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

description: A dataset containing 138 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((170.6913 -45.77737,170.63694 -45.78916,170.5528 -45.85786,170.51973 -45.86699,170.50186 -45.88447,170.51576 -45.90041,170.53249 -45.88635,170.60433 -45.87304,170.60813 -45.8548,170.65602 -45.84416,170.68643 -45.82781,170.68643 -45.81641,170.72254 -45.80386,170.73927 -45.78486,170.73965 -45.77649,170.7214 -45.76851,170.6913 -45.77737))",
 "TaxonKey is Polyplacophora"
 ]
}

 The dataset includes 138 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033448-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17592605
title: Ancillary files for "Two-loop all-plus helicity amplitudes for self-dual Higgs boson with gluons via unitarity cut constraints"

description: Ancillary files for "Two-loop all-plus helicity amplitudes for self-dual Higgs boson with gluons via unitarity cut constraints" by Simon Badger, Christian Biello, Colomba Brancaccio, Federico Ripani.  See the README files and the article for a description of the files.
DOI: 10.5281/zenodo.17592604
title: Ancillary files for "Two-loop all-plus helicity amplitudes for self-dual Higgs boson with gluons via unitarity cut constraints"

description: Ancillary files for "Two-loop all-plus helicity amplitudes for self-dual Higgs boson with gluons via unitarity cut constraints" by Simon Badger, Christian Biello, Colomba Brancaccio, Federico Ripani.  See the README files and the article for a description of the files.
DOI: 10.15468/dl.wwdzw8
title: Occurrence Download

description: A dataset containing 6487 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "TaxonKey is Clemmys guttata (Schneider, 1792)"
 ]
}

 The dataset includes 6487 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001594-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15269681
title: Ethical Requirements Stack data for Fitness App

description: An Ethical Requirements Stack.
DOI: 10.5281/zenodo.15269680
title: Ethical Requirements Stack data for Fitness App

description: An Ethical Requirements Stack.
DOI: 10.5281/zenodo.15282574
title: Ethical Requirements Stack data for Fitness App

description: An Ethical Requirements Stack.
DOI: 10.6084/m9.figshare.25461052
title: Vision to Specification: Automating the Transition from Conceptual Features to Functional Requirements

description: We delve into the potential for automating the drafting of functional requirements from basic conceptual ideas, expressed through tokens and their syntactic roles, aiming to streamline the process of writing and revising SRS. We present EasyFR, an approach designed to distill high-frequency Semantic Role Labeling (SRL) templates from existing public functional requirements statements, recommend suitable templates for specific token-role combinations, and integrate these functions with the powerful ChatGLM-6B open language model to automate the generation of functional requirements drafts.
DOI: 10.5281/zenodo.15461524
title: Optimizing Training Settings for Explainable Chemprop Model using Layer-Wise Relevance Propagation

description: This repository contains data for the thesis titled "Optimizing Training Settings for Explainable Chemprop Model Using Layer-Wise Relevance Propagation." Each zip file is associated with 1 target and includes the following:



Datasets: All datasets associated with the specified target.

Metrics: Evaluations of the model's performance, faithfulness, and correctness.

Models: Checkpoints for all models related to the given target.


For more details about the study, check this GitHub repo: https://github.com/DinhLongHuynh/lrp_chemprop
DOI: 10.34740/kaggle/dsv/11907221
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.25326/mwft-tg73
title: Terebic acid + OH - Aerosol study - particle formation - 2018-06-29

description: Publication of the dataset
DOI: 10.25326/dhq6-dx66
title: Terebic acid + OH - Aerosol study - particle formation - 2018-07-02

description: Publication of the dataset
DOI: 10.15468/dl.644ahj
title: Occurrence Download

description: A dataset containing 634 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grampus griseus (G.Cuvier, 1812)",
 "HasCoordinate is true",
 "Geometry POLYGON ((-98.05392 3.850193, -49.40613 3.850193, -49.40613 30.8906, -98.05392 30.8906, -98.05392 3.850193))"
 ]
}

 The dataset includes 634 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009820-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25326/gfqv-9n36
title: Terebic acid + OH - Aerosol study - particle formation - 2018-07-04

description: Publication of the dataset
DOI: 10.25326/z3eg-h157
title: e. coli + None - Reference experiment - 2019-11-18

description: Publication of the dataset
DOI: 10.25326/46ec-p012
title: e. coli + None - Reference experiment - 2019-11-19

description: Publication of the dataset
DOI: 10.15468/dl.np3vkn
title: Occurrence Download

description: A dataset containing 174029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Marasmius wynneae Berk. & Broome, Marasmius bulliardii Quél., Lepiota boudieri Bres., Lentinus tigrinus (Bull.) Fr., Lepiota ignivolvata Bousset & Joss. ex Joss., Lepiota ochraceofulva P.D.Orton, Macrotyphula fistulosa var. contorta (Holmsk.) Nannf. & L.Holm, Lentinellus cochleatus (Pers.) P.Karst., Leucocoprinus brebissonii (Godey) Locq., Lepiota castanea Quél., Megacollybia platyphylla (Pers.) Kotl. & Pouzar, Marasmiellus vaillantii (Pers.) Singer, Marasmius saccharinus (Batsch) Fr., Lepiota subalba Kühner ex P.D.Orton, Lepiota subgracilis Kühner, Lepiota xanthophylla P.D.Orton, Lepiota magnispora Murrill, Lepiota perplexa Knudsen, Leucoagaricus badhamii (Berk. & Broome) Singer, Lentinellus ursinus (Fr.) Kühner, Marasmius cohaerens (Pers.) Cooke & Quél., Lepiota jacobi Vellinga & Knudsen, Mycena polyadelpha (Lasch) Kühner, Mycena picta (Fr.) Harmaja, Mycena pelianthina (Fr.) Quél., Mycena polygramma (Bull.) Gray, Mucronella calva (Alb. & Schwein.) Fr., Mycena capillaris (Schumach.) P.Kumm., Mycena diosma Krieglst. & Schwöbel, Merismodes fasciculata (Schwein.) Donk, Mycena haematopus (Pers.) P.Kumm., Mycena capillaripes Peck, Mycena cinerella (P.Karst.) P.Karst., Mycoacia fuscoatra (Fr.) Donk, Merismodes anomala (Pers.) Singer, Mycena crocata (Schrad.) P.Kumm., Mycena arcangeliana Bres., Phlebiella christiansenii (Parmasto) K.H.Larss. & Hjortstam, Postia floriformis (Quél.) Jülich, Phellinus laevigatus (P.Karst.) Bourdot & Galzin, Pluteus romellii (Britzelm.) Lapl., Pholiota tuberculosa (Schaeff.) P.Kumm., Pluteus aurantiorugosus (Trog) Sacc., Postia lowei (Pilát) Jülich, Phanerochaete radicata (Henn.) Nakasone, C.R.Bergman & Burds., Pholiota limonella (Peck) Sacc., Pluteus luctuosus Boud., Phlebia lilascens (Bourdot) J.Erikss. & Hjortstam, Pluteus inquilinus Romagn., Panellus serotinus (Pers.) Kühner)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 174029 records from 590 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009811-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25326/rrft-d010
title: ambient air + None - Other - 2019-08-26

description: Publication of the dataset
DOI: 10.25326/pt82-tj36
title: Synthetic air + None - Other - 2019-08-26

description: Publication of the dataset
DOI: 10.25326/512f-mf91
title: + OH - Aerosol study - particle formation - 2019-04-12

description: Publication of the dataset
DOI: 10.25326/h5ba-gt89
title: + OH - Aerosol study - particle formation - 2019-04-13

description: Publication of the dataset
DOI: 10.5281/zenodo.15474782
title: Storytelling in Social Media from People, Process, Technology Framework Systematic Literature Review
DOI: 10.5281/zenodo.15474783
title: Storytelling in Social Media from People, Process, Technology Framework Systematic Literature Review
DOI: 10.15468/dl.92apdp
title: Occurrence Download

description: A dataset containing 94890 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Alopecurus pratensis L.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 94890 records from 297 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010743-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m58p39
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonicera pileata",
 "Country is Spain",
 "HasCoordinate is true"
 ]
}

 The dataset includes 26 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049545-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v648rr
title: Occurrence Download

description: A dataset containing 12003 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cosmos sulphureus Cav."
 ]
}

 The dataset includes 12003 records from 417 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009964-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uemvdj
title: Occurrence Download

description: A dataset containing 14623 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Daucus carota",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 14623 records from 523 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008006-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rtkg5hf3dt
title: QTNANO - Identification of Sodiation Mechanisms in Graphite-based Negative Electrodes by Molecular Dynamics Simulations Combined with Potential of Mean Force, Electrochimica Acta, Volume 468, 10 November 2023, 143149.

description: This repository contains the primary GROMACS input and output files used in the molecular dynamics simulations, including force field parameters, topology files, molecular dynamics protocols, and production simulation box structures. It also includes the output files generated from the analyses used to construct the figures presented in the Results section of the manuscript “Identification of Sodiation Mechanisms in Graphite-based Negative Electrodes by Molecular Dynamics Simulations Combined with Potential of Mean Force” (DOI:https://doi.org/10.1016/j.electacta.2023.143149). All files and the overall organization of the repository were prepared by Dr. Tuanan C. Lourenço. For any questions or further information, please contact Dr. Tuanan C. Lourenço at lourenco.tuanan@gmail.com.
DOI: 10.17632/rtkg5hf3dt.1
title: QTNANO - Identification of Sodiation Mechanisms in Graphite-based Negative Electrodes by Molecular Dynamics Simulations Combined with Potential of Mean Force, Electrochimica Acta, Volume 468, 10 November 2023, 143149.

description: This repository contains the primary GROMACS input and output files used in the molecular dynamics simulations, including force field parameters, topology files, molecular dynamics protocols, and production simulation box structures. It also includes the output files generated from the analyses used to construct the figures presented in the Results section of the manuscript “Identification of Sodiation Mechanisms in Graphite-based Negative Electrodes by Molecular Dynamics Simulations Combined with Potential of Mean Force” (DOI:https://doi.org/10.1016/j.electacta.2023.143149). All files and the overall organization of the repository were prepared by Dr. Tuanan C. Lourenço. For any questions or further information, please contact Dr. Tuanan C. Lourenço at lourenco.tuanan@gmail.com.
DOI: 10.17632/7p3h9dcww5
title: Replication for Volatility Regimes and Jumps in Crude Oil Futures: Uncovering How Market Shocks Trigger Extreme Comovements

description: Codes to replication the results in the paper: Volatility Regimes and Jumps in Crude Oil Futures: Uncovering How market Shocks Trigger Extreme Comovements
DOI: 10.17632/7p3h9dcww5.1
title: Replication for Volatility Regimes and Jumps in Crude Oil Futures: Uncovering How Market Shocks Trigger Extreme Comovements

description: Codes to replication the results in the paper: Volatility Regimes and Jumps in Crude Oil Futures: Uncovering How market Shocks Trigger Extreme Comovements
DOI: 10.17632/np2tdz5wvj
title: IL-17A Inhibitors for Guttate Psoriasis Treatment: A Retrospective Cohort Study

description: Supplementary material of the manuscript entitled "IL-17A Inhibitors for Guttate Psoriasis Treatment: A Retrospective Cohort Study."
DOI: 10.17632/np2tdz5wvj.1
title: IL-17A Inhibitors for Guttate Psoriasis Treatment: A Retrospective Cohort Study

description: Supplementary material of the manuscript entitled "IL-17A Inhibitors for Guttate Psoriasis Treatment: A Retrospective Cohort Study."
DOI: 10.17632/mn3g5c5zdf.2
title: Palmaria_global_change_data

description: Data and code for the manuscript "Resilience of Palmaria palmata to anthropogenic environmental changes: combined effects of marine heatwaves, hyposalinity, and pollution" submitted to the Journal of Applied Phycology.
DOI: 10.17632/n9pc2dpn95
title: DFODE-Kit: Deep learning package for solving flame chemical kinetics with high-dimensional stiff ordinary differential equations

description: Recent advances in deep learning for solving flame chemical kinetics offer promising solutions to the long-standing trade-off between accuracy and computational efficiency in combustion simulations. This work introduces DFODE-kit, an open-source Python package designed to replace the conventional, computationally intensive integration of chemical source terms governed by high-dimensional, stiff ordinary differential equations (ODEs), thereby substantially accelerating chemistry evaluation in combustion simulations. The package provides: i) an efficient sampling module that extracts high-quality thermochemical states from low-dimensional manifolds in canonical flames; ii) an effective data augmentation module that enriches the dataset to approximate the high-dimensional composition space encountered in turbulent flames; and (iii) an optimized neural network training module with multiscale preprocessing and physics-informed constraints to enhance model fidelity and stability. The trained models are seamlessly integrated into our previously released CFD solver DeepFlame, and can also be adapted for use with other widely used platforms such as OpenFOAM via custom interface modifications. Illustrative examples for a posteriori validations demonstrate that DFODE-kit models achieve excellent predictive accuracy. Furthermore, in isolated chemistry evaluations, the DNN models attain up to O(10^2) acceleration compared with CVODE, while end-to-end CFD runs typically see multi-fold speed-ups. The package, dataset, and example scripts are released to support reproducible benchmarking and community adoption.
DOI: 10.17632/n9pc2dpn95.1
title: DFODE-Kit: Deep learning package for solving flame chemical kinetics with high-dimensional stiff ordinary differential equations

description: Recent advances in deep learning for solving flame chemical kinetics offer promising solutions to the long-standing trade-off between accuracy and computational efficiency in combustion simulations. This work introduces DFODE-kit, an open-source Python package designed to replace the conventional, computationally intensive integration of chemical source terms governed by high-dimensional, stiff ordinary differential equations (ODEs), thereby substantially accelerating chemistry evaluation in combustion simulations. The package provides: i) an efficient sampling module that extracts high-quality thermochemical states from low-dimensional manifolds in canonical flames; ii) an effective data augmentation module that enriches the dataset to approximate the high-dimensional composition space encountered in turbulent flames; and (iii) an optimized neural network training module with multiscale preprocessing and physics-informed constraints to enhance model fidelity and stability. The trained models are seamlessly integrated into our previously released CFD solver DeepFlame, and can also be adapted for use with other widely used platforms such as OpenFOAM via custom interface modifications. Illustrative examples for a posteriori validations demonstrate that DFODE-kit models achieve excellent predictive accuracy. Furthermore, in isolated chemistry evaluations, the DNN models attain up to O(10^2) acceleration compared with CVODE, while end-to-end CFD runs typically see multi-fold speed-ups. The package, dataset, and example scripts are released to support reproducible benchmarking and community adoption.
DOI: 10.17632/yz79cwt7t6.1
title: Replication Package for "Which Investors Drive Anomaly Returns and How?"

description: This repository contains the code and data for the paper "Which Investors Drive Anomaly Returns and How?". Please see the read-me file for details.
DOI: 10.17632/bb4hhnzktp
title: soil sample

description: 577 soil samples
DOI: 10.17632/bb4hhnzktp.1
title: soil sample

description: 577 soil samples
DOI: 10.17632/tjgmf8pzn4
title: Instances for "Touting Occasional Drivers for Mid-haul Delivery"

description: Instances for "Touting Occasional Drivers for Mid-haul Delivery"
DOI: 10.17632/tjgmf8pzn4.1
title: Instances for "Touting Occasional Drivers for Mid-haul Delivery"

description: Instances for "Touting Occasional Drivers for Mid-haul Delivery"
DOI: 10.17632/6vf3tbcsfz
title: Majes-Soil-Nutrient-DeepLearning

description: Synergistic Integration of Sentinel-1/2 Data and Deep Learning for High-Resolution Soil Nutrient Mapping in the Arid Majes Valley, Peru.
DOI: 10.17632/6vf3tbcsfz.1
title: Majes-Soil-Nutrient-DeepLearning

description: Synergistic Integration of Sentinel-1/2 Data and Deep Learning for High-Resolution Soil Nutrient Mapping in the Arid Majes Valley, Peru.
DOI: 10.17632/8nch9tx8v7
title: InSAR data Processing using MintPy in jupyter Notebook

description: This Jupyter Notebook presents a structured workflow for Interferometric Synthetic Aperture Radar (InSAR) time-series processing using the Miami InSAR Time-series software in Python (MintPy). It provides an edited and modularised implementation of standard MintPy processing scripts, designed to streamline deformation time-series analysis using HyP3-derived SAR data from the ASF portal and a study-area shapefile for geohazard and surface-deformation applications.

The workflow guides users through each critical processing stage, with clear explanations of required steps and parameter choices. Emphasis is placed on reproducibility, modular execution, and transparent parameter control within an interactive Jupyter environment. The notebook is particularly well-suited for researchers and students engaged in landslide monitoring, tectonic deformation analysis, subsidence assessment, and infrastructure stability studies.

This implementation builds upon the core MintPy framework and established InSAR time-series methodologies, with adaptations to support flexible application across different study areas and datasets.

Users are encouraged to adapt and extend the notebook for their own case studies, provided that appropriate citation of MintPy and related methodological literature is maintained.

Core MintPy Reference along with Notebook Citation:

Yunjun, Z., Fattahi, H., & Amelung, F. (2019). Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331.
DOI: 10.17632/8nch9tx8v7.1
title: InSAR data Processing using MintPy in jupyter Notebook

description: This Jupyter Notebook presents a structured workflow for Interferometric Synthetic Aperture Radar (InSAR) time-series processing using the Miami InSAR Time-series software in Python (MintPy). It provides an edited and modularised implementation of standard MintPy processing scripts, designed to streamline deformation time-series analysis using HyP3-derived SAR data from the ASF portal and a study-area shapefile for geohazard and surface-deformation applications.

The workflow guides users through each critical processing stage, with clear explanations of required steps and parameter choices. Emphasis is placed on reproducibility, modular execution, and transparent parameter control within an interactive Jupyter environment. The notebook is particularly well-suited for researchers and students engaged in landslide monitoring, tectonic deformation analysis, subsidence assessment, and infrastructure stability studies.

This implementation builds upon the core MintPy framework and established InSAR time-series methodologies, with adaptations to support flexible application across different study areas and datasets.

Users are encouraged to adapt and extend the notebook for their own case studies, provided that appropriate citation of MintPy and related methodological literature is maintained.

Core MintPy Reference along with Notebook Citation:

Yunjun, Z., Fattahi, H., & Amelung, F. (2019). Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331.
DOI: 10.17632/y2d5pcyrzz
title: Dataset for Sentiment and Named Entity Analysis in Uzbek Texts

description: This dataset contains 15,000 synthetically generated Uzbek sentences annotated for sentiment (positive/neutral/negative) and named entities in three categories: PER, ORG, and LOC. It includes two subsets: Hybrid Synthetic Corpus (12,000) generated via templates with lexical polarity resources and curated NER gazetteers, and Manual-Style Synthetic Corpus (3,000) created using short natural-style patterns with higher emoji frequency to reflect conversational usage. Each record provides: id, text, sentiment, entities (JSON), entity_type (JSON aligned with entities), polarity_score, polarity_source, token_count, and emojis (JSON). Emoji presence is ~30% in the hybrid subset and ~39% in the manual-style subset, with emojis grouped into positive/neutral/negative classes. The dataset is released in CSV and XLSX (UTF-8) and distributed under CC BY 4.0.