cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.dpy8uw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Trimium expandum Reitter, 1884",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102497-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.12723770
title: BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2021

description: Belgian National Burden of Disease Study

Estimates of the fatal burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

More information

For additional background on BeBOD, please visit https://www.sciensano.be/en/projects/belgian-national-burden-disease-study.

Explore the estimates via https://burden.sciensano.be/shiny/mortality.
description: For more information on the Parquet format, please see https://arrow.apache.org/docs/r/reference/read_parquet.html
DOI: 10.15468/dl.8npsm8
title: Occurrence Download

description: A dataset containing 5132 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coreopsis grandiflora Hogg ex Sweet"
 ]
}

 The dataset includes 5132 records from 234 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102496-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13378462
title: ENDGAME - Laboratory Experiment 2024-03-20 Exp. 001 - High Speed Camera data

description: Preliminary 2D Shock-tube experiments in combination with high speed Schlieren shadow photography. 

We developed a 2D shock-tube setup using 2 glass sheets (1 cm width) separated by 2 lateral bars (gap between glass sheets 1.3 cm). We injected compressed air into the 2D setup at different overpressures (up to 8 bar). The high-pressure reservoir is connected with the 2D apparatus through a diaphragm pulse valve which allows a fast release of pressurized gas into the system. Images were collected at a frame rate of 30000 fps. The field of view of the images show the jet flow dynamics within the 2 glass sheets (vent is outside the field of view).
DOI: 10.5281/zenodo.13378463
title: ENDGAME - Laboratory Experiment 2024-03-20 Exp. 001 - High Speed Camera data

description: Preliminary 2D Shock-tube experiments in combination with high speed Schlieren shadow photography. 

We developed a 2D shock-tube setup using 2 glass sheets (1 cm width) separated by 2 lateral bars (gap between glass sheets 1.3 cm). We injected compressed air into the 2D setup at different overpressures (up to 8 bar). The high-pressure reservoir is connected with the 2D apparatus through a diaphragm pulse valve which allows a fast release of pressurized gas into the system. Images were collected at a frame rate of 30000 fps. The field of view of the images show the jet flow dynamics within the 2 glass sheets (vent is outside the field of view).
DOI: 10.15468/dl.h6vvzh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Didymosphenia geminatum (Lyngbye) M.Schmidt, 1899)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1900",
 "BasisOfRecord is one of (Specimen)",
 "Country is one of (Argentina)",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than or equal to 20000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102498-240626123714530/datasets/export for details.
DOI: 10.15468/dl.5vmua9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Lithobius simplex Folkmanová, 1946",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102499-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.12723769
title: BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2021

description: Belgian National Burden of Disease Study

Estimates of the fatal burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

More information

For additional background on BeBOD, please visit https://www.sciensano.be/en/projects/belgian-national-burden-disease-study.

Explore the estimates via https://burden.sciensano.be/shiny/mortality.
description: For more information on the Parquet format, please see https://arrow.apache.org/docs/r/reference/read_parquet.html
DOI: 10.5281/zenodo.13377800
title: BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2021

description: Belgian National Burden of Disease Study

Estimates of the fatal burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

More information

For additional background on BeBOD, please visit https://www.sciensano.be/en/projects/belgian-national-burden-disease-study.

Explore the estimates via https://burden.sciensano.be/shiny/mortality.
description: For more information on the Parquet format, please see https://arrow.apache.org/docs/r/reference/read_parquet.html
DOI: 10.15468/dl.yn4c3w
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Neriene avida Blackwall, 1844",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102500-240626123714530/datasets/export for details.
DOI: 10.6084/m9.figshare.26840203
title: Underlying data used to generate phylogeny of ABCB homologs from various eukaryotes closely related to characterized ABCB6 and ABCB7 homologs from selected model organisms.

description: The protein sequence, alignment, and tree inference output files used to generate the phylogeny, inferred from amino acid sequences of ABCB homologs from various eukaryotes closely related to characterized ABCB6 and ABCB7 homologs from selected model organisms, in Fig. 1B and Fig. S2 in "The <i>Toxoplasma gondii</i> mitochondrial transporter ABCB7L is essential for the biogenesis of cytosolic and nuclear iron-sulfur cluster proteins and cytosolic translation".
DOI: 10.6084/m9.figshare.26840203.v1
title: Underlying data used to generate phylogeny of ABCB homologs from various eukaryotes closely related to characterized ABCB6 and ABCB7 homologs from selected model organisms.

description: The protein sequence, alignment, and tree inference output files used to generate the phylogeny, inferred from amino acid sequences of ABCB homologs from various eukaryotes closely related to characterized ABCB6 and ABCB7 homologs from selected model organisms, in Fig. 1B and Fig. S2 in "The <i>Toxoplasma gondii</i> mitochondrial transporter ABCB7L is essential for the biogenesis of cytosolic and nuclear iron-sulfur cluster proteins and cytosolic translation".
DOI: 10.15468/dl.eg45em
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Trimium raffrayi Guillebeau, 1890",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102501-240626123714530/datasets/export for details.
DOI: 10.15468/dl.ahtsej
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Lithobius ignotus Muralevitch, 1906",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102503-240626123714530/datasets/export for details.
DOI: 10.15468/dl.a4qe9q
title: Occurrence Download

description: A dataset containing 2757 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coreopsis basalis (Otto & A.Dietr.) S.F.Blake"
 ]
}

 The dataset includes 2757 records from 152 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102504-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ypfb8t
title: Occurrence Download

description: A dataset containing 3151 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Neriene peltata (Wider, 1834)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 3151 records from 181 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102505-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.efbbn3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Trimium besucheti Sabella, 1989",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102506-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.13309710
title: Pyhon code and raw data for our proposed algorithm (GANMA)
title: Pseudo code and data for benchmark functions

description: Description of Source Code and Raw Data

Overview: The provided source code and raw data files are designed for evaluating the performance of a proposed algorithm using 15 widely recognized benchmark functions. These functions are critical for assessing the algorithm's efficiency, robustness, and effectiveness in optimization tasks. The evaluation is conducted across three different dimensions: 10, 20, and 30, providing a comprehensive analysis of the algorithm's capability to handle varying complexities.

Components:

1. Source Code:

The source code is implemented to execute the proposed algorithm on the benchmark functions. It includes modules for initializing populations, applying genetic operations (selection, crossover, mutation), and measuring performance metrics such as fitness value, convergence rate, and computational time.

The code is adaptable for different dimensional settings (10, 20, 30 dimensions) and can be easily modified to adjust parameters such as population size, iteration count, and genetic operators' specifics.

The algorithm is tested against a suite of 15 benchmark functions, each representing a unique challenge in the optimization landscape, including unimodal, multimodal, separable, and non-separable functions.

2. Raw Data:

The raw data consists of the results generated by running the proposed algorithm on each benchmark function across the three dimensional settings (10, 20, and 30 dimensions).

Data includes multiple runs to ensure statistical significance, capturing metrics like the best and average fitness values, standard deviation, and convergence behavior over the iterations.

This data is crucial for performing comparative analysis, highlighting the strengths and weaknesses of the proposed algorithm relative to existing methods.

Benchmark Functions:

· The 15 benchmark functions include a mix of well-known test cases such as Sphere, Rosenbrock, Rastrigin, Ackley, and others. Each function is crafted to test different aspects of optimization algorithms, from dealing with high-dimensional search spaces to escaping local optima.

· The functions are provided in the standard mathematical form, and the source code includes implementation details for each.

Purpose:

· The primary goal of this package is to validate the effectiveness of the proposed algorithm against standard benchmarks in the field. The source code enables reproducibility of results, while the raw data serves as a baseline for further research and comparison with other optimization techniques.

Usage:

· Researchers can use the provided source code to replicate the experiments or adapt the algorithm for other benchmark functions or dimensional settings.

· The raw data can be analyzed using statistical tools to derive insights into the algorithm's performance across different scenarios.
DOI: 10.5281/zenodo.13375454
title: Pyhon code and raw data for our proposed algorithm(GANMA)
title: Pseudo code and data for benchmark functions

description: Description of Source Code and Raw Data

Overview: The provided source code and raw data files are designed for evaluating the performance of a proposed algorithm using 15 widely recognized benchmark functions. These functions are critical for assessing the algorithm's efficiency, robustness, and effectiveness in optimization tasks. The evaluation is conducted across three different dimensions: 10, 20, and 30, providing a comprehensive analysis of the algorithm's capability to handle varying complexities.

Components:

1. Source Code:

The source code is implemented to execute the proposed algorithm on the benchmark functions. It includes modules for initializing populations, applying genetic operations (selection, crossover, mutation), and measuring performance metrics such as fitness value, convergence rate, and computational time.

The code is adaptable for different dimensional settings (10, 20, 30 dimensions) and can be easily modified to adjust parameters such as population size, iteration count, and genetic operators' specifics.

The algorithm is tested against a suite of 15 benchmark functions, each representing a unique challenge in the optimization landscape, including unimodal, multimodal, separable, and non-separable functions.

2. Raw Data:

The raw data consists of the results generated by running the proposed algorithm on each benchmark function across the three dimensional settings (10, 20, and 30 dimensions).

Data includes multiple runs to ensure statistical significance, capturing metrics like the best and average fitness values, standard deviation, and convergence behavior over the iterations.

This data is crucial for performing comparative analysis, highlighting the strengths and weaknesses of the proposed algorithm relative to existing methods.

Benchmark Functions:

· The 15 benchmark functions include a mix of well-known test cases such as Sphere, Rosenbrock, Rastrigin, Ackley, and others. Each function is crafted to test different aspects of optimization algorithms, from dealing with high-dimensional search spaces to escaping local optima.

· The functions are provided in the standard mathematical form, and the source code includes implementation details for each.

Purpose:

· The primary goal of this package is to validate the effectiveness of the proposed algorithm against standard benchmarks in the field. The source code enables reproducibility of results, while the raw data serves as a baseline for further research and comparison with other optimization techniques.

Usage:

· Researchers can use the provided source code to replicate the experiments or adapt the algorithm for other benchmark functions or dimensional settings.

· The raw data can be analyzed using statistical tools to derive insights into the algorithm's performance across different scenarios.
DOI: 10.5281/zenodo.13378468
title: Pyhon code and raw data for our proposed algorithm (GANMA)
title: Pseudo code and data for benchmark functions

description: Description of Source Code and Raw Data

Overview: The provided source code and raw data files are designed for evaluating the performance of a proposed algorithm using 15 widely recognized benchmark functions. These functions are critical for assessing the algorithm's efficiency, robustness, and effectiveness in optimization tasks. The evaluation is conducted across three different dimensions: 10, 20, and 30, providing a comprehensive analysis of the algorithm's capability to handle varying complexities.

Components:

1. Source Code:

The source code is implemented to execute the proposed algorithm on the benchmark functions. It includes modules for initializing populations, applying genetic operations (selection, crossover, mutation), and measuring performance metrics such as fitness value, convergence rate, and computational time.

The code is adaptable for different dimensional settings (10, 20, 30 dimensions) and can be easily modified to adjust parameters such as population size, iteration count, and genetic operators' specifics.

The algorithm is tested against a suite of 15 benchmark functions, each representing a unique challenge in the optimization landscape, including unimodal, multimodal, separable, and non-separable functions.

2. Raw Data:

The raw data consists of the results generated by running the proposed algorithm on each benchmark function across the three dimensional settings (10, 20, and 30 dimensions).

Data includes multiple runs to ensure statistical significance, capturing metrics like the best and average fitness values, standard deviation, and convergence behavior over the iterations.

This data is crucial for performing comparative analysis, highlighting the strengths and weaknesses of the proposed algorithm relative to existing methods.

Benchmark Functions:

· The 15 benchmark functions include a mix of well-known test cases such as Sphere, Rosenbrock, Rastrigin, Ackley, and others. Each function is crafted to test different aspects of optimization algorithms, from dealing with high-dimensional search spaces to escaping local optima.

· The functions are provided in the standard mathematical form, and the source code includes implementation details for each.

Purpose:

· The primary goal of this package is to validate the effectiveness of the proposed algorithm against standard benchmarks in the field. The source code enables reproducibility of results, while the raw data serves as a baseline for further research and comparison with other optimization techniques.

Usage:

· Researchers can use the provided source code to replicate the experiments or adapt the algorithm for other benchmark functions or dimensional settings.

· The raw data can be analyzed using statistical tools to derive insights into the algorithm's performance across different scenarios.
DOI: 10.15468/dl.4ywhm7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Lithobius strandzanicus Ribarov, 1987",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102507-240626123714530/datasets/export for details.
DOI: 10.15468/dl.9uq5xa
title: Occurrence Download

description: A dataset containing 265 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Entelecara flavipes (Blackwall, 1834)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 265 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102508-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.