cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27047617
title: Deciphering the History of ERK Activity from Fixed-Cell Immunofluorescence Measurements

description: We generated a dataset that enables correlation of ERK activation to downstream target protein expression and modification by first collecting live ERK activity measurements under differential activation states of the RAS/ERK pathway. In MCF10A mammary epithelial cells, MCF7 breast cancer cells, and Hcc827 and A549 lung adenocarcinoma cells, we used EKAR3.1, a calibrated FRET-based biosensor that measures the balance between phosphorylation by ERK activity and dephosphorylation by competing phosphatases. ERK activity was stimulated in a dose-dependent manner with varying Epidermal Growth Factor (EGF) concentrations. To increase the temporal diversity of activity patterns, we added MEK inhibitor (MEKi) at varying times after EGF stimulation and included treatments where EGF was added at different timepoints of the experiment. These treatments generated a wide range of ERK signaling patterns, including sustained and pulsatile activity with varying duration and magnitude. Consistent with previous studies, we found that ERK activation is heterogenous from cell to cell within each stimulation condition. Immediately following live-cell data collection, we fixed the cells and conducted cyclic immunofluorescence (4i) staining to measure nuclear and cytoplasmic levels of protein targets potentially modulated by ERK activity. 
This repository contains all code and data needed to generate the figures in the paper.

Quick start guide (see the README file for more detailed instructions):

-Unzip Code_files.zip to view the custom code associated with processing and plotting data.

-To access the source MCF10A supplementary data tables, unzip SourceData.zip (in the same folder as you unzipped Code_files.zip)

-To access the (large) cancer cell line data files needed to run the figure generation code, unzip Data_files.zip WITHIN the MATLABfiles subfolder that was extracted from Code_files.zip
DOI: 10.6084/m9.figshare.27047617.v1
title: Deciphering the History of ERK Activity from Fixed-Cell Immunofluorescence Measurements

description: We generated a dataset that enables correlation of ERK activation to downstream target protein expression and modification by first collecting live ERK activity measurements under differential activation states of the RAS/ERK pathway. In MCF10A mammary epithelial cells, MCF7 breast cancer cells, and Hcc827 and A549 lung adenocarcinoma cells, we used EKAR3.1, a calibrated FRET-based biosensor that measures the balance between phosphorylation by ERK activity and dephosphorylation by competing phosphatases. ERK activity was stimulated in a dose-dependent manner with varying Epidermal Growth Factor (EGF) concentrations. To increase the temporal diversity of activity patterns, we added MEK inhibitor (MEKi) at varying times after EGF stimulation and included treatments where EGF was added at different timepoints of the experiment. These treatments generated a wide range of ERK signaling patterns, including sustained and pulsatile activity with varying duration and magnitude. Consistent with previous studies, we found that ERK activation is heterogenous from cell to cell within each stimulation condition. Immediately following live-cell data collection, we fixed the cells and conducted cyclic immunofluorescence (4i) staining to measure nuclear and cytoplasmic levels of protein targets potentially modulated by ERK activity. 
This repository contains all code and data needed to generate the figures in the paper.

Quick start guide (see the README file for more detailed instructions):

-Unzip Code_files.zip to view the custom code associated with processing and plotting data.

-To access the source MCF10A supplementary data tables, unzip SourceData.zip (in the same folder as you unzipped Code_files.zip)

-To access the (large) cancer cell line data files needed to run the figure generation code, unzip Data_files.zip WITHIN the MATLABfiles subfolder that was extracted from Code_files.zip
DOI: 10.6084/m9.figshare.28505138
title: Reveal genomic insights into cotton domestication and improvement using gene level functional haplotype-based GWAS

description: Source data presented in graphs within figures of "Reveal genomic insights into cotton domestication and improvement using gene level functional haplotype-based GWAS".
DOI: 10.6084/m9.figshare.28505138.v1
title: Reveal genomic insights into cotton domestication and improvement using gene level functional haplotype-based GWAS

description: Source data presented in graphs within figures of "Reveal genomic insights into cotton domestication and improvement using gene level functional haplotype-based GWAS".
DOI: 10.5522/04/30640043
title: C3VD-Raycasting-10k: A Clinical Point Cloud Registration Dataset for Image-Guided Colonoscopy

description: <b>C3VD-Raycasting-10k</b> is a clinically grounded benchmark dataset for 3D point cloud registration in image-guided colonoscopy. It contains <b>10,014 geometrically aligned point-cloud pairs</b> that simulate the cross-modal alignment problem between preoperative CT anatomy and intraoperative endoscopic observations.The dataset is derived from clinical CT and endoscopy data provided by the Colonoscopy 3D Video Dataset (C3VD)<b> </b>[Bobrow et al., MedIA 2023]. Starting from complete CT-based colon meshes and recorded endoscope trajectories, we use <b>physics-based ray casting</b> to generate realistic intraoperative viewpoints. For each recorded camera pose, we cast rays from the endoscopic viewpoint onto the CT-derived surface to obtain a <b>partial target point cloud</b> that mimics what is observable during colonoscopy. The corresponding <b>source point cloud</b> is sampled from the <b>dense CT mesh</b> representing the underlying preoperative anatomy.Each sample in C3VD-Raycasting-10k therefore consists of:A <b>dense source point cloud</b> derived from the preoperative CT colon mesh.A <b>partial target point cloud</b> generated by ray casting from an endoscopic viewpoint, with occlusions and visibility constraints that reflect realistic intraoperative conditions.By construction, the dataset emphasizes challenging but clinically relevant cases, including:<b>Partial-to-partial alignment</b> with varying field-of-view, coverage, and missing regions.<b>Locally homogeneous geometry</b> and repetitive structures that cause feature degeneracy on tubular organ surfaces.<b>Cross-modal variability</b> between CT-derived anatomy and endoscopic appearance, while still providing precise geometric ground truth.C3VD-Raycasting-10k is designed to support <b>rigorous and reproducible benchmarking</b> of 3D registration algorithms for image-guided colonoscopy and related minimally invasive procedures.<b>Citing the Dataset</b>Cite [Linzhe:arXiv2025] whenever research making use of this dataset is reported in any academic publication or research report.<b>Declaration</b>This point cloud dataset is derived from the Colonoscopy 3D Video Dataset (C3VD) (https://durrlab.github.io/C3VD/).<br><br>Original data: Bobrow et al., "Colonoscopy 3D video dataset with paired depth from 2D-3D registration", Medical Image Analysis, 2023.<br><br>In accordance with the original C3VD dataset license, our derived point cloud dataset is also released under the CC BY-NC-SA 4.0 license and may only be used for non-commercial purposes.
DOI: 10.5522/04/30640043.v1
title: C3VD-Raycasting-10k: A Clinical Point Cloud Registration Dataset for Image-Guided Colonoscopy

description: <b>C3VD-Raycasting-10k</b> is a clinically grounded benchmark dataset for 3D point cloud registration in image-guided colonoscopy. It contains <b>10,014 geometrically aligned point-cloud pairs</b> that simulate the cross-modal alignment problem between preoperative CT anatomy and intraoperative endoscopic observations.The dataset is derived from clinical CT and endoscopy data provided by the Colonoscopy 3D Video Dataset (C3VD)<b> </b>[Bobrow et al., MedIA 2023]. Starting from complete CT-based colon meshes and recorded endoscope trajectories, we use <b>physics-based ray casting</b> to generate realistic intraoperative viewpoints. For each recorded camera pose, we cast rays from the endoscopic viewpoint onto the CT-derived surface to obtain a <b>partial target point cloud</b> that mimics what is observable during colonoscopy. The corresponding <b>source point cloud</b> is sampled from the <b>dense CT mesh</b> representing the underlying preoperative anatomy.Each sample in C3VD-Raycasting-10k therefore consists of:A <b>dense source point cloud</b> derived from the preoperative CT colon mesh.A <b>partial target point cloud</b> generated by ray casting from an endoscopic viewpoint, with occlusions and visibility constraints that reflect realistic intraoperative conditions.By construction, the dataset emphasizes challenging but clinically relevant cases, including:<b>Partial-to-partial alignment</b> with varying field-of-view, coverage, and missing regions.<b>Locally homogeneous geometry</b> and repetitive structures that cause feature degeneracy on tubular organ surfaces.<b>Cross-modal variability</b> between CT-derived anatomy and endoscopic appearance, while still providing precise geometric ground truth.C3VD-Raycasting-10k is designed to support <b>rigorous and reproducible benchmarking</b> of 3D registration algorithms for image-guided colonoscopy and related minimally invasive procedures.<b>Citing the Dataset</b>Cite [Linzhe:arXiv2025] whenever research making use of this dataset is reported in any academic publication or research report.<b>Declaration</b>This point cloud dataset is derived from the Colonoscopy 3D Video Dataset (C3VD) (https://durrlab.github.io/C3VD/).<br><br>Original data: Bobrow et al., "Colonoscopy 3D video dataset with paired depth from 2D-3D registration", Medical Image Analysis, 2023.<br><br>In accordance with the original C3VD dataset license, our derived point cloud dataset is also released under the CC BY-NC-SA 4.0 license and may only be used for non-commercial purposes.
DOI: 10.5517/ccdc.csd.cc2pcstp
title: CCDC 2483993: Experimental Crystal Structure Determination

description: Related Article: Shupeng Liu, Yilin Wang, Yuting Liu, Haifeng Lin, Ningbo Li, Jie Wang, Xinyu Zhang, Ayesha Tajammal, Muhammad Tahir, Haihan Yan, Weilin Wang, Yongliang Zhang, Youliang Wang|2025|ChemRxiv|||doi:10.26434/chemrxiv-2025-0dk9s
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.cc2pcsxs
title: CCDC 2483996: Experimental Crystal Structure Determination

description: Related Article: Shupeng Liu, Yilin Wang, Yuting Liu, Haifeng Lin, Ningbo Li, Jie Wang, Xinyu Zhang, Ayesha Tajammal, Muhammad Tahir, Haihan Yan, Weilin Wang, Yongliang Zhang, Youliang Wang|2025|ChemRxiv|||doi:10.26434/chemrxiv-2025-0dk9s
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.cc2pcsyt
title: CCDC 2483997: Experimental Crystal Structure Determination

description: Related Article: Shupeng Liu, Yilin Wang, Yuting Liu, Haifeng Lin, Ningbo Li, Jie Wang, Xinyu Zhang, Ayesha Tajammal, Muhammad Tahir, Haihan Yan, Weilin Wang, Yongliang Zhang, Youliang Wang|2025|ChemRxiv|||doi:10.26434/chemrxiv-2025-0dk9s
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.cc2pcszv
title: CCDC 2483998: Experimental Crystal Structure Determination

description: Related Article: Shupeng Liu, Yilin Wang, Yuting Liu, Haifeng Lin, Ningbo Li, Jie Wang, Xinyu Zhang, Ayesha Tajammal, Muhammad Tahir, Haihan Yan, Weilin Wang, Yongliang Zhang, Youliang Wang|2025|ChemRxiv|||doi:10.26434/chemrxiv-2025-0dk9s
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.cc2pct0x
title: CCDC 2483999: Experimental Crystal Structure Determination

description: Related Article: Shupeng Liu, Yilin Wang, Yuting Liu, Haifeng Lin, Ningbo Li, Jie Wang, Xinyu Zhang, Ayesha Tajammal, Muhammad Tahir, Haihan Yan, Weilin Wang, Yongliang Zhang, Youliang Wang|2025|ChemRxiv|||doi:10.26434/chemrxiv-2025-0dk9s
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.cc2pd2hp
title: CCDC 2484262: Experimental Crystal Structure Determination

description: Related Article: Shupeng Liu, Yilin Wang, Yuting Liu, Haifeng Lin, Ningbo Li, Jie Wang, Xinyu Zhang, Ayesha Tajammal, Muhammad Tahir, Haihan Yan, Weilin Wang, Yongliang Zhang, Youliang Wang|2025|ChemRxiv|||doi:10.26434/chemrxiv-2025-0dk9s
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: 10.6084/m9.figshare.26771080.v2
title: Optimizing Assessment Reliability of Communication Skills in Clinical Dentistry: A Generalizability Study

description: This includes the row dataset for the Optimizing Assessment Reliability of Communication Skills in Clinical Dentistry: Generalizability Study for a Calgary-Cambridge Guide Adaptation study and an Example R code used for calculating the Generalizability coefficient and conducting the decision study.
DOI: 10.15468/dl.vhbh8s
title: Occurrence Download

description: A dataset containing 19559 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Phylloscopus borealis",
 "Year 2024-2025"
 ]
}

 The dataset includes 19559 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002186-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e847rv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Passer domesticus",
 "DatasetKey is f2f5561e-aa2b-4f95-8c89-3df0a5a1c7c6",
 "Geometry POLYGON((12.4 55.3, 14.6 55.3, 14.6 56.5, 12.4 56.5, 12.4 55.3))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002187-251120083545085/datasets/export for details.
DOI: 10.60887/4k36qu3nuuw97m7b
title: Fundberichte aus Österreich Band 62 2023
DOI: 10.60887/5y1h82r0qvpweqhc
title: Fundberichte aus Österreich, Band 62 2023 (Digitalteil)
DOI: 10.25934/pr00009324.0
title: Available datapackage for study 'A Multicentre, Randomised, Double-blind, Placebo-Controlled Phase 3 Extension Study to Characterise the Long-term Safety and Tolerability of Anifrolumab in Adult Subjects With Active Systemic Lupus Erythematosus.'

description: The purpose of this study is to characterise long-term safety and tolerability of intravenous anifrolumab.
DOI: 10.5167/uzh-280588
title: Crafts of Life : Among the Hunters of East Asia
title: CRAFTS OF LIFE – Hunters of East Asia
DOI: 10.5281/zenodo.17652414
title: Aerosol-only parameterizations for liquid cloud microphysics

description: The gradient-boosted tree framework LightGBM [51] is used to train separate models for predicting cloud droplet number concentration (CDNC) and median volume diameter (MVD).Each cloud identified in the combined ACTIVATE and Airbus Norway dataset, constitutesa single datapoint. The input variables for each datapoint are the mass mixing ratios of the eleven aerosol species

The target variables are the in situ CDNC and MVD measured by the CDP. To test howsensitive the results are to the train–test division, the model was trained one hundred times using different randomsplits, each with 75 percent of the data for training and 25 percent for testing.

The clean data for training the decision trees is available cloud_data_MVD.csv.The aerosol data is from CAMS EAC4 reanalysis.The python script lightGBM_train_plot.py has functions for training and evaluating the decision trees for CDNC and MVD.The raw data files from the Norwegian part of the training data are in the Airbus_Norway_2023_2024.zip file. 

Data from the ACTIVATE campaign can be accessed here: https://www-air.larc.nasa.gov/missions/activate/index.html
DOI: 10.5281/zenodo.17652413
title: Aerosol-only parameterizations for liquid cloud microphysics

description: The gradient-boosted tree framework LightGBM [51] is used to train separate models for predicting cloud droplet number concentration (CDNC) and median volume diameter (MVD).Each cloud identified in the combined ACTIVATE and Airbus Norway dataset, constitutesa single datapoint. The input variables for each datapoint are the mass mixing ratios of the eleven aerosol species

The target variables are the in situ CDNC and MVD measured by the CDP. To test howsensitive the results are to the train–test division, the model was trained one hundred times using different randomsplits, each with 75 percent of the data for training and 25 percent for testing.

The clean data for training the decision trees is available cloud_data_MVD.csv.The aerosol data is from CAMS EAC4 reanalysis.The python script lightGBM_train_plot.py has functions for training and evaluating the decision trees for CDNC and MVD.The raw data files from the Norwegian part of the training data are in the Airbus_Norway_2023_2024.zip file. 

Data from the ACTIVATE campaign can be accessed here: https://www-air.larc.nasa.gov/missions/activate/index.html
DOI: 10.18150/ri4yyi
title: Data on fuel prices, COVID-19 new cases and Google search intensity for terms related to public transport, alternative transport and electric cars in V4, Germany and Sweden, used in the article "Fear or price? Vulnerability of the interest in green transport to COVID dynamics and fuel prices in V4 economies"

description: Data used in the article "Fear or price? Vulnerability of the interest in green transport to COVID dynamics and fuel prices in V4 economies" (DOI: 10.14254/2071-789X.2025/18-1/4). The data covers the period from January 2020 to the end of February 2023. It includes weekly data on consumer prices of petroleum products (PB95) inclusive of duties and taxes (data source: weekly oil bulletin prepared by the European Commission) and the results of Google search terms related to public transport, alternative transport and electric cars in Czechia, Germany, Hungary, Poland, Slovakia and Sweden. We considered the total Google search volume related to a given means of transport for various phrases in national languages. Lastly, we include the data on the number of confirmed COVID-19 cases per week cases provided by World Health Organization.The dataset consists of 6 files: Czechia, Germany, Hungary, Poland, Slovakia and Slovenia. Each file stores the analogous data collected for each country. Column names:PB - price (in Euro) of the 95-PB petrol;ecar - number of Google searches for the keywords related to electric cars;pubTR - number of Google searches for the keywords related to public transport;susTR - number of Google searches for the keywords related to alternative transport;pubTR_sa - de-seasoned number of searches for the keywords related to public transport;susTR_sa - de-seasoned number of searches for the keywords related to alternative transport;COV - number of COVID deaths.
DOI: 10.17026/ar/3lkaex
title: Altena Andel Beatrixstraat

description: &lt;p&gt;In augustus van 2020 heeft ADC ArcheoProjecten een opgraving uitgevoerd aan de Beatrixstraat te Andel, gemeente Altena. Hierbij zijn elf werkputten aangelegd op twee delen van het plangebied op locaties waarvan op basis van het vooronderzoek een vindplaats werd verwacht. In het zuidelijke deel, waar één werkput is aangelegd zijn op een kuil na geen andere archeologische sporen gevonden. In het noordelijke deel zijn delen van zeker vijf middeleeuwse percelen aangetroffen.&lt;br&gt;Binnen de percelen zijn kuilen, paalkuilen een waterput en stakenrijen aangetroffen maar concrete sporen van huizen of bijgebouwen echter niet. Het is dan ook zeer aannemelijk dat grote delen van de vindplaatsen buiten het plangebied zijn gelegen. De sporen laten een lang en intensief gebruik van het terrein zien die zijn weerspiegeling kent in de nieuwebouw ontwikkeling die daar op het moment plaatsvindt.&lt;/p&gt;
DOI: 10.15468/dl.t7ud5e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acaulon muticum",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is Germany",
 "CoordinateUncertaintyInMeters is less than or equal to 30",
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Year is greater than or equal to 2014",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002188-251120083545085/datasets/export for details.
DOI: 10.6084/m9.figshare.30675585.v1
title: Hybrid imputation of missing values using KNN on MEWMA-based adaptive process control

description: Missing data, a common issue in production processes due to factors like sample contamination and equipment malfunctions, can lead to a decrease in the recognition accuracy of control charts, especially in cases of shifting. To address this, we introduce an online adaptive weighted imputation technique that combines the strengths of K-Nearest Neighbor (KNN) and Exponentially Weighted Moving Average (EWMA) imputations. It utilizes an adaptive weight matrix for weighting both methods and an adaptive covariance matrix to optimize for missing structures. When dealing with data fluctuation, we assign a higher weight to the KNN method for its sensitivity, while the EWMA method is preferred for stationary data. This approach does not require data stacking; thus, the imputation process for missing data is conducted online. Consequently, based on the online Multivariate EWMA (MEWMA) control chart, real-time process monitoring can be achieved. To optimize the use of available information, we also adjust the covariance matrix with a weight matrix to emphasize complete data. The proposed technique outperforms traditional methods in performance monitoring by avoiding false alarms and quickly detecting anomalies during process shifts.