cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.gg3cte
title: Occurrence Download

description: A dataset containing 148540 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ondatra zibethicus (Linnaeus, 1766)",
 "DatasetKey is one of (VMM - Rat control occurrences in Flanders, Belgium, Muskrat captures in Flanders, Belgium, MICA - Muskrat occurrences collected by RATO in East Flanders, Belgium, MICA - Muskrat occurrences collected by VMM in Flanders, Belgium, RATO - daily operations commissioned by the province East Flanders, Belgium)"
 ]
}

 The dataset includes 148540 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015439-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15073682
title: Foretelling microstructural interface with multi-generational convolutional-LSTM framework

description: The dataset is arranged within folders inside the  "microstructure-model-computer-vision.zip" zip folder in such a way that they holistically represent a step-by-step workflow for integrating phase-field simulations with machine learning methodologies. Thus the overall methodology is organized into seven structured steps, each represented by corresponding dataset type contained within a dedicated folder.The instructions are provided in each folder sequentially to promote the understandability of the codes/data contained therein. The seven steps represented by seven dataset folders are as following:

Contents:



 Step 0: Phase-Field SimulationsInstructions and data for running initial phase-field simulations.Folder: Step_0_phase_field_simulations

Step 1: Train-Test Data PreparationScripts and guidance for generating and organizing training/testing datasets.Folder: Step_1_train_test_data



Step 2: Model DevelopmentTools and models for training machine learning algorithms using simulation data.Folder: Step_2_model_development




Step 3: PredictionsApplication of trained models to generate predictions on unseen data.Folder: Step_3_predictions




Step 4: Quantification and AnalysisEvaluation of model outputs, including error quantification and comparative metrics.Folder: Step_4_quantification




Step 5: Engineering Alloys ApplicationsCase studies and use-cases focused on engineering alloy systems.Folder: Step_5_Engg_Alloys




Step 6: Appendix and Graphical AbstractSupporting materials, visual summaries, and appendix content.Folder: Step_6_Appendix_And_Graphical_Abstract



Usage:Each folder (corresponding to each of the steps above) again contains a README.txt or instruction file detailing how to run the code or interpret the data within that step. 
description: This work was supported by the National Science Centre, Poland (UMO-2021/42/E/ST5/00339) and the HORIZON EUROPE European Research Council (101123107-µTWIN).
DOI: 10.5281/zenodo.15073683
title: Foretelling microstructural interface with multi-generational convolutional-LSTM framework

description: The dataset is arranged within folders inside the  "microstructure-model-computer-vision.zip" zip folder in such a way that they holistically represent a step-by-step workflow for integrating phase-field simulations with machine learning methodologies. Thus the overall methodology is organized into seven structured steps, each represented by corresponding dataset type contained within a dedicated folder.The instructions are provided in each folder sequentially to promote the understandability of the codes/data contained therein. The seven steps represented by seven dataset folders are as following:

Contents:



 Step 0: Phase-Field SimulationsInstructions and data for running initial phase-field simulations.Folder: Step_0_phase_field_simulations

Step 1: Train-Test Data PreparationScripts and guidance for generating and organizing training/testing datasets.Folder: Step_1_train_test_data



Step 2: Model DevelopmentTools and models for training machine learning algorithms using simulation data.Folder: Step_2_model_development




Step 3: PredictionsApplication of trained models to generate predictions on unseen data.Folder: Step_3_predictions




Step 4: Quantification and AnalysisEvaluation of model outputs, including error quantification and comparative metrics.Folder: Step_4_quantification




Step 5: Engineering Alloys ApplicationsCase studies and use-cases focused on engineering alloy systems.Folder: Step_5_Engg_Alloys




Step 6: Appendix and Graphical AbstractSupporting materials, visual summaries, and appendix content.Folder: Step_6_Appendix_And_Graphical_Abstract



Usage:Each folder (corresponding to each of the steps above) again contains a README.txt or instruction file detailing how to run the code or interpret the data within that step. 
description: This work was supported by the National Science Centre, Poland (UMO-2021/42/E/ST5/00339) and the HORIZON EUROPE European Research Council (101123107-µTWIN).
DOI: 10.15468/dl.beyuak
title: Occurrence Download

description: A dataset containing 920 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chrysuronia lilliae (Stone, 1917)",
 "Year 2019-2024"
 ]
}

 The dataset includes 920 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015444-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jvs7w5
title: Occurrence Download

description: A dataset containing 3564402 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Magnoliopsida"
 ]
}

 The dataset includes 3564402 records from 377 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015440-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rq2kwn
title: Occurrence Download

description: A dataset containing 920 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chrysuronia lilliae (Stone, 1917)",
 "Year 2019-2024"
 ]
}

 The dataset includes 920 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015445-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ff6akw
title: Occurrence Download

description: A dataset containing 9625853 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Squamata"
 ]
}

 The dataset includes 9625853 records from 7952 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015443-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18200184
title: The importance of the sampling design in mapping woody cover in arid ecosystems

description: Accurate calibration data remains a major constraint in ecological remote sensing, particularly in arid ecosystems where sparse and heterogeneous woody vegetation is challenging to detect. Despite advances in sensor technology and modelling algorithms, optimal calibration sampling design has received limited attention, with current approaches varying unsystematically (35–1,000 plots/1,000 km²) without empirical benchmarks. Here, we develop and evaluate a quantitative framework for optimizing sampling strategies in remote-sensing models of woody cover through systematic comparison of (i) calibration data source (field surveys versus photointerpretation), (ii) spatial configuration (clustered versus dispersed), and (iii) sampling density. This integrated approach enables isolating the relative contributions of each design component to mapping accuracy—a critical gap in current remote sensing methodology.

We applied this framework to Sentinel-1, Sentinel-2, combined Sentinel-1+2, and AlphaEarth Foundations across Madagascar's arid southwest, validating predictions through spatial cross-validation using independent field plots. Photointerpretation substantially outperformed field-based calibration under typical arid-zone constraints (R²=0.88, RMSE=0.11 versus R²=0.46–0.66, RMSE=0.17–0.21). Performance saturated at 20.7 - 41.4 dispersed calibration plots per 1,000 km² across all predictors, beyond which gains became marginal. Dispersed strategies required half as many samples as clustered designs to achieve comparable accuracy, demonstrating that spatial distribution outweighs sample size. Once adequate sampling density and distribution were achieved, Sentinel-1+2 and AlphaEarth performed similarly (R²≈0.85–0.86), indicating that sampling design outweighs predictor complexity.

Our framework provides empirically derived operational thresholds (minimum: 15.5 plots/1,000 km²; optimal: 20.7 -41.4 plots/1,000 km²) and a transferable methodology for determining optimal calibration densities in heterogeneous ecosystems where logistical constraints limit field sampling.
DOI: 10.5281/zenodo.18235471
title: The importance of the sampling design in mapping woody cover in arid ecosystems

description: Accurate calibration data remains a major constraint in ecological remote sensing, particularly in arid ecosystems where sparse and heterogeneous woody vegetation is challenging to detect. Despite advances in sensor technology and modelling algorithms, optimal calibration sampling design has received limited attention, with current approaches varying unsystematically (35–1,000 plots/1,000 km²) without empirical benchmarks. Here, we develop and evaluate a quantitative framework for optimizing sampling strategies in remote-sensing models of woody cover through systematic comparison of (i) calibration data source (field surveys versus photointerpretation), (ii) spatial configuration (clustered versus dispersed), and (iii) sampling density. This integrated approach enables isolating the relative contributions of each design component to mapping accuracy—a critical gap in current remote sensing methodology.

We applied this framework to Sentinel-1, Sentinel-2, combined Sentinel-1+2, and AlphaEarth Foundations across Madagascar's arid southwest, validating predictions through spatial cross-validation using independent field plots. Photointerpretation substantially outperformed field-based calibration under typical arid-zone constraints (R²=0.88, RMSE=0.11 versus R²=0.46–0.66, RMSE=0.17–0.21). Performance saturated at 20.7 - 41.4 dispersed calibration plots per 1,000 km² across all predictors, beyond which gains became marginal. Dispersed strategies required half as many samples as clustered designs to achieve comparable accuracy, demonstrating that spatial distribution outweighs sample size. Once adequate sampling density and distribution were achieved, Sentinel-1+2 and AlphaEarth performed similarly (R²≈0.85–0.86), indicating that sampling design outweighs predictor complexity.

Our framework provides empirically derived operational thresholds (minimum: 15.5 plots/1,000 km²; optimal: 20.7 -41.4 plots/1,000 km²) and a transferable methodology for determining optimal calibration densities in heterogeneous ecosystems where logistical constraints limit field sampling.
DOI: 10.17632/9nhkk4s4f4
title: Unsaturated lipids as key control points for caveola formation and disassembly

description: Raw data for western blot images.
DOI: 10.17632/9nhkk4s4f4.1
title: Unsaturated lipids as key control points for caveola formation and disassembly

description: Raw data for western blot images.
DOI: 10.5281/zenodo.15321900
title: Data underlying the figures of The Sequestration Efficiency of the Deep Ocean, submitted to Geophysical Research Letters

description: This files contain the data used to produce the figures in the aforementioned manuscript submitted to GRL, of which a preprint is available at XXX.
DOI: 10.5281/zenodo.15321901
title: Data underlying the figures of The Sequestration Efficiency of the Deep Ocean, submitted to Geophysical Research Letters

description: This files contain the data used to produce the figures in the aforementioned manuscript submitted to GRL, of which a preprint is available at XXX.
DOI: 10.5281/zenodo.14967221
title: Delivering Data: A Real-World Dataset for Last-Mile Delivery Optimization

description: The dataset captures approximately one month of pharmaceutical delivery operations from a 3PL provider. It represents a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), incorporating real-world constraints like vehicle capacity, delivery time windows, and operational limits. With nine daily instances covering two delivery days per week, it reflects four to five weeks of routing challenges, providing a benchmark for optimizing last-mile pharmaceutical logistics. 

The dataset  is divided into two main folders:

1. Time and Distance Matrices:

This folder contains travel distances and estimated travel times between delivery nodes for each of the nine days. To maintain confidentiality, exact node locations are not provided.





Distance Matrices: Provide the pairwise distances (in kilometers) between delivery locations




Time Matrices: Represent travel durations (in minutes) under three different traffic conditions:






Optimistic: Best-case scenario with minimal traffic 




Most Likely: Expected travel time 




Pessimistic: Worst-case scenario with heavy traffic delays




Each dataset version (1-9) has four corresponding Excel files:





distance_matrix_X.xlsx




time_matrix_mostlikely_X.xlsx




time_matrix_optimistic_X.xlsx




time_matrix_pessimistic_X.xlsx



where X represents the dataset number (1-9).

2. Order Characteristics:

This folder contains detailed information about daily delivery requests. It includes an Excel file (orders.xlsx) with nine sheets, each corresponding to a specific day's delivery requests. Each order is defined by the following attributes:



NODE_ID: Unique identifier for each delivery request

WEIGHT: Weight of the shipment (kg)

VOLUME: Volume of the shipment (cubic meters)

SERVICE_TIME: Time required to complete the delivery (minutes)

EAT: Earliest Arrival Time allowed for delivery

LAT: Latest Arrival Time allowed for delivery

TIME_WINDOW: Allowed delivery time window, defined by the EAT and LAT
DOI: 10.5281/zenodo.15310106
title: Delivering Data: A Real-World Dataset for Last-Mile Delivery Optimization

description: The dataset captures approximately one month of pharmaceutical delivery operations from a 3PL provider. It represents a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), incorporating real-world constraints like vehicle capacity, delivery time windows, and operational limits. With nine daily instances covering two delivery days per week, it reflects four to five weeks of routing challenges, providing a benchmark for optimizing last-mile pharmaceutical logistics. 

The dataset  is divided into two main folders:

1. Time and Distance Matrices:

This folder contains travel distances and estimated travel times between delivery nodes for each of the nine days. To maintain confidentiality, exact node locations are not provided.





Distance Matrices: Provide the pairwise distances (in kilometers) between delivery locations




Time Matrices: Represent travel durations (in minutes) under three different traffic conditions:






Optimistic: Best-case scenario with minimal traffic 




Most Likely: Expected travel time 




Pessimistic: Worst-case scenario with heavy traffic delays




Each dataset version (1-9) has four corresponding Excel files:





distance_matrix_X.xlsx




time_matrix_mostlikely_X.xlsx




time_matrix_optimistic_X.xlsx




time_matrix_pessimistic_X.xlsx



where X represents the dataset number (1-9).

2. Order Characteristics:

This folder contains detailed information about daily delivery requests. It includes an Excel file (orders.xlsx) with nine sheets, each corresponding to a specific day's delivery requests. Each order is defined by the following attributes:



NODE_ID: Unique identifier for each delivery request

WEIGHT: Weight of the shipment (kg)

VOLUME: Volume of the shipment (cubic meters)

SERVICE_TIME: Time required to complete the delivery (minutes)

EAT: Earliest Arrival Time allowed for delivery

LAT: Latest Arrival Time allowed for delivery

TIME_WINDOW: Allowed delivery time window, defined by the EAT and LAT
DOI: 10.25549/inscriptifact-ouc110597282
title: Persepolis Fortification Aramaic Text 373, PFAT 373

description: [Text Description:] Administrative text with seals PFS 1098; PFS 0017. ❧ [Text or Publication Number Note:] PFAT 373, Bowman 373 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription.Seal impressions PFS 1098 on obverse, upper edge, reverse, bottom edge; PFS 0017 on flat edge.
DOI: 10.25549/inscriptifact-ouc110598957
title: Persepolis Fortification Elamite Tablet 0522, PF 0522

description: [Text Description:] Administrative text with seals PFS 0255, PFS 0122 ❧ [Text or Publication Number Note:] PF 0522 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. Seal impressions PFS 0255 reverse; PFS 0122 left edge. ❧ [Physical Object Note:] Some damage on left edge.
DOI: 10.25549/inscriptifact-ouc110598953
title: Persepolis Fortification Elamite Tablet 0479, PF 0479

description: [Text Description:] Administrative text with seals PFS 0044, second seal impression ❧ [Text or Publication Number Note:] PF 0479 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform, Aramaic ❧ [Physical Object Description:] Clay tablet with cuneiform text, Aramaic ink inscription. Seal impression PFS 0044 reverse, bottom edge; another seal impression left edge. ❧ [Physical Object Note:] Damaged reverse.
DOI: 10.25549/inscriptifact-ouc110597281
title: Persepolis Fortification Aramaic Text 374, PFAT 374

description: [Text Description:] Administrative text with seals PFATS 0362; PFS 0319. ❧ [Text or Publication Number Note:] PFAT 374, Bowman 374 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription. Seal impressions PFATS 0362 on obverse, bottom edge; PFS 0319 on upper edge, reverse, flat edge.
DOI: 10.25549/inscriptifact-ouc110597268
title: Persepolis Fortification Aramaic Text 373, PFAT 373

description: [Text Description:] Administrative text with seals PFS 1098; PFS 0017. ❧ [Text or Publication Number Note:] PFAT 373, Bowman 373 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription.Seal impressions PFS 1098 on obverse, upper edge, reverse, bottom edge; PFS 0017 on flat edge.
DOI: 10.25549/inscriptifact-ouc110598952
title: Persepolis Fortification Elamite Tablet 0522, PF 0522

description: [Text Description:] Administrative text with seals PFS 0255, PFS 0122 ❧ [Text or Publication Number Note:] PF 0522 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. Seal impressions PFS 0255 reverse; PFS 0122 left edge. ❧ [Physical Object Note:] Some damage on left edge.
DOI: 10.25549/inscriptifact-ouc110598951
title: Persepolis Fortification Elamite Tablet 0522, PF 0522

description: [Text Description:] Administrative text with seals PFS 0255, PFS 0122 ❧ [Text or Publication Number Note:] PF 0522 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. Seal impressions PFS 0255 reverse; PFS 0122 left edge. ❧ [Physical Object Note:] Some damage on left edge.
DOI: 10.25549/inscriptifact-ouc110597257
title: Persepolis Fortification Aramaic Text 373, PFAT 373

description: [Text Description:] Administrative text with seals PFS 1098; PFS 0017. ❧ [Text or Publication Number Note:] PFAT 373, Bowman 373 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription.Seal impressions PFS 1098 on obverse, upper edge, reverse, bottom edge; PFS 0017 on flat edge.
DOI: 10.25549/inscriptifact-ouc110598923
title: Persepolis Fortification Elamite Tablet 0479, PF 0479

description: [Text Description:] Administrative text with seals PFS 0044, second seal impression ❧ [Text or Publication Number Note:] PF 0479 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform, Aramaic ❧ [Physical Object Description:] Clay tablet with cuneiform text, Aramaic ink inscription. Seal impression PFS 0044 reverse, bottom edge; another seal impression left edge. ❧ [Physical Object Note:] Damaged reverse.
DOI: 10.25549/inscriptifact-ouc110601555
title: Persepolis Fortification Aramaic Text 437, PFAT 437

description: [Text Description:] Administrative text with seals PFS 0075 ❧ [Text or Publication Number Note:] PFAT 437, Bowman 437 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription. Seal impression PFS 0075 on obverse, upper edge, reverse, bottom edge, flat edge.
DOI: 10.25549/inscriptifact-ouc110597214
title: Persepolis Fortification Aramaic Text 371, PFAT 371

description: [Text Description:] Administrative text with seal PFATS 0075. ❧ [Text or Publication Number Note:] PFAT 371, Bowman 371 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription. Seal impression PFATS 0075 on obverse, upper edge, reverse, bottom edge.