cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/2a9ac1f0f0e8a4aa1e72d693383de345
title: Temperature, Salinity, Sound Velocity, Location, Depth, Heading, and Velocity measured aboard the R/V Nanuq for the Northern Gulf of Alaska LTER site, 2020-2021

description: This dataset describes measurements from a thermosalinograph and navigation device used aboard the R/V Nanuq during cruises in Resurrection Bay and the Gulf of Alaska. Thermosalinograph data includes temperature, salinity, conductivity, and sound speed measurements every five seconds while the instrument was in use. Navigation data describes latitude, longitude, depth, heading, course over ground, and speed over ground.
 
 Data are collected on R/V Nanuq using the ship's GPS devices and a Seabird Electronics SBE-45 thermosalinograph (TSG) that samples uncontaminated pumped seawater. Temperature and conductivity data are sampled every 5 seconds and salinity and sound velocity are derived parameters. No data quality control has been applied to this data, so users should be cautious that conductivity, salinity and sound speed dropouts to due bubbles are common when the ship is plowing through large waves. 
 
 Data are collected by a variety of projects, including from the NSF-funded Northern Gulf of Alaska Long Term Ecological Research (NGA LTER) program, the Exxon Valdez Oil Spill Trustee Council (EVOSTC) Gulf Watch Alaska GAK1 project, the UAF Sub-Arctic Oceanography Field Course, the Alaska Ocean Observing System (AOOS) glider program, and others.
DOI: 10.25934/pr00008341.1
title: Available datapackage for study 'A Phase 3, Multicenter, Randomized, Double-blind Study Evaluating the Comparative Efficacy of CNTO 1959 (Guselkumab) and Secukinumab for the Treatment of Moderate to Severe Plaque-type Psoriasis'

description: The purpose of this study is to evaluate the efficacy of guselkumab compared with secukinumab for the treatment of participants with moderate to severe plaque-type psoriasis.
DOI: 10.6084/m9.figshare.29120426
title: Replication datasets for Classification of soft decision-making methods via fuzzy parameterized fuzzy soft matrices and their performance-based statistical analysis in machine learning

description: Replication datasets for Classification of soft decision-making methods via fuzzy parameterized fuzzy soft matrices and their performance-based statistical analysis in machine learning
DOI: 10.15468/dl.vu9skx
title: Occurrence Download

description: A dataset containing 3136054 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations, Observation.org, Nature data from around the World, UF Invertebrate Zoology)",
 "TaxonKey is Mollusca"
 ]
}

 The dataset includes 3136054 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012467-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/tbmca-wbj97
title: Supplementary Data: D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

description: Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN constructs probabilistic models of regulatory interactions between genes or gene-expression programs to fit the cell state distributions under different perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal key regulators of cell fate decisions and the coordination of distant cellular pathways in response to gene knockdown perturbations. D-SPIN also dissects gene-level drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs acting on distinct regulators induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene regulatory networks to reveal principles of cellular information processing and physiological control.
DOI: 10.15468/dl.zvfm6r
title: Occurrence Download

description: A dataset containing 16183 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Abroscopus superciliaris (Blyth, 1859)"
 ]
}

 The dataset includes 16183 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012487-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e6vdp7
title: Occurrence Download

description: A dataset containing 118606 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((30.30387 -13.91786, 33.18264 -13.91786, 33.18264 -10.67257, 30.30387 -10.67257, 30.30387 -13.91786))"
 ]
}

 The dataset includes 118606 records from 194 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012466-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3g49t5
title: Occurrence Download

description: A dataset containing 302835 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Atlapetes Wagler, 1831"
 ]
}

 The dataset includes 302835 records from 298 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012472-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25838/d5p-78
title: Data for "Influence of ionic liquids on enzymatic asymmetric carboligations"

description: The asymmetric mixed carboligation of aldehydes catalyzed by thiamine diphosphate (ThDP)-dependent enzymes provides a sensitive system for monitoring changes in activity, chemo-, and enantioselectivity. While previous studies have shown that organic cosolvents influence these parameters, we now demonstrate that similar effects occur upon addition of water-miscible ionic liquids (ILs). In this study, six ThDP-dependent enzymes were analyzed in the presence of 14 ILs under comparable conditions to assess their influence on enzymatic carboligation reactions yielding 2-hydroxy ketones. ILs exerted a moderate to strong influence on activity and, more notably, altered enantioselectivity. (R)-selective reactions were generally stable upon IL addition, while (S)-selective reactions frequently showed reduced selectivity or even inversion to the (R)-enantiomer. The most significant change was observed for the ApPDC_E469G variant of pyruvate decarboxylase from Acetobacter pasteurianus, where the enantiomeric excess shifted from 86% (S) to 60% (R) in the presence of 9% (w/v) Ammoeng 102. Control experiments indicated that this shift was primarily due to the Ammoeng cation rather than the anion. To explore the molecular basis of this phenomenon, all-atom molecular dynamics (MD) simulations were performed on wild-type ApPDC and the E469G variant in Ammoeng 101 and Ammoeng 102. The simulations revealed that hydrophobic and hydrophilic regions of the Ammoeng cations interact with the (S)-selective binding pocket, thereby favoring formation of the (R)-product. These results highlight the potential of solvent engineering for modulating enzyme selectivity and demonstrate that MD simulations can capture functionally relevant enzyme–solvent interactions at the atomic level.
DOI: 10.25345/c5f47h64d
title: MassIVE MSV000097955 - Integrated multi-omic analysis reveals novel subtype-specific regulatory interactions in pediatric B-cell acute lymphoblastic leukemia
title: In this study, we performed proteomic and phosphoproteomic profiling of B-cell acute lymphoblastic leukemia (B-ALL) samples collected at diagnosis and remission from pediatric leukemia patients with one of two subtypes of B-ALL: BCR::ABL1-like (Ph-like) and ETV6::RUNX1. We analyzed each dataset, as well as existing RNAseq data at diagnosis, to identify subtype-specific features, including increased calcium-dependent signaling in Ph-like samples. We then performed an integrated analysis of all three datasets, which enabled us to identify multiple layers of regulation, including subtype-specific phosphorylation of known cancer-associated proteins. Taken together, these data add to our understanding of the molecular profile of Ph-like and ETV6::RUNX1 B-ALL, demonstrating the utility of multi-omic comparison in pediatric leukemia subtype characterization.
DOI: 10.25345/c59c6sd1r
title: MassIVE MSV000097956 - Proteostasis sustains T cell differentiation potential and tumor-infiltrating lymphocyte function
title: Tumors that contain tumor-infiltrating lymphocytes (TIL) with tissue-resident memory T cells (TRM) features are associated with better patient prognoses. While CD8+ T cells that target tumors often fail to restrain tumor growth due to progressive T cell exhaustion, pathogen-specific TRM survey tissues and sustain protective capacity for years. We identified protein homeostasis (proteostasis)-related E3 ubiquitin ligase genes shared by TRM and progenitor-exhausted TIL (TPEX), that are lost as TIL become terminally-exhausted (TEX). Sustained ligase expression led to increased TCF1+ T cell accumulation and improved anti-tumor function and less T cell exhaustion in chronic infection. Loss-of-function alleles in T cells challenged with cancer models impaired TIL accumulation and function and altered T cell differentiation in response to acute infection. Mass spectrometry revealed abundant expression of protein degradation pathways by TEX, yet they accumulated unfolded proteins. Ligase overexpression rescued unfolded protein accumulation in TEX, demonstrating their functional contribution to proteostasis. Ligase overexpression in combination with immune checkpoint blockade led to improved survival from murine melanoma, demonstrating that the relationship between proteostasis and T cell differentiation opens up new avenues for cancer immunotherapy.
DOI: 10.15468/dl.zhvdjb
title: Occurrence Download

description: A dataset containing 81858 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Mammalia)",
 "HasCoordinate is true",
 "DecimalLatitude is greater than or equal to 42.0327356",
 "DecimalLatitude is less than or equal to 73.8012356",
 "DecimalLongitude is greater than or equal to 58.5436",
 "DecimalLongitude is less than or equal to 179.999"
 ]
}

 The dataset includes 81858 records from 152 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012473-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bej5e7
title: Occurrence Download

description: A dataset containing 506909 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is IND.16_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2017-2017"
 ]
}

 The dataset includes 506909 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012451-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/qtm95-b2j72
title: Mass-Spectrometry data for "The Environment-Dependent Regulatory Landscape of the E. coli Genome "

description: Code to process this data and identify sample names can be found at: https://github.com/RPGroup-PBoC/reg-seq_env
DOI: 10.15468/dl.4em2ta
title: Occurrence Download

description: A dataset containing 41271185 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Observation.org, Nature data from around the World, International Barcode of Life project (iBOL))",
 "TaxonKey is Arthropoda"
 ]
}

 The dataset includes 41271185 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012478-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15484477
title: REBIPP Plant-Pollinator Interactions Download - 8c0de685d0a45dfcfd913f361235641e0fdac62565318511ae2336a00a7df623

description: A dataset containing all interactions available in REBIPP-PPI matching the query:
            
{"and":["Plant is Sapium glandulosum","Year anyany"]}
The dataset includes records from the following constituent datasets.
            The full metadata for each constituent is also included in this archive:
[object Promise]
DOI: 10.5281/zenodo.15484478
title: REBIPP Plant-Pollinator Interactions Download - 8c0de685d0a45dfcfd913f361235641e0fdac62565318511ae2336a00a7df623

description: A dataset containing all interactions available in REBIPP-PPI matching the query:
            
{"and":["Plant is Sapium glandulosum","Year anyany"]}
The dataset includes records from the following constituent datasets.
            The full metadata for each constituent is also included in this archive:
[object Promise]
DOI: 10.34740/kaggle/dsv/11900214
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.7910/dvn/glputm
title: Replication Data for: "How do Cyber-Risks Vary Across Smart City Technologies?" Journal of Urban Technology. 2025.

description: These replication materials contain the data and R code used to generate the analyses included in our paper.
DOI: 10.6084/m9.figshare.28590278
title: Sparse Bayesian Multidimensional Item Response Theory

description: Multivariate Item Response Theory (MIRT) is sought-after widely by applied researchers looking for interpretable (sparse) explanations underlying response patterns in questionnaire data. There is, however, an unmet demand for such sparsity discovery tools in practice. Our article develops a Bayesian platform for binary and ordinal item MIRT which requires minimal tuning and scales well on large datasets due to its parallelizable features. Bayesian methodology for MIRT models has traditionally relied on MCMC simulation, which cannot only be slow in practice, but also often renders exact sparsity recovery impossible without additional thresholding. In this work, we develop a scalable Bayesian EM algorithm to estimate sparse factor loadings from mixed continuous, binary, and ordinal item responses. We address the seemingly insurmountable problem of unknown latent factor dimensionality with tools from Bayesian nonparametrics which enable estimating the number of factors. Rotations to sparsity through parameter expansion further enhance convergence and interpretability without identifiability constraints. In our simulation study, we show that our method reliably recovers both the factor dimensionality as well as the latent structure on high-dimensional synthetic data even for small samples. We demonstrate the practical usefulness of our approach on three datasets: an educational assessment dataset, a quality-of-life measurement dataset, and a bio-behavioral dataset. All demonstrations show that our tool yields interpretable estimates, facilitating interesting discoveries that might otherwise go unnoticed under a pure confirmatory factor analysis setting. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.28590278.v1
title: Sparse Bayesian Multidimensional Item Response Theory

description: Multivariate Item Response Theory (MIRT) is sought-after widely by applied researchers looking for interpretable (sparse) explanations underlying response patterns in questionnaire data. There is, however, an unmet demand for such sparsity discovery tools in practice. Our paper develops a Bayesian platform for binary and ordinal item MIRT which requires minimal tuning and scales well on large datasets due to its parallelizable features. Bayesian methodology for MIRT models has traditionally relied on MCMC simulation, which cannot only be slow in practice, but also often renders exact sparsity recovery impossible without additional thresholding. In this work, we develop a scalable Bayesian EM algorithm to estimate sparse factor loadings from mixed continuous, binary, and ordinal item responses. We address the seemingly insurmountable problem of unknown latent factor dimensionality with tools from Bayesian nonparametrics which enable estimating the number of factors. Rotations to sparsity through parameter expansion further enhance convergence and interpretability without identifiability constraints. In our simulation study, we show that our method reliably recovers both the factor dimensionality as well as the latent structure on high-dimensional synthetic data even for small samples. We demonstrate the practical usefulness of our approach on three datasets: an educational assessment dataset, a quality-of-life measurement dataset, and a bio-behavioral dataset. All demonstrations show that our tool yields interpretable estimates, facilitating interesting discoveries that might otherwise go unnoticed under a pure confirmatory factor analysis setting.
DOI: 10.6084/m9.figshare.28590278.v2
title: Sparse Bayesian Multidimensional Item Response Theory

description: Multivariate Item Response Theory (MIRT) is sought-after widely by applied researchers looking for interpretable (sparse) explanations underlying response patterns in questionnaire data. There is, however, an unmet demand for such sparsity discovery tools in practice. Our article develops a Bayesian platform for binary and ordinal item MIRT which requires minimal tuning and scales well on large datasets due to its parallelizable features. Bayesian methodology for MIRT models has traditionally relied on MCMC simulation, which cannot only be slow in practice, but also often renders exact sparsity recovery impossible without additional thresholding. In this work, we develop a scalable Bayesian EM algorithm to estimate sparse factor loadings from mixed continuous, binary, and ordinal item responses. We address the seemingly insurmountable problem of unknown latent factor dimensionality with tools from Bayesian nonparametrics which enable estimating the number of factors. Rotations to sparsity through parameter expansion further enhance convergence and interpretability without identifiability constraints. In our simulation study, we show that our method reliably recovers both the factor dimensionality as well as the latent structure on high-dimensional synthetic data even for small samples. We demonstrate the practical usefulness of our approach on three datasets: an educational assessment dataset, a quality-of-life measurement dataset, and a bio-behavioral dataset. All demonstrations show that our tool yields interpretable estimates, facilitating interesting discoveries that might otherwise go unnoticed under a pure confirmatory factor analysis setting. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29120516
title: Tail calibration of probabilistic forecasts

description: Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we introduce a general notion of tail calibration for probabilistic forecasts, which allows forecasters to assess the reliability of their predictions for extreme outcomes. We study the relationships between tail calibration and standard notions of forecast calibration, and discuss connections to peaks-over-threshold models in extreme value theory. Diagnostic tools are introduced and applied in a case study on European precipitation forecasts.
DOI: 10.6084/m9.figshare.29120516.v1
title: Tail calibration of probabilistic forecasts

description: Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we introduce a general notion of tail calibration for probabilistic forecasts, which allows forecasters to assess the reliability of their predictions for extreme outcomes. We study the relationships between tail calibration and standard notions of forecast calibration, and discuss connections to peaks-over-threshold models in extreme value theory. Diagnostic tools are introduced and applied in a case study on European precipitation forecasts.
DOI: 10.15468/dl.n82z7f
title: Occurrence Download

description: A dataset containing 3420 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Brazil",
 "TaxonKey is Compsothraupis loricata (M.H.K.Lichtenstein, 1819)"
 ]
}

 The dataset includes 3420 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012488-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25574/01199
title: Chandra X-ray Observatory ObsId 01199
DOI: 10.5517/ccdc.csd.cc2m621z
title: CCDC 2418900: Experimental Crystal Structure Determination

description: Related Article: Najoua Sbei, Seyfeddine Rahali, Nisar Ahmed|2025|Org.Biomol.Chem.|23|4917|doi:10.1039/D5OB00522A
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: 10.5517/ccdc.csd.cc2m99hr
title: CCDC 2422014: Experimental Crystal Structure Determination

description: Related Article: Dongmei Wu, Jianwu Wei, Binbin Luo, Liya Zhou, Peican Chen, Jie Tian, Jiahong Pan, Alexei V Emeline, Jin Zhong Zhang, Qi Pang|2025|J.Phys.Chem.Lett.|16|4181|doi:10.1021/acs.jpclett.5c00713
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: 10.15139/s3/4snnjb
title: EEG Data

description: This data contains raw EEG data for each subject's EEG session, including resting state and reward tasks.
DOI: 10.5281/zenodo.15484502
title: Anexo 1. Movimientos feministas europeos, estadounidense y mexicano en el posicionamiento del bienestar en el centro de las políticas públicas.
DOI: 10.5281/zenodo.15484503
title: Anexo 1. Movimientos feministas europeos, estadounidense y mexicano en el posicionamiento del bienestar en el centro de las políticas públicas.