cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fcsjwd9gr6.2
title: Forest Fire Dataset

description: The "Forest Fire Dataset" is a comprehensive and meticulously curated resource, specifically designed to support the development of algorithms for forest fire detection and object detection tasks. The dataset consists of 2,974 images dedicated to fire classification, which are divided into two primary categories: the first category includes images documenting active forest fires, while the second category contains images of intact, fire-free forest environments. This clear distinction within the dataset is crucial for training models to accurately differentiate between fire-affected and unaffected areas in forested regions. In addition to the fire classification data, the dataset includes 1,690 images dedicated to object detection, enhancing its applicability in machine learning and computer vision research.

The dataset is carefully structured with a thoughtful distribution across training, validation, and test sets, with proportions of 80%, 15%, and 5%, respectively, to ensure that models trained on this data can generalize effectively to new, unseen data. The data were collected from various online sources and underwent rigorous manual filtering to maintain high data integrity. Additionally, a portion of the dataset was generated through controlled simulations of forest fires, conducted after obtaining the necessary approvals from relevant authorities. This simulated portion adds diversity and reliability to the dataset, providing a more comprehensive training ground for algorithms.

By integrating both real-world and simulated scenarios, the "Forest Fire Dataset" offers a robust foundation for developing advanced fire detection systems, significantly contributing to forest conservation and disaster management efforts.

For scientific research and advanced applications in the fields of forest fire detection and computer vision, the "Forest Fire Dataset" is a valuable tool. Researchers and practitioners are encouraged to refer to the published article that details the development of the system based on this dataset. To cite the article related to this dataset, the following citation can be used:
=======================================================
I. Shamta and B. E. Demir, “Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV,” ِ Artica: PLoS One, vol. 19, no. 3, p. e0299058, 2024.
=======================================================

Link to Article: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299058
ORCID: https://orcid.org/my-orcid?orcid=0009-0003-1280-679X
Google Academik: https://scholar.google.com/citations?user=xP6CvtQAAAAJ&hl=tr
DOI: 10.25810/fq7x-q521.61
title: Middle Eastern Newspaper Coverage of Climate Change or Global Warming, 2004-2024 - December 2024

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.11409896
title: Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"

description: Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".

Inputs: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.



Real-life: preprocessed event logs (xes and csv) corresponding to the real-life processes used in the evaluation. Process models (pnml) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (txt) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder split).

Synthetic: simulated event logs (csv) corresponding to the synthetic processes used in the evaluation. Designed process models (bpmn and pnml). Ongoing cases result from splitting each case in the preprocessed event logs (under folder split). Ongoing cases with injected noise as described in the publication (under folders noise_1, noise_2, and noise_3).
DOI: 10.5281/zenodo.11409897
title: Evaluation datasets, results, and supplementary material of the paper "Efficient State Computation for Log Animation and Short-Term Simulation Using N-Gram Indexing"

description: Event logs, process models, results, and supplementary material corresponding to the paper "Efficient State Computation for Log Animation and Short-Term Simulation Using N-Gram Indexing".

Inputs: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.



Real-life: preprocessed event logs (xes and csv) corresponding to the real-life processes used in the evaluation. Process models (bpmn and pnml) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (txt) of the event logs and process models. Ongoing cases result of splitting each case in the preprocessed event logs (under folder split).

Synthetic: simulated event logs (csv) corresponding to the synthetic processes used in the evaluation. Designed process models (bpmn and pnml). Ongoing cases result of splitting each case in the preprocessed event logs (under folder split). Ongoing cases with injected noise as described in the publication (under folders noise_1, noise_2, and noise_3).
DOI: 10.5281/zenodo.13625880
title: Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"

description: Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".

Inputs: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.



Real-life: preprocessed event logs (xes and csv) corresponding to the real-life processes used in the evaluation. Process models (bpmn and pnml) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (txt) of the event logs and process models. Ongoing cases result of splitting each case in the preprocessed event logs (under folder split).

Synthetic: simulated event logs (csv) corresponding to the synthetic processes used in the evaluation. Designed process models (bpmn and pnml). Ongoing cases result of splitting each case in the preprocessed event logs (under folder split). Ongoing cases with injected noise as described in the publication (under folders noise_1, noise_2, and noise_3).
DOI: 10.5281/zenodo.14679920
title: Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"

description: Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".

Inputs: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.



Real-life: preprocessed event logs (xes and csv) corresponding to the real-life processes used in the evaluation. Process models (pnml) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (txt) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder split).

Synthetic: simulated event logs (csv) corresponding to the synthetic processes used in the evaluation. Designed process models (bpmn and pnml). Ongoing cases result from splitting each case in the preprocessed event logs (under folder split). Ongoing cases with injected noise as described in the publication (under folders noise_1, noise_2, and noise_3).
DOI: 10.15468/dl.tmwsdu
title: Occurrence Download

description: A dataset containing 15169 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "GadmGid is USA.24_1",
 "TaxonKey is Anura"
 ]
}

 The dataset includes 15169 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000149-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jb4uex
title: Occurrence Download

description: A dataset containing 46581 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cecropia Loefl."
 ]
}

 The dataset includes 46581 records from 729 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000148-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28194947
title: Dengue treatment systematic review

description: Dengue fever is a vector-borne infectious disease, caused by dengue virus. With no feasible, widely applicable prevention method available in the near term, the need for an effective treatment is of great importance. This systematic review aims to provide a comprehensive overview of potential antiviral, immunomodulatory, and platelet-enhancing therapies for the treatment of dengue
DOI: 10.6084/m9.figshare.28194947.v1
title: Dengue treatment systematic review

description: Dengue fever is a vector-borne infectious disease, caused by dengue virus. With no feasible, widely applicable prevention method available in the near term, the need for an effective treatment is of great importance. This systematic review aims to provide a comprehensive overview of potential antiviral, immunomodulatory, and platelet-enhancing therapies for the treatment of dengue
DOI: 10.15468/dl.keqxs4
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((1.694738 41.821071, 1.694738 41.835623, 1.718007 41.835623, 1.718007 41.821071, 1.694738 41.821071))"
 ]
}

 The dataset includes 71 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000152-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qna5k4
title: Occurrence Download

description: A dataset containing 340 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Prunus L.",
 "Year 2010-2025"
 ]
}

 The dataset includes 340 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000154-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jte8f8
title: Occurrence Download

description: A dataset containing 1507 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Panama",
 "Geometry POLYGON((-78.88247 9.12301,-79.11267 9.1717,-79.30746 9.11563,-79.5834 9.05365,-79.59963 9.00791,-79.51995 9.03004,-79.35763 9.06103,-79.08463 9.09202,-78.881 9.08612,-78.88247 9.12301))",
 "IucnRedListCategory is one of (VU, EN, CR, NT)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1507 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000155-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b6skha
title: Occurrence Download

description: A dataset containing 4287 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((1.591467 41.768239, 1.591467 41.836587, 1.760449 41.836587, 1.760449 41.768239, 1.591467 41.768239))"
 ]
}

 The dataset includes 4287 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000156-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qq2h92
title: Occurrence Download

description: A dataset containing 371 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence, Observation)",
 "Country is one of (Chinese Taipei, Hong Kong)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lithocarpus konishii (Hayata) Hayata"
 ]
}

 The dataset includes 371 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000158-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14615739
title: Dynamic Time Warping Intermediate Signal Approach (DTW-ISA)

description: MANUSCRIPT SUPPLEMENTARY MATERIAL

This supplementary material is provided for the manuscript titled Full-waveform inversion cycle-skipping mitigation with dynamic time warping, Part 2: an intermediate signal approach, submitted to the journal Pure and Applied Geophysics. For additional context and details, please refer to the manuscript.
description: SUPPLEMENTARY RESULTS



The files provided include the results of the FWI numerical simulations presented in Table 2 of the manuscript. Due to Zenodo's size limitations, some files could not be uploaded, as:



Seismograms from the l_bfgs_b_... folders.

DTW best path results from the l_bfgs_b_..., initial_proximity_eval, and proximity_eval... folders.


The l_bfgs_b_... folders contain data for each multiscale step. The folder name indicates the low-pass frequency filter (FF) applied at each step and the intermediate step (IS) (e.g., l_bfgs_b_FF-10.0Hz_IS-1).

The initial_proximity_eval folders contain files from the Dynamic Time Warping Proximity Analysis (DTW-PA) conducted prior to the first multiscale step using a 5 Hz low-pass filter. These files assess the initial model's proximity to the target data.

The proximity_eval... folders contain DTW-PA results used to evaluate the low-pass frequency filters for subsequent multiscale steps. Each folder name specifies the filter tested (e.g., proximity_eval-10.0Hz).

CODE

Each simulation folder contains a code subfolder, which includes the scripts and code used to run the numerical simulations. The following files are included in each folder:



A .py script for running the FWI numerical simulation.

..._pa_plt.py to generate DTW-PA figures.

..._plt.py to produce additional figures.

A .npy velocity model, when needed.

The requirements.txt.


Additionally, .slurm files are provided, as the simulations were executed on a cluster using the SLURM job scheduler. These files are supplied AS-IS and must be adjusted to fit your specific computational environment.
description: SOFTWARE INSTALLATION

To run the provided codes, follow these steps to set up a virtual Python environment:



Ensure Python 3.8 is installed and set as your default Python3 version.

Install virtualenv if it is not already installed.

Execute the following commands:




python3 -m venv my_venv

source my_venv/bin/activate

python -m pip install --upgrade pip

pip install -r requirements.txt


In each code folder is a requirements.txt file.
DOI: 10.5281/zenodo.14615740
title: Dynamic Time Warping Intermediate Signal Approach (DTW-ISA)

description: MANUSCRIPT SUPPLEMENTARY MATERIAL

This supplementary material is provided for the manuscript titled Full-waveform inversion cycle-skipping mitigation with dynamic time warping, Part 2: an intermediate signal approach, submitted to the journal Pure and Applied Geophysics. For additional context and details, please refer to the manuscript.
description: SUPPLEMENTARY RESULTS



The files provided include the results of the FWI numerical simulations presented in Table 2 of the manuscript. Due to Zenodo's size limitations, some files could not be uploaded, as:



Seismograms from the l_bfgs_b_... folders.

DTW best path results from the l_bfgs_b_..., initial_proximity_eval, and proximity_eval... folders.


The l_bfgs_b_... folders contain data for each multiscale step. The folder name indicates the low-pass frequency filter (FF) applied at each step and the intermediate step (IS) (e.g., l_bfgs_b_FF-10.0Hz_IS-1).

The initial_proximity_eval folders contain files from the Dynamic Time Warping Proximity Analysis (DTW-PA) conducted prior to the first multiscale step using a 5 Hz low-pass filter. These files assess the initial model's proximity to the target data.

The proximity_eval... folders contain DTW-PA results used to evaluate the low-pass frequency filters for subsequent multiscale steps. Each folder name specifies the filter tested (e.g., proximity_eval-10.0Hz).

CODE

Each simulation folder contains a code subfolder, which includes the scripts and code used to run the numerical simulations. The following files are included in each folder:



A .py script for running the FWI numerical simulation.

..._pa_plt.py to generate DTW-PA figures.

..._plt.py to produce additional figures.

A .npy velocity model, when needed.

The requirements.txt.


Additionally, .slurm files are provided, as the simulations were executed on a cluster using the SLURM job scheduler. These files are supplied AS-IS and must be adjusted to fit your specific computational environment.
description: SOFTWARE INSTALLATION

To run the provided codes, follow these steps to set up a virtual Python environment:



Ensure Python 3.8 is installed and set as your default Python3 version.

Install virtualenv if it is not already installed.

Execute the following commands:




python3 -m venv my_venv

source my_venv/bin/activate

python -m pip install --upgrade pip

pip install -r requirements.txt


In each code folder is a requirements.txt file.
DOI: 10.15468/dl.ebx4me
title: Occurrence Download

description: A dataset containing 318 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.52487 -18.41993,-68.91672 -18.41993,-68.91672 -14.21033,-75.52487 -14.21033,-75.52487 -18.41993))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Podilymbus podiceps (Linnaeus, 1758)",
 "Year 2021-2025"
 ]
}

 The dataset includes 318 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000159-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11360727
title: Dynamic Time Warping Proximity Analysis (DTW-PA)

description: MANUSCRIPT SUPPLEMENTARY MATERIAL

This supplementary material is provided for the manuscript titled Full-waveform inversion cycle-skipping mitigation with dynamic time warping, Part 1: a method for a proximity analysis between the current and the observed data, submitted to the journal Pure and Applied Geophysics. For additional context and details, please refer to the manuscript.
description: SUPPLEMENTARY RESULTS



The files provided include the results of the FWI numerical simulations presented in Table 2 of the manuscript. Due to Zenodo's size limitations, some files could not be uploaded, as:



Seismograms from the l_bfgs_b_... folders.

DTW best path results from the l_bfgs_b_..., initial_proximity_eval, and proximity_eval... folders.


The l_bfgs_b_... folders contain data for each multiscale step. The folder name indicates the low-pass frequency filter (FF) applied at each step and the intermediate step (IS) (e.g., l_bfgs_b_FF-10.0Hz_IS-1).

The initial_proximity_eval folders contain files from the Dynamic Time Warping Proximity Analysis (DTW-PA) conducted prior to the first multiscale step using a 5 Hz low-pass filter. These files assess the initial model's proximity to the target data.

The proximity_eval... folders contain DTW-PA results used to evaluate the low-pass frequency filters for subsequent multiscale steps. Each folder name specifies the filter tested (e.g., proximity_eval-10.0Hz).

CODE

Each simulation folder contains a code subfolder, which includes the scripts and code used to run the numerical simulations. The following files are included in each folder:



A .py script for running the FWI numerical simulation.

..._pa_plt.py to generate DTW-PA figures.

..._plt.py to produce additional figures.

A .npy velocity model, when needed.

The requirements.txt.


Additionally, .slurm files are provided, as the simulations were executed on a cluster using the SLURM job scheduler. These files are supplied AS-IS and must be adjusted to fit your specific computational environment.
description: SOFTWARE INSTALLATION

To run the provided codes, follow these steps to set up a virtual Python environment:



Ensure Python 3.8 is installed and set as your default Python3 version.

Install virtualenv if it is not already installed.

Execute the following commands:




python3 -m venv my_venv

source my_venv/bin/activate

python -m pip install --upgrade pip

pip install -r requirements.txt


In each code folder is a requirements.txt file.
description: HARDWARE REQUIREMENTS







These simulations require substantial computational resources. Running them on a personal computer is not recommended.

 

We used five Intel Xeon Gold 6148 nodes for the numerical simulations, each running at 2.4 GHz with 27 MB cache and 10.4 GT/s. Each node had 192 GB of DDR4 2,666 MHz ECC Reg memory. Due to the scheduler using one node, only four were used for FWI computations. Similarly, the scheduler limited the number of workers employed for shot parallelisation.