cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.s9de9t
title: Occurrence Download

description: A dataset containing 3962 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 5,-45 5,-45 90,-180 90,-180 5))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carterocephalus palaemon (Pallas, 1771)",
 "Year 1000-2020"
 ]
}

 The dataset includes 3962 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0322740-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s9kxnc
title: Occurrence Download

description: A dataset containing 12994 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 5,-45 5,-45 90,-180 90,-180 5))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Euptoieta claudia (Cramer, 1775)",
 "Year 1000-2020"
 ]
}

 The dataset includes 12994 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0325957-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sjppnp
title: Occurrence Download

description: A dataset containing 1784 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cupido amyntula (Boisduval, 1852)",
 "Year 1000-2020"
 ]
}

 The dataset includes 1784 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0323558-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.skh79w
title: Occurrence Download

description: A dataset containing 1258 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lycaena hyllus (Cramer, 1775)"
 ]
}

 The dataset includes 1258 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030310-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.snybgv
title: Occurrence Download

description: A dataset containing 546 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Neominois ridingsii (Edwards, 1865)"
 ]
}

 The dataset includes 546 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030383-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sr3urb
title: Occurrence Download

description: A dataset containing 17573 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Limenitis archippus Cramer, 1775"
 ]
}

 The dataset includes 17573 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030224-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t9qnr5
title: Occurrence Download

description: A dataset containing 4870 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 -90,0 -90,0 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aglais milberti (Godart, 1819)",
 "Year 1000-2020"
 ]
}

 The dataset includes 4870 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0157807-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tkwbgr
title: Occurrence Download

description: A dataset containing 6240 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 10,-45 10,-45 90,-180 90,-180 10))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Eurema mexicana (Boisduval, 1836)",
 "Year 1000-2020"
 ]
}

 The dataset includes 6240 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0326536-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tx3rcm
title: Occurrence Download

description: A dataset containing 1875 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Callophrys niphon (Hübner, 1819)",
 "Year 1000-2020"
 ]
}

 The dataset includes 1875 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0168483-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ej5xed
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pleiospilos nelii Schwantes)"
 ]
}

 The dataset includes 15 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078464-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u62984
title: Occurrence Download

description: A dataset containing 202 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 -90,-45 -90,-45 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Agriades glandon (von Prunner, 1798)",
 "Year 1000-2020"
 ]
}

 The dataset includes 202 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0157827-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ujmfgy
title: Occurrence Download

description: A dataset containing 15305 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 5,-45 5,-45 90,-180 90,-180 5))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cupido comyntas (Godart, 1824)",
 "Year 1000-2020"
 ]
}

 The dataset includes 15305 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0323603-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/cyz5k2jnzc.1
title: The Impact of Lifestyle Factors on Cardiometabolic Comorbidities and Mortality of Psoriatic Diseases: A Multi-State Model Analysis

description: Figure S1. Study design 
Table S1. The standard UKB endpoints used in the present study 
Table S2. Effects of genetic and lifestyle factors for the trajectory of psoriatic and cardiometabolic multimorbidity 
Table S3. Subgroup analyses by gender 
Table S4. The lifestyle score and the trajectory of cardiometabolic diseases 
Table S5. Life expectancy in different lifestyle groups in participants with PD
DOI: 10.17632/cyz5k2jnzc.2
title: The Impact of Lifestyle Factors on Cardiometabolic Comorbidities and Mortality of Psoriatic Diseases: A Multi-State Model Analysis

description: Figure S1. Study design
Table S1. The standard UKB endpoints used in the present study
Table S2. Effects of genetic and lifestyle factors for the trajectory of psoriatic and cardiometabolic multimorbidity
Table S3. Effects of genetic and lifestyle factors for the trajectory of cutaneous-only psoriasis (PsO) and cardiometabolic multimorbidity
Table S4. Subgroup analyses by gender
Table S5. The lifestyle score and the trajectory of cardiometabolic diseases
Table S6. Life expectancy in different lifestyle groups in participants with PD
DOI: 10.17632/cyz5k2jnzc.3
title: The Impact of Lifestyle Factors on Cardiometabolic Comorbidities and Mortality of Psoriatic Diseases: A Multi-State Model Analysis

description: Supplementary Fig 1. Study design
Supplementary Table I. The standard UKB endpoints used in the present study
Supplementary Table II. Effects of genetic and lifestyle factors for the trajectory of psoriatic and cardiometabolic multimorbidity
Supplementary Table III. Effects of genetic and lifestyle factors for the trajectory of cutaneous-only psoriasis (PsO) and cardiometabolic multimorbidity
Supplementary Table IV. Subgroup analyses by gender
Supplementary Table V. The lifestyle score and the trajectory of cardiometabolic diseases
Supplementary Table VI. Life expectancy in different lifestyle groups in participants with psoriatic diseases
DOI: 10.17632/9y4wvxszkd
title: Good and bad classification of Fenugreek leaves

description: Title: Good and Bad Classification of Fenugreek Leaves: Techniques, Parameters, and Perspectives

1. Introduction

Fenugreek (Trigonella foenum-graecum) is a widely cultivated herb in the Fabaceae family, used both as a spice and a medicinal plant. Its fresh leaves are commonly consumed as a leafy vegetable in many parts of Asia and the Middle East. Given its high nutritional and therapeutic value, the quality of fenugreek leaves is of paramount importance to ensure consumer satisfaction and safety.

One crucial step in the post-harvest handling of fenugreek leaves is the classification or sorting of good and bad leaves. Manual sorting is time-consuming and inconsistent, leading to the growing interest in automated classification using image processing and machine learning. This essay provides an in-depth analysis of the classification of fenugreek leaves into "good" and "bad" categories, covering the criteria, techniques, technologies, challenges, and future outlook.


---

2. Importance of Classification

Classification of fenugreek leaves is essential for several reasons:

Quality Control: Ensures only fresh, uncontaminated leaves reach consumers.

Market Value: Improves pricing and demand for higher-quality produce.

Shelf Life: Discarding spoiled or infected leaves extends the storage life of the batch.

Processing Efficiency: Reduces contamination during processing for frozen or dried leaf products.



---

3. Classification Criteria

To effectively classify fenugreek leaves, various quality parameters are considered:

a. Visual Appearance

Color: Good leaves exhibit a vibrant green color, while bad leaves may show yellowing, browning, or black spots.

Texture: Healthy leaves are turgid and crisp; bad ones are wilted, dry, or slimy.

Size and Shape: Good leaves maintain uniform shape; damaged, malformed, or shriveled leaves are considered bad.


b. Physiological Condition

Decay or Rot: Fungal or bacterial infection can cause soft rot, making the leaf unfit.

Pest Damage: Holes, discoloration, or patterns indicating pest attacks disqualify leaves from the good category.


c. Foreign Matter

Leaves contaminated with soil, dust, or foreign plant matter are categorized as bad unless cleaned and inspected.


d. Odor

A fresh herbal aroma indicates good leaves; any sour, foul, or fermented smell implies spoilage.



---

4. Manual Classification Techniques

Traditionally, manual sorting is conducted at collection centers or processing units. Workers rely on:

Visual inspection under natural or artificial light.

Touch and feel to identify wilting or softness.

Occasionally smell for spoilage detection.


Limitations:

Inconsistent results due to fatigue or subjective judgment.

Time- and labor-intensive.

Higher chances of contamination.
5. Automated Classification Using Image Processing

Advancements in machine vision and artificial intelligence have enabled the development of automated classification systems.

a. Image Acquisition
DOI: 10.17632/9y4wvxszkd.1
title: Good and bad classification of Fenugreek leaves

description: Title: Good and Bad Classification of Fenugreek Leaves: Techniques, Parameters, and Perspectives

1. Introduction

Fenugreek (Trigonella foenum-graecum) is a widely cultivated herb in the Fabaceae family, used both as a spice and a medicinal plant. Its fresh leaves are commonly consumed as a leafy vegetable in many parts of Asia and the Middle East. Given its high nutritional and therapeutic value, the quality of fenugreek leaves is of paramount importance to ensure consumer satisfaction and safety.

One crucial step in the post-harvest handling of fenugreek leaves is the classification or sorting of good and bad leaves. Manual sorting is time-consuming and inconsistent, leading to the growing interest in automated classification using image processing and machine learning. This essay provides an in-depth analysis of the classification of fenugreek leaves into "good" and "bad" categories, covering the criteria, techniques, technologies, challenges, and future outlook.


---

2. Importance of Classification

Classification of fenugreek leaves is essential for several reasons:

Quality Control: Ensures only fresh, uncontaminated leaves reach consumers.

Market Value: Improves pricing and demand for higher-quality produce.

Shelf Life: Discarding spoiled or infected leaves extends the storage life of the batch.

Processing Efficiency: Reduces contamination during processing for frozen or dried leaf products.



---

3. Classification Criteria

To effectively classify fenugreek leaves, various quality parameters are considered:

a. Visual Appearance

Color: Good leaves exhibit a vibrant green color, while bad leaves may show yellowing, browning, or black spots.

Texture: Healthy leaves are turgid and crisp; bad ones are wilted, dry, or slimy.

Size and Shape: Good leaves maintain uniform shape; damaged, malformed, or shriveled leaves are considered bad.


b. Physiological Condition

Decay or Rot: Fungal or bacterial infection can cause soft rot, making the leaf unfit.

Pest Damage: Holes, discoloration, or patterns indicating pest attacks disqualify leaves from the good category.


c. Foreign Matter

Leaves contaminated with soil, dust, or foreign plant matter are categorized as bad unless cleaned and inspected.


d. Odor

A fresh herbal aroma indicates good leaves; any sour, foul, or fermented smell implies spoilage.



---

4. Manual Classification Techniques

Traditionally, manual sorting is conducted at collection centers or processing units. Workers rely on:

Visual inspection under natural or artificial light.

Touch and feel to identify wilting or softness.

Occasionally smell for spoilage detection.


Limitations:

Inconsistent results due to fatigue or subjective judgment.

Time- and labor-intensive.

Higher chances of contamination.
5. Automated Classification Using Image Processing

Advancements in machine vision and artificial intelligence have enabled the development of automated classification systems.

a. Image Acquisition
DOI: 10.17632/mjcjxdsjbg.1
title: Nano-silica-modified alkali activated multi-solid waste-based concrete: Hydration characteristics and seawater corrosion mechanism

description: This study addresses the issue of seawater corrosion faced by solid waste-based concrete in marine environments by introducing nano-silica (NS) to optimize the alkaline-activated gelling material system. It investigates the impact of activator types and gelling material proportions on the hydration characteristics of neat paste and the seawater resistance of concrete. The results indicate that when the ratio of fly ash to steel slag is 1:1 and dual activation with NaOH and Na₂SiO₃ is employed, the 28-day compressive strength reaches 28.41 MPa, which is 2.41 MPa higher than that of the group with only NaOH. After adding 2% NS, the 28-day strengths of the B1N2 and B2N2 groups increase to 29.67 MPa and 29.41 MPa, respectively. In the seawater corrosion test, the strength of the B1 group without NS decreased by 28.6% after soaking in high-concentration seawater for 90 days, while the strength of B2N2 only decreased by 2.1%, with a corrosion resistance coefficient of 97%. Moreover, the chloride ion concentration at the surface layer (5 mm) was only 10% at 90 days. NS significantly inhibits the intrusion of Cl⁻ and SO₄²⁻ by filling pores and promoting the densification of C-A-S-H gel. Additionally, ICP-MS results show that after soaking B2N2 in seawater for 90 days, the concentrations of Al and Fe in the seawater were 2.6 µg/L and 1.2µg/L, respectively, indicating that this multi-solid waste-based concrete has an extremely low risk of metal leaching in marine environments. This study holds significant value for the development of high-performance, environmentally friendly marine engineering concrete.
DOI: 10.15468/dl.csyg2n
title: Occurrence Download

description: A dataset containing 445 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cereus jamacaru DC.",
 "Country is South Africa",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 445 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028136-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/354rs5m8tp.1
title: Astral-DIA depicts the dynamic landscape of milk fat globule membrane proteins in yak colostrum, transition milk and mature milk

description: Astral-DIA provides supplementary data on the dynamic landscape of milk fat globule membrane proteins in yak colostrum, transitional milk and mature milk.
DOI: 10.17632/9pvcnngdv3.1
title: Effect of crack on soil damage response induced by wetting

description: Effect of crack on soil damage response induced by wetting
DOI: 10.17632/dwhnr26sfd.1
title: Determination of shelf life of Solanum tuberosum

description: Good Classification of Solanum tuberosum
1. Correct Species Identification
Modern science correctly identifies Solanum tuberosum as a distinct species within the genus Solanum, family Solanaceae.
It is distinguished by key characteristics:
Underground tuber production 
Distinct flower structure
Particular arrangement of leaves 
Tuber skin and flesh variations
Thus, potatoes are well-separated from their close relatives like tomatoes (Solanum lycopersicum) and eggplants (Solanum melongena).
2. Recognition of Subspecies
Today, cultivated potatoes are organized into two main subspecies:
This division reflects evolutionary adaptation and supports better crop management. Recognizing subspecies helps match varieties to appropriate climates and environments.
3. Use of Cytogenetic Evidence
The potato shows various ploidy levels.
4. Genetic and Molecular Advances
Modern taxonomy incorporates genetic methods:
Chloroplast DNA analysis.
Microsatellite markers (short repeating DNA sequences).
SNP genotyping (single nucleotide polymorphisms).
Whole genome sequencing.
These tools confirm that Solanum tuberosum is closely related to several wild species but genetically distinct from them.
Molecular markers have helped create a clear evolutionary tree ("phylogeny") for potatoes and their relatives.
This molecular classification is more robust and objective compared to relying only on appearance.
5. Practical Impact
Good classification directly impacts real-world issues:
Breeding: Creating potatoes resistant to diseases like late blight (caused by Phytophthora infestans).
Conservation: Protecting wild relatives critical for future
breeding.
Without solid classification, efforts in these areas would be fragmented and inefficient.
Bad Classification of Solanum tuberosum
1.Cultivation practices.
This over-splitting caused Taxonomic confusion.
Difficulty in identifying germplasm.
Hindrance to breeding programs.
2. Confusion Between Wild and Cultivated Forms
Wild relatives such as Solanum acaule, Solanum demissum, and Solanum stoloniferum share many features with cultivated potatoes.
In the absence of genetic evidence, early scientists sometimes:
Lumped different species together wrongly.
Mistook hybrid forms as new species.
This blurring made understanding the domestication history of the potato extremely difficult
3. Lack of Genetic Basis
Until the 20th century, taxonomy relied heavily on visible traits (phenotypes), without understanding underlying genetics.
Problems included:
Phenotypic plasticity: The same genotype producing different appearances under different environments.
Convergent evolution: Different species developing similar traits independently.
Thus, without genetic evidence, many early classifications were inaccurate or incomplete.
4. Regional Bias and Misinterpretation
European botanists in particular often misinterpreted the diversity of Andean potatoes.
They failed to:
Recognize the sophistication of Indigenous agricultural systems.
DOI: 10.17632/dwhnr26sfd.2
title: Determination of shelf life of Solanum tuberosum

description: Good Classification of Solanum tuberosum
1. Correct Species Identification
Modern science correctly identifies Solanum tuberosum as a distinct species within the genus Solanum, family Solanaceae.
It is distinguished by key characteristics:
Underground tuber production 
Distinct flower structure
Particular arrangement of leaves 
Tuber skin and flesh variations
Thus, potatoes are well-separated from their close relatives like tomatoes (Solanum lycopersicum) and eggplants (Solanum melongena).
2. Recognition of Subspecies
Today, cultivated potatoes are organized into two main subspecies:
This division reflects evolutionary adaptation and supports better crop management. Recognizing subspecies helps match varieties to appropriate climates and environments.
3. Use of Cytogenetic Evidence
The potato shows various ploidy levels.
4. Genetic and Molecular Advances
Modern taxonomy incorporates genetic methods:
Chloroplast DNA analysis.
Microsatellite markers (short repeating DNA sequences).
SNP genotyping (single nucleotide polymorphisms).
Whole genome sequencing.
These tools confirm that Solanum tuberosum is closely related to several wild species but genetically distinct from them.
Molecular markers have helped create a clear evolutionary tree ("phylogeny") for potatoes and their relatives.
This molecular classification is more robust and objective compared to relying only on appearance.
5. Practical Impact
Good classification directly impacts real-world issues:
Breeding: Creating potatoes resistant to diseases like late blight (caused by Phytophthora infestans).
Conservation: Protecting wild relatives critical for future
breeding.
Without solid classification, efforts in these areas would be fragmented and inefficient.
Bad Classification of Solanum tuberosum
1.Cultivation practices.
This over-splitting caused Taxonomic confusion.
Difficulty in identifying germplasm.
Hindrance to breeding programs.
2. Confusion Between Wild and Cultivated Forms
Wild relatives such as Solanum acaule, Solanum demissum, and Solanum stoloniferum share many features with cultivated potatoes.
In the absence of genetic evidence, early scientists sometimes:
Lumped different species together wrongly.
Mistook hybrid forms as new species.
This blurring made understanding the domestication history of the potato extremely difficult
3. Lack of Genetic Basis
Until the 20th century, taxonomy relied heavily on visible traits (phenotypes), without understanding underlying genetics.
Problems included:
Phenotypic plasticity: The same genotype producing different appearances under different environments.
Convergent evolution: Different species developing similar traits independently.
Thus, without genetic evidence, many early classifications were inaccurate or incomplete.
4. Regional Bias and Misinterpretation
European botanists in particular often misinterpreted the diversity of Andean potatoes.
They failed to:
Recognize the sophistication of Indigenous agricultural systems.