cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.88ryu6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Platyhelminthes",
 "DatasetKey is Observations naturalistes indépendantes d'origine privée partagées sur la base de données Kollect Nouvelle-Aquitaine-Observations faunistiques de Thomas GACHET",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-25.03991 31.75, 41.65954 31.75, 41.65954 71.53037, -25.03991 71.53037, -25.03991 31.75))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0218441-230224095556074/datasets/export for details.
DOI: 10.15468/dl.q7sxfv
title: Occurrence Download

description: A dataset containing 18456 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((150.26405 -50.89949,199.73595 -50.89949,199.73595 -31.10051,150.26405 -31.10051,150.26405 -50.89949))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dicksonia squarrosa (G.Forst.) Sw."
 ]
}

 The dataset includes 18456 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007986-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc112044974
title: Cuneiform Tablet 1913.14.0169, Spurlock 1913.14.0169

description: [Text Description:] Administrative tablet ❧ [Text or Publication Number Note:] Spurlock 1913.14.0169; CDLI P420550 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Yellowish White (10YR 9/2) Brownish Pink (5YR 8/3) ❧ [Physical Object Note:] 7.5 x 4.5 x 2.3 cm (82g) ❧ [Physical Object Note:] Seal impressions
DOI: 10.15468/dl.63vfr7
title: Occurrence Download

description: A dataset containing 184 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cephalophus callipygus Peters, 1876"
 ]
}

 The dataset includes 184 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0349010-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ba7yvq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Platyhelminthes",
 "DatasetKey is Visiolittoral : Base de données de la communauté de travail du Conservatoire du littoral-Visiolittoral : surveillance naturaliste des sites du Conservatoire du littoral",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-25.03991 31.75, 41.65954 31.75, 41.65954 71.53037, -25.03991 71.53037, -25.03991 31.75))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0218445-230224095556074/datasets/export for details.
DOI: 10.15468/dl.t4qdqu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Platyhelminthes",
 "DatasetKey is La Planète revisitée - Corse 2019-2022-La Planète Revisitée Corse : observations lors des phases de reconnaissance et d'installation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-25.03991 31.75, 41.65954 31.75, 41.65954 71.53037, -25.03991 71.53037, -25.03991 31.75))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0218446-230224095556074/datasets/export for details.
DOI: 10.25549/inscriptifact-ouc112019884
title: Cuneiform Tablet 1913.14.0257, Spurlock 1913.14.0257

description: [Text Description:] Administrative tablet ❧ [Text or Publication Number Note:] Spurlock 1913.14.0257; CDLI P420639 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Moderate Yellowish Pink (5YR 7/4) ❧ [Physical Object Note:] 4.5 x 4.3 x 1.5 cm (66g) ❧ [Physical Object Note:] Seal impressions
DOI: 10.15468/dl.uvrvva
title: Occurrence Download

description: A dataset containing 2063 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "Continent is Europe",
 "Year is greater than 1978",
 "Year is less than 2014",
 "HasGeospatialIssue is false",
 "TaxonKey is Galium triflorum Michx."
 ]
}

 The dataset includes 2063 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020851-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3usrx4
title: Occurrence Download

description: A dataset containing 364 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Orsillus depressus (Mulsant & Rey, 1852) Mulsant & Rey, 1852"
 ]
}

 The dataset includes 364 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004705-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc112044899
title: Cuneiform Tablet 1913.14.0169, Spurlock 1913.14.0169

description: [Text Description:] Administrative tablet ❧ [Text or Publication Number Note:] Spurlock 1913.14.0169; CDLI P420550 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Yellowish White (10YR 9/2) Brownish Pink (5YR 8/3) ❧ [Physical Object Note:] 7.5 x 4.5 x 2.3 cm (82g) ❧ [Physical Object Note:] Seal impressions
DOI: 10.15468/dl.cedg34
title: Occurrence Download

description: A dataset containing 2420758 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Prunella ocularis (Radde, 1884), Prunella collaris (Scopoli, 1769), Prunella modularis (Linnaeus, 1758))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2420758 records from 2396 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004714-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31310356
title: Supp 2 - WGCNA Module Trait Correlations

description: Pearson correlation coefficients and p-values between WGCNA co-expression modules and experimental traits (treatment, sex, drug). Contains 11 modules (including grey/unassigned). CSV format, 12 rows including header.
DOI: 10.6084/m9.figshare.31310356.v1
title: Supp 2 - WGCNA Module Trait Correlations

description: Pearson correlation coefficients and p-values between WGCNA co-expression modules and experimental traits (treatment, sex, drug). Contains 11 modules (including grey/unassigned). CSV format, 12 rows including header.
DOI: 10.25345/c59s1m01j
title: MassIVE MSV000100793 - LC-MS/MS analysis of Pseudonocardia sp. extracts
title: This dataset comprises untargeted LC MS/MS data generated from 36 Pseudonocardia strains isolated from Amazonian fungus-growing ants of the genus Paratrachymyrmex. The dataset includes RAW LC MS/MS files and corresponding converted mzXML files, organized by strain and biological replicate.
DOI: 10.34740/kaggle/dsv/14800632
title: Berita UNDIP
DOI: 10.25934/pr00008428
title: Type 1 Diabetes EXercise Initiative: The Effect of Exercise on Glycemic Control in Type 1 Diabetes Study

description: 
Brief Summary: The Type 1 Diabetes Exercise Initiave (T1-DEXI) was an observational study of adults living with type 1 diabetes in the U.S., conducted remotely outside of clinics, designed to develop a better understanding of the effects of different levels of exercise intensity and duration on glycemic control during and after exercise across a wide range of patient characteristics.  This dataset incorporates aggregated data around exercise events including pertinent diabetes management information (insulin and continuous glucose monitoring data), self-reported and objectively measured physical activity levels (Polar H10 sensor and Verily Study Watch), self-reported stress levels and life-event data such as the timing and composition of meals (Remote Food Photography Method). Genotyping, completed for a subset of participants, may help researchers understand how variations in DNA may be associated with exercise, diabetes, and glycemic response to exercise.
DOI: 10.25934/pr00008429
title: Type 1 Diabetes EXercise Initiative Pediatric Study (T1DexiP): The Effect of Exercise on Glycemic Control in Youth with Type 1 Diabetes

description: Brief Summary: The Type 1 Diabetes Exercise Initiave for Pediatrics (T1-DEXIP) is an observational study of children living with type 1 diabetes in the U.S., conducted remotely outside of clinics, designed to develop a better understanding of the effects of different levels of exercise intensity and duration on glycemic control during and after exercise across a wide range of patient characteristics.  This dataset incorporates aggregated data around exercise events including pertinent diabetes management information (insulin and continuous glucose monitoring data), self-reported and objectively measured physical activity levels (Garmin), self-reported life-event data such as the timing and composition of meals (Remote Food Photography Method). Genotyping, completed for a subset of participants, may help researchers understand how variations in DNA may be associated with exercise, diabetes, and glycemic response to exercise.
DOI: 10.25934/pr00008885
title: ReplayBG-E: Expansion of the ReplayBG software to assess exercise-aware treatments of type 1 diabetes

description: N/A
DOI: 10.6084/m9.figshare.30936958
title: Feature Selection in Cox Model with Partially Observed Covariates: Application to Oncology Trials

description: In many real-life experiments with human subjects, missing data are common. Multiple imputation is widely used to handle unobserved data points. In statistical research, selecting important variables from multiple imputed datasets can be challenging, as each imputed dataset may yield different sets of variables. Over the last decade, stacking imputed datasets and analyzing the resulting integrated data has gained attention. In this article, we consider both horizontal and vertical stacking approaches. The horizontal stacking approach in conjunction with different group penalties is discussed alongside the recently proposed vertical appending method, for identifying predominant variables under time-to-event data. The proposed methods are investigated numerically. Finally, the methods are illustrated in two real-world oncology experiments.
DOI: 10.6084/m9.figshare.30936958.v2
title: Feature Selection in Cox Model with Partially Observed Covariates: Application to Oncology Trials

description: In many real-life experiments with human subjects, missing data are common. Multiple imputation is widely used to handle unobserved data points. In statistical research, selecting important variables from multiple imputed datasets can be challenging, as each imputed dataset may yield different sets of variables. Over the last decade, stacking imputed datasets and analyzing the resulting integrated data has gained attention. In this article, we consider both horizontal and vertical stacking approaches. The horizontal stacking approach in conjunction with different group penalties is discussed alongside the recently proposed vertical appending method, for identifying predominant variables under time-to-event data. The proposed methods are investigated numerically. Finally, the methods are illustrated in two real-world oncology experiments.
DOI: 10.15468/dl.nmgnhg
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is one of (CAS Entomology (ENT), Field Museum of Natural History (Zoology) Insect, Arachnid and Myriapod Collection, Museum of Comparative Zoology, Harvard University, Illinois Natural History Survey Insect Collection, NEON Biorepository Benthic Macroinvertebrate Collection (Taxonomy Subsample), Bee Biology and Systematics Laboratory, C.P. Gillette Museum of Arthropod Diversity, Denver Museum of Nature & Science - Arachnology)",
 "Geometry POLYGON((-115.68577 33.1499,-108.82638 32.04025,-101.69728 33.1499,-100.59809 35.86262,-101.69728 48.03302,-115.68577 48.03302,-115.68577 33.1499))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Thanatophilus coloradensis"
 ]
}

 The dataset includes 102 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006601-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30936958.v1
title: Feature selection in Cox model with partially observed covariates: Application to oncology trials

description: In many real-life experiments with human subjects, missing data are common. Multiple imputation is widely used to handle unobserved data points. In statistical research, selecting important variables from multiple imputed datasets can be challenging, as each imputed data set may yield different sets of variables. Over the last decade, stacking imputed datasets and analyzing the resulting integrated data has gained attention. In this article, we consider both horizontal and vertical stacking approaches. The horizontal stacking approach in conjunction with different group penalties is discussed alongside the recently proposed vertical appending method, for identifying predominant variables under time-to-event data. The proposed methods are investigated numerically. Finally, the methods are illustrated in two real-world oncology experiments.
DOI: 10.6084/m9.figshare.30788395
title: Momentum Effects in Team Sports: Analyzing the Interplay between Offense and Defense in the NBA

description: The phenomenon of momentum in sports has been explored in various studies; however, to date, there is limited evidence regarding the interplay between offense and defense. Using play-by-play data from the NBA spanning seasons 2015/16 to 2018/19, we investigate potential team-level momentum effects. Within a state-space modeling framework, we initially analyze offensive and defensive performances independently before integrating them into a joint framework. Our findings do not reveal evidence for momentum effects when the offense and the defense are considered in isolation. In contrast, we identify a (weak) momentum effect in the combined model. These results suggest that a successful defense performance affects the offense and vice versa. In addition, it underscores the necessity of including the interplay between offense and defense for an accurate analysis of momentum in team sports.
DOI: 10.6084/m9.figshare.30788395.v2
title: Momentum Effects in Team Sports: Analyzing the Interplay between Offense and Defense in the NBA

description: The phenomenon of momentum in sports has been explored in various studies; however, to date, there is limited evidence regarding the interplay between offense and defense. Using play-by-play data from the NBA spanning seasons 2015/16 to 2018/19, we investigate potential team-level momentum effects. Within a state-space modeling framework, we initially analyze offensive and defensive performances independently before integrating them into a joint framework. Our findings do not reveal evidence for momentum effects when the offense and the defense are considered in isolation. In contrast, we identify a (weak) momentum effect in the combined model. These results suggest that a successful defense performance affects the offense and vice versa. In addition, it underscores the necessity of including the interplay between offense and defense for an accurate analysis of momentum in team sports.
DOI: 10.6084/m9.figshare.30788395.v1
title: Momentum effects in team sports: analyzing the interplay between offense and defense in the NBA

description: The phenomenon of momentum in sports has been explored in various studies; however, to date, there is limited evidence regarding the interplay between offense and defense. Utilizing play-by-play data from the NBA spanning seasons 2015/16 to 2018/19, we investigate potential team-level momentum effects. Within a state-space modeling framework, we initially analyze offensive and defensive performances independently before integrating them into a joint framework. Our findings do not reveal evidence for momentum effects when the offense and the defense are considered in isolation. In contrast, we identify a (weak) momentum effect in the combined model. These results suggest that a successful defense performance affects the offense and vice versa. In addition, it underscores the necessity of including the interplay between offense and defense for an accurate analysis of momentum in team sports.
DOI: 10.6084/m9.figshare.30753199
title: DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting

description: To unlock access to stronger winds, the offshore wind industry is advancing toward significantly larger and taller wind turbines. This massive upscaling motivates a departure from wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE—a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate nonstationary kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high-dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from offshore wind energy areas in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods.
DOI: 10.6084/m9.figshare.30753199.v1
title: DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting

description: To unlock access to stronger winds, the offshore wind industry is advancing toward significantly larger and taller wind turbines. This massive upscaling motivates a departure from wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE—a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate nonstationary kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high-dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from offshore wind energy areas in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods.
DOI: 10.26300/gb26-kp55
title: Data and code for Reciprocal signals mediate cleaning interactions between cleaner shrimp and their reef fish clients
DOI: 10.25574/cdc.547
title: Chandra X-ray Observatory Chandra Data Collection for request #547

description: Chandra Data Collection for request #547
DOI: 10.34740/kaggle/dsv/14800647
title: Berita UNNES
DOI: 10.15468/dl.brc4ad
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Atriplex cordifolia"
 ]
}

 The dataset includes 60 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006605-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18602493
title: micromeria_tenensis_spades_02.fasta.gz
DOI: 10.5281/zenodo.18602494
title: micromeria_tenensis_spades_02.fasta.gz
DOI: 10.15468/dl.2taawe
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Holocheilus pinnatifidus",
 "Year 1995-2026"
 ]
}

 The dataset includes 12 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006607-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18601774
title: HLVK O5 Observing Scenario Simulations Using the GWTC-4 Population Model: Detection Rates, Skymaps, and Multimessenger Parameters — February 2026 Data Release

description: We performed simulations of the HLVK detector network configuration for the upcoming O5 observing run, estimating the expected gravitational-wave (GW) detection rates using a signal-to-noise ratio (S/N) threshold of 8. The simulated compact-binary population is drawn from the GWTC-4 Broken Power Law + Two Gaussian Peaks (BP2P) population model, ensuring consistency with the latest observational constraints on compact-object mass, spin, and merger-rate distributions.

The statistics folder contains summary statistics for both the O4 and projected O5 observing scenarios, enabling direct comparisons of detection rates, source classifications, and population properties between current and anticipated detector sensitivities.

Events are classified according to component masses using a standard threshold: neutron stars (NS) are defined as having masses < 3 M⊙, while black holes (BH) are defined as having masses ≥ 3 M⊙.

These simulations build on previous observing-scenario studies, including the O4HL, O4HLV, and O5a/O5b/O5c sensitivity projections.
DOI: 10.5281/zenodo.18602491
title: DEVELOPMENT OF CHILDREN'S CREATIVE THINKING BASED ON AN INTEGRATIVE APPROACH

description: This article describes the components of integration, the integration approach and the advantages of the integration approach in developing the creative thinking of primery school students.
DOI: 10.5281/zenodo.18602492
title: DEVELOPMENT OF CHILDREN'S CREATIVE THINKING BASED ON AN INTEGRATIVE APPROACH

description: This article describes the components of integration, the integration approach and the advantages of the integration approach in developing the creative thinking of primery school students.
DOI: 10.15468/dl.7uuujw
title: Occurrence Download

description: A dataset containing 210 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Serrapinnus micropterus",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 210 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006609-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.