cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11401393
title: Collection of scRNA-Seq datasets with clonal information

description: Collection of scRNA-Seq datasets with lineage tracing information at the clonal level. They're prepared for the Python package scLiTr (GitHub).

Available datasets:



In vitro hematopoiesis from Weinreb et al., 2020 (Weinreb_in_vitro.h5ad).

In vivo E7.5/E8.5 → E13.5 murine development dataset (all files with the prefix Erickson_), splitted into



Head region (control with and without perturbations) — Erickson_Head_[...].h5ad,

Trunk region (control with and without perturbations) — Erickson_Trunk_[...].h5ad.
DOI: 10.5281/zenodo.11401394
title: Collection of scRNA-Seq datasets with clonal information

description: Collection of scRNA-Seq datasets with lineage tracing information at the clonal level. They're prepared for the Python package scLiTr (GitHub).

Available datasets:



In vitro hematopoiesis from Weinreb et al., 2020 (Weinreb_in_vitro.h5ad).


If you want to add some of your datasets here (so they might be easily downloaded via scLiTr interface), please feel free to text me at sergey.isaev[at]meduniwien.ac.at.
DOI: 10.5281/zenodo.15334396
title: Collection of scRNA-Seq datasets with clonal information

description: Collection of scRNA-Seq datasets with lineage tracing information at the clonal level. They're prepared for the Python package scLiTr (GitHub).

Available datasets:



In vitro hematopoiesis from Weinreb et al., 2020 (Weinreb_in_vitro.h5ad).

In vivo E7.5/E8.5 → E13.5 murine development dataset (all files with the prefix Erickson_), splitted into



Head region (control with and without perturbations) — Erickson_Head_[...].h5ad,

Trunk region (control with and without perturbations) — Erickson_Trunk_[...].h5ad.
DOI: 10.5281/zenodo.15396687
title: Collection of scRNA-Seq datasets with clonal information

description: Collection of scRNA-Seq datasets with lineage tracing information at the clonal level. They're prepared for the Python package scLiTr (GitHub).

Available datasets:



In vitro hematopoiesis from Weinreb et al., 2020 (Weinreb_in_vitro.h5ad).

In vivo E7.5/E8.5 → E13.5 murine development dataset (all files with the prefix Erickson_), splitted into



Head region (control with and without perturbations) — Erickson_Head_[...].h5ad,

Trunk region (control with and without perturbations) — Erickson_Trunk_[...].h5ad.
DOI: 10.15468/dl.6ve7k6
title: Occurrence Download

description: A dataset containing 13181 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leonotis nepetifolia (L.) R.Br."
 ]
}

 The dataset includes 13181 records from 440 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028381-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xwntma
title: Occurrence Download

description: A dataset containing 770 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Occurrences of aquatic invertebrates in the Antarctic and Subantarctic regions"
 ]
}

 The dataset includes 770 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028383-250426092105405/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.29048165
title: Data in Selangor River Estuary_MG

description: This study investigates the sediment transport processes and ultimate deposition patterns in the Selangor River estuary, Malaysia- a representative tropical macrotidal estuary characterized by complex nearshore topography and weak Coriolis effects. By combining field measurements (2023-2024) with sediment analysis, this research reveals three key findings. First, approximately 73.5% of the annual sediment discharge (19.75×10⁴ tonnes)accumulates in estuarine tidal flats, with the northern tidal flats receiving twice the deposition of the southern tidal flats. Second, distinct seasonal transport regimes were evident: (i) during the dry season wave-driven sediment resuspension and tidal forces result in northwestward-southeastward transport, (ii) during the wet season stratified flows lead to significant sediment-freshwater decoupling, where river plumes disperse offshore while terrestrial sediments remain trapped near the estuary. Third, the unique sediment trapping mechanism, different from both large river systems and wave-dominated tropical SMRs, resulting from the interaction of strong tidal currents, seasonal river discharge, and offshore water intrusion form the Strait of Malacca. These findings provide a new conceptual framework for understanding sediment retention in tropical macrotidal estuaries, with implications for coastal management under changing climate change and intensifying anthropogenic pressures.
DOI: 10.15468/dl.4dh2gf
title: Occurrence Download

description: A dataset containing 73818 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nezara viridula (Linnaeus, 1758)"
 ]
}

 The dataset includes 73818 records from 643 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028373-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zv4f4z
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is Geodetic datum assumed WGS84",
 "TaxonKey is Aquilegia paui Font Quer"
 ]
}

 The dataset includes 23 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028384-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2692p9
title: CCDC 2034520: Experimental Crystal Structure Determination

description: Related Article: Jinkui Chai, Yu Nie, Zhao Wang, Li Cheng, Ying-Guo Liu, Junliang Wu|2021|Chem.-Eur.J.|27|17565|doi:10.1002/chem.202103219
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.cc2692qb
title: CCDC 2034521: Experimental Crystal Structure Determination

description: Related Article: Jinkui Chai, Yu Nie, Zhao Wang, Li Cheng, Ying-Guo Liu, Junliang Wu|2021|Chem.-Eur.J.|27|17565|doi:10.1002/chem.202103219
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: 10.5281/zenodo.15396703
title: carbon density data for species richness experiment

description: This dataset originates from a biodiversity platform experiment designed to assess how different species combinations and species richness levels influence ecosystem carbon dynamics.

The dataset includes measurements from different vegetation layers (aboveground biomass, litter, belowground biomass, and total vegetation carbon density), plant traits, and soil carbon fractions (POC, MAOC, and SOC densities at 0–20 cm depth), as well as associated soil physicochemical properties.

Statistical analyses include one-way ANOVA, linear regression, and correlation analysis, which were used to explore the relationships between biodiversity and both vegetation and soil carbon variables.

This dataset was used in the manuscript currently under review and will be made publicly available upon publication. An accompanying R script is provided for reproducing the main statistical analyses and figures.
DOI: 10.5281/zenodo.15396704
title: carbon density data for species richness experiment

description: This dataset originates from a biodiversity platform experiment designed to assess how different species combinations and species richness levels influence ecosystem carbon dynamics.

The dataset includes measurements from different vegetation layers (aboveground biomass, litter, belowground biomass, and total vegetation carbon density), plant traits, and soil carbon fractions (POC, MAOC, and SOC densities at 0–20 cm depth), as well as associated soil physicochemical properties.

Statistical analyses include one-way ANOVA, linear regression, and correlation analysis, which were used to explore the relationships between biodiversity and both vegetation and soil carbon variables.

This dataset was used in the manuscript currently under review and will be made publicly available upon publication. An accompanying R script is provided for reproducing the main statistical analyses and figures.
DOI: 10.5517/ccdc.csd.cc2lr67t
title: CCDC 2405576: Experimental Crystal Structure Determination

description: Related Article: Lorena S. R. Martelli, Lucas G. Furniel, Pedro H. O. Santiago, Javier Ellena, Arlene G. Corrêa|2025|ACS Omega|10|18017|doi:10.1021/acsomega.5c01416
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.cc2lr68v
title: CCDC 2405577: Experimental Crystal Structure Determination

description: Related Article: Lorena S. R. Martelli, Lucas G. Furniel, Pedro H. O. Santiago, Javier Ellena, Arlene G. Corrêa|2025|ACS Omega|10|18017|doi:10.1021/acsomega.5c01416
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.cc2lr69w
title: CCDC 2405578: Experimental Crystal Structure Determination

description: Related Article: Lorena S. R. Martelli, Lucas G. Furniel, Pedro H. O. Santiago, Javier Ellena, Arlene G. Corrêa|2025|ACS Omega|10|18017|doi:10.1021/acsomega.5c01416
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.cc2lr6bx
title: CCDC 2405579: Experimental Crystal Structure Determination

description: Related Article: Lorena S. R. Martelli, Lucas G. Furniel, Pedro H. O. Santiago, Javier Ellena, Arlene G. Corrêa|2025|ACS Omega|10|18017|doi:10.1021/acsomega.5c01416
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.cc2lyqy7
title: CCDC 2411860: Experimental Crystal Structure Determination

description: Related Article: Dino Kuzman, Mario Pajić, Lucija Drempetić, Josipa Sarjanović, Jana Pisk, Tomica Hrenar, Marina Cindrić, Višnja Vrdoljak|2025|ACS Omega|10|16668|doi:10.1021/acsomega.5c00186
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.cc2lyqz8
title: CCDC 2411861: Experimental Crystal Structure Determination

description: Related Article: Dino Kuzman, Mario Pajić, Lucija Drempetić, Josipa Sarjanović, Jana Pisk, Tomica Hrenar, Marina Cindrić, Višnja Vrdoljak|2025|ACS Omega|10|16668|doi:10.1021/acsomega.5c00186
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.cc2lyr0b
title: CCDC 2411862: Experimental Crystal Structure Determination

description: Related Article: Dino Kuzman, Mario Pajić, Lucija Drempetić, Josipa Sarjanović, Jana Pisk, Tomica Hrenar, Marina Cindrić, Višnja Vrdoljak|2025|ACS Omega|10|16668|doi:10.1021/acsomega.5c00186
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.cc2lyr1c
title: CCDC 2411863: Experimental Crystal Structure Determination

description: Related Article: Dino Kuzman, Mario Pajić, Lucija Drempetić, Josipa Sarjanović, Jana Pisk, Tomica Hrenar, Marina Cindrić, Višnja Vrdoljak|2025|ACS Omega|10|16668|doi:10.1021/acsomega.5c00186
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.cc2lyr2d
title: CCDC 2411864: Experimental Crystal Structure Determination

description: Related Article: Dino Kuzman, Mario Pajić, Lucija Drempetić, Josipa Sarjanović, Jana Pisk, Tomica Hrenar, Marina Cindrić, Višnja Vrdoljak|2025|ACS Omega|10|16668|doi:10.1021/acsomega.5c00186
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.cc2lyr3f
title: CCDC 2411865: Experimental Crystal Structure Determination

description: Related Article: Dino Kuzman, Mario Pajić, Lucija Drempetić, Josipa Sarjanović, Jana Pisk, Tomica Hrenar, Marina Cindrić, Višnja Vrdoljak|2025|ACS Omega|10|16668|doi:10.1021/acsomega.5c00186
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.cc2lyr4g
title: CCDC 2411866: Experimental Crystal Structure Determination

description: Related Article: Dino Kuzman, Mario Pajić, Lucija Drempetić, Josipa Sarjanović, Jana Pisk, Tomica Hrenar, Marina Cindrić, Višnja Vrdoljak|2025|ACS Omega|10|16668|doi:10.1021/acsomega.5c00186
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.cc2lyr5h
title: CCDC 2411867: Experimental Crystal Structure Determination

description: Related Article: Dino Kuzman, Mario Pajić, Lucija Drempetić, Josipa Sarjanović, Jana Pisk, Tomica Hrenar, Marina Cindrić, Višnja Vrdoljak|2025|ACS Omega|10|16668|doi:10.1021/acsomega.5c00186
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: 10.5281/zenodo.14873410
title: Enhanced spray-wall interaction model for port fuel injection under medium load conditions

description: In the Eulerian-Lagrangian framework, the term consisting the drag force together with relative velocity (Eq. 13 in the paper), is an important expression which blends the aspects of both Eulerian (flow) and Lagrangian (particle ) regimes. The datasets in the csv format is related to drag coefficient. 

Datasets:



A_dataset_fitted_Cd.csv: The Reyonolds Number (Re) and Coefficient of Drag (C_D) are fitted in accordance to the equation provided by Morsi and Alexander, 1972.

B_dataset_fitted_Cd.csv: The data is fitted in accordance to the equation provided by Terfous et al., 2013. 


The web app for fitting the drag coefficient is available at Empirical Drag Coefficient . 

Usage of the web app: 

 

The concepts, and illustrations of Eulerian-Lagrangian framework in engineering applications have been provided in the Eulerian-Lagrangian Formulation Explorer

 

 
description: This work was supported by the National Science Centre, Poland (UMO-2021/42/E/ST5/00339).
DOI: 10.5281/zenodo.14873411
title: Enhanced spray-wall interaction model for port fuel injection under medium load conditions

description: In the Eulerian-Lagrangian framework, the term consisting the drag force together with relative velocity (Eq. 13 in the paper), is an important expression which blends the aspects of both Eulerian (flow) and Lagrangian (particle ) regimes. The datasets in the csv format is related to drag coefficient. 

Datasets:



A_dataset_fitted_Cd.csv: The Reyonolds Number (Re) and Coefficient of Drag (C_D) are fitted in accordance to the equation provided by Morsi and Alexander, 1972.

B_dataset_fitted_Cd.csv: The data is fitted in accordance to the equation provided by Terfous et al., 2013. 


The web app for fitting the drag coefficient is available at Empirical Drag Coefficient . 

Usage of the web app: 

 

The concepts, and illustrations of Eulerian-Lagrangian framework in engineering applications have been provided in the Eulerian-Lagrangian Formulation Explorer

 

 
description: This work was supported by the National Science Centre, Poland (UMO-2021/42/E/ST5/00339).
DOI: 10.15468/dl.se4kee
title: Occurrence Download

description: A dataset containing 1085022 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Moscow University Herbarium (MW)"
 ]
}

 The dataset includes 1085022 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028379-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15396406
title: A recurrent neural network surrogate model with few-shot learning strategy for CO2 storage in deep subsurface saline aquifer
title: Reptile CRU-Net++

description: Carbon dioxide (CO2) storage in deep subsurface saline aquifer is a crucial technology for achieving long-term and safe CO2 storage. Conducting numerical simulations of deep subsurface saline aquifer CO2 storage projects to evaluate engineering plans before implementation demands significant computational resources. The application of deep learning surrogate models can effectively enhance simulation efficiency and reduce the consumption of computational resources. However, constructing a deep learning surrogate model in practical tasks typically requires a substantial number of simulations to gather sufficient training samples. Therefore, the advantages of reduced resource consumption offered by deep learning surrogate model are limited compared to the deployment cost associated with the simulation process. To address these deployment costs, this study proposes a deep learning surrogate model, 3D ConvLSTM ResU-Net++ (hereafter referred to as 3D CRU-Net++), optimized using the Reptile algorithm. By employing a few-shot strategy based on Reptile, this model can simulate the CO2 storage process under varying reservoir conditions with relatively low computational resource requirements. The surrogate model was trained using diverse geological storage scenarios, and a target task was selected to validate its effectiveness. The experimental results show that the model optimized using Reptile demonstrated a 70% reduction in RMSE, a 60% reduction in MAE, and a 7% improvement in R². These results suggests that Reptile-optimized model achieves higher accuracy with the same number of samples compared to the standard model. This study shows that the Reptile algorithm significantly reduces the number of samples required for training surrogate models, thus facilitating the application of these models in various CO2 sequestration simulations, such as varying reservoir conditions, injection well optimization, and uncertainty analysis.
DOI: 10.5281/zenodo.15396407
title: A recurrent neural network surrogate model with few-shot learning strategy for CO2 storage in deep subsurface saline aquifer
title: Reptile CRU-Net++

description: Carbon dioxide (CO2) storage in deep subsurface saline aquifer is a crucial technology for achieving long-term and safe CO2 storage. Conducting numerical simulations of deep subsurface saline aquifer CO2 storage projects to evaluate engineering plans before implementation demands significant computational resources. The application of deep learning surrogate models can effectively enhance simulation efficiency and reduce the consumption of computational resources. However, constructing a deep learning surrogate model in practical tasks typically requires a substantial number of simulations to gather sufficient training samples. Therefore, the advantages of reduced resource consumption offered by deep learning surrogate model are limited compared to the deployment cost associated with the simulation process. To address these deployment costs, this study proposes a deep learning surrogate model, 3D ConvLSTM ResU-Net++ (hereafter referred to as 3D CRU-Net++), optimized using the Reptile algorithm. By employing a few-shot strategy based on Reptile, this model can simulate the CO2 storage process under varying reservoir conditions with relatively low computational resource requirements. The surrogate model was trained using diverse geological storage scenarios, and a target task was selected to validate its effectiveness. The experimental results show that the model optimized using Reptile demonstrated a 70% reduction in RMSE, a 60% reduction in MAE, and a 7% improvement in R². These results suggests that Reptile-optimized model achieves higher accuracy with the same number of samples compared to the standard model. This study shows that the Reptile algorithm significantly reduces the number of samples required for training surrogate models, thus facilitating the application of these models in various CO2 sequestration simulations, such as varying reservoir conditions, injection well optimization, and uncertainty analysis.