximal Policy Optimization (PPO) augmented by entropy regularization and recurrent neural networks (RNN/LSTM) for delayed Markov Decision Processes (MDPs) and interpretable decision-making. It includes Unscented Kalman Filters (UKF) with algorithmic details and Mars-specific applications, Approximate Bayesian Computation (ABC), multi-agent collision avoidance using B-plane parameters with collision probability gradients and game-theoretic coordination, solar radiation pressure modeling, structural uncertainty quantification distinguishing aleatoric and epistemic types using Gaussian Processes (GP) for discrepancy, and Fokker-Planck equations for probability evolution. Applications are tailored to Mars missions, including entry navigation with stochastic atmospheric density modeling, rover autonomy, and sample return; lunar missions with PINNs for trajectory prediction, thermal management with radiative heat transfer constraints, and low-thrust propulsion in CR3BP; asteroid missions for exploration and mining; and deep space missions such as asteroid exploration and outer planet probes. Validation uses NASA SPICE kernels integrated with CR3BP coordinate transformations, 10,000-run Monte Carlo simulations, Pareto optimality for trade-offs in Delta-v, arrival time, and collision risks, ensuring compliance with COSPAR planetary protection guidelines for Mars special regions. The framework is supported by mathematical derivations, including PINN loss functions for CR3BP with symplectic integration, non-spherical harmonics (J2/J3), Poincaré maps for stability, convergence proofs for PINNs under SDE noise, reproducible Python simulations, quantitative statistics, Popperian falsifiability criteria, computational complexity analysis for onboard RAD750 processors, Lyapunov stability for neural controllers, quantization error analysis in fixed-point arithmetic, Fisher information matrix for sensor sensitivity, stress testing for sensor failures, Yarkovsky effect on debris, and debris modeling as stochastic fluid.
DOI: 10.15468/dl.n548x8
title: Occurrence Download

description: A dataset containing 4502 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Miconia xalapensis",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 4502 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003846-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4z22xb
title: Occurrence Download

description: A dataset containing 388 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ceiba glaziovii",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 388 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003847-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18571383
title: Why Superman ain't coming

description: Abstract

This paper presents a timeline-based analytical case study examining how modern U.S. institutions would plausibly respond to the emergence of a superhuman, benevolent, non-human actor (“Superman”). Drawing on existing emergency management doctrine, public health authority, national security frameworks, and organizational risk theory, the analysis demonstrates that institutional responses would prioritize uncertainty reduction, narrative control, and administrative containment over coexistence or autonomy. The study argues that such an outcome is not driven by malicious intent but by structural incentives inherent to bureaucratic systems, which are optimized to manage risk, prevent precedent, and preserve control. The case illustrates a broader socio-technical pattern: human institutions systematically convert miracles into managed problems through policy, procedure, and classification.
DOI: 10.5281/zenodo.18571384
title: Why Superman ain't coming

description: Abstract

This paper presents a timeline-based analytical case study examining how modern U.S. institutions would plausibly respond to the emergence of a superhuman, benevolent, non-human actor (“Superman”). Drawing on existing emergency management doctrine, public health authority, national security frameworks, and organizational risk theory, the analysis demonstrates that institutional responses would prioritize uncertainty reduction, narrative control, and administrative containment over coexistence or autonomy. The study argues that such an outcome is not driven by malicious intent but by structural incentives inherent to bureaucratic systems, which are optimized to manage risk, prevent precedent, and preserve control. The case illustrates a broader socio-technical pattern: human institutions systematically convert miracles into managed problems through policy, procedure, and classification.
DOI: 10.6075/j0445mvm
title: Sharing Impact: CAITIE 2024 Dissemination Videos. In California American Indian Tobacco Initiative Evaluation (CAITIE)

description: This collection includes a series of three videos developed to share key findings from the California American Indian Tobacco Initiative Evaluation (CAITIE). The videos were produced in late 2024 as part of the evaluation’s dissemination strategy. The first video introduces the evaluation team; the second presents major findings from the 2019-2024 evaluation cycle; and the third highlights overarching takeaways and policy recommendations. The videos were designed to support community accessibility and stakeholder engagement, and are grounded in the evaluation data. The content was developed by the CAITIE evaluation team, with direction, editing, and creative support from Fnu Moazzma.
DOI: 10.6075/j0gm87nf
title: CAITIE Final Report 2019-2024. In California American Indian Tobacco Initiative Evaluation (CAITIE)

description: This report documents the evaluation as conducted by the CAITIE team for the 2019 to 2024 cycle of the California Tobacco Prevention Program's (CTPP) American Indian Initiative.
DOI: 10.15468/dl.886b9e
title: Occurrence Download

description: A dataset containing 177 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pombalia glabra",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 177 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003849-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kkuj49
title: Occurrence Download

description: A dataset containing 11880 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acacia auriculiformis",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 11880 records from 197 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003851-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x22apy
title: Occurrence Download

description: A dataset containing 171897 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Thunnus albacares",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 171897 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003811-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18568500
title: Data for "Localization behavior in a Hermitian and non-Hermitian Raman lattice"

description: This dataset is for research article "Localization behavior in a Hermitian and non-Hermitian Raman lattice".
DOI: 10.5281/zenodo.18568499
title: Data for "Localization behavior in a Hermitian and non-Hermitian Raman lattice"

description: This dataset is for research article "Localization behavior in a Hermitian and non-Hermitian Raman lattice".
DOI: 10.15468/dl.cvyy6m
title: Occurrence Download

description: A dataset containing 322269 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pterygophora",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 322269 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003813-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kev52w
title: Occurrence Download

description: A dataset containing 507 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeocarpus serratus",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 507 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003853-260208012135463/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/13558596
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.30490835
title: AMSTAR 2 – Assessing the Methodological Quality of Systematic Reviews: Evaluation Applied to “Mathematical Competencies and Critical Thinking in Secondary Education”

description: This dataset contains the AMSTAR v2 (A MeaSurement Tool to Assess Systematic Reviews) checklist applied to the systematic review titled “Mathematical Competencies and Critical Thinking in Secondary Education (2019–2025)”.The checklist was adapted for the field of educational research, where most studies are qualitative or mixed-methods rather than randomized controlled trials. The checklist was uploaded as part of the open science commitment associated with the submission to Education Sciences (MDPI), supporting transparency and reproducibility.
DOI: 10.6084/m9.figshare.30490835.v1
title: AMSTAR 2 – Assessing the Methodological Quality of Systematic Reviews: Evaluation Applied to “Mathematical Competencies and Critical Thinking in Secondary Education”

description: This dataset contains the AMSTAR v2 (A MeaSurement Tool to Assess Systematic Reviews) checklist applied to the systematic review titled “Mathematical Competencies and Critical Thinking in Secondary Education (2019–2025)”.The checklist was adapted for the field of educational research, where most studies are qualitative or mixed-methods rather than randomized controlled trials. The checklist was uploaded as part of the open science commitment associated with the submission to Education Sciences (MDPI), supporting transparency and reproducibility.
DOI: 10.24431/rw1k9hw
title: Cormorant Oceanography Project: Biologging Data from Neotropic Cormorants (Nannopterum brasilianus), Peru, 2023

description: Oceanographic sampling of the coastal ocean is often sparse in space and time and in situ observations are needed to understand the complex physical processes in this zone. 

This dataset is provided by the Cormorant Oceanography Project (https://www.osudashcams.com/), an interdisciplinary collaboration between oceanographers and seabird ecologists based at Oregon State University, and in partnership with collaborators globally. We are advancing biologging tag technologies for use with cormorant species to measure in situ oceanographic conditions. This also offers a unique opportunity to study the distribution and behaviors of cormorants relative to environmental conditions along their daily foraging paths. 

Our biologging tags include combinations of fast-response sensors to measure water temperature, conductivity, and pressure. Each tag contains an inertial measurement unit (IMU) to monitor acceleration and orientation. A GPS unit, triggered when a bird surfaces, provides locations for georeferencing measurements. The sensors collect large volumes of data that are transmitted and retrieved using two-way cellular communications. 

This dataset consists of GPS tracks and dive profiles from Neotropic cormorants (Nannopterum brasilianum) captured and tagged in Peru during the 2023 breeding season. Done of the tagged birds were nesting. Cormorants were fitted with GPS-GSM tags with integrated fast response temperature and depth sensors.
DOI: 10.3535/4pz-rct-l20
title: Naticarius canrena (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/kt7-4aq-abw
title: Triphoridae J. E. Gray, 1847

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wem-7wm-72g
title: Assiminea J. Fleming, 1828

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/y2d-6q2-8j8
title: Ranularia caudata (Gmelin, 1791)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hy1-78x-45q
title: Monoplex vespaceus (Lamarck, 1822)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/f0q-bvk-vaz
title: Farcimen gundlachi (L. Pfeiffer, 1856)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.