cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27999089
title: Data and Scripts for 'Application of Multidimensional Cognitive Ability Tests in Computer-Assisted Diagnosis of Attention-Deficit/Hyperactivity Disorder and Its Subtypes in Children: Based on A Genetic Algorithm Optimized Back-Propagation Neural Network Predictive Model'

description: Accurately identifying and diagnosing whether a child has attention-deficit/hyperactivity disorder, as well as determining the specific subtype, are crucial for selecting and implementing targeted intervention and treatment strategies. However, previous artificial intelligence-based diagnostic models for attention-deficit/hyperactivity disorder have faced challenges in achieving a balance among cost-effectiveness, low operational threshold, and high predictive accuracy. The present study recruited 316 children aged 6 to 14 years, including 98 with the inattentive subtype, 80 with the combined subtype, and 138 typically developing controls. Using multidimensional cognitive ability tests and a back-propagation neural network optimized by a genetic algorithm, this study developed a computer-aided diagnostic model to distinguish children with attention-deficit/hyperactivity disorder and its specific subtypes. The model achieved an overall predictive accuracy of 90.82%, outperforming traditional logistic regression and support vector machine models. The cancellation test was identified as the strongest predictor for differentiating attention-deficit/hyperactivity disorder subtypes. Additionally, the cancellation test, digits forward test, paper folding test, and digits backward test were critical in reflecting the cognitive ability measured by that test was more reflective of the essential differences in cognitive deficits among attention-deficit/hyperactivity disorder subtypes. Specifically, the cancellation test and digits forward test positively predicted the inattentive subtype but negatively predicted the combined subtype. In contrast, the paper folding test and digits backward test negatively predicted the inattentive subtype while positively predicting combined subtype. Moreover, the predictive contribution of cognitive tests, including the cancellation test, paper folding test, digits forward test, visual search test, and go/no-go test, varied greatly across different genders and age groups in predicting attention-deficit/hyperactivity disorder subtypes. In conclusion, the artificial intelligence-based aided diagnostic model demonstrated robust predictive performance in identifying attention-deficit/hyperactivity disorder and its subtypes in children. These findings provide a strong foundation for developing artificial intelligence-driven expert diagnostic systems for attention-deficit/hyperactivity disorder in the future.
DOI: 10.6084/m9.figshare.27999089.v1
title: Data and Scripts for 'Application of Multidimensional Cognitive Ability Tests in Computer-Assisted Diagnosis of Attention-Deficit/Hyperactivity Disorder and Its Subtypes in Children: Based on A Genetic Algorithm Optimized Back-Propagation Neural Network Predictive Model'

description: Accurately identifying and diagnosing whether a child has attention-deficit/hyperactivity disorder, as well as determining the specific subtype, are crucial for selecting and implementing targeted intervention and treatment strategies. However, previous artificial intelligence-based diagnostic models for attention-deficit/hyperactivity disorder have faced challenges in achieving a balance among cost-effectiveness, low operational threshold, and high predictive accuracy. The present study recruited 316 children aged 6 to 14 years, including 98 with the inattentive subtype, 80 with the combined subtype, and 138 typically developing controls. Using multidimensional cognitive ability tests and a back-propagation neural network optimized by a genetic algorithm, this study developed a computer-aided diagnostic model to distinguish children with attention-deficit/hyperactivity disorder and its specific subtypes. The model achieved an overall predictive accuracy of 90.82%, outperforming traditional logistic regression and support vector machine models. The cancellation test was identified as the strongest predictor for differentiating attention-deficit/hyperactivity disorder subtypes. Additionally, the cancellation test, digits forward test, paper folding test, and digits backward test were critical in reflecting the cognitive ability measured by that test was more reflective of the essential differences in cognitive deficits among attention-deficit/hyperactivity disorder subtypes. Specifically, the cancellation test and digits forward test positively predicted the inattentive subtype but negatively predicted the combined subtype. In contrast, the paper folding test and digits backward test negatively predicted the inattentive subtype while positively predicting combined subtype. Moreover, the predictive contribution of cognitive tests, including the cancellation test, paper folding test, digits forward test, visual search test, and go/no-go test, varied greatly across different genders and age groups in predicting attention-deficit/hyperactivity disorder subtypes. In conclusion, the artificial intelligence-based aided diagnostic model demonstrated robust predictive performance in identifying attention-deficit/hyperactivity disorder and its subtypes in children. These findings provide a strong foundation for developing artificial intelligence-driven expert diagnostic systems for attention-deficit/hyperactivity disorder in the future.
DOI: 10.60952/begz-qb12
title: Połączenia międzykomórkowe  w przedimplantacyjnych stadiach rozwoju zarodkowego myszy - zarodek 8-komórkowy (3)

description: Celem badania było ultrastrukturalne badanie kontaktów międzykomórkowych zachodzących w przedimplantacyjnych stadiach rozwoju zarodkowego myszy - zarodek 8-komórkowy. Doświadczenie przeprowadzono na samicach myszy szczepu Swiss w wieku od 6 do 8 tygodni. Zarodki 8-komórkowe przygotowane zostały do badań standardową techniką mikroskopowo-elektronową oraz z  zastosowaniem czerwieni rutenowej. Na zdjęciu zarodek 8-komórkowy. Przestrzeń międzykomórkowa wypełniona czerwienią rutenową. Preparat nie barwiony octanem uranylu i cytrynianem ołowiu. Powiększenie 40 000x. Dokumentacja zdjęciowa powstała przy użyciu mikroskopu elektronowego JEM 100C firmy Jeol.
DOI: 10.15468/dl.ubrq22
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((36.64083 -1.7175, 36.6425 -1.7175, 36.6425 -1.715833, 36.645 -1.715833, 36.645 -1.7075, 36.64083 -1.7075, 36.64083 -1.7175))",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027787-241126133413365/datasets/export for details.
DOI: 10.15468/dl.xyexj8
title: Occurrence Download

description: A dataset containing 3536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhynchophorus ferrugineus (A.G.Olivier, 1791)",
 "Year 1986-2024"
 ]
}

 The dataset includes 3536 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027786-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xj9zm8
title: Occurrence Download

description: A dataset containing 93750 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Primulaceae"
 ]
}

 The dataset includes 93750 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027783-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10633637
title: J-AESOP Rating Data (6_02)

description: Test description.
DOI: 10.5281/zenodo.10633827
title: J-AESOP Rating Data (6_01 & 6_02)

description: This dataset contains the rating results of the speech data from the J-AESOP corpus (Kondo et al., 2015), which have been used in our previous studies (e.g., Yazawa et al., 2023). More details will be available soon.
DOI: 10.6084/m9.figshare.27999116
title: Is There Body District-Specificity in Sequence-Specific Implicit Motor Learning?Comparison between Individuals with Parkinson’s Disease and Controls

description: Alterations in motor learning are associated with Parkinson’s disease (PD). Possible body district-specificity remains unexplored and could have practical implications for testing and rehabilitation.<b>PURPOSE</b>: To investigate possible sequence-specific implicit motor learning (<sub>i</sub>ML) differences in the standard hand movement to alternative, arm and leg movements in people with early stages PD vs age-matched healthy controls (HC).
DOI: 10.15468/dl.pbx69t
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((36.05167 -1.684167, 36.05167 -1.689167, 36.0525 -1.689167, 36.0525 -1.691667, 36.05667 -1.691667, 36.05667 -1.694167, 36.05833 -1.694167, 36.05833 -1.6975, 36.065 -1.6975, 36.065 -1.695833, 36.06667 -1.695833, 36.06667 -1.691667, 36.05833 -1.691667, 36.05833 -1.684167, 36.05167 -1.684167))",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027788-241126133413365/datasets/export for details.
DOI: 10.15468/dl.uzcf9n
title: Occurrence Download

description: A dataset containing 100212854 species occurrences available in GBIF matching the query:
{
 "and" : [
 "MediaType is one of (Image, Video, Audio)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 100212854 records from 857 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027653-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ttbn3u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((36.06667 -1.671667, 36.06833 -1.671667, 36.06833 -1.6675, 36.06667 -1.6675, 36.06667 -1.671667))",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027790-241126133413365/datasets/export for details.
DOI: 10.15468/dl.2cugsa
title: Occurrence Download

description: A dataset containing 4899 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhynchophorus ferrugineus (A.G.Olivier, 1791)"
 ]
}

 The dataset includes 4899 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027791-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14103573
title: OP in Europe: test

description: This repository was used as a test during the review process for article Tassel et al. 2025. This version is not up to date. Please refer to repository https://doi.org/10.5281/zenodo.15424165.
DOI: 10.15468/dl.plporf
title: Occurrence Download

description: A dataset containing 41208 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rupicapra rupicapra (Linnaeus, 1758)"
 ]
}

 The dataset includes 41208 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049844-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gmk2m5
title: Occurrence Download

description: A dataset containing 2646 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)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Bambusicola fytchii Anderson, 1871"
 ]
}

 The dataset includes 2646 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002450-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xjqq4m
title: Occurrence Download

description: A dataset containing 154215 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Human Observation"
 ]
 },
 {
 "or" : [
 "Country is Papua New Guinea",
 "Country is New Zealand",
 "Country is Australia"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anthus novaeseelandiae (Gmelin, 1789)",
 "Year 1950-2020"
 ]
}

 The dataset includes 154215 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0099500-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wbvr72
title: Occurrence Download

description: A dataset containing 350574 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Viet Nam"
 ]
}

 The dataset includes 350574 records from 690 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049845-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b65bkt
title: Occurrence Download

description: A dataset containing 1919724 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "Geometry POLYGON ((138.437 -35.34896, 138.8481 -35.34896, 138.8481 -34.61298, 138.437 -34.61298, 138.437 -35.34896))"
 ]
}

 The dataset includes 1919724 records from 350 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002464-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6fif1t
title: Occurrence Download

description: A dataset containing 67122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Norwegian Species Observation Service",
 "Year 2013-2016",
 "TaxonKey is Ardeidae"
 ]
}

 The dataset includes 67122 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049845-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ttdyxu
title: Occurrence Download

description: A dataset containing 21080 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Specimen"
 ]
 },
 "OccurrenceStatus is Present",
 "TaxonKey is Eptesicus fuscus (Palisot de Beauvois, 1796)"
 ]
}

 The dataset includes 21080 records from 149 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0099513-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vsbw88
title: Occurrence Download

description: A dataset containing 2479 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)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Tyranneutes virescens (Pelzeln, 1868)"
 ]
}

 The dataset includes 2479 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056321-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rzqn6x
title: Occurrence Download

description: A dataset containing 215197 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erinaceus europaeus Linnaeus, 1758"
 ]
}

 The dataset includes 215197 records from 418 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049846-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc111316811
title: Lambert Inscription

description: [Text Description:] Lambert Inscription, Cuneiform on Stone ❧ [Medium:] Clay ❧ [Script Note:] Cuneiform, Non-Alphabetic ❧ [Physical Object Description:] Clay tablet with cuneiform script
DOI: 10.5281/zenodo.11208095
title: SunspotsYoloDataset: annotated solar images captured with smart telescopes (January 2023 - May 2024)

description: SunspotsYoloDataset is a set of 1690+190 high-resolution RGB astronomical images captured with smart telescopes with specific solar filters and annotated with the positions of sunspots that are effectively in the images. Two instruments were used for several months from Luxembourg and France between January 2023 and May 2024: a Stellina smart telescope (https://vaonis.com/stellina) and a Vespera smart telescope (https://vaonis.com/vespera).

SunspotsYoloDataset  can be used to train YOLO detection models on solar images, enabling the prediction of unexpected events such as Borealis Aurora with astronomical equipment accessible to the public.

SunspotsYoloDataset is formatted with the YOLO standard, i.e., with separated files for images and annotations, usable by state-of-the-art training tools and graphical software like MakeSense (https://www.makesense.ai). More precisely, there is a ZIP file containing RGB images in JPEG format (minimal compression), and text files containing the positions of sunspots. Each RGB image has a resolution of 640 × 640 pixels.

For more details about the dataset, please contact the author: olivier.parisot@list.lu .

For more information about Luxembourg of Science and Technology (LIST), please consult: https://www.list.lu .
DOI: 10.15468/dl.m6wwc9
title: Occurrence Download

description: A dataset containing 297 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Leptotyphlopidae"
 ]
}

 The dataset includes 297 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042756-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc111293904
title: Nehemiah 2:13-16, DSS F.201

description: [Text Description:] DSS F.201; inspecting the walls of Jerusalem ❧ [Text or Publication Number Note:] MOTB.SCR.003175; DSS F.Neh2; DSS F.201 ❧ [Medium:] Parchment ❧ [Script Note:] Jewish ❧ [Physical Object Description:] Substrate is dark brown, surface worn in some places, revealing lighter areas. ❧ [Physical Object Note:] Two pieces vertically joined to measure a combined 2.1 x 3.4cm. ❧ [Physical Object Note:] Four lines of text in the middle of a column. Letters visible to naked eye. No visible ruling lines. ❧
DOI: 10.5281/zenodo.11208094
title: SunspotsYoloDataset: annotated solar images captured with smart telescopes (January 2023 - May 2024)

description: SunspotsYoloDataset is a set of 1690+380+128 high-resolution RGB astronomical images captured with smart telescopes with specific solar filters and annotated with the positions of sunspots that are effectively in the images. Two instruments were used for several months from Luxembourg and France between January 2023 and May 2024: a Stellina smart telescope (https://vaonis.com/stellina) and a Vespera smart telescope (https://vaonis.com/vespera).

SunspotsYoloDataset  can be used to train YOLO detection models on solar images, enabling the prediction of unexpected events such as Borealis Aurora with astronomical equipment accessible to the public.

SunspotsYoloDataset is formatted with the YOLO standard, i.e., with separated files for images and annotations, usable by state-of-the-art training tools and graphical software like MakeSense (https://www.makesense.ai). More precisely, there is a ZIP file containing RGB images in JPEG format (minimal compression), and text files containing the positions of sunspots. Each RGB image has a resolution of 640 × 640 pixels.

For more details about the dataset, please contact the author: olivier.parisot@list.lu .

For more information about Luxembourg of Science and Technology (LIST), please consult: https://www.list.lu .
DOI: 10.5281/zenodo.11235127
title: SunspotsYoloDataset: annotated solar images captured with smart telescopes (January 2023 - May 2024)

description: SunspotsYoloDataset is a set of 1690+380 high-resolution RGB astronomical images captured with smart telescopes with specific solar filters and annotated with the positions of sunspots that are effectively in the images. Two instruments were used for several months from Luxembourg and France between January 2023 and May 2024: a Stellina smart telescope (https://vaonis.com/stellina) and a Vespera smart telescope (https://vaonis.com/vespera).

SunspotsYoloDataset  can be used to train YOLO detection models on solar images, enabling the prediction of unexpected events such as Borealis Aurora with astronomical equipment accessible to the public.

SunspotsYoloDataset is formatted with the YOLO standard, i.e., with separated files for images and annotations, usable by state-of-the-art training tools and graphical software like MakeSense (https://www.makesense.ai). More precisely, there is a ZIP file containing RGB images in JPEG format (minimal compression), and text files containing the positions of sunspots. Each RGB image has a resolution of 640 × 640 pixels.

For more details about the dataset, please contact the author: olivier.parisot@list.lu .

For more information about Luxembourg of Science and Technology (LIST), please consult: https://www.list.lu .
DOI: 10.5281/zenodo.11441091
title: SunspotsYoloDataset: annotated solar images captured with smart telescopes (January 2023 - May 2024)

description: SunspotsYoloDataset is a set of 1690+380+128 high-resolution RGB astronomical images captured with smart telescopes with specific solar filters and annotated with the positions of sunspots that are effectively in the images. Two instruments were used for several months from Luxembourg and France between January 2023 and May 2024: a Stellina smart telescope (https://vaonis.com/stellina) and a Vespera smart telescope (https://vaonis.com/vespera).

SunspotsYoloDataset  can be used to train YOLO detection models on solar images, enabling the prediction of unexpected events such as Borealis Aurora with astronomical equipment accessible to the public.

SunspotsYoloDataset is formatted with the YOLO standard, i.e., with separated files for images and annotations, usable by state-of-the-art training tools and graphical software like MakeSense (https://www.makesense.ai). More precisely, there is a ZIP file containing RGB images in JPEG format (minimal compression), and text files containing the positions of sunspots. Each RGB image has a resolution of 640 × 640 pixels.

For more details about the dataset, please contact the author: olivier.parisot@list.lu .

For more information about Luxembourg of Science and Technology (LIST), please consult: https://www.list.lu .