cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8169352
title: CLDF dataset derived from Liu et al.'s "Comparative Wordlist of Newari Varieties" from 2023

description: Cite the source of the dataset as:




Liu, Zhenyang; Jacques, Guillaume; List, Johann-Mattis (2023): Creating a Standardized Comparative Wordlist of Newari Varieties. (26/07/2023), URL: https://calc.hypotheses.org/6269.
DOI: 10.15468/dl.5588n2
title: Occurrence Download

description: A dataset containing 523 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Elliptio arca (Conrad, 1834))"
 ]
}

 The dataset includes 523 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056116-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14597366
title: paraliparis_alius_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14597367
title: paraliparis_alius_zanfona_1.fasta.gz
DOI: 10.25345/c5qj7894g
title: MassIVE MSV000096773 - Freshwater Assembly Experiment (Exudates 2024) - Jackrel Lab
title: With the intent to understand the drivers of microbial assembly, we used phytoplankton as a model host organism. Thirty-one strains of freshwater algae were inoculated as sterile monocultures in jars covered with a filter that allows for the passage of bacteria, but not the larger phytoplankton cells. The jars were submerged in aquarium tanks filled with a natural freshwater inoculum for 72 hours. After 72 hours, the jars were removed, and the microbial communities were sequenced to know the composition and function of the microbiomes recruited. To begin to understand the possible drivers of microbial assembly, we will have three different metrics of phytoplankton genotypic and phenotypic traits to compare to the microbiomes assembled. One of these metrics of phytoplankton phenotype is the metabolite profile of the phytoplankton exudates. To collect the metabolite profile, sterile monocultures of the 31 phytoplankton strains were grown under identical conditions. When each strain reached carrying capacity, the culture was filtered through a 0.22um filter, removing all phytoplankton cells. This filtrate was then acidified to a pH of 2.5 and stored at 4C for 2-29 hours, until it could be processed through the solid phase extraction column. To prep the column, 1 cartridge volume (1mL) of 100% methanol was passed through the column. Once the column was prepped, 50mL of cell-free, acidified culture was passed through the column to collect the organic material. The column was cleaned with 2 cartridge volume of 0.01M HCl and air dried for 5 minutes. The sample was eluted into a deep-well plate over dry ice with 1mL of 100% methanol. The plates were stored at -80C until ready for processing.
DOI: 10.15468/dl.zmwzry
title: Occurrence Download

description: A dataset containing 1069941 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Iran (Islamic Republic of)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1069941 records from 1220 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056113-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18746/bmth.data.00000474
title: Large-Scale Archaeological Prospection of Early Modern Battlefields: Geophysical Surveys at the Battlefield of Waterloo - raw data
DOI: 10.5281/zenodo.17473864
title: Tree-Main-Part-Extraction Public

description: This project contains the code and data for the paper "Houyu Liang, Xiang Zhou et al., Tree Main-Part Extraction: A Method for Individual Tree Point Cloud and Parameter Extraction in Complex Forest Environments Integrating Structural Characteristics and Growth Constraint Rules". 

The main function of this code is to perform individual tree extraction from MLS forest point clouds.

The dataset can be downloaded on this page. The code needs to be downloaded from GitHub, and the code link is provided [https://github.com/lhy245/Tree-Main-Part-Extraction.git].

The dataset contains the generated results of "This project" on multiple datasets including: L1W dataset (Henrich et al., 2024), LAUx dataset (Andreas et al., 2022), NIBIO_MLS test dataset (Wielgosz et al., 2024), Boreal3D dataset (Liu et al., 2025), and a self-evaluated dataset (GenHe). The results are stored in the "dataset" folder, with each subfolder named after the original dataset it corresponds to.

The "example_data" folder provides the sample dataset used in the code repository [https://github.com/lhy245/Tree-Main-Part-Extraction.git].

 

Reference

Andreas, T., Christoph, G., Tim, R., Arne, N., 2022. LAUTx - Individual Tree Point Clouds from Austrian forest Inventory plots. https://doi.org/10.5281/ZENODO.6560112

Henrich, J., Van Delden, J., Seidel, D., Kneib, T., Ecker, A.S., 2024. TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds. Ecol. Inform. 84, 102888. https://doi.org/10.1016/j.ecoinf.2024.102888

Liu, J., Wang, Duanchu, Gong, H., Wang, C., Zhu, J., Wang, Di, 2025. Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets. https://doi.org/10.48550/ARXIV.2501.03637

Wielgosz, M., Puliti, S., Xiang, B., Schindler, K., Astrup, R., 2024. SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. Remote Sens. Environ. 313, 114367. https://doi.org/10.1016/j.rse.2024.114367
DOI: 10.5281/zenodo.17473865
title: Tree-Main-Part-Extraction Public

description: This project contains the code and data for the paper "Houyu Liang, Xiang Zhou et al., Tree Main-Part Extraction: A Method for Individual Tree Point Cloud and Parameter Extraction in Complex Forest Environments Integrating Structural Characteristics and Growth Constraint Rules". 

The main function of this code is to perform individual tree extraction from MLS forest point clouds.

The dataset can be downloaded on this page. The code needs to be downloaded from GitHub, and the code link is provided [https://github.com/lhy245/Tree-Main-Part-Extraction.git].

The dataset contains the generated results of "This project" on multiple datasets including: L1W dataset (Henrich et al., 2024), LAUx dataset (Andreas et al., 2022), NIBIO_MLS test dataset (Wielgosz et al., 2024), Boreal3D dataset (Liu et al., 2025), and a self-evaluated dataset (GenHe). The results are stored in the "dataset" folder, with each subfolder named after the original dataset it corresponds to.

The "example_data" folder provides the sample dataset used in the code repository [https://github.com/lhy245/Tree-Main-Part-Extraction.git].

 

Reference

Andreas, T., Christoph, G., Tim, R., Arne, N., 2022. LAUTx - Individual Tree Point Clouds from Austrian forest Inventory plots. https://doi.org/10.5281/ZENODO.6560112

Henrich, J., Van Delden, J., Seidel, D., Kneib, T., Ecker, A.S., 2024. TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds. Ecol. Inform. 84, 102888. https://doi.org/10.1016/j.ecoinf.2024.102888

Liu, J., Wang, Duanchu, Gong, H., Wang, C., Zhu, J., Wang, Di, 2025. Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets. https://doi.org/10.48550/ARXIV.2501.03637

Wielgosz, M., Puliti, S., Xiang, B., Schindler, K., Astrup, R., 2024. SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. Remote Sens. Environ. 313, 114367. https://doi.org/10.1016/j.rse.2024.114367
DOI: 10.5281/zenodo.17399072
title: URANS–FSI Case Files for Axial Flow-Induced Vibration of a Cantilever Rod for Nuclear Application

description: This repository contains the input files, setup scripts, and documentation for the URANS–FSI simulation framework developed during the author’s PhD at the University of Manchester.

The case models the axial flow-induced vibration (FIV) of a blunt-ended cantilevered rod in turbulent axial flow using an unsteady Reynolds-averaged Navier–Stokes (URANS) turbulence model coupled with a structural solver through two-way fluid–structure interaction (FSI). The work forms part of the following publications and supplementary materials:

1. Muhamad Pauzi, A., Iacovides, H., Cioncolini, A., Li, H., & Nabawy, M. R. A. (2024). Application of URANS Simulation and Experimental Validation of Axial Flow-Induced Vibrations on a Blunt-End Cantilever Rod for Nuclear Applications. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-024-09505-5

2. Muhamad Pauzi, A. (2025). Axial Flow-Induced Vibrations over a Blunt-Ended Cantilevered Rod. PhD thesis, University of Manchester. https://research.manchester.ac.uk/en/studentTheses/axial-flow-induced-vibrations-over-a-blunt-ended-cantilevered-rod

3. Muhamad Pauzi, A. (2024a). Video of axial flow-induced vibration (FIV) simulations at different axial positions using RSM LRR and EVM k–ω SST models. https://doi.org/10.48420/27925344.v1

4. Muhamad Pauzi, A. (2024b). Video of axial flow-induced vibration (FIV) simulations at varying annulus Reynolds numbers using RSM LRR and EVM k–ω SST models. https://doi.org/10.48420/27936300.v1

These simulations were run using foam-extend 4.0 with the solids4Foam library for two-way FSI coupling.

The data are intended to support reproducibility and benchmarking of future FSI research in nuclear and energy applications.
DOI: 10.3929/ethz-c-000785281
title: Iron bioaccessibility assessment and bioaccumulation enrichment in microalgae under different production conditions
DOI: 10.5281/zenodo.17473868
title: Dataset and Code Availability for Article with title "Machine Learning Prediction of Reverted Austenite in 18% Ni Maraging Steel"

description: The dataset aus_rev.csv contains 204 entries with eight numerical variables that describe the chemical composition, heat-treatment parameters, and resulting phase fraction in an alloy system, likely related to maraging or austenitic steel reversion studies. The columns include the elemental compositions of Nickel (Ni %), Molybdenum (Mo %), Cobalt (Co %), Iron (Fe %), and Titanium (Ti %) in weight percent, along with the heat treatment duration (Time (hrs)) and temperature (Temperature (°C)). The output variable, %fraction, represents the fraction (in percent) of a specific phase, probably reverted austenite or a precipitate phase formed during treatment. All variables are numeric and free from missing values, making the dataset well-suited for data-driven modeling or regression analysis. Overall, it captures the relationship between alloy composition and thermal processing conditions, and how these factors influence the resulting phase fraction in the material.

The provided Python script builds a machine learning model to predict the volume fraction of reverted austenite in alloys based on their chemical composition and heat treatment conditions. It loads and cleans training and test datasets, applies a nonlinear feature transformation for scaling, and trains multiple regression models including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting. A Voting Regressor combines these models to improve prediction accuracy. The code evaluates model performance using cross-validation, Mean Squared Error (MSE), and R² score, and visualizes the results through actual vs. predicted plots, providing a complete workflow for data-driven materials analysis.
DOI: 10.5281/zenodo.17473869
title: Dataset and Code Availability for Article with title "Machine Learning Prediction of Reverted Austenite in 18% Ni Maraging Steel"

description: The dataset aus_rev.csv contains 204 entries with eight numerical variables that describe the chemical composition, heat-treatment parameters, and resulting phase fraction in an alloy system, likely related to maraging or austenitic steel reversion studies. The columns include the elemental compositions of Nickel (Ni %), Molybdenum (Mo %), Cobalt (Co %), Iron (Fe %), and Titanium (Ti %) in weight percent, along with the heat treatment duration (Time (hrs)) and temperature (Temperature (°C)). The output variable, %fraction, represents the fraction (in percent) of a specific phase, probably reverted austenite or a precipitate phase formed during treatment. All variables are numeric and free from missing values, making the dataset well-suited for data-driven modeling or regression analysis. Overall, it captures the relationship between alloy composition and thermal processing conditions, and how these factors influence the resulting phase fraction in the material.

The provided Python script builds a machine learning model to predict the volume fraction of reverted austenite in alloys based on their chemical composition and heat treatment conditions. It loads and cleans training and test datasets, applies a nonlinear feature transformation for scaling, and trains multiple regression models including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting. A Voting Regressor combines these models to improve prediction accuracy. The code evaluates model performance using cross-validation, Mean Squared Error (MSE), and R² score, and visualizes the results through actual vs. predicted plots, providing a complete workflow for data-driven materials analysis.
DOI: 10.5517/ccdc.csd.cc275wfr
title: CCDC 2061234: Experimental Crystal Structure Determination

description: Related Article: Olaf Nachtigall,  Andrew I. VanderWeide,  William. W. Brennessel,  William D. Jones|2021|Z.Anorg.Allg.Chem.|647|1442|doi:10.1002/zaac.202100056
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.cc2d34vj
title: CCDC 2207567: Experimental Crystal Structure Determination

description: Related Article: Richard J. Puddephatt|2022|CSD Communication|||
description: Related Article: Anwar Abo-Amer,  Paul D. Boyle,  Richard J. Puddephatt|2025|Can.J.Chem.|103|2|doi:10.1139/cjc-2024-0125
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.cc2d34wk
title: CCDC 2207568: Experimental Crystal Structure Determination

description: Related Article: Richard J. Puddephatt|2022|CSD Communication|||
description: Related Article: Anwar Abo-Amer,  Paul D. Boyle,  Richard J. Puddephatt|2025|Can.J.Chem.|103|2|doi:10.1139/cjc-2024-0125
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.cc2d34xl
title: CCDC 2207569: Experimental Crystal Structure Determination

description: Related Article: Richard J. Puddephatt|2022|CSD Communication|||
description: Related Article: Anwar Abo-Amer,  Paul D. Boyle,  Richard J. Puddephatt|2025|Can.J.Chem.|103|2|doi:10.1139/cjc-2024-0125
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.cc2d34ym
title: CCDC 2207570: Experimental Crystal Structure Determination

description: Related Article: Richard J. Puddephatt|2022|CSD Communication|||
description: Related Article: Anwar Abo-Amer,  Paul D. Boyle,  Richard J. Puddephatt|2025|Can.J.Chem.|103|2|doi:10.1139/cjc-2024-0125
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.cc2d34zn
title: CCDC 2207571: Experimental Crystal Structure Determination

description: Related Article: Richard J. Puddephatt|2022|CSD Communication|||
description: Related Article: Anwar Abo-Amer,  Paul D. Boyle,  Richard J. Puddephatt|2025|Can.J.Chem.|103|2|doi:10.1139/cjc-2024-0125
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.cc2d350q
title: CCDC 2207572: Experimental Crystal Structure Determination

description: Related Article: Richard J. Puddephatt|2022|CSD Communication|||
description: Related Article: Anwar Abo-Amer,  Paul D. Boyle,  Richard J. Puddephatt|2025|Can.J.Chem.|103|2|doi:10.1139/cjc-2024-0125
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.cc2d351r
title: CCDC 2207573: Experimental Crystal Structure Determination

description: Related Article: Richard J. Puddephatt|2022|CSD Communication|||
description: Related Article: Anwar Abo-Amer,  Paul D. Boyle,  Richard J. Puddephatt|2025|Can.J.Chem.|103|2|doi:10.1139/cjc-2024-0125
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.cc2d352s
title: CCDC 2207574: Experimental Crystal Structure Determination

description: Related Article: Richard J. Puddephatt|2022|CSD Communication|||
description: Related Article: Anwar Abo-Amer,  Paul D. Boyle,  Richard J. Puddephatt|2025|Can.J.Chem.|103|2|doi:10.1139/cjc-2024-0125
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.cc2d9lbn
title: CCDC 2213751: Experimental Crystal Structure Determination

description: Related Article: Natalia A. Elkina, Evgeny V. Shchegolkov, Yanina V. Burgart, Alexander S. Steparuk, Denis A. Gazizov, Alexander E. Osminin, Ekaterina F. Zhilina, Artem S. Minin, Natalia A. Gerasimova, Natalia P. Evstigneeva, Victor I. Saloutin|2025|Dyes Pigm.|246||doi:10.1016/j.dyepig.2025.113264
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.cc2gn8q4
title: CCDC 2283606: Experimental Crystal Structure Determination

description: Related Article: Ashutosh Panigrahi, Kiran Kumar Pulukuri|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c03541
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.cc2gn8t7
title: CCDC 2283609: Experimental Crystal Structure Determination

description: Related Article: Ashutosh Panigrahi, Kiran Kumar Pulukuri|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c03541
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.cc2gn8v8
title: CCDC 2283610: Experimental Crystal Structure Determination

description: Related Article: Ashutosh Panigrahi, Kiran Kumar Pulukuri|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c03541
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.cc2gn8yc
title: CCDC 2283613: Experimental Crystal Structure Determination

description: Related Article: Ashutosh Panigrahi, Kiran Kumar Pulukuri|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c03541
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.cc2gn8zd
title: CCDC 2283614: Experimental Crystal Structure Determination

description: Related Article: Ashutosh Panigrahi, Kiran Kumar Pulukuri|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c03541
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.cc2kcll4
title: CCDC 2364636: Experimental Crystal Structure Determination

description: Related Article: Yi-Dan Qiao, Meng-Meng Zhai, Qin-Ying Jin, Yin-Hang Chai, Ya-Pan Wu, Ying Zhao, Lu-Fang Ma|2025|J.Mol.Struct.|1322|140692|doi:10.1016/j.molstruc.2024.140692
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.