cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/vsdmkpz78y.1
title: Dataset from a Quasi-Experimental Study on Enhancing Academic Integrity and Reducing Plagiarism through a Generative AI-Based Paraphrasing Tool in Multilingual Higher Education

description: This dataset is derived from a quasi-experimental study titled “Enhancing Academic Integrity and Reducing Plagiarism through a Generative AI-Based Paraphrasing Tool: A Quasi-Experimental Study in Multilingual Writing Education.” The study investigated the impact of Languafrasa, a generative AI-based paraphrasing tool specifically designed to support ethical academic writing, on the performance, ethical awareness, and perception of undergraduate students engaged in multilingual education.

The study involved 300 undergraduate participants divided equally into experimental and control groups. Over a six-week period, the experimental group utilized the AI tool to complete weekly paraphrasing tasks integrated into a learning management system, while the control group performed the same tasks without access to AI. The dataset includes demographic profiles (age, gender, and field of study), pretest and posttest similarity scores (including gain scores), rubric-based evaluations of paraphrasing quality across five dimensions—semantic fidelity, syntactic transformation, lexical diversity, citation ethics, and clarity—along with measures of engagement and ethical awareness, and student perception survey responses in both Likert and open-ended formats.

All statistical procedures were conducted using JASP, including descriptive statistics, assumption testing, paired and independent samples t-tests, one-way ANOVA, MANOVA, and Wilcoxon signed-rank tests. Results are presented in six summary PDF files corresponding to the study’s three research questions. Each file contains structured outputs, effect size calculations, and narrative interpretations.

The dataset offers a comprehensive, transparent, and replicable resource for researchers, educators, and instructional designers interested in academic integrity, AI-assisted writing instruction, and multilingual learning environments in higher education.
DOI: 10.17632/9n4wzwc4mc.1
title: The right to benefit: Using videos to encourage citizen involvement in resource revenue management (Replication data and instructions)

description: The analysis for this article was performed in STATA/MP 18.0. 
The replication instructions include all code needed to reproduce the regression results presented in the main text of the article and the appendices.
DOI: 10.17632/c8dbc2kp54.1
title: Data and syntax for 'Ordered or Scrambled: How the forward sequencing of Multiple Choice questions affects test item scores'

description: Data and syntax for paper Ordered or Scrambled
DOI: 10.5281/zenodo.15395540
title: xerosicyos_pubescens_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.15395541
title: xerosicyos_pubescens_zanfona_1.fasta.gz
DOI: 10.17632/nxz2ypr8pm.1
title: Data for Optimum Estimation of Sea-Air CO₂ Fluxes considering Diurnal Variations: A Case Study in the Southern Yellow Sea

description: This dataset consists of CO₂ source–sink observations conducted in the Xiaomai Island region of the southern Yellow Sea from March to September 2012, and was generated in support of Paper "Optimum Estimation of Sea-Air CO₂ Fluxes considering Diurnal Variations: A Case Study in the Southern Yellow Sea".
DOI: 10.17632/2dyxncgw7j.1
title: Good and Bad classification of Citrus sinensis

description: Here's a data description within 3000 characters for your project titled "Good and Bad Classification of Oranges":-


Project Title: Good and Bad Classification of Oranges

Dataset Description:-

This dataset is designed for a binary classification task aimed at distinguishing between good and bad quality oranges based on visual or measurable characteristics. It contains a total of 2000 samples, split evenly into:

1000 good orange samples

1000 bad orange samples

Each sample represents an individual orange, captured or measured through consistent methods to ensure quality and comparability.

Class Definitions:-

Good Orange:- An orange that meets criteria such as ripeness, vibrant color, uniform shape, lack of surface damage or mold, proper size, and firmness. These are suitable for sale and consumption.

Bad Orange:- An orange exhibiting qualities such as over-ripeness, under-ripeness, discoloration, mold growth, deformation, bruising, or soft/rotten spots, making them unsuitable for consumer use.


Features (may include, depending on data type):-

Visual Features:- Color histograms, texture, shape parameters, presence of defects or spots.

Physical Features:- Weight, diameter, firmness level.

Image Data (if applicable):- High-resolution images under uniform lighting and background.

Label:-Binary label indicating class: 1 for good, 0 for bad.

Data Collection Method:- Samples were collected from a variety of sources including local markets, farms, and storage facilities to include a diverse representation of both good and bad oranges. If image-based, photos were taken using a consistent camera setup. If physical measurements are involved, instruments like calipers, scales, and firmness testers were used.

Purpose of the Dataset:- The dataset is intended for machine learning model development and evaluation in tasks such as:

Quality control automation

Agricultural product grading

Computer vision-based fruit sorting systems


Applications:-

Deployment in smart sorting machines in fruit processing units

Mobile or embedded quality inspection tools

Consumer applications for home use to detect fruit quality

Dataset Format:

If structured data:-CSV or Excel format with each row as a sample and columns as features plus label

If image data:- Folder structure with labeled directories (e.g., /good/ and /bad/) or metadata file containing image names and labels

Ethical Considerations:- The dataset only contains non-personal, agricultural data. Usage should be aligned with fair and transparent machine learning practices, especially in agricultural automation that may impact farmers' livelihood.

Let me know if you need this description tailored for a report, website, or presentation format.
DOI: 10.17632/2dyxncgw7j.2
title: Good and Bad classification of Citrus sinensis

description: Here's a data description within 3000 characters for your project titled "Good and Bad Classification of Oranges":-


Project Title: Good and Bad Classification of Oranges

Dataset Description:-

This dataset is designed for a binary classification task aimed at distinguishing between good and bad quality oranges based on visual or measurable characteristics. It contains a total of 2000 samples, split evenly into:

1000 good orange samples

1000 bad orange samples

Each sample represents an individual orange, captured or measured through consistent methods to ensure quality and comparability.

Class Definitions:-

Good Orange:- An orange that meets criteria such as ripeness, vibrant color, uniform shape, lack of surface damage or mold, proper size, and firmness. These are suitable for sale and consumption.

Bad Orange:- An orange exhibiting qualities such as over-ripeness, under-ripeness, discoloration, mold growth, deformation, bruising, or soft/rotten spots, making them unsuitable for consumer use.


Features (may include, depending on data type):-

Visual Features:- Color histograms, texture, shape parameters, presence of defects or spots.

Physical Features:- Weight, diameter, firmness level.

Image Data (if applicable):- High-resolution images under uniform lighting and background.

Label:-Binary label indicating class: 1 for good, 0 for bad.

Data Collection Method:- Samples were collected from a variety of sources including local markets, farms, and storage facilities to include a diverse representation of both good and bad oranges. If image-based, photos were taken using a consistent camera setup. If physical measurements are involved, instruments like calipers, scales, and firmness testers were used.

Purpose of the Dataset:- The dataset is intended for machine learning model development and evaluation in tasks such as:

Quality control automation

Agricultural product grading

Computer vision-based fruit sorting systems


Applications:-

Deployment in smart sorting machines in fruit processing units

Mobile or embedded quality inspection tools

Consumer applications for home use to detect fruit quality

Dataset Format:

If structured data:-CSV or Excel format with each row as a sample and columns as features plus label

If image data:- Folder structure with labeled directories (e.g., /good/ and /bad/) or metadata file containing image names and labels

Ethical Considerations:- The dataset only contains non-personal, agricultural data. Usage should be aligned with fair and transparent machine learning practices, especially in agricultural automation that may impact farmers' livelihood.

Let me know if you need this description tailored for a report, website, or presentation format.
DOI: 10.15468/dl.dj99x9
title: Occurrence Download

description: A dataset containing 3962339 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Accipiter badius (Gmelin, 1788), Accipiter gentilis (Linnaeus, 1758), Accipiter gularis (Temminck & Schlegel, 1844), Accipiter nisus (Linnaeus, 1758), Accipiter soloensis (Horsfield, 1821), Accipiter trivirgatus (Temminck, 1824), Accipiter virgatus (Temminck, 1822), Aegypius monachus (Linnaeus, 1766), Aquila chrysaetos (Linnaeus, 1758), Aquila fasciata Vieillot, 1822, Aquila heliaca Savigny, 1809, Aquila nipalensis Hodgson, 1833, Aviceda jerdoni (Blyth, 1842), Aviceda leuphotes (Dumont, 1820), Butastur indicus (Gmelin, 1788), Butastur liventer (Temminck, 1827), Butastur teesa (Franklin, 1831), Buteo buteo (Linnaeus, 1758), Buteo hemilasius Temminck & Schlegel, 1844, Buteo japonicus Temminck & Schlegel, 1844, Buteo lagopus (Pontoppidan, 1763), Buteo refectus Portenko, 1935, Buteo rufinus (Cretzschmar, 1829), Circaetus gallicus (Gmelin, 1788), Circus aeruginosus (Linnaeus, 1758), Circus cyaneus (Linnaeus, 1766), Circus macrourus (S.G.Gmelin, 1770), Circus melanoleucos (Pennant, 1769), Circus pygargus (Linnaeus, 1758), Circus spilonotus Kaup, 1847, Aquila clanga Pallas, 1811, Elanus caeruleus (Desfontaines, 1789), Gypaetus barbatus (Linnaeus, 1758), Gyps bengalensis (Gmelin, 1788), Gyps fulvus (Hablizl, 1783), Gyps himalayensis Hume, 1869, Gyps tenuirostris G.R.Gray, 1844, Haliaeetus albicilla (Linnaeus, 1758), Haliaeetus humilis, Haliaeetus leucogaster (Gmelin, 1788), Haliaeetus leucoryphus (Pallas, 1771), Haliaeetus pelagicus (Pallas, 1811), Haliastur indus (Boddaert, 1783), Hieraaetus pennatus (Gmelin, 1788), Ictinaetus malayensis (Temminck, 1822), Lophotriorchis kienerii (Sparre, 1835), Milvus migrans (Boddaert, 1783), Neophron percnopterus (Linnaeus, 1758), Nisaetus cirrhatus (Gmelin, 1788), Nisaetus nipalensis Hodgson, 1836, Pernis apivorus (Linnaeus, 1758), Pernis ptilorhynchus (Temminck, 1821), Sarcogyps calvus (Scopoli, 1786), Spilornis cheela (Latham, 1790), Pandion haliaetus (Linnaeus, 1758), Aix galericulata (Linnaeus, 1758), Anas acuta Linnaeus, 1758, Anas crecca Linnaeus, 1758, Anas luzonica Fraser, 1839, Anas platyrhynchos Linnaeus, 1758)",
 "Geometry POLYGON((73.4990129 3.8378886,135.0873769 3.8378886,135.0873769 53.5616571,73.4990129 53.5616571,73.4990129 3.8378886))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation)"
 ]
}

 The dataset includes 3962339 records from 137 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028127-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15395346
title: Filtered SNP Variants in the ABCB1, ANKK1, and APOE Genes in 20 Domestic Goats  (Capra hircus)

description: This dataset contains variant call format (VCF) files with single nucleotide polymorphisms (SNPs) filtered by quality and depth, extracted from whole-genome sequencing data of domestic goats (Capra hircus). The files include genomic variants located in or near three annotated genes: ABCB1, ANKK1, and APOE. The data are intended as a gene-specific genomic resource and can be used for reference, variant cataloging, or further functional annotation. No phenotypic associations are included or implied in this dataset. Variants were filtered using standard bioinformatic tools based on coverage and call quality thresholds.
DOI: 10.5281/zenodo.15395347
title: Filtered SNP Variants in the ABCB1, ANKK1, and APOE Genes in 20 Domestic Goats  (Capra hircus)

description: This dataset contains variant call format (VCF) files with single nucleotide polymorphisms (SNPs) filtered by quality and depth, extracted from whole-genome sequencing data of domestic goats (Capra hircus). The files include genomic variants located in or near three annotated genes: ABCB1, ANKK1, and APOE. The data are intended as a gene-specific genomic resource and can be used for reference, variant cataloging, or further functional annotation. No phenotypic associations are included or implied in this dataset. Variants were filtered using standard bioinformatic tools based on coverage and call quality thresholds.
DOI: 10.15468/dl.kjjj3k
title: Occurrence Download

description: A dataset containing 1429324 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 PRINTF('%04d-%02d', "year", "month") yearmonth,
 GBIF_EQDGCODE(2, decimallatitude, decimallongitude, 0.0) eqdcellcode,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, GBIF_EQDGCODE(2, decimallatitude, decimallongitude, 0.0), PRINTF('%04d-%02d', "year", "month"))) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, GBIF_EQDGCODE(2, decimallatitude, decimallongitude, 0.0), PRINTF('%04d-%02d', "year", "month"))) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, GBIF_EQDGCODE(2, decimallatitude, decimallongitude, 0.0), PRINTF('%04d-%02d', "year", "month"))) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, GBIF_EQDGCODE(2, decimallatitude, decimallongitude, 0.0), PRINTF('%04d-%02d', "year", "month"))) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, GBIF_EQDGCODE(2, decimallatitude, decimallongitude, 0.0), PRINTF('%04d-%02d', "year", "month"))) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, GBIF_EQDGCODE(2, decimallatitude, decimallongitude, 0.0), PRINTF('%04d-%02d', "year", "month"))) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'CN'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 2000 AND occurrence."year" &lt;= 2025)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence."month" IS NOT NULL AND occurrence.hascoordinate = TRUE)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 PRINTF('%04d-%02d', "year", "month"),
 GBIF_EQDGCODE(2, decimallatitude, decimallongitude, 0.0)

 The dataset's 1429324 records were derived from 1563 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028133-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ugcsq3
title: Occurrence Download

description: A dataset containing 153239 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Museum of Texas Tech University Mammalogy Collection"
 ]
}

 The dataset includes 153239 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001893-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ku48uv
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-103.63713 44.37423,-103.60651 44.37423,-103.60651 44.39481,-103.63713 44.39481,-103.63713 44.37423))",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 83 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001891-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zjfawg
title: Occurrence Download

description: A dataset containing 130568 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudocheirus peregrinus (Boddaert, 1785)"
 ]
}

 The dataset includes 130568 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001892-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/dfp3-4pt
title: World Catalogue of Opiliones
DOI: 10.15468/dl.dtv8az
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cynodon dactylon",
 "Country is Kiribati",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027804-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/dfpx-4rc
title: World List of Crinoidea
DOI: 10.15468/dl.2r8h7f
title: Occurrence Download

description: A dataset containing 12622 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is one of (Amphibolis antarctica, Amphibolis griffithii, Cymodocea angustata, Cymodocea nodosa, Cymodocea rotundata, Halodule bermudensis, Halodule ciliata, Halodule emarginata, Halodule pinifolia, Halodule uninervis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than or equal to 27000",
 "CoordinateUncertaintyInMeters is null"
 ]
 },
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2010"
 ]
}

 The dataset includes 12622 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027803-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/dfqt-4rf
title:  Nemys, World Database of Nematodes
DOI: 10.48580/dfqt-4rx
title: 3i World Auchenorrhyncha Database
DOI: 10.48580/dfqz
title: Catalogue of Life Checklist
DOI: 10.48580/dfq8-37w
title: Fulgoromorpha Lists On the Web
DOI: 10.48580/dfqz-38g
title: World Scarabaeidae Database
DOI: 10.15468/dl.84mpej
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is ",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027802-250811113504898/datasets/export for details.
DOI: 10.48580/dfqz-392
title: World Porifera Database
DOI: 10.48580/dfqj-39k
title: World Ophiuroidea Database
DOI: 10.48580/dfqz-3bb
title: World List of Bryozoa
DOI: 10.48580/dfp3-3bg
title: World Nemertea Database
DOI: 10.48580/dfqz-3bm
title: World Polychaeta Database
DOI: 10.48580/dfqz-3br
title: World Marine, Freshwater and Terrestrial Isopod Crustaceans database
DOI: 10.48580/dfqz-3bs
title: World Asteroidea Database
DOI: 10.48580/dfqz-3bx
title: World List of Marine Oligochaeta
DOI: 10.48580/dfpx-3c5
title: World List of Leptostraca
DOI: 10.48580/dfqt-3c7
title: World List of Holothuroidea
DOI: 10.48580/dfqz-3c8
title: World List of marine Brachyura
DOI: 10.15468/dl.d6x346
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cynodon dactylon",
 "Country is Kyrgyzstan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 16 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027805-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/dfqz-3cd
title: World Hydrozoa Database
DOI: 10.15468/dl.5y5sw9
title: Occurrence Download

description: A dataset containing 66618 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amphibolis antarctica, Amphibolis griffithii, Cymodocea angustata, Cymodocea nodosa, Cymodocea rotundata, Halodule bermudensis, Halodule ciliata, Halodule emarginata, Halodule pinifolia, Halodule uninervis, Halodule wrightii, Oceana serrulata, Syringodium filiforme, Syringodium isoetifolium, Thalassodendron ciliatum, Thalassodendron leptocaule, Thalassodendron pachyrhizum, Enhalus acoroides, Halophila australis, Halophila baillonis, Halophila beccarii, Halophila capricorni, Halophila decipiens, Halophila engelmannii, Halophila hawaiiana, Halophila major, Halophila mikii, Halophila minor, Halophila nipponica, Halophila okinawensis, Halophila ovalis, Halophila spinulosa, Halophila stipulacea, Halophila sulawesii, Halophila tricostata, Thalassia hemprichii, Thalassia testudinum, Posidonia angustifolia, Posidonia australis, Posidonia coriacea, Posidonia denhartogii, Posidonia kirkmanii, Posidonia oceanica, Posidonia ostenfeldii, Posidonia sinuosa, Althenia marina, Zostera nigricaulis, Zostera polychlamys, Zostera tasmanica, Phyllospadix iwatensis, Phyllospadix japonicus, Phyllospadix scouleri, Phyllospadix serrulatus, Phyllospadix torreyi, Zostera capensis, Zostera capricorni, Zostera japonica, Zostera mucronata, Zostera muelleri, Zostera noltii, Zostera novazelandica, Zostera asiatica, Zostera caespitosa, Zostera caulescens, Zostera marina, Ruppia cirrhosa, Ruppia maritima, Ruppia megacarpa, Ruppia tuberosa)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than or equal to 27000",
 "CoordinateUncertaintyInMeters is null"
 ]
 },
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2010"
 ]
}

 The dataset includes 66618 records from 194 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027776-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/dfqz-3cv
title: World List of Monogenea
DOI: 10.48580/dfqz-3cw
title: World List of Cestoda
DOI: 10.48580/dfqz-3cx
title: World List of Trematoda