cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18036836
title: SuNeRF-CME - reconstruction results

description: This Zenodo archive contains the reconstructed three-dimensional data products generated with the SuNeRF-CME framework and used in the analysis and figures of the associated manuscript. The dataset consists of time-dependent reconstruction snapshots representing the volumetric plasma properties inferred from multi-viewpoint observations.

The archive includes all reconstructed 3D snapshots for all time steps analyzed in the paper.

The data are provided in their native file formats as produced by SuNeRF. No raw observational data are included; those remain available from the original mission and challenge archives referenced in the manuscript.

Instructions for reading, visualizing, and reproducing the reconstructions are provided in the accompanying SuNeRF GitHub repository:https://github.com/USERNAME/SuNeRF

The code repository includes example scripts, configuration files, and documentation describing the data structure and recommended usage.

This repository is for the paper“SuNeRF-CME: Physics-Informed Neural Radiance Fields for Tomographic Reconstruction of Coronal Mass Ejections”by Robert Jarolim.It is associated with manuscript number AAS68873R1.
DOI: 10.5281/zenodo.18036835
title: SuNeRF-CME - reconstruction results

description: This Zenodo archive contains the reconstructed three-dimensional data products generated with the SuNeRF-CME framework and used in the analysis and figures of the associated manuscript. The dataset consists of time-dependent reconstruction snapshots representing the volumetric plasma properties inferred from multi-viewpoint observations.

The archive includes all reconstructed 3D snapshots for all time steps analyzed in the paper.

The data are provided in their native file formats as produced by SuNeRF. No raw observational data are included; those remain available from the original mission and challenge archives referenced in the manuscript.

Instructions for reading, visualizing, and reproducing the reconstructions are provided in the accompanying SuNeRF GitHub repository:https://github.com/USERNAME/SuNeRF

The code repository includes example scripts, configuration files, and documentation describing the data structure and recommended usage.

This repository is for the paper“SuNeRF-CME: Physics-Informed Neural Radiance Fields for Tomographic Reconstruction of Coronal Mass Ejections”by Robert Jarolim.It is associated with manuscript number AAS68873R1.
DOI: 10.15468/dl.3fw8ah
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bolitoglossa decora"
 ]
}

 The dataset includes 12 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064127-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/mbga1k
title: Lorenz System Time Series Across Multiple Parameter Regimes

description: &lt;p&gt;&lt;b&gt;Dataset Description&lt;/b&gt;&lt;/p&gt;

&lt;p&gt;
The datasets consist of simulated trajectories of the &lt;b&gt;Lorenz system&lt;/b&gt; generated using MATLAB.
&lt;/p&gt;

&lt;p&gt;&lt;b&gt;Data generation details:&lt;/b&gt;&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;Numerical integration performed using the &lt;b&gt;ODE45 solver&lt;/b&gt; with multistep integration&lt;/li&gt;
 &lt;li&gt;Initial conditions sampled uniformly at random from the interval &lt;b&gt;[0, 1]&lt;/b&gt;&lt;/li&gt;
 &lt;li&gt;System parameter (&lt;b&gt;ρ&lt;/b&gt;) range: &lt;b&gt;5 to 225&lt;/b&gt;&lt;/li&gt;
 &lt;li&gt;Simulation time span: &lt;b&gt;1 to 500&lt;/b&gt;&lt;/li&gt;
 &lt;li&gt;Time step: &lt;b&gt;0.05&lt;/b&gt;&lt;/li&gt;
 &lt;li&gt;Number of simulations: &lt;b&gt;10&lt;/b&gt; independent runs for each &lt;b&gt;ρ&lt;/b&gt; value&lt;/li&gt;
 &lt;li&gt;Length per simulation: &lt;b&gt;19,810&lt;/b&gt; data points&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;b&gt;Datasets:&lt;/b&gt;&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;b&gt;Training dataset:&lt;/b&gt; 891,450 data points&lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Testing dataset:&lt;/b&gt; 891,450 data points&lt;/li&gt;
 &lt;li&gt;Training and testing sets are generated independently&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;b&gt;Noise model:&lt;/b&gt;&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;At each simulation step, &lt;b&gt;additive Gaussian noise&lt;/b&gt; is injected into all state variables&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;
These datasets are intended for evaluating &lt;b&gt;regime-dependent predictability&lt;/b&gt; and &lt;b&gt;long-term forecasting performance&lt;/b&gt; of sequence models under parameter variations.
&lt;/p&gt;
DOI: 10.15468/dl.je77tq
title: Occurrence Download

description: A dataset containing 22018 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anthocerotophyta",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 22018 records from 393 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064126-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18018249
title: ResMobiLys test dataset: long-read and hybrid sequencing data for plasmid and mobilome analysis

description: This dataset contains example long-read (and, where applicable, short-read) sequencing data used to test and demonstrate the ResMobiLys Nextflow pipeline.

The data are provided to enable users to validate pipeline installation, reproduce the example workflow, and explore plasmid reconstruction, antimicrobial resistance genes, and mobile genetic element detection in bacterial genomes.

The dataset is intended for testing and benchmarking purposes only and is referenced by the ResMobiLys GitHub repository and manuscript.
DOI: 10.5281/zenodo.18034584
title: ResMobiLys test dataset: long-read and hybrid sequencing data for plasmid and mobilome analysis

description: This dataset contains example long-read (and, where applicable, short-read) sequencing data used to test and demonstrate the ResMobiLys Nextflow pipeline.

The data are provided to enable users to validate pipeline installation, reproduce the example workflow, and explore plasmid reconstruction, antimicrobial resistance genes, and mobile genetic element detection in bacterial genomes.

The dataset is intended for testing and benchmarking purposes only and is referenced by the ResMobiLys GitHub repository and manuscript.
DOI: 10.15468/dl.jufh5k
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((13.19649 41.22494,13.72972 41.23206,13.72423 41.51365,13.1887 41.50646,13.19649 41.22494)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bufo",
 "Year 1990-2025"
 ]
}

 The dataset includes 27 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064128-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18034629
title: Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland

description: Der Datensatz "Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland" des Robert Koch-Instituts dokumentiert die tägliche intensivmedizinische Versorgungslage seit der COVID-19-Pandemie. Basierend auf Meldungen aller intensivbettenführenden Krankenhäuser in Deutschland erfasst das DIVI-Intensivregister Echtzeitdaten zu belegten und freien Intensivbetten. Die Erhebung differenziert nach Altersgruppen, Regionen und Versorgungsstufen. COVID-19-Fälle auf Intensivstationen werden gesondert ausgewiesen. Die Daten stehen aggregiert auf Bundes-, Landes- und Kreisebene zur Verfügung. Damit bildet der Datensatz eine Grundlage für die Überwachung von Kapazitäten, die Koordination von Behandlungskapazitäten und politische Entscheidungsprozesse während der Pandemie und darüber hinaus.
DOI: 10.5281/zenodo.18036771
title: Ant Social Complexity - Genomic Data

description: These datasets are for ant social complexity project, including genomic sequences and annotations.
DOI: 10.5281/zenodo.18036770
title: Ant Social Complexity - Genomic Data

description: These datasets are for ant social complexity project, including genomic sequences and annotations.
DOI: 10.15468/dl.ct2haw
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bolitoglossa diaphora"
 ]
}

 The dataset includes 83 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064129-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/joq7sj
title: Replication Data for: Team production on the battlefield: Evidence from NATO in Afghanistan

description: Replication Data for: Team production on the battlefield: Evidence from NATO in Afghanistan
DOI: 10.6084/m9.figshare.30940664
title: COVID-19 reminders studies_Data

description: Data from five studies testing whether thoughts related to COVID-19 are associated to pro-social behavioral intentions. 1 correlational and 4 experimental studies using COVID-19 reminders test this hypothesis.
DOI: 10.6084/m9.figshare.30940664.v1
title: COVID-19 reminders studies_Data

description: Data from five studies testing whether thoughts related to COVID-19 are associated to pro-social behavioral intentions. 1 correlational and 4 experimental studies using COVID-19 reminders test this hypothesis.
DOI: 10.15468/dl.q48e2s
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Greece",
 "TaxonKey is Pentaphyllus chrysomeloides"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064131-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uafmss
title: Occurrence Download

description: A dataset containing 492 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bolitoglossa dofleini"
 ]
}

 The dataset includes 492 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064130-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/0h4zhg
title: Data science academic programs in the pre-ChatGPT era in the Midwestern United States : a curated dataset

description: This dataset documents and classifies academic programs in data science offered by higher education institutions in the Midwestern United States prior to widespread adoption of generative artificial intelligence tools (up to 2023). Detailed program-level metadata, including program names and types, institutional types, degree levels, and state-by-state information are systematically compiled using a reproducible typology. The resource enables investigation of regional trends, curriculum benchmarking, and comparative analysis of data science education in the pre-generative-AI era. By providing a standardized and transparent overview of academic offerings, the dataset supports research, educational planning, and policy decisions related to workforce development and the evolving landscape of data science education.
DOI: 10.5281/zenodo.18037224
title: 200-mysterious-final-countdown : covers : A-B
DOI: 10.5281/zenodo.18037223
title: 200-mysterious-final-countdown : covers : A-B
DOI: 10.7910/dvn/4d6v8c
title: Powers et al 2026 dataset

description: These are data for "A shared ensemble in the prelimbic cortex links impulsivity and anxiety-like behavior" by Powers et al, 2026. Abstract is as follows:

Mental health disorders often share overlapping behavioral and neural features, yet it remains unclear why these relationships emerge or whether they reflect common underlying neural processes. To explore this, we recorded prelimbic (PL) activity using endoscopic calcium imaging as rats completed a battery of tasks assessing impulsivity, distress tolerance, anxiety-like behavior, incentive salience (Pavlovian conditioned approach), and sensation-seeking in a novel environment (locomotor activity). By collecting neural and behavioral measures across all tasks within each animal, we were able to investigate whether PL activity tracked processes that were either unique to individual behaviors, shared across multiple behaviors, or both. We found that PL activity was significantly predictive of each of the behaviors except locomotor activity. Subsequent analyses revealed shared behavioral structure across tasks, with one latent dimension reflecting high impulsivity and low anxiety-like behavior. Individual variability across this particular dimension was strongly predicted by a neural structure comprising shared PL activity across four of the behaviors. Furthermore, this relationship was driven by a subset of PL neurons that shared patterns of activity across multiple tasks, forming a shared neural ensemble. Importantly, animals with a greater tendency to share neural ensembles exhibited a stronger link between high impulsivity and low anxiety-like behavior. These findings suggest that a small, shared ensemble of PL neurons tracked activity across multiple clinically-relevant behaviors to predict an approach/avoidance phenotype characterized by high impulsivity and low anxiety. This points toward a targetable neural population that may help explain why diverse psychiatric symptoms often co-occur within individuals.
DOI: 10.15468/dl.4jfrmm
title: Occurrence Download

description: A dataset containing 165 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bolitoglossa dunni"
 ]
}

 The dataset includes 165 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064132-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6uww27
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cicer rechingeri"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064133-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18036801
title: Non native species reported in the mountain range of the Pyrenees.

description: This checklist is the result of the compilation of non-native species reported in the Pyrenees according to the following inclusion criteria:

- Status. We included terrestrial and freshwater species considered non-native according to national checklists or regional atlases (FloraPyr, FaunaPyr). Species native to continental France or Spain were excluded to avoid confusion. This may underestimate species native to other parts of the country but introduced into the Pyrenees. However, it ensures the database is homogeneous and relevant for management. One exception was made for fish species native to continental Spain and/or France, but deliberately introduced into high-altitude Pyrenean lakes, where they are non-native and have strong ecological impacts.

- Taxonomic scope. All taxonomic groups were included except pathogens and microorganisms whose native range is often difficult to determine. The taxonomic level of the database is species. Subspecies were included at the species level only when the entire species can be considered non-native.

- Spatial information. We only included species with georeferenced records in the Pyrenees. Species without any information of presence were excluded.

- Naturalisation in the wild. We included species that are naturalised in the wild, excluding those clearly associated with cultivated fields and indoor environments with no evidence of escape potential (e.g. home pets, greenhouse species).

Given the difficulty of discerning established from casual records and their persistence over time, we followed the precautionary approach and kept all NNS meeting our inclusion criteria. This choice may overestimate the number and distribution of NNS but avoids underreporting.
DOI: 10.5281/zenodo.18036802
title: Non native species reported in the mountain range of the Pyrenees.

description: This checklist is the result of the compilation of non-native species reported in the Pyrenees according to the following inclusion criteria:

- Status. We included terrestrial and freshwater species considered non-native according to national checklists or regional atlases (FloraPyr, FaunaPyr). Species native to continental France or Spain were excluded to avoid confusion. This may underestimate species native to other parts of the country but introduced into the Pyrenees. However, it ensures the database is homogeneous and relevant for management. One exception was made for fish species native to continental Spain and/or France, but deliberately introduced into high-altitude Pyrenean lakes, where they are non-native and have strong ecological impacts.

- Taxonomic scope. All taxonomic groups were included except pathogens and microorganisms whose native range is often difficult to determine. The taxonomic level of the database is species. Subspecies were included at the species level only when the entire species can be considered non-native.

- Spatial information. We only included species with georeferenced records in the Pyrenees. Species without any information of presence were excluded.

- Naturalisation in the wild. We included species that are naturalised in the wild, excluding those clearly associated with cultivated fields and indoor environments with no evidence of escape potential (e.g. home pets, greenhouse species).

Given the difficulty of discerning established from casual records and their persistence over time, we followed the precautionary approach and kept all NNS meeting our inclusion criteria. This choice may overestimate the number and distribution of NNS but avoids underreporting.
DOI: 10.5281/zenodo.17952050
title: Forecasting of Seasonal Groundwater Levels Using Geoelectrical Coupled Inversion

description: This code repository accompanies the manuscript entitled "Forecasting of Seasonal Groundwater Levels Using Geoelectrical Coupled Inversion Based on the Ensemble Kalman Filter", which is currently submitted at the WRR.

The project provides implementations of the model experiments, sensitivity analyses, and field applications presented in the manuscript.

1. wat_DC_modeltest1: This example is designed for model testing. A simplified one-dimensional soil model is constructed to simulate groundwater level fluctuations. The posterior results obtained from coupled, uncoupled, and non-seasonal frozen soil models are compared to evaluate the impact of coupling and seasonal processes.

2. sensitivity_test: This module focuses on model uncertainty analysis, including: Dual error distributions for coupled and uncoupled inversions, Parameter sensitivity analysis. The results are used to assess the robustness and uncertainty characteristics of the proposed inversion framework.

3. wat_DC_noname and wat_DC_SpearO: These folders contain field application examples for two monitoring sites. The datasets used in these case studies were obtained from previous studies by Pleasant et al. (2022, 2023).

4. processing: This module provides the ERT data pre-processing workflow for the Noname and SpearO sites. The script automatically reads and integrates ERT measurements to generate time-lapse profiles.Users may download the original datasets from the sources cited in the original publications and place them into the corresponding folders to run the processing scripts.Alternatively, data access may be requested from the authors of this study.

5. ERT_inv: This folder contains the ERT inversion profiles for the three study sites presented in the manuscript.

How to Run the Code: Set a unified path for all subfunctions located in the WAT_function directory. Run the main script "wat_DC_modeltest1". Additional examples and analyses can be executed by running the corresponding scripts in each module.

Contact: If you encounter any issues or have questions regarding data access or code usage, please contact the author: bailg19@mails.jlu.edu.cn
DOI: 10.5281/zenodo.17952049
title: Forecasting of Seasonal Groundwater Levels Using Geoelectrical Coupled Inversion

description: This code repository accompanies the manuscript entitled "Forecasting of Seasonal Groundwater Levels Using Geoelectrical Coupled Inversion Based on the Ensemble Kalman Filter", which is currently submitted at the WRR.

The project provides implementations of the model experiments, sensitivity analyses, and field applications presented in the manuscript.

1. wat_DC_modeltest1: This example is designed for model testing. A simplified one-dimensional soil model is constructed to simulate groundwater level fluctuations. The posterior results obtained from coupled, uncoupled, and non-seasonal frozen soil models are compared to evaluate the impact of coupling and seasonal processes.

2. sensitivity_test: This module focuses on model uncertainty analysis, including: Dual error distributions for coupled and uncoupled inversions, Parameter sensitivity analysis. The results are used to assess the robustness and uncertainty characteristics of the proposed inversion framework.

3. wat_DC_noname and wat_DC_SpearO: These folders contain field application examples for two monitoring sites. The datasets used in these case studies were obtained from previous studies by Pleasant et al. (2022, 2023).

4. processing: This module provides the ERT data pre-processing workflow for the Noname and SpearO sites. The script automatically reads and integrates ERT measurements to generate time-lapse profiles.Users may download the original datasets from the sources cited in the original publications and place them into the corresponding folders to run the processing scripts.Alternatively, data access may be requested from the authors of this study.

5. ERT_inv: This folder contains the ERT inversion profiles for the three study sites presented in the manuscript.

How to Run the Code: Set a unified path for all subfunctions located in the WAT_function directory. Run the main script "wat_DC_modeltest1". Additional examples and analyses can be executed by running the corresponding scripts in each module.

Contact: If you encounter any issues or have questions regarding data access or code usage, please contact the author: bailg19@mails.jlu.edu.cn
DOI: 10.13140/rg.2.2.29193.30568
title: Estimating Tumor "Rim" volume relative to Tumor Body-Center Volume when Scoring Differential Protein Expression, a Python Based Calculator
DOI: 10.7910/dvn/g68u6p
title: Data for the paper "Impact of the Sustainable Entrepreneurship Ecosystem on Sustainable Entrepreneurial Intentions: A Hybrid Literature Review and Future Research Agenda"

description: This is Dataset for the paper "Impact of the Sustainable Entrepreneurship Ecosystem on Sustainable Entrepreneurial Intentions: A Hybrid Literature Review and Future Research Agenda"