cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e246090
title: Pre 9/11 Gulf War Cohort | Department of Veterans Affairs Open Data Portal

description: On this thirtieth anniversary of the Gulf War the Department of Veterans
 Affairs (VA) wishes to recognize Veterans who served during that war 
and prior to September 11, 2001 (1). Over 695,000 service members were 
deployed, while 2.2 million were in service during the war (1990-1991), 
known as Operations Desert Storm and Desert Shield (2). As of 2019, 
there were nearly 5 million living Veterans in the cohort (3), of whom 
2.7 million served exclusively during the Pre-9/11 time period (4).<br>
description: US Veterans.
DOI: 10.3886/e246090v1
title: Pre 9/11 Gulf War Cohort | Department of Veterans Affairs Open Data Portal

description: On this thirtieth anniversary of the Gulf War the Department of Veterans
 Affairs (VA) wishes to recognize Veterans who served during that war 
and prior to September 11, 2001 (1). Over 695,000 service members were 
deployed, while 2.2 million were in service during the war (1990-1991), 
known as Operations Desert Storm and Desert Shield (2). As of 2019, 
there were nearly 5 million living Veterans in the cohort (3), of whom 
2.7 million served exclusively during the Pre-9/11 time period (4).<br>
description: US Veterans.
DOI: 10.15468/dl.6g5vw3
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ulmeritoides amajari",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032654-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bq86rc
title: Occurrence Download

description: A dataset containing 28354 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidotrigla",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 28354 records from 153 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032655-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14844542
title: D1.1_metadata

description: The file contains rheometry, IR camera, DSC, TGA, conductivity etc metadata.
DOI: 10.5281/zenodo.18715557
title: D1.1_metadata

description: The file contains rheometry, IR camera, DSC, TGA, conductivity etc metadata.
DOI: 10.15468/dl.pqfark
title: Occurrence Download

description: A dataset containing 1232 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rana arvalis",
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 2023-2024"
 ]
}

 The dataset includes 1232 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032656-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7tnq3y
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ulmeritoides angelus",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032658-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m3x38v
title: Occurrence Download

description: A dataset containing 6947 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "StateProvince is Mississippi",
 "TaxonKey is Bryophyta"
 ]
}

 The dataset includes 6947 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032659-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18715598
title: Individual  Questionnaire Responses

description: This is a dataset that presents the individual questionnaire responses
DOI: 10.5281/zenodo.18715597
title: Individual  Questionnaire Responses

description: This is a dataset that presents the individual questionnaire responses
DOI: 10.6084/m9.figshare.31378936
title: A practitioner study on preferences for informal learning resources and collaboration between social studies teachers and historic site supervisors

description: Historic sites offer valuable opportunities for experiential learning but are often underused in social studies education. This practitioner study explored how secondary social studies teachers—including those who teach geography—and historic site supervisors from an upper Midwestern U.S. state prefer to collaborate and which informal learning resources they find most effective. Using survey data from 136 middle and high school teachers and interviews with eight historic site supervisors, the study found that teachers prefer brief, flexible pre- and post-visit lesson materials, such as 10–15-minute activities and videos. Both groups expressed strong interest in collaborating to develop student-centered learning experiences beyond the classroom. Teachers preferred electronic resource distribution and suggested summer institutes, online collaboration, and graduate credit as ways to incentivize the co-creation of materials. Site supervisors emphasized the importance of tailoring visits to student backgrounds and highlighted the role of educational outreach teams. The study concludes with practical recommendations for resource design and collaboration models, such as developing site-specific lesson materials and establishing teacher–supervisor summer institutes. These initiatives can strengthen partnerships between teachers and historic sites and support informal learning in social studies classrooms, particularly focusing on enhancing geoscience education.
DOI: 10.6084/m9.figshare.31378936.v1
title: A practitioner study on preferences for informal learning resources and collaboration between social studies teachers and historic site supervisors

description: Historic sites offer valuable opportunities for experiential learning but are often underused in social studies education. This practitioner study explored how secondary social studies teachers—including those who teach geography—and historic site supervisors from an upper Midwestern U.S. state prefer to collaborate and which informal learning resources they find most effective. Using survey data from 136 middle and high school teachers and interviews with eight historic site supervisors, the study found that teachers prefer brief, flexible pre- and post-visit lesson materials, such as 10–15-minute activities and videos. Both groups expressed strong interest in collaborating to develop student-centered learning experiences beyond the classroom. Teachers preferred electronic resource distribution and suggested summer institutes, online collaboration, and graduate credit as ways to incentivize the co-creation of materials. Site supervisors emphasized the importance of tailoring visits to student backgrounds and highlighted the role of educational outreach teams. The study concludes with practical recommendations for resource design and collaboration models, such as developing site-specific lesson materials and establishing teacher–supervisor summer institutes. These initiatives can strengthen partnerships between teachers and historic sites and support informal learning in social studies classrooms, particularly focusing on enhancing geoscience education.
DOI: 10.5281/zenodo.18715629
title: "PSIXODRAMMA METODINING GURUHIY TRENINGDAGI IMKONIYATLARI"

description: Ushbu maqolada psixodramma metodining guruhiy trening jarayonlaridagi psixologik ahamiyati va samaradorligi nazariy va empirik jihatdan tahlil qilinadi. Psixodramma – shaxsning ichki kechinmalarini, hissiyot va qaror qabul qilish jarayonlarini bevosita amaliy faoliyat orqali ifodalash imkonini beruvchi interaktiv metod bo‘lib, guruh ichidagi muloqot, empatiya va ijtimoiy moslashuv ko‘nikmalarini shakllantirishda samarali vosita hisoblanadi.

Maqolada psixodramma metodining nazariy asoslari, guruhiy trening jarayonida qo‘llash mexanizmlari, turli yosh va ijtimoiy guruhlar bilan ishlashda metodik jihatlari ko‘rib chiqilgan. Empirik tadqiqot natijalari psixodramma metodining trening samaradorligini oshirish, ishtirokchilarning emotsional barqarorligi va guruh ichidagi o‘zaro ishonchini kuchaytirishdagi rolini tasdiqlaydi.

Maqola natijalari guruhiy treninglar va pedagogik-psixologik amaliyotda psixodramma metodidan tizimli foydalanish bo‘yicha metodik tavsiyalar ishlab chiqish uchun ilmiy asos bo‘lib xizmat qiladi.
DOI: 10.5281/zenodo.18715628
title: "PSIXODRAMMA METODINING GURUHIY TRENINGDAGI IMKONIYATLARI"

description: Ushbu maqolada psixodramma metodining guruhiy trening jarayonlaridagi psixologik ahamiyati va samaradorligi nazariy va empirik jihatdan tahlil qilinadi. Psixodramma – shaxsning ichki kechinmalarini, hissiyot va qaror qabul qilish jarayonlarini bevosita amaliy faoliyat orqali ifodalash imkonini beruvchi interaktiv metod bo‘lib, guruh ichidagi muloqot, empatiya va ijtimoiy moslashuv ko‘nikmalarini shakllantirishda samarali vosita hisoblanadi.

Maqolada psixodramma metodining nazariy asoslari, guruhiy trening jarayonida qo‘llash mexanizmlari, turli yosh va ijtimoiy guruhlar bilan ishlashda metodik jihatlari ko‘rib chiqilgan. Empirik tadqiqot natijalari psixodramma metodining trening samaradorligini oshirish, ishtirokchilarning emotsional barqarorligi va guruh ichidagi o‘zaro ishonchini kuchaytirishdagi rolini tasdiqlaydi.

Maqola natijalari guruhiy treninglar va pedagogik-psixologik amaliyotda psixodramma metodidan tizimli foydalanish bo‘yicha metodik tavsiyalar ishlab chiqish uchun ilmiy asos bo‘lib xizmat qiladi.
DOI: 10.6084/m9.figshare.31378934.v1
title: Nonparametric Prior Learning in Differential Equation Modeling

description: This paper addresses Bayesian inference related to partial differential equations (PDEs), particularly nonparametric regression constrained by PDEs. To effectively encode prior information, we propose a novel framework that learns a prediction function of the prior distribution from historical training datasets. We introduce hyper-prior and hyper-posterior distributions and derive a generalization error estimate, which accommodates data-dependent priors by extending the concept of differential privacy. Some mild conditions are given to validate the error estimate, where various typical PDEs such as diffusion and Darcy flow equations can be integrated. We thus formulate an infinite-dimensional optimization problem to obtain the point estimate of the hyper-posterior. Numerical examples demonstrate the performance of our proposed method in learning the prediction function of priors.
DOI: 10.6084/m9.figshare.31378934
title: Nonparametric Prior Learning in Differential Equation Modeling

description: This paper addresses Bayesian inference related to partial differential equations (PDEs), particularly nonparametric regression constrained by PDEs. To effectively encode prior information, we propose a novel framework that learns a prediction function of the prior distribution from historical training datasets. We introduce hyper-prior and hyper-posterior distributions and derive a generalization error estimate, which accommodates data-dependent priors by extending the concept of differential privacy. Some mild conditions are given to validate the error estimate, where various typical PDEs such as diffusion and Darcy flow equations can be integrated. We thus formulate an infinite-dimensional optimization problem to obtain the point estimate of the hyper-posterior. Numerical examples demonstrate the performance of our proposed method in learning the prediction function of priors.
DOI: 10.15468/dl.8weq3s
title: Occurrence Download

description: A dataset containing 33397 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenolabrus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 33397 records from 166 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032660-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26208/ehb5-4w65
title: Susquehanna Shale Hills CZO - Vegetation Survey for LiDAR Validation

description: A vegetation survey was conducted May through July, 2010 to validate the LiDAR data collected in July. Thirty-nine 30-diameter circular plots were chosen. Within each plot, trees over 18cm diameter were counted, species names recorded, and height and diameter measured. In meadow and wetland plots, average vegetation height and dominant plant species were recorded. In meadow and wetland plots, average vegetation height and dominant plant species were recorded. Leaf Area Index (LAI) and canopy closure (CC) data were collected between July 14 and July 23, 2010 for meadow (M), wetland (W), and forest (Leading Ridge - LR) sites in addition to the Shale Hills catchment. LAI was measured using a LAI-2200 instrument (LI-COR Biosciences). Canopy closure was measured with a spherical densiometer, Model C Forest Densiometers). See "Notes" and "Key" sheets for additional details.
DOI: 10.6084/m9.figshare.31378937
title: Bayesian Optimization for Branching and Nested Hyperparameters in Deep Learning

description: Hyperparameter optimization plays a crucial role in the success of neural networks as hyperparameters directly control the behavior and performance of the training algorithms. To obtain efficient tuning, Bayesian optimization based on Gaussian process is widely used. Despite numerous applications in deep learning, the existing methods rely on a convenient but restrictive assumption that the tuning parameters are independent of each other. However, tuning parameters with conditional dependence are common in practice. In this paper, we focus on two types of them: branching and nested parameters. Nested parameters refer to those tuning parameters that exist only within a particular setting of another tuning parameter, and a parameter that contains other nested parameters is referred to as a branching parameter. To capture the conditional dependence between branching and nested parameters, a unified Bayesian optimization framework is proposed. The sufficient conditions are rigorously derived to guarantee the validity of the kernel function, and the asymptotic convergence of the proposed optimization framework is proven under the continuum-armed-bandit setting. The new model, which accounts for the dependent structure among input variables through a new kernel function, provides a higher prediction accuracy and a better optimization efficiency in simulations and real data applications in neural networks.
DOI: 10.6084/m9.figshare.31378937.v1
title: Bayesian Optimization for Branching and Nested Hyperparameters in Deep Learning

description: Hyperparameter optimization plays a crucial role in the success of neural networks as hyperparameters directly control the behavior and performance of the training algorithms. To obtain efficient tuning, Bayesian optimization based on Gaussian process is widely used. Despite numerous applications in deep learning, the existing methods rely on a convenient but restrictive assumption that the tuning parameters are independent of each other. However, tuning parameters with conditional dependence are common in practice. In this paper, we focus on two types of them: branching and nested parameters. Nested parameters refer to those tuning parameters that exist only within a particular setting of another tuning parameter, and a parameter that contains other nested parameters is referred to as a branching parameter. To capture the conditional dependence between branching and nested parameters, a unified Bayesian optimization framework is proposed. The sufficient conditions are rigorously derived to guarantee the validity of the kernel function, and the asymptotic convergence of the proposed optimization framework is proven under the continuum-armed-bandit setting. The new model, which accounts for the dependent structure among input variables through a new kernel function, provides a higher prediction accuracy and a better optimization efficiency in simulations and real data applications in neural networks.
DOI: 10.5281/zenodo.18715500
title: Appendix A - Quality assessment
DOI: 10.5281/zenodo.18715499
title: Appendix A - Quality assessment
DOI: 10.15468/dl.v5heay
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ulmeritoides aurantifrons",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032662-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25824/redu/4ieeky
title: Estimativa da estatura humana por meio de ossos longos de indivíduos de Cuiabá

description: Os dados não podem ser disponibilizados por questões de proteção intelectual. Para mais informações, contatar o (s) responsável (is).
DOI: 10.15468/dl.42d4zg
title: Occurrence Download

description: A dataset containing 29971 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ophiogomphus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 29971 records from 216 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032663-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18715468
title: ALS Cross-Platform Classification - CyTOF and scRNA-seq Data

description: This dataset contains mass cytometry (CyTOF) and single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) of ALS patients and healthy controls, used for developing cross-platform machine learning models to classify disease progression rates.

Dataset 1: als_cytof_data.h5ad



Technology: Mass cytometry (CyTOF)

Samples: 44 samples across 4 batches

Groups: Healthy controls, ALS patients (slow, standard, and fast progression)

Cell types: 25 immune cell populations profiled

Contains: Single-cell protein expression, cell type annotations, clinical metadata (progression rate ALSFRS-R/time, progression group)


Dataset 2: Itou2024_scrna_data.h5ad



Technology: 10x Genomics scRNA-seq

Source: Itou et al. 2024 (GSE244263)

Samples: 40 PBMC samples (30 ALS, 10 controls)

Cells: 29,883 (750 per donor, subsampled with random_state=42)

Genes: 3,054 (highly variable genes only)

Groups: Healthy controls, rapid ALS, non-rapid ALS (column: Classifier)

Contains: Log-normalized expression (X), cell type annotations, Leiden clusters, PCA/Harmony/UMAP embeddings

Use case: Cross-platform classification analyses (companion to CyTOF data)


Dataset 3: Itou2024_scrna_data_full_with_counts.h5ad (new)



Technology: 10x Genomics scRNA-seq

Source: Itou et al. 2024 (GSE244263) — same cells as Dataset 2

Samples: 40 PBMC samples (30 ALS, 10 controls)

Cells: 29,883 (750 per donor, subsampled with random_state=42)

Genes: 36,601 (full transcriptome)

Groups: Healthy controls, rapid ALS, non-rapid ALS

Contains:



X: Log-normalized expression (sparse, float64)

layers['counts']: Raw integer counts (sparse, int32)

Cell type annotations (26 cell types), harmonized labels, ALS subtype metadata

PCA, Harmony-corrected PCA, and UMAP embeddings



Use case: Differential expression (DESeq2/edgeR require raw counts), cell-cell communication analysis, and any reanalysis requiring the full gene set

Note: This is the same set of 29,883 cells as Dataset 2, expanded to the full transcriptome with raw counts preserved. Dataset 2 contains only highly variable genes and is sufficient for the cross-platform classification analyses described in the manuscript.


These datasets enable cross-platform validation of immune signatures in ALS, demonstrating that disease-associated immune patterns identified in CyTOF data generalize to scRNA-seq data. The datasets support reproducible machine learning analyses for ALS patient stratification based on immune profiling.

Manuscript: Peripheral immune patterns enable robust cross-platform prediction of ALS onset and progression
DOI: 10.5281/zenodo.16039495
title: ALS Cross-Platform Classification - CyTOF and scRNA-seq Data

description: This dataset contains mass cytometry (CyTOF) and single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) of ALS patients and healthy controls, used for developing cross-platform machine learning models to classify disease progression rates.

Dataset 1: als_cytof_data.h5ad



Technology: Mass cytometry (CyTOF)

Samples: 44 samples across 4 batches

Groups: Healthy controls, ALS patients (slow, standard, and fast progression)

Cell types: 25 immune cell populations profiled

Contains: Single-cell protein expression, cell type annotations, clinical metadata (progression rate ALSFRS-R/time, progression group)


Dataset 2: Itou2024_scrna_data.h5ad



Technology: 10x Genomics scRNA-seq

Source: Itou et al. 2024 (GSE244263)

Samples: 40 PBMC samples (30 ALS, 10 controls)

Cells: 29,883 (750 per donor, subsampled with random_state=42)

Genes: 3,054 (highly variable genes only)

Groups: Healthy controls, rapid ALS, non-rapid ALS (column: Classifier)

Contains: Log-normalized expression (X), cell type annotations, Leiden clusters, PCA/Harmony/UMAP embeddings

Use case: Cross-platform classification analyses (companion to CyTOF data)


Dataset 3: Itou2024_scrna_data_full_with_counts.h5ad (new)



Technology: 10x Genomics scRNA-seq

Source: Itou et al. 2024 (GSE244263) — same cells as Dataset 2

Samples: 40 PBMC samples (30 ALS, 10 controls)

Cells: 29,883 (750 per donor, subsampled with random_state=42)

Genes: 36,601 (full transcriptome)

Groups: Healthy controls, rapid ALS, non-rapid ALS

Contains:



X: Log-normalized expression (sparse, float64)

layers['counts']: Raw integer counts (sparse, int32)

Cell type annotations (26 cell types), harmonized labels, ALS subtype metadata

PCA, Harmony-corrected PCA, and UMAP embeddings



Use case: Differential expression (DESeq2/edgeR require raw counts), cell-cell communication analysis, and any reanalysis requiring the full gene set

Note: This is the same set of 29,883 cells as Dataset 2, expanded to the full transcriptome with raw counts preserved. Dataset 2 contains only highly variable genes and is sufficient for the cross-platform classification analyses described in the manuscript.


These datasets enable cross-platform validation of immune signatures in ALS, demonstrating that disease-associated immune patterns identified in CyTOF data generalize to scRNA-seq data. The datasets support reproducible machine learning analyses for ALS patient stratification based on immune profiling.

Manuscript: Peripheral immune patterns enable robust cross-platform prediction of ALS onset and progression
DOI: 10.5281/zenodo.18715658
title: Artistic basic income analysis
DOI: 10.5281/zenodo.18715657
title: Artistic basic income analysis