cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29944694
title: Global survey on the utilisation and experiences with different retrobulbar anaesthesia techniques in horses

description: Retrobulbar anaesthesia (RBA) is relevant for ocular surgery in standing sedated horses. An online survey collected information on the respondents' professional background, indications, injection methods, use of ultrasound assistance, medication, and complications associated with RBA in horses.
DOI: 10.3535/mxk-t3t-m5w
title: Echinodermata

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zmw-98c-dc2
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9an-714-kvv
title: Cetacea Brisson, 1762

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ndn-gqc-myx
title: †Spirifer Sowerby, 1818

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.5281/zenodo.17398471
title: Kinetic Energy statistics in the Western Arctic Ocean from Ice-Tethered Profiler with Velocity observations

description: This dataset contains kinetic energy statistics for the sea ice and the ocean over 9 - 246 m depth that is representative of the Western Arctic Ocean during the 2010's. Statistics are derived from over 12,000 velocity profiles collected by Ice-Tethered Profilers with Velocity between July 2013 and January 2021. Statistics consists of a vertical profile at 1 m vertical resolution of KE at specific horizontal scales and scale ranges (including near-inertial KE). Statistics include winter and summertime mean, standard deviation, 25th, 50th, and 75th percentiles of total kinetic energy and kinetic energy at scales larger than 7, 30, 60, 120, 240, and 480 km, as well as scales larger than 10, 15, 20, 25, 40, 50, 75, 100, 150, 200, 300, and 400 km. Summertime mean KE statistics are included for three ice-concentration ranges: 0-15%, 15-80%, and 80-100%. Horizontal wavenumber spectra for the ice velocity and at each depth are also included for kinetic energy as well as density over 7 to 600 km scales. Statistics are estimated as described in 'Cole, S. T. (2026). Kinetic energy in the Western Arctic Ocean: Vertical structure partitioned by horizontal scale, J. Geophys. Res.'
DOI: 10.5281/zenodo.15450471
title: Underwater images collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2025-05-15

description: This dataset was collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2025-05-15. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.15468/dl.x7qhyq
title: Occurrence Download

description: A dataset containing 1523 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Solanum atropurpureum Schrank"
 ]
}

 The dataset includes 1523 records from 134 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014762-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18234972
title: Supplementary Data and Model Weights for BioSeq2Seq
DOI: 10.5281/zenodo.18234973
title: Supplementary Data and Model Weights for BioSeq2Seq
DOI: 10.5281/zenodo.18253879
title: Chloroluma Baill. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:44
DOI: 10.5281/zenodo.18253880
title: Chloroluma Baill. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:44
DOI: 10.6084/m9.figshare.31072813
title: <b>Research on the “economy-society-environment” </b><b>s</b><b>ustainability of </b><b>u</b><b>rban </b><b>a</b><b>gglomerations in the Yellow River Basin</b>

description: Assessing the coordinated development of the economy, society, and environment is fundamental to achieving urban sustainability and reducing regional disparities. This study examined 72 cities across seven urban agglomerations in the Yellow River Basin. A comprehensive evaluation system was constructed based on the United Nations Sustainable Development Goals (SDGs), employing the Coupling Coordination Degree Model (CCDM) and Random Forest algorithm (RFA)-SHapley Additive exPlanations (SHAP) model to analyze the spatial distribution characteristics, spatiotemporal evolution, and driving factors of the coordinated development level of the “economy-society-environment” (ESE) system from 2007 to 2022. Results indicate significant spatial differentiation in the sustainable development levels of urban agglomerations in the Yellow River Basin. Cities with higher SDG scores are concentrated in the eastern coastal areas of the Shandong Peninsula urban agglomeration (SPUA), while those with lower scores are predominantly distributed in the Ningxia Yellow River urban agglomeration (NXUA) and the Lanzhou-Xining urban agglomeration (LXUA). The overall degree of coupling coordination of the ESE system increased from 2007 to 2022. Among the urban agglomerations, the SPUA and the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration (HBOYUA) are in a barely balanced development state, while core cities such as Xi'an and Zhengzhou have achieved a state of slight balance. However, development levels remain uneven within and across urban agglomerations. Driving factor analysis shows that the urban sprawl index, economic development level, and urbanization level are the primary factors influencing economic sustainability. Social sustainability is mainly driven by fiscal expenditure per unit area, urbanization level, and economic development level. Environmental sustainability is closely related to the proportions of water bodies, cropland, and impervious surfaces. This study provides theoretical support for the sustainable development of urban agglomerations in the Yellow River Basin.
DOI: 10.6084/m9.figshare.31072813.v1
title: <b>Research on the “economy-society-environment” </b><b>s</b><b>ustainability of </b><b>u</b><b>rban </b><b>a</b><b>gglomerations in the Yellow River Basin</b>

description: Assessing the coordinated development of the economy, society, and environment is fundamental to achieving urban sustainability and reducing regional disparities. This study examined 72 cities across seven urban agglomerations in the Yellow River Basin. A comprehensive evaluation system was constructed based on the United Nations Sustainable Development Goals (SDGs), employing the Coupling Coordination Degree Model (CCDM) and Random Forest algorithm (RFA)-SHapley Additive exPlanations (SHAP) model to analyze the spatial distribution characteristics, spatiotemporal evolution, and driving factors of the coordinated development level of the “economy-society-environment” (ESE) system from 2007 to 2022. Results indicate significant spatial differentiation in the sustainable development levels of urban agglomerations in the Yellow River Basin. Cities with higher SDG scores are concentrated in the eastern coastal areas of the Shandong Peninsula urban agglomeration (SPUA), while those with lower scores are predominantly distributed in the Ningxia Yellow River urban agglomeration (NXUA) and the Lanzhou-Xining urban agglomeration (LXUA). The overall degree of coupling coordination of the ESE system increased from 2007 to 2022. Among the urban agglomerations, the SPUA and the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration (HBOYUA) are in a barely balanced development state, while core cities such as Xi'an and Zhengzhou have achieved a state of slight balance. However, development levels remain uneven within and across urban agglomerations. Driving factor analysis shows that the urban sprawl index, economic development level, and urbanization level are the primary factors influencing economic sustainability. Social sustainability is mainly driven by fiscal expenditure per unit area, urbanization level, and economic development level. Environmental sustainability is closely related to the proportions of water bodies, cropland, and impervious surfaces. This study provides theoretical support for the sustainable development of urban agglomerations in the Yellow River Basin.
DOI: 10.15468/dl.53ty2u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phoxinus phoxinus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1805",
 "Year is less than or equal to 1809"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011114-260108223611665/datasets/export for details.
DOI: 10.5281/zenodo.18042276
title: Reproducibility package (code + synthetic dataset) for: Assessing mortality risk in pulmonary tuberculosis and severe malnutrition: development of the IIR marker via artificial intelligence

description: This Zenodo record (record: 18042276; DOI: 10.5281/zenodo.18042276) contains the code and a fully synthetic dataset sufficient to reproduce the analyses reported in the manuscript “Assessing mortality risk in pulmonary tuberculosis and severe malnutrition: development of the IIR marker via artificial intelligence” (submitted to Scientific Reports).

Contents:





Google Colab notebook reproducing model training and evaluation (Random Forest and Logistic Regression), LIME explanations, calibration, and an IIR-only validation (in-sample AUC, repeated CV out-of-fold AUC, bootstrap 95% CI).




Synthetic dataset pro1.csv (fully synthetic; no real patient data).




Outputs exported by the notebook (figures and CSV tables), including beta coefficients (univariate and multivariate) and cross-validation metrics.



How to run (Colab):





Upload the provided synthetic pro1.csv to Google Drive under: My Drive/pro1.csv




Open and run the notebook from top to bottom. All outputs will be shown in the notebook.



Notes:





The dataset is fully synthetic and is provided only to enable reproducibility without sharing the original clinical dataset.




The original clinical dataset is not included due to participant confidentiality and GDPR constraints.




Library versions are printed at the start of the notebook for reproducibility.



License:



The code in this record is released under the MIT License (see the attached LICENSE file).
DOI: 10.5281/zenodo.18253885
title: Chloroluma gonocarpa (Mart. & Eichler ex Miq.) Baill. ex Aubrév. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:44
DOI: 10.5281/zenodo.18253886
title: Chloroluma gonocarpa (Mart. & Eichler ex Miq.) Baill. ex Aubrév. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:44
DOI: 10.5281/zenodo.18042275
title: Reproducibility package (code + synthetic dataset) for: Assessing mortality risk in pulmonary tuberculosis and severe malnutrition: development of the IIR marker via artificial intelligence

description: This Zenodo record (record: 18042276; DOI: 10.5281/zenodo.18042276) contains the code and a fully synthetic dataset sufficient to reproduce the analyses reported in the manuscript “Assessing mortality risk in pulmonary tuberculosis and severe malnutrition: development of the IIR marker via artificial intelligence” (submitted to Scientific Reports).

Contents:





Google Colab notebook reproducing model training and evaluation (Random Forest and Logistic Regression), LIME explanations, calibration, and an IIR-only validation (in-sample AUC, repeated CV out-of-fold AUC, bootstrap 95% CI).




Synthetic dataset pro1.csv (fully synthetic; no real patient data).




Outputs exported by the notebook (figures and CSV tables), including beta coefficients (univariate and multivariate) and cross-validation metrics.



How to run (Colab):





Upload the provided synthetic pro1.csv to Google Drive under: My Drive/pro1.csv




Open and run the notebook from top to bottom. All outputs will be shown in the notebook.



Notes:





The dataset is fully synthetic and is provided only to enable reproducibility without sharing the original clinical dataset.




The original clinical dataset is not included due to participant confidentiality and GDPR constraints.




Library versions are printed at the start of the notebook for reproducibility.



License:



The code in this record is released under the MIT License (see the attached LICENSE file).
DOI: 10.15468/dl.fz9pz6
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.47.28_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 38 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011115-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18253893
title: Chondrachyrum Nees. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:44
DOI: 10.5281/zenodo.18253892
title: Chondrachyrum Nees. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:44
DOI: 10.15468/dl.456d47
title: Occurrence Download

description: A dataset containing 36515 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Taxon occurrence data for the FungalRoot database"
 ]
}

 The dataset includes 36515 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011116-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ud68ey
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sesamum indicum",
 "Year 1970-2000"
 ]
}

 The dataset includes 54 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011117-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18163540
title: Studies Found for Scoping Review: Artificial Intelligence in Youth Mental Health Counseling

description: This dataset contains the extraction table for a scoping review titled "Artificial Intelligence in Youth Mental Health Counseling: A Scoping Review". It mapping the development, gaps, implementation challenges, and features of Artificial Intelligence (AI) in Youth Mental Health Counseling. The table summarizes 36 key studies found through a systematic search of academic databases (from Scopus).
DOI: 10.5281/zenodo.18163541
title: Studies Found for Scoping Review: Artificial Intelligence in Youth Mental Health Counseling

description: This dataset contains the extraction table for a scoping review titled "Artificial Intelligence in Youth Mental Health Counseling: A Scoping Review". It mapping the development, gaps, implementation challenges, and features of Artificial Intelligence (AI) in Youth Mental Health Counseling. The table summarizes 36 key studies found through a systematic search of academic databases (from Scopus).
DOI: 10.24403/jp.1517994
title: [Sammelrezension] / [rezensiert von] Steffen Raßloff
DOI: 10.15468/dl.7na63c
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.47.29_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011118-260108223611665/datasets/export for details.
DOI: 10.15468/dl.tq2vjd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phoxinus phoxinus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1810",
 "Year is less than or equal to 1814"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011119-260108223611665/datasets/export for details.
DOI: 10.5281/zenodo.18201750
title: Chronetic Rate: A Testable Change-Rate Framework  for Relativistic and Quantum Systems

description: Project: The Quantum Lenz’s Law (QLL) Series (Part 2 of 5)

This second installment defines the Chronetic Rate, providing the necessary temporal framework to quantify the rate of change in gravitational flux. By establishing how time-translation symmetry breaking in dynamic spacetime modulates vacuum energy, this report bridges the gap between the general postulate and formal geometric proofs.

THE COMPLETE SERIES:

Part 1: A Quantum Lenz's Law – The Fundamental Postulate. [DOI: 10.5281/zenodo.17858086]Part 2: Chronetic Rate – (This Report) The temporal framework for induction. [DOI: 10.5281/zenodo.18201750]Part 3: The Semiclassical Master Equation – The Formal Geometric Proof. [DOI: 10.5281/zenodo.18212708]Part 4: Primordial Quantum Lenz's Law – Application to the Cosmic Bounce. [DOI: 10.5281/zenodo.18214970]Part 5: B-Mode Polarization Signatures – Observational Evidence. [DOI: 10.5281/zenodo.18218853]

AUTHOR ORCID: 0009-0002-8135-0645

ABSTRACT: CHRONETIC RATE

This report introduces the Chronetic Rate, a testable change-rate framework for relativistic and quantum systems. While standard General Relativity assumes a static coordinate time progress, we propose that the passage of proper time (Tau) is a dynamic variable influenced by local curvature and energy density. By defining the "rate of change of time" relative to the system's evolution (d_Tau/dt), we provide the necessary mathematical denominator for calculating the magnitude of Lenzian vacuum opposition.

INTEGRATION WITH THE QLL PROOF

 

This dataset provides the formal derivation and temporal metric for the Chronetic Rate, serving as the foundational variable for the Semiclassical Master Equation (Part 3 of the QLL Series).

REMARK ON THEORETICAL GLUE: 

 

The Chronetic Rate serves as the operational engine for the Semiclassical Master Equation (Part 3). In the derivation of the expansion scalar (Theta), the Chronetic Rate prevents mathematical divergence at the singularity. By normalizing the approach to the Planck density, it allows for the emergence of the Induced Stress-Energy Tensor (RSET), which provides the repulsive force for the non-singular bounce.

Without the temporal scaling (d_Tau) established in this report, the "Opposition Force" of the vacuum would lack a defined magnitude. This framework bridges the gap between the philosophical postulate of vacuum induction and the geometric proof of singularity avoidance.

 



Project Enrichment: Solving the Hubble Tension

 

The Chronetic Rate (the variable flow of time) provided in this dataset is the primary tool for resolving the Hubble Tension. By accounting for how time scales differently in high-curvature environments compared to the local universe, this metric aligns early-universe expansion data with current supernovae measurements without requiring new physics beyond the QLL framework.