cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.7l83du
title: Occurrence Download

description: A dataset containing 52670 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Cymbalaria muralis G.Gaertn., B.Mey. & Scherb."
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 52670 records from 288 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012529-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fb5s2k
title: Occurrence Download

description: A dataset containing 369457 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is izeltlabuak.hu observations validated to species level"
 ]
}

 The dataset includes 369457 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054222-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5d795p41
title: MassIVE MSV000096760 - GNPS: Time-lapse Quantitative Phosphoproteomic Analyses Reveal Bhallataka Taila's Role in Lung Cancer Cell Apoptosis and Survival
title: Lung cancer ranks second in terms of new cases and is the leading cause of cancer-related deaths with 5-year survival of less than 18%. This is due to the poor prognosis with approximately half of people die within one year of diagnosis with lung cancer. This necessitates the quest for alternative, safe and effective treatments for lung cancer. The molecularization of traditional medicines for cancer treatment by applying modern scientific techniques such as genomics, proteomics, and metabolomics, will enable to transform traditional medicine into newer adjuvant therapies or targeted therapies with enhanced efficacy. Bhallataka taila, an Ayurvedic formulation derived from the nuts of Semecarpus anacardium is traditionally used in cancer treatment. The understanding of the signaling mechanisms of Bhallataka taila in lung cancer is currently limited. Our study aimed to assess the anticancer potential of Bhallataka taila on lung cancer cells by examining changes in signaling pathways and kinases. We performed a temporal quantitative multiplexed phosphoproteomic analysis using tandem mass tag (TMT) labelling to investigate the temporal signaling dynamics induced by Bhallataka taila in A549 cells. This approach provides insight into the Bhallataka taila signaling mechanisms and also aid in the identification of potential kinases as therapeutic targets for lung cancer monitoring and treatment. Gene Ontology (GO) analysis and pathway analysis was done for the significantly altered proteins and was later validated by immunoblotting.
DOI: 10.34740/kaggle/dsv/10354732
title: World Stock Prices ( Daily Updating )
DOI: 10.15468/dl.a7az55
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bathygadidae, Bathygadidae, 210654722, Bathygadidae, Bathygadidae, Bathygadidae, Bathygadidae, Bathygadidae, Bathygadidae, Bathygadidae Jordan & Evermann 1898, Bathygadidae Jordan & Evermann, 1898, Bathygadidae Jordan & Evermann, 1898, Bathygadidae Jordan & Evermann, 1898, Bathygadidae (Jordan & Evermann, 1898), Macrouridae Bonaparte, 1831, Macrouridae)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((109.5 20.15,117.25 20.15,117.25 24,109.5 24,109.5 20.15))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054225-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pk8jdm
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (205001038, 205193100, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, 209432313, 204774793, Lotidae, Lotidae, Lotidae, 212273342, 209454848, Lotidae, Lotidae, Lotidae, 209404977, Lotidae, Lotidae, 212282762, 212386846, 211759489, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, 193369923, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, LOTIDAE, Lotidae, Lotidae, Lotidae, 209345965, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, 116776129, 210477345, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae, Lotidae Bonaparte, 1835, Lotidae Bonaparte, 1835, Lotidae Bonaparte, 1835, Lotidae Bonaparte, 1835, Lotidae Bonaparte, 1835, Lotidae Bonaparte, 1832, Lotidae (Bonaparte, 1842), 161463013, Lotidae Bonaparte, 1832, 161419301, Lottiidae Gray, 1840, 211767585, Lottidae, Lottiidae Gray, 1840, 204359236, 210221386, Lottiidae, Lottiidae, Lottiidae, Lottiidae Gray, 1840, 181425143, Lottiidae, Lottiidae, Lottiidae, 205967301, Lottiidae Gray, 1840, Lottiidae, Lottiidae, 203876326, Lottiidae Gray, 1840, Lottiidae, 203396174, Lottiidae Gray, 1840, 212282422, Lottiidae, Lottiidae J.E.Gray, 1840, Lottiidae?, Lottiidae, Lottiidae, Lottiidae, 212274788, Lottiidae Gray, 1840, Lottiidae Gray, 1840, Lottiidae, Lottiidae, Lottidae [sic], Lottiidae, 212357708, 212112410, 212105664, 157568679, Lottiidae Gray, 1840, Lottiidae, Lottiidae Gray, 1840, Lottiidae, Lottiidae, Lottiidae, Lottiidae, Lottiidae, LOTTIIDAE, Lottiidae, Lottiidae Gray, 1840, Lottidae, 211934851, Lottiidae, Lottidae, Lottidae, Gadidae Rafinesque, 1810, Gadidae Rafinesque, 1810, Patelloididae Chapman & Gabriel, 1923, Patelloididae Chapman & Gabriel, 1923, Tecturidae J. E. Gray, 1847, 211934723, 211934773, Tecturidae J.E.Gray, 1847)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((109.5 20.15,117.25 20.15,117.25 24,109.5 24,109.5 20.15))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 39 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054226-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc278k69
title: CCDC 2063800: Experimental Crystal Structure Determination

description: Related Article: Danbo Wang, Guangming Guo, Renzeng Chen, Yuzhen Gong, Lishui Sun, Yingjie Zhao|2021|Sci.China.Chem.|64|1510|doi:10.1007/s11426-021-1030-7
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.beyf6x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Macrouroididae, Macrouroididae, Macrouroididae)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((109.5 20.15,117.25 20.15,117.25 24,109.5 24,109.5 20.15))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054227-241126133413365/datasets/export for details.
DOI: 10.60656/a21c54c0858842f2
title: Custom collection of categorize, classification, drizzle, ice water content, liquid water content, and model data from Potenza on 17 Oct 2012
DOI: 10.5281/zenodo.14040230
title: Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity

description: Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. 
description: The dataset is available on reuest from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
DOI: 10.5281/zenodo.14040231
title: Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity

description: Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. 
description: The dataset is available on reuest from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. 

e-mail: tomas.hajek@dal.ca
DOI: 10.5281/zenodo.14163108
title: Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity

description: Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. 
description: The dataset is available on reuest from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
DOI: 10.15468/dl.2batx8
title: Occurrence Download

description: A dataset containing 12577 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus glauca Thunb."
 ]
}

 The dataset includes 12577 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054229-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60656/9ab30d3195764590
title: Custom collection of categorize, classification, drizzle, ice water content, liquid water content, and model data from Potenza on 26 Oct 2012
DOI: 10.15468/dl.83yrkp
title: Occurrence Download

description: A dataset containing 334 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Cuba",
 "OccurrenceStatus is Present",
 "TaxonKey is Megachilidae"
 ]
}

 The dataset includes 334 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054230-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9pu9nc
title: Occurrence Download

description: A dataset containing 117030 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Angola",
 "MediaType is Image",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 117030 records from 160 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054224-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.55pgy8
title: Occurrence Download

description: A dataset containing 6523 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-98.52841 3.99939,-42.35341 3.99939,-42.35341 26.49939,-98.52841 26.49939,-98.52841 3.99939))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Procyon lotor (Linnaeus, 1758)"
 ]
}

 The dataset includes 6523 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054234-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ytfbt2
title: Occurrence Download

description: A dataset containing 34865 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abutilon theophrasti Medik."
 ]
}

 The dataset includes 34865 records from 660 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054235-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s6q954
title: Occurrence Download

description: A dataset containing 8245 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acalypha australis L."
 ]
}

 The dataset includes 8245 records from 229 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054236-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/x6xr4vptn6.1
title: research data for H isotope fractionation at high P-T conditions

description: Raw data for all figures in the manuscript by Gao and Liu to be published by Geochimica et Cosmochimica Acta as "Path-integral molecular dynamics predictions of H isotope fractionation between brucite and water at elevated temperatures and pressures".
DOI: 10.17632/p3bc4d8css.1
title: miRNAs patents

description: miRNAs patents retrieved from Derwent Innovation.
DOI: 10.17632/4vfnscgrvt.1
title: TGRS support information

description: Supprt information for Simulation of Pantana phyllostachysae Chao hazard spread in Moso bamboo (Phyllostachys pubescens) forests based on XGBoost-CA model
DOI: 10.17632/z3s6mnj76g
title: Multi-omics profiling reveals ortho-topolin riboside and protocatechualdehyde combination exhibits anti-proliferative activity by modulating metabolic pathways in in vitro and in vivo radio-resistant MDA-MB-231 cell models

description: Transcriptome and proteome data analysis of radio-resistant MDA-MB-231 Cells following co-treatment of oTR and PA
DOI: 10.17632/z3s6mnj76g.1
title: Multi-omics profiling reveals ortho-topolin riboside and protocatechualdehyde combination exhibits anti-proliferative activity by modulating metabolic pathways in in vitro and in vivo radio-resistant MDA-MB-231 cell models

description: Transcriptome and proteome data analysis of radio-resistant MDA-MB-231 Cells following co-treatment of oTR and PA
DOI: 10.17632/vx3kv6md5j.1
title: Yunjia data

description: the data of the SSCE article . Please use matlab2021b to use the data and code.
DOI: 10.17632/st2t7dv3xz.1
title: CODES-ON TORSION SUBGROUPS OF ELLIPTIC CURVES OVER QUARTIC, QUINTIC AND SEXTIC NUMBER FIELDS

description: Codes relevant to the paper named "ON TORSION SUBGROUPS OF ELLIPTIC CURVES OVER QUARTIC, QUINTIC AND SEXTIC NUMBER FIELDS"
DOI: 10.17632/c4ptw8r2c8.1
title: The geochemical component and typical parameters of surface samples across the First Meander of the Yellow River and relevant climatic and environmental variables

description: Geochemical component and typical parameters of surface samples across the First Meander of the Yellow River and corresponding climatic and environmental variables extract from the Arc GIS
DOI: 10.15468/dl.tyjcg7
title: Occurrence Download

description: A dataset containing 216 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.15.4_1",
 "TaxonKey is one of (Caprimulgiformes, Chiroptera, Strigiformes)"
 ]
}

 The dataset includes 216 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054239-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rpvmhjkhf4.1
title: SES_HH_2013_2019_2021

description: Thailand's SES 2013-2019-2021 and R code
DOI: 10.25345/c58g8fw0w
title: MassIVE MSV000096761 - Proteomics of South African archaeological bead residues
title: This study examines residues taken from archaeological ostrich eggshell beads.
DOI: 10.6084/m9.figshare.28123403
title: Mapping human impacts to seagrass meadows across the world’s marine protected areas

description: *Data and code for: Mapping human impacts to seagrass meadows across the world’s marine protected areasAll data from SeagrassSpotter is available open access: http://seagrassspotter.org*World Marine Protected Areas shapefiles are not uploaded.
DOI: 10.6084/m9.figshare.28099585
title: An <i>in-silico</i> study of FIKK9.5 protein of <i>Plasmodium falciparum</i> for identification of therapeutics

description: The FIKK protein family, encompassing 21 serine-threonine protein kinases, is a distinctive cluster exclusive to the Apicomplexa phylum. Predominantly located in <i>Plasmodium falciparum</i> which is a malarial parasite, with a solitary gene identified in a distinct apicomplexan species, this family derives its nomenclature from - phenylalanine, isoleucine, lysine, lysine (FIKK), a conserved amino acid motif. Integral to the parasite’s life cycle and consequential to malaria pathogenesis, the absence of orthologous proteins in eukaryotic organisms designates it as a promising antimalarial drug target. Among the FIKKs, FIKK9.5 plays a pivotal role in the parasite’s development within red blood cells (RBCs). This investigation acquired the three-dimensional structure of FIKK9.5 and its ligands through extensive database searches and literature review. Computational screening of natural phytochemicals derived from plants traditionally used in antimalarial remedies was conducted by employing the Glide docking suite. AutoDock Vina was utilized to discern the inhibitor exhibiting optimal binding affinity. Subsequently, Molecular Dynamics (MD) simulations employing GROMACS validated Rufigallol as the most potent inhibitory compound against FIKK9.5. The robustness of the protein-ligand complex was scrutinized through a 200 nanosecond molecular dynamics (MD) trajectory. Trajectory analysis and determination of binding free energies were accomplished using MM-GBSA and MM-PBSA approaches. The ligand-binding exhibited sustained stability throughout the simulation, manifesting an approximate binding free energy of −25.5986 kcal/mol. This comprehensive computational study lays the groundwork for potential experimental validation in the laboratory, paving the way for the development of novel therapeutics targeting FIKK9.5 in the pursuit of innovative antimalarial.