cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31041946.v1
title: A Machine Learning and Quantum Chemical Strategy to Disrupt Oncogenic Arginine Methylation

description: Protein arginine N-methyltransferase 1 (PRMT1) plays a key role in gene regulation by modifying proteins through asymmetric arginine dimethylation, a process often linked to cancer development. Due to its elevated activity in cancer, PRMT1 is a promising target for therapeutic intervention. This study used an integrated approach to identify and optimize selective inhibitors of PRMT1, starting from the known compound GSK3368715. Over 1,000 structurally related analogs were screened for their binding potential. Top candidates underwent quantum mechanical optimization and docking studies, which confirmed strong interactions with key active-site residues, including hydrogen bonding with Tyr57 and Glu171, and <i>π-π</i> stacking with Tyr53 and Phe54. Molecular dynamics simulations revealed stable binding behavior, supported by MM/GBSA binding energy calculations. One compound, 118971667, showed particularly high binding affinity and structural stability. Principal component analysis and free energy landscapes further confirmed its favorable dynamics. Additionally, a machine learning model trained on 290 known PRMT1 inhibitors predicted 118971667 to have strong inhibitory potential (pIC<sub>50</sub> of 6.66), outperforming a reference compound. This comprehensive strategy, combining structural modeling, simulations, quantum calculations, and predictive analytics, highlights 118971667 as a promising PRMT1 inhibitor and offers valuable insights into targeting arginine methylation in cancer.
DOI: 10.15156/bio/sh1049888.10fu
title: SH1049888.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049891.10fu
title: SH1049891.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049892.10fu
title: SH1049892.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049893.10fu
title: SH1049893.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049895.10fu
title: SH1049895.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049896.10fu
title: SH1049896.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049899.10fu
title: SH1049899.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049900.10fu
title: SH1049900.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1049903.10fu
title: SH1049903.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.14984115
title: Simulated data from the article "MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates"

description: The repository includes alignments of simulated RNA-seq data for evaluating differential splicing detection with covariates. Starting from an empirical transcript expression matrix trained on an RNA-seq data set from lung fibroblasts (GenBank A# SRR493366) and using GENCODE v.41 as reference, 11.5 million 100 bp long paired-end reads were generated per sample, from 2,000 genes with two or more expressed isoforms. RNA-seq data was simulated for one ‘condition’, with values ‘control’, ‘disease’ and ‘stage2’, with one covariate, ‘biological sex’, with values ‘M’ and ‘F’.  10 samples each were simulated for each (condition x sex) category. Changes were simulated in the expression (DE) and/or the splicing ratio (DS) of genes as follows. Changes in expression (DE) were simulated by either halving or doubling the expression level of the gene. Changes in splicing ratios (DS) were simulated by swapping the expression levels of the gene’s top two transcript isoforms. All RNA-seq data was mapped to the hg38 genome with the spliced alignment tool STAR v2.7.10a.

 Pairwise comparison alignment set: Differences due to ‘condition’ between two states, ‘control’ and ‘disease’, were simulated at 600 genes, including 200 DE, 200 DS and 200 DE+DS genes. Differences in ‘biological sex’ (covariate) were represented as changes in 300 genes, with 100 genes from each of the DS, DE and DE+DS categories. Hence, the target gene set for differential splicing ratio (DSR) pairwise comparisons consists of the pooled 200 DS and 200 DS+DE genes differentially spliced between the ‘control’ and ‘disease’ states, while for differential splicing abundance (DSA) pairwise comparisons the target gene set is the set of 600 modified genes, 200 in each of the DS, DE and DS+DE categories.

 Multiway (3-way) comparison alignment set: To create the 'stage2' data, changes in expression and splicing levels were maintained for 3 x 100 of the 'disease' genes (100 in each category). Further, 'stage2'-specific changes were made to a set of 200 additional genes not encountered previously, for each of the categories DE, DS and DE+DS. Therefore, for DSR three-way comparisons, the target gene set represents the 800 genes simulated as being DS or DE+DS between any of the ‘control’, ‘disease’ and ’stage2’ categories, while for the multi-way DSA comparisons the target is the full set of 1,200 genes (400 DE, 400 DS and 400 DE+DS) simulated to have changed between any of the 'control', 'disease’ and ‘stage2’ states.

 Further details: See the ‘key’ package for the gene lists and sample metadata.
DOI: 10.5281/zenodo.14984116
title: Simulated data from the article "MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates"

description: The repository includes alignments of simulated RNA-seq data for evaluating differential splicing detection with covariates. Starting from an empirical transcript expression matrix trained on an RNA-seq data set from lung fibroblasts (GenBank A# SRR493366) and using GENCODE v.41 as reference, 11.5 million 100 bp long paired-end reads were generated per sample, from 2,000 genes with two or more expressed isoforms. RNA-seq data was simulated for one ‘condition’, with values ‘control’, ‘disease’ and ‘stage2’, with one covariate, ‘biological sex’, with values ‘M’ and ‘F’.  10 samples each were simulated for each (condition x sex) category. Changes were simulated in the expression (DE) and/or the splicing ratio (DS) of genes as follows. Changes in expression (DE) were simulated by either halving or doubling the expression level of the gene. Changes in splicing ratios (DS) were simulated by swapping the expression levels of the gene’s top two transcript isoforms. All RNA-seq data was mapped to the hg38 genome with the spliced alignment tool STAR v2.7.10a.

 Pairwise comparison alignment set: Differences due to ‘condition’ between two states, ‘control’ and ‘disease’, were simulated at 600 genes, including 200 DE, 200 DS and 200 DE+DS genes. Differences in ‘biological sex’ (covariate) were represented as changes in 300 genes, with 100 genes from each of the DS, DE and DE+DS categories. Hence, the target gene set for differential splicing ratio (DSR) pairwise comparisons consists of the pooled 200 DS and 200 DS+DE genes differentially spliced between the ‘control’ and ‘disease’ states, while for differential splicing abundance (DSA) pairwise comparisons the target gene set is the set of 600 modified genes, 200 in each of the DS, DE and DS+DE categories.

 Multiway (3-way) comparison alignment set: To create the 'stage2' data, changes in expression and splicing levels were maintained for 3 x 100 of the 'disease' genes (100 in each category). Further, 'stage2'-specific changes were made to a set of 200 additional genes not encountered previously, for each of the categories DE, DS and DE+DS. Therefore, for DSR three-way comparisons, the target gene set represents the 800 genes simulated as being DS or DE+DS between any of the ‘control’, ‘disease’ and ’stage2’ categories, while for the multi-way DSA comparisons the target is the full set of 1,200 genes (400 DE, 400 DS and 400 DE+DS) simulated to have changed between any of the 'control', 'disease’ and ‘stage2’ states.

 Further details: See the ‘key’ package for the gene lists and sample metadata.
DOI: 10.15468/dl.t3tks3
title: Occurrence Download

description: A dataset containing 6199 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Elodea densa (Planch.) Casp.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 6199 records from 442 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007581-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g7epwa
title: Occurrence Download

description: A dataset containing 7343 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Camelina sativa (L.) Crantz)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7343 records from 359 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007580-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cdx4dv
title: Occurrence Download

description: A dataset containing 4204 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Theobroma L.",
 "HasCoordinate is true",
 "Country is Colombia"
 ]
}

 The dataset includes 4204 records from 134 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007582-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5736mf08
title: MassIVE MSV000097256 - Systems-Level Interactomic Mapping Reveals Epichaperome-Mediated Protein Network Dysregulation in Alzheimer's Disease
title: Alzheimer's disease (AD) progresses as a continuum from pre clinical stages through to late-stage cognitive decline, yet the molecular mechanisms driving this remain poorly understood. Here, we show that dynamic and continuous remodeling of protein-protein interaction (PPI) networks by epichaperomes large scaffolding platforms composed of chaperones and other factors acts as a driver of disease progression across this continuum. Using over 100 human brain specimens, mouse models, and human neurons, we demonstrate that epichaperomes form early, even in preclinical AD, and progressively reshape multiple PPI networks, including those crucial for synaptic function and plasticity.
DOI: 10.15468/dl.b6bwm8
title: Occurrence Download

description: A dataset containing 3370 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Camponotus atriceps (Smith, 1858))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3370 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007583-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10960855
title: DATA: Narrative cave art in Indonesia by 51,200 years ago

description: Raw data that support the findings of the study Narrative cave art in Indonesia by 51,200 years ago published in Nature can be found here.
DOI: 10.5281/zenodo.10960856
title: DATA: Narrative cave art in Indonesia by 51,200 years ago

description: Raw data that support the findings of the study Narrative cave art in Indonesia by 51,200 years ago published in Nature can be found here.
DOI: 10.15468/dl.pa6mz2
title: Occurrence Download

description: A dataset containing 4703 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Australian National Insect Collection Image Library"
 ]
}

 The dataset includes 4703 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007516-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7udqgf
title: Occurrence Download

description: A dataset containing 8638 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calystegia macrostegia (Greene) Brummitt"
 ]
}

 The dataset includes 8638 records from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007517-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xrsfrf
title: Occurrence Download

description: A dataset containing 631 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Candoia carinata (Schneider, 1801))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 631 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007584-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xzcapq
title: Occurrence Download

description: A dataset listing the 51 species recorded in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Capparidastrum Hutch."
 ]
}

 The dataset's 51 records were derived from 247 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007513-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d88h3v
title: Occurrence Download

description: A dataset containing 398989 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "GadmGid is USA.23_1",
 "TaxonKey is Plantae",
 "Year 2015-2025"
 ]
}

 The dataset includes 398989 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007579-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kxwqhn
title: Occurrence Download

description: A dataset containing 24342 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hedypnois rhagadioloides (L.) F.W.Schmidt"
 ]
}

 The dataset includes 24342 records from 335 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007585-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.