cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr39320.v1
title: Strategic National Arts Alumni Project (SNAAP), 2022 [United States]
title: Version 1

description: The Strategic National Arts Alumni Project (SNAAP) survey collects data on the perspectives and experiences of alumni from arts, design, and adjacent fields. The 2022 SNAAP survey data offer evidence-based insights to help inform the important work being undertaken by postsecondary institutions offering studies and training in arts and design amid shifts and changes affecting higher education.
SNAAP's national, online survey was launched in 2008 to meet the growing need for reliable alumni outcomes data, as articulated by arts training institutions across the country. The survey draws generalizable insights about "artists" with postsecondary degrees (as defined by Classification of Instructional Programs (CIP) codes) in arts, design, and media fields. Alumni of independent arts and design colleges, conservatories, and arts schools and departments within comprehensive colleges and universities are surveyed.
SNAAP's survey was successfully relaunched in 2022 with numerous changes to its design, methodology, and administration. This included a probabilistic sampling frame to ensure a more representative sample; a more inclusive recruitment process to secure a more diverse group of institutions' participation in SNAAP; and a revised survey questionnaire to expand on important substantive areas and improve the respondent's experience completing the questionnaire. The revised questionnaire gave new attention to alumni's "sense of belonging" at their educational institution; the impact of the COVID-19 pandemic on their lives and careers; and paying for postsecondary arts education.
description: The data includes the weighting variable <strong>WEIGHT_FINAL.</strong>
description: Alumni from 128 institutional data files
description: Datasets: 
DS1: Strategic National Arts Alumni Project (SNAAP), 2022 [United States]
description: Individuals with arts, design, and related degrees.
description: web-based surveyFor additional information on the Strategic National Arts Alumni Project (SNAAP) study, please visit the SNAAP website.
DOI: 10.7910/dvn/pkxxkw
title: Precision Medicine in Ischemic Stroke and Atrial Fibrillation (PreMISe-AF)

description: Clinical trial data which can be used to recreate our models. It includes survival data, outcomes (monitoring), time to event and clinical risk scores in 2 cohorts. We also included data on the PRS study which can be used to recreate our models. It includes polygenic risk scores for AF, data on monitoring, and time to event data. Included is a list of SNPs used to calculate the polygenic risk score and their weights.
DOI: 10.15468/dl.eema2p
title: Occurrence Download

description: A dataset containing 1398 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cenostigma eriostachys (Benth.) Gagnon & G.P.Lewis"
 ]
}

 The dataset includes 1398 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020170-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15346249
title: Molecular dynamics simulations of intrinsically disordered protein regions with pathogenic and benign missense variants

description: This study aimed to address pathogenic variants in intrinsically disordered regions (IDRs) using molecular dynamics simulations. This dataset contains:



Coarse-grained MD simulations using CALVADOS2 of mutated IDR sequences.

Metadata for these simulations of mutated sequences and their groupings.

IDRome wide predictions of MDmis. MDmis is our model trained on features extracted from MD simulations of IDRs.

Data used in training and other analysis in the manuscript. Features used for training, for plotting labels with proteomic information such as conservation, pLDDT, AlphaMissense and ESM1b scores.


Please note that the folders of coarse-grained and processed MD simulations use the term "Low" and "High" RMSF, which were initially used to denote the "Short" and "Long" IDR dichotomy addressed in the paper. However, this mapping may not be 1:1, so we recommend using the metadata named "calvados_mutation_differences.csv" to map each mutation to the category of interest."Shorter_simulations" are wild-type simulations of long IDRs for very short timescales (70ns) to study the role of time in RMSF calculations.

Other data used in training and analysis are also provided.



feature_table.csv contains extracted features from MD simulations of IDRs.



This can be merged with sequence_composition_MSA.csv which contains features extracted from Zoonomia MSAs such as entropy, charge pattern, and compositional variation.



GPCRmd_train_val has extracted MD features from GPCRmd

DMS_train_val is for IDRs but focusing on DMS labels.

Merged Proteome Information is a folder containing all the merged proteomic information from different sources for all labels used in our analysis. 

DMS and MD metadata provide information useful for processing these datasets. 

training.csv file that contains all the labels used for the analysis in IDRs and other primarily structured regions.
DOI: 10.5281/zenodo.15346250
title: Molecular dynamics simulations of intrinsically disordered protein regions with pathogenic and benign missense variants

description: This study aimed to address pathogenic variants in intrinsically disordered regions (IDRs) using molecular dynamics simulations. This dataset contains:



Coarse-grained MD simulations using CALVADOS2 of mutated IDR sequences.

Metadata for these simulations of mutated sequences and their groupings.

IDRome wide predictions of MDmis. MDmis is our model trained on features extracted from MD simulations of IDRs.

Data used in training and other analysis in the manuscript. Features used for training, for plotting labels with proteomic information such as conservation, pLDDT, AlphaMissense and ESM1b scores.


Please note that the folders of coarse-grained and processed MD simulations use the term "Low" and "High" RMSF, which were initially used to denote the "Short" and "Long" IDR dichotomy addressed in the paper. However, this mapping may not be 1:1, so we recommend using the metadata named "calvados_mutation_differences.csv" to map each mutation to the category of interest."Shorter_simulations" are wild-type simulations of long IDRs for very short timescales (70ns) to study the role of time in RMSF calculations.

Other data used in training and analysis are also provided.



feature_table.csv contains extracted features from MD simulations of IDRs.



This can be merged with sequence_composition_MSA.csv which contains features extracted from Zoonomia MSAs such as entropy, charge pattern, and compositional variation.



GPCRmd_train_val has extracted MD features from GPCRmd

DMS_train_val is for IDRs but focusing on DMS labels.

Merged Proteome Information is a folder containing all the merged proteomic information from different sources for all labels used in our analysis. 

DMS and MD metadata provide information useful for processing these datasets. 

training.csv file that contains all the labels used for the analysis in IDRs and other primarily structured regions.
DOI: 10.3886/icpsr39320.v2
title: Strategic National Arts Alumni Project (SNAAP), 2022 [United States]
title: Version 2

description: The Strategic National Arts Alumni Project (SNAAP) survey collects data on the perspectives and experiences of alumni from arts, design, and adjacent fields. The 2022 SNAAP survey data offer evidence-based insights to help inform the important work being undertaken by postsecondary institutions offering studies and training in arts and design amid shifts and changes affecting higher education.
SNAAP's national, online survey was launched in 2008 to meet the growing need for reliable alumni outcomes data, as articulated by arts training institutions across the country. The survey draws generalizable insights about "artists" with postsecondary degrees (as defined by Classification of Instructional Programs (CIP) codes) in arts, design, and media fields. Alumni of independent arts and design colleges, conservatories, and arts schools and departments within comprehensive colleges and universities are surveyed.
SNAAP's survey was successfully relaunched in 2022 with numerous changes to its design, methodology, and administration. This included a probabilistic sampling frame to ensure a more representative sample; a more inclusive recruitment process to secure a more diverse group of institutions' participation in SNAAP; and a revised survey questionnaire to expand on important substantive areas and improve the respondent's experience completing the questionnaire. The revised questionnaire gave new attention to alumni's "sense of belonging" at their educational institution; the impact of the COVID-19 pandemic on their lives and careers; and paying for postsecondary arts education.
description: The data includes the weighting variable <strong>WEIGHT_FINAL.</strong>
description: Alumni from 128 institutional data files
description: Datasets: 
DS1: Strategic National Arts Alumni Project (SNAAP), 2022 [United States]
description: Individuals with arts, design, and related degrees.
description: web-based surveyFor additional information on the Strategic National Arts Alumni Project (SNAAP) study, please visit the SNAAP website.
DOI: 10.3886/icpsr39320
title: Strategic National Arts Alumni Project (SNAAP), 2022 [United States]
title: Archival Version

description: The Strategic National Arts Alumni Project (SNAAP) survey collects data on the perspectives and experiences of alumni from arts, design, and adjacent fields. The 2022 SNAAP survey data offer evidence-based insights to help inform the important work being undertaken by postsecondary institutions offering studies and training in arts and design amid shifts and changes affecting higher education.
SNAAP's national, online survey was launched in 2008 to meet the growing need for reliable alumni outcomes data, as articulated by arts training institutions across the country. The survey draws generalizable insights about "artists" with postsecondary degrees (as defined by Classification of Instructional Programs (CIP) codes) in arts, design, and media fields. Alumni of independent arts and design colleges, conservatories, and arts schools and departments within comprehensive colleges and universities are surveyed.
SNAAP's survey was successfully relaunched in 2022 with numerous changes to its design, methodology, and administration. This included a probabilistic sampling frame to ensure a more representative sample; a more inclusive recruitment process to secure a more diverse group of institutions' participation in SNAAP; and a revised survey questionnaire to expand on important substantive areas and improve the respondent's experience completing the questionnaire. The revised questionnaire gave new attention to alumni's "sense of belonging" at their educational institution; the impact of the COVID-19 pandemic on their lives and careers; and paying for postsecondary arts education.
description: The data includes the weighting variable <strong>WEIGHT_FINAL.</strong>
description: Alumni from 128 institutional data files
description: Datasets: 
DS1: Strategic National Arts Alumni Project (SNAAP), 2022 [United States]
description: Individuals with arts, design, and related degrees.
description: web-based surveyFor additional information on the Strategic National Arts Alumni Project (SNAAP) study, please visit the SNAAP website.
DOI: 10.15468/dl.wnqb6h
title: Occurrence Download

description: A dataset containing 428 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "DatasetKey is one of (SFSU - Vascular Plants of the Harry D. Thiers Herbarium at San Francisco State University, The University and Jepson Herbaria -- Vascular Plants, CAS Botany (BOT), San Francisco State University, Harry D. Thiers Herbarium)",
 "Geometry POLYGON((-122.44868 37.85706,-122.43783 37.84864,-122.41447 37.84836,-122.41434 37.86913,-122.43278 37.87577,-122.44874 37.86372,-122.44868 37.85706))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 428 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020172-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9tdi0a6
title: Water Surface Elevation Data Derived from Submersible Pressure Transducers Deployed in the Susitna, Yukon, and Nushagak Rivers, Alaska, Summers 2020-2021

description: This dataset includes locations and water surface elevation measurements from submersible pressure transducers deployed in channels of three rivers in Alaska: Susitna and Yukon Rivers (July-October 2020), and Nushagak River (May to September 2021).
DOI: 10.5281/zenodo.15360381
title: IODP Expedition 399, Hole U1601C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 399: Building Blocks of Life - Atlantis Massif
DOI: 10.5281/zenodo.15360382
title: IODP Expedition 399, Hole U1601C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 399: Building Blocks of Life - Atlantis Massif
DOI: 10.15468/dl.u43yea
title: Occurrence Download

description: A dataset containing 5722 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Albizia lebbeck (L.) Benth.",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Occurrence evidence)"
 ]
}

 The dataset includes 5722 records from 149 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020173-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15360342
title: Quantum precursors to Kolmogorov-Arnold-Moser theorem in Floquet spin-J systems

description: Raw data for Figures 1(c), 3(a-c) in the main text, and Fig 9 in the supplementary material for arxiv:2504.13257.
DOI: 10.5281/zenodo.15360343
title: Quantum precursors to Kolmogorov-Arnold-Moser theorem in Floquet spin-J systems

description: Raw data for Figures 1(c), 3(a-c) in the main text, and Fig 9 in the supplementary material for arxiv:2504.13257.
DOI: 10.5281/zenodo.15360375
title: DATA MINING
title: DATA MINING
DOI: 10.5281/zenodo.15360376
title: DATA MINING
title: DATA MINING
DOI: 10.18430/m39od6
title: X-ray diffraction data for the crystal structure of nucleoside-diphosphate kinase from Cryptosporidium parvum in complex with citrate (9OD6)
DOI: 10.5281/zenodo.13709550
title: Suco - PBMC

description: Suco (Single cell universal classification omnibus) is a large standardized reference dataset for cell type classification in single cell RNA sequencing data. Suco seeks to tackle the lack of standardized datasets for classification tasks in the single cell genomics field. In other fields of artificial intelligence, like computer vision, standardized datasets such as MNIST or ImageNet have transformed the development of powerful new machine learning classifiers.

Suco features manual uniform standardized hierarchical cell type annotations in independently analyzed datasets. This collection of independent datasets ensures that machine learning classifiers can be tested using statistically independent data & labels. 

Here, we present the peripheral blood mononuclear cell dataset within Suco which includes > 1200 independent manual cell type cluster labels from 10 datasets totalling >500 individuals and >5 millions cells.
DOI: 10.5281/zenodo.15108444
title: Suco - PBMC

description: Suco (Single cell universal classification omnibus) is a large standardized reference dataset for cell type classification in single cell RNA sequencing data. Suco seeks to tackle the lack of standardized datasets for classification tasks in the single cell genomics field. In other fields of artificial intelligence, like computer vision, standardized datasets such as MNIST or ImageNet have transformed the development of powerful new machine learning methods.

Suco features manual uniform standardized hierarchical cell type annotations in independently analyzed datasets. This collection of independent datasets ensures that machine learning classifiers can be tested using statistically independent data & labels. 

Here, we present the peripheral blood mononuclear cell dataset within Suco which includes > 1200 independent manual cell type cluster labels from 12 datasets totalling >500 individuals and >5 millions cells. 

______________________________________________________________________________________________________

Structure of the dataset:

.zip compressed folder containing datasets from individual studies which have been reprocessed, clustered and annotated independently by two different expert raters (human immunology)



Filenames: DATASET_ID.h5ad



The dataset ID has the following format (each line followed by ‘-X-‘ separator



Tissue/cell type: here PBMC

Disease context

Publication year

First author (optional: followed by _BATCHNAME)

DOI (/ in DOI is replace by _ for compatibility with file systems



the .h5ad files have the following structure



load using the scanpy python package adata = sc.read(FILE_PATH)

cell barcode (adata.obs_names)

Study-ID + '-X-' + internal barcode

adata.obs[‘sample_id’]



the sample ID should be the patient ID + '-X-' separator + internal sample ID



e.g. TIL-X-BRCA-X-scRNAseq-X-Bassez-X-2021-X-10.1038_s41591-021-01323-8-X-2-X-Pre



with TIL-X-BRCA-X-scRNAseq-X-Bassez-X-2021-X-10.1038_s41591-021-01323-8-X-2 being the patient ID

-X-  the separator







adata.obs['patient_id']



dataset id followed by an '-X-' separator and the internal patient id

e.g. TIL-X-BRCA-X-scRNAseq-X-Bassez-X-2021-X-10.1038_s41591-021-01323-8-X-35

-X- is the separator

35 is the internal patient 



adata.obs[cluster_final']



final clustering used for the cell type annotation

granularity can differ between subsets --> e.g. clustering from myeloid cells can originate from myeloid subset, clustering from TNK from TNK subset and epithelial from all leukocyte subset

should be preceeded by the prefix used for subtyping e.g. 'TNK' for TNK cells followed by a '_' seperator and the cluster number:·  

e.g.



cluster 0 in TNK would be 'TNK_0'

cluster 1 in M would be 'M_1'





adata.obs[cluster_all']



containing coarse clustering format 'all_CLUSTERNUMBER'     



adata.obs[‘annotation’]



Most granular annotation based on adata.obs[‘cluster_final’]



adata.obs[‘annotation_all’]



 annotation based on adata.obs[‘cluster_all’]
DOI: 10.15468/dl.c6dvrj
title: Occurrence Download

description: A dataset containing 428 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "DatasetKey is one of (SFSU - Vascular Plants of the Harry D. Thiers Herbarium at San Francisco State University, The University and Jepson Herbaria -- Vascular Plants, CAS Botany (BOT), San Francisco State University, Harry D. Thiers Herbarium)",
 "Geometry POLYGON((-122.44868 37.85706,-122.43783 37.84864,-122.41447 37.84836,-122.41434 37.86913,-122.43278 37.87577,-122.44874 37.86372,-122.44868 37.85706))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 428 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020176-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7kpgrn
title: Occurrence Download

description: A dataset containing 59217 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Swiss National Databank of Vascular Plants, Atlas des Orchidées de France métropolitaine (2010), INPN - Données flore des CBN agrégées par la FCBN, iNaturalist Research-grade Observations, Centralisation des observations floristiques - Observations floristiques floutées issues de la base de données flore du Conservatoire botanique national alpin, Observation.org, Nature data from around the World, 16 - Inventaire général de la flore et de la fonge - SFO - inventaire de la flore, Artportalen, Occurrence Data of Vascular Plants collected or compiled for the Flora of Bavaria)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Orchis militaris L."
 ]
}

 The dataset includes 59217 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020171-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5prhka
title: Occurrence Download

description: A dataset containing 4527 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Culicoides chiopterus (Meigen, 1830)"
 ]
}

 The dataset includes 4527 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020177-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gwp7dp
title: Occurrence Download

description: A dataset containing 723 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hydrangea petiolaris Siebold & Zucc.",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 723 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020178-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/0h5-gr3-269
title: Eubalaena Gray, 1864

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4c7-0md-6yl
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4eh-4ys-3s2
title: †Latiphyllia de Fromentel, 1861

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4sd-098-9g1
title: Equus (Equus) caballus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/l8m-1sc-3ax
title: Cetacea Brisson, 1762

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ysl-4a9-c34
title: Balclutha Kirkaldy, 1900

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/15s-aha-2n3
title: †Pustula Thomas, 1914

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wrf-6gj-gls
title: Bison C. H. Smith, 1827

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.