cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30505906.v1
title: Change Point Detection in Pairwise Comparison Data with Covariates

description: This paper introduces the novel piecewise stationary covariate-assisted ranking estimation (PS-CARE) model for analyzing time-evolving pairwise comparison data, enhancing item ranking accuracy through the integration of covariate information. By partitioning the data into distinct, stationary segments, the PS-CARE model adeptly detects temporal shifts in item rankings, known as change points, whose number and positions are initially unknown. Leveraging the minimum description length (MDL) principle, this paper establishes a statistically consistent model selection criterion to estimate these unknowns. The practical optimization of this MDL criterion is done with the pruned exact linear time (PELT) algorithm. Empirical evaluations reveal the method’s promising performance in accurately locating change points across various simulated scenarios. An application to an NBA dataset yielded meaningful insights that aligned with significant historical events, highlighting the method’s practical utility and the MDL criterion’s effectiveness in capturing temporal ranking changes. To the best of the authors’ knowledge, this research pioneers change point detection in pairwise comparison data with covariate information, representing a significant leap forward in the field of dynamic ranking analysis. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.18190355
title: Unveiling the role of Ndrg1 gene  on the oxidative stress induction  behind the anticancer potential of  styrylquinazoline derivatives

description: This dataset contains raw data from the analyses published in the article entitled “Unveiling the role of Ndrg1 gene on the oxidative stress induction behind the anticancer potential of styrylquinazoline derivatives" (doi: 10.1038/s41598-025-99277-1) published in Scientific Reports.

The raw data were generated during cytotoxicity experiments, flow cytometry measurements (including cell cycle, apoptosis, and autophagy induction), studies on ROS and GSH generation, Lumit assays, and gene expression analysis. The data provided correspond to figures published in the main article and supporting material file.
DOI: 10.60707/9cq2-p973
title: OREX-803096-102 STEM data
DOI: 10.6084/m9.figshare.31032647
title: Balancing Weights for Causal Inference in Observational Factorial Studies

description: Many scientific questions in biomedical, environmental, and psychological research involve understanding the effects of multiple factors on outcomes. While factorial experiments are ideal for this purpose, randomized controlled treatment assignment is generally infeasible in many empirical studies. Therefore, investigators must rely on observational data, where drawing reliable causal inferences for multiple factors remains challenging. As the number of treatment combinations grows exponentially with the number of factors, some treatment combinations can be rare or missing by chance in observed data, further complicating factorial effects estimation. To address these challenges, we propose a novel weighting method tailored to observational studies with multiple factors. Our approach uses weighted observational data to emulate a randomized factorial experiment, enabling simultaneous estimation of the effects of multiple factors and their interactions. Our investigations reveal a crucial nuance: achieving balance among covariates, as in single-factor scenarios, is necessary but insufficient for unbiasedly estimating factorial effects; balancing the factors is also essential in multi-factor settings. Moreover, we extend our weighting method to handle missing treatment combinations in observed data. Finally, we study the asymptotic behavior of the new weighting estimators and propose a consistent variance estimator, providing reliable inferences on factorial effects in observational studies.
DOI: 10.6084/m9.figshare.31032647.v1
title: Balancing Weights for Causal Inference in Observational Factorial Studies

description: Many scientific questions in biomedical, environmental, and psychological research involve understanding the effects of multiple factors on outcomes. While factorial experiments are ideal for this purpose, randomized controlled treatment assignment is generally infeasible in many empirical studies. Therefore, investigators must rely on observational data, where drawing reliable causal inferences for multiple factors remains challenging. As the number of treatment combinations grows exponentially with the number of factors, some treatment combinations can be rare or missing by chance in observed data, further complicating factorial effects estimation. To address these challenges, we propose a novel weighting method tailored to observational studies with multiple factors. Our approach uses weighted observational data to emulate a randomized factorial experiment, enabling simultaneous estimation of the effects of multiple factors and their interactions. Our investigations reveal a crucial nuance: achieving balance among covariates, as in single-factor scenarios, is necessary but insufficient for unbiasedly estimating factorial effects; balancing the factors is also essential in multi-factor settings. Moreover, we extend our weighting method to handle missing treatment combinations in observed data. Finally, we study the asymptotic behavior of the new weighting estimators and propose a consistent variance estimator, providing reliable inferences on factorial effects in observational studies.
DOI: 10.60707/sz22-e555
title: OREX-803096-102 STEM data
DOI: 10.5281/zenodo.18176995
title: Classifying Biophysical Subpopulations of Insulin Secretory Granules using Quantitative Whole Cell Structure Analysis

description: Pancreatic β-cells contain insulin secretory granules (ISGs), organelles where proinsulin is converted into insulin. As ISGs mature, they undergo extensive biophysical remodeling, producing a spectrum of subpopulations with heterogeneous molecular and spatial characteristics. However, systematic methods to define ISG subpopulations remain underdeveloped. To address this gap in knowledge, we employed soft x-ray tomography (SXT), which can quantitatively measure the biochemical density of ISGs within whole β-cells. Using unsupervised clustering, we classified subpopulations based on molecular density, size, and spatial positioning. Across different insulin secretory stimuli, we observed shifts towards mature and releasable subtypes, demonstrating that exogenous signals can dynamically remodel ISG subpopulation distributions. We extended this methodology to primary β-cells characterized using volume electron microscopy (vEM). Integrating subpopulations from SXT and vEM uncovered insights inaccessible by a single method in isolation. This strategy establishes a framework for defining therapeutic approaches aimed at enriching physiologically beneficial ISG subpopulations.
DOI: 10.5281/zenodo.18176996
title: Classifying Biophysical Subpopulations of Insulin Secretory Granules using Quantitative Whole Cell Structure Analysis

description: Pancreatic β-cells contain insulin secretory granules (ISGs), organelles where proinsulin is converted into insulin. As ISGs mature, they undergo extensive biophysical remodeling, producing a spectrum of subpopulations with heterogeneous molecular and spatial characteristics. However, systematic methods to define ISG subpopulations remain underdeveloped. To address this gap in knowledge, we employed soft x-ray tomography (SXT), which can quantitatively measure the biochemical density of ISGs within whole β-cells. Using unsupervised clustering, we classified subpopulations based on molecular density, size, and spatial positioning. Across different insulin secretory stimuli, we observed shifts towards mature and releasable subtypes, demonstrating that exogenous signals can dynamically remodel ISG subpopulation distributions. We extended this methodology to primary β-cells characterized using volume electron microscopy (vEM). Integrating subpopulations from SXT and vEM uncovered insights inaccessible by a single method in isolation. This strategy establishes a framework for defining therapeutic approaches aimed at enriching physiologically beneficial ISG subpopulations.
DOI: 10.15468/dl.zyqhne
title: Occurrence Download

description: A dataset containing 173 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Geometry POLYGON((-444.94102 39.49946,-438.33517 41.16821,-437.90864 43.4288,-438.39253 43.68638,-445.78785 43.61895,-444.94102 39.49946))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hexagenia limbata"
 ]
}

 The dataset includes 173 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000013-260108223611665/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.30011638
title: Technical Lag as Latent Technical Debt: A Rapid Review

description: Dataset for Technical Lag as Latent Technical Debt: A Rapid Review conference paper
DOI: 10.6084/m9.figshare.30011638.v1
title: Technical Lag as Latent Technical Debt: A Rapid Review

description: Dataset for Technical Lag as Latent Technical Debt: A Rapid Review conference paper
DOI: 10.15468/dl.hhf56x
title: Occurrence Download

description: A dataset containing 173 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Geometry POLYGON((-444.94102 39.49946,-438.33517 41.16821,-437.90864 43.4288,-438.39253 43.68638,-445.78785 43.61895,-444.94102 39.49946))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hexagenia limbata"
 ]
}

 The dataset includes 173 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000014-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4vgfuf
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((73.288146 23.722413, 73.211443 23.716836, 73.13664799999999 23.700244, 73.065619 23.67305, 73.000119 23.635929, 72.94177000000001 23.589802, 72.89201 23.535816, 72.852063 23.475307, 72.822901 23.409773, 72.80523100000001 23.340833, 72.799469 23.270189, 72.80574 23.199582, 72.823869 23.130749, 72.85339500000001 23.06538, 72.893576 23.005079, 72.943417 22.951324, 73.00168499999999 22.905429, 73.066951 22.868516, 73.13761599999999 22.841487, 73.211952 22.825001, 73.288146 22.819461, 73.36434 22.825001, 73.438676 22.841487, 73.50934100000001 22.868516, 73.574607 22.905429, 73.632875 22.951324, 73.682716 23.005079, 73.722897 23.06538, 73.75242299999999 23.130749, 73.770552 23.199582, 73.77682299999999 23.270189, 73.771061 23.340833, 73.75339099999999 23.409773, 73.72422899999999 23.475307, 73.684282 23.535816, 73.634522 23.589802, 73.576173 23.635929, 73.510673 23.67305, 73.439644 23.700244, 73.36484900000001 23.716836, 73.288146 23.722413))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000017-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18190394
title: VCFcache blueprint gnomAD v4.1 joint GRCh37 (AF ≥ 0.1, lifted from GRCh38)

description: Abstract VCFcache blueprint derived from gnomAD v4.1 joint frequency data, lifted over from GRCh38 to GRCh37 (hg19).

Note This blueprint does not contain annotations. A combined cache+blueprint bundle can be downloaded from the corresponding cache record.

Note This is a liftover from GRCh38. For native GRCh38 analysis, use the GRCh38 blueprint instead.

Contents This blueprint contains 8M high-quality variant sites lifted over from GRCh38 to GRCh37 using CrossMap and then filtered to allele frequency (AF) ≥ 0.1 10% or 1 in 10 individuals). All genotype and INFO fields have been removed, retaining only genomic positions (CHROM, POS, REF, ALT). Multiallelic sites have been split into separate records.

Technical detailsCoverage: chr1-22, chrX, chrY (GRCh37 coordinates)Source: gnomAD v4.1.0 joint frequency (807,162 samples)Processing: GRCh38→GRCh37 liftover, AF-filtered (≥0.1%), normalized,GT/INFO removed, multiallelics split

Purpose This blueprint serves as a foundation for building annotation caches with tools like VEP, SnpEff, or bcftools +split-vep, enabling rapid annotation of sample VCFs using GRCh37/hg19 coordinates with common variants (≥1% population frequency).

Setup: vcfcache blueprint-init --doi <DOI> -o <output_dir>

Usage: vcfcache cache-build --db <blueprint_dir> -a annotation.yaml -n <name>

Documentation: VCFcache wiki
description: Liftover from GRCh38 to GRCh37 performed using CrossMap.
Reference: hg38ToHg19.over.chain.gz from UCSC Genome Browser.
DOI: 10.5281/zenodo.18190393
title: VCFcache blueprint gnomAD v4.1 joint GRCh37 (AF ≥ 0.1, lifted from GRCh38)

description: Abstract VCFcache blueprint derived from gnomAD v4.1 joint frequency data, lifted over from GRCh38 to GRCh37 (hg19).

Note This blueprint does not contain annotations. A combined cache+blueprint bundle can be downloaded from the corresponding cache record.

Note This is a liftover from GRCh38. For native GRCh38 analysis, use the GRCh38 blueprint instead.

Contents This blueprint contains 8M high-quality variant sites lifted over from GRCh38 to GRCh37 using CrossMap and then filtered to allele frequency (AF) ≥ 0.1 10% or 1 in 10 individuals). All genotype and INFO fields have been removed, retaining only genomic positions (CHROM, POS, REF, ALT). Multiallelic sites have been split into separate records.

Technical detailsCoverage: chr1-22, chrX, chrY (GRCh37 coordinates)Source: gnomAD v4.1.0 joint frequency (807,162 samples)Processing: GRCh38→GRCh37 liftover, AF-filtered (≥0.1%), normalized,GT/INFO removed, multiallelics split

Purpose This blueprint serves as a foundation for building annotation caches with tools like VEP, SnpEff, or bcftools +split-vep, enabling rapid annotation of sample VCFs using GRCh37/hg19 coordinates with common variants (≥1% population frequency).

Setup: vcfcache blueprint-init --doi <DOI> -o <output_dir>

Usage: vcfcache cache-build --db <blueprint_dir> -a annotation.yaml -n <name>

Documentation: VCFcache wiki
description: Liftover from GRCh38 to GRCh37 performed using CrossMap.
Reference: hg38ToHg19.over.chain.gz from UCSC Genome Browser.
DOI: 10.15468/dl.z245ne
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((73.288146 23.722413, 73.211443 23.716836, 73.13664799999999 23.700244, 73.065619 23.67305, 73.000119 23.635929, 72.94177000000001 23.589802, 72.89201 23.535816, 72.852063 23.475307, 72.822901 23.409773, 72.80523100000001 23.340833, 72.799469 23.270189, 72.80574 23.199582, 72.823869 23.130749, 72.85339500000001 23.06538, 72.893576 23.005079, 72.943417 22.951324, 73.00168499999999 22.905429, 73.066951 22.868516, 73.13761599999999 22.841487, 73.211952 22.825001, 73.288146 22.819461, 73.36434 22.825001, 73.438676 22.841487, 73.50934100000001 22.868516, 73.574607 22.905429, 73.632875 22.951324, 73.682716 23.005079, 73.722897 23.06538, 73.75242299999999 23.130749, 73.770552 23.199582, 73.77682299999999 23.270189, 73.771061 23.340833, 73.75339099999999 23.409773, 73.72422899999999 23.475307, 73.684282 23.535816, 73.634522 23.589802, 73.576173 23.635929, 73.510673 23.67305, 73.439644 23.700244, 73.36484900000001 23.716836, 73.288146 23.722413))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000019-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/80y-vex-5hw
title: Carex ferruginea Scop.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/fle-nnf-4p8
title: Carex sempervirens Vill.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.15468/dl.pfpzhk
title: Occurrence Download

description: A dataset listing the 365586 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 365586 records were derived from 4124 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000009-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.4694911
title: NMR data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands

description: 1H, 13C, COSY, HMBC, and HSQC NMR data in fid format for (NPA)6Zn3(H2O)2 (NPA = 2-(phenylamino) benzoate) in DMSO-d6.
DOI: 10.5281/zenodo.4694912
title: NMR data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands

description: 1H, 13C, COSY, HMBC, and HSQC NMR data in fid format for (NPA)6Zn3(H2O)2 (NPA = 2-(phenylamino) benzoate) in DMSO-d6.
DOI: 10.5281/zenodo.11373487
title: Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps

description: This data set contains model output presented in the following paper:  I. Brangers, H. Lievens, A. Getirana, and G. J. M. De Lannoy. (2024). Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps. Water Resources Research. Under review.

The model simulations were carried out in NASA's Land Information System (LIS), using the NoahMP v3.6 land surface model, forced with ERA5. The land surface model was coupled to the HyMAP routing algorithm to produce streamflow estimates. The data contains model results for the western European Alps for the period of 2015-2021 for two seperate cases. 1) The OL run: model run without assimilation of external observations; and 2) DA run:  model run with the assimilation of Sentinel-1 snow depth observations.

 

The zip-folders contain netcdf files for each day of the simulation period, for 1) the river discharge (_ROUTING), 

2) land surface model variables such as snow depth and SWE (_SURFACEMODEL_yyyy) grouped per year, and 3) variables related to the data assimilation such as the spread and innovations (_EnKF).
DOI: 10.15468/dl.kujxwk
title: Occurrence Download

description: A dataset containing 2083844008 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2083844008 records from 35253 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028988-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dm7xkw
title: Occurrence Download

description: A dataset containing 1387 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heleioporus australiacus (Shaw & Nodder, 1795)"
 ]
}

 The dataset includes 1387 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069460-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mgg69z
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Eryx miliaris (Pallas, 1773)"
 ]
}

 The dataset includes 7 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0219916-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.lht7zt
title: Occurrence Download

description: A dataset containing 234 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Phytoplankton samples - 2006/07 V3 SAZ-Sense"
 ]
}

 The dataset includes 234 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012412-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qvmd28
title: Occurrence Download

description: A dataset containing 2452 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is United States of America",
 "Country is Mexico"
 ]
 },
 "OccurrenceStatus is Present",
 "TaxonKey is Ischnura denticollis (Burmeister, 1839)"
 ]
}

 The dataset includes 2452 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0231445-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sfk3cb
title: Occurrence Download

description: A dataset containing 3653 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Espeletia grandiflora Humb. & Bonpl."
 ]
}

 The dataset includes 3653 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0237667-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jznhwq
title: Occurrence Download

description: A dataset containing 470 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anotheca spinosa (Steindachner, 1864)"
 ]
}

 The dataset includes 470 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012412-180412121330197/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qmxcpw
title: Occurrence Download

description: A dataset containing 1319518 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cetacea"
 ]
}

 The dataset includes 1319518 records from 1291 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008334-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.