cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2p6kl2
title: CCDC 2478964: Experimental Crystal Structure Determination

description: Related Article: Philipp Wahlhütter, Roland C. Fischer, Ana Torvisco, Christoph Garstenauer, Frank Uhlig|2025|J.Organomet.Chem.|1042|123885|doi:10.1016/j.jorganchem.2025.123885
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.cc2p6km3
title: CCDC 2478965: Experimental Crystal Structure Determination

description: Related Article: Philipp Wahlhütter, Roland C. Fischer, Ana Torvisco, Christoph Garstenauer, Frank Uhlig|2025|J.Organomet.Chem.|1042|123885|doi:10.1016/j.jorganchem.2025.123885
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.cc2p6kn4
title: CCDC 2478966: Experimental Crystal Structure Determination

description: Related Article: Philipp Wahlhütter, Roland C. Fischer, Ana Torvisco, Christoph Garstenauer, Frank Uhlig|2025|J.Organomet.Chem.|1042|123885|doi:10.1016/j.jorganchem.2025.123885
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: 10.15468/dl.bcsu7x
title: Occurrence Download

description: A dataset containing 47014 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to -45.914722",
 "DecimalLatitude is less than or equal to -40.914722",
 "DecimalLongitude is greater than or equal to -75.0",
 "DecimalLongitude is less than or equal to -70.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 47014 records from 173 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014120-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qkk2p9
title: Occurrence Download

description: A dataset containing 367416 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Europe, Africa, Asia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aquila chrysaetos",
 "Year 1969-2025"
 ]
}

 The dataset includes 367416 records from 554 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014079-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13908978
title: BRACS-WSI-Group-MT-Type-DCIS
DOI: 10.15468/dl.b2xp2a
title: Occurrence Download

description: A dataset containing 91967 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Rana arvalis"
 ]
}

 The dataset includes 91967 records from 230 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014070-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16044839
title: Coccolith δ¹³C and δ¹⁸O records from IODP Site U1476 during the Piacenzian

description: This dataset provides stable isotope (δ¹³C and δ¹⁸O) measurements from coccolith-rich fine fractions (<20 µm) obtained from sediments at IODP Site U1476 in the Mozambique Channel. A small amount of sediment (~1 g) was wet-sieved using a 20 µm mesh with buffered water. The <20 µm fraction was concentrated, dried, and homogenised before isotopic analysis. Measurements were performed at the Stable Isotope Facility, School of Earth and Environmental Sciences, Cardiff University (UK).
DOI: 10.5281/zenodo.16044840
title: Coccolith δ¹³C and δ¹⁸O records from IODP Site U1476 during the Piacenzian

description: This dataset provides stable isotope (δ¹³C and δ¹⁸O) measurements from coccolith-rich fine fractions (<20 µm) obtained from sediments at IODP Site U1476 in the Mozambique Channel. A small amount of sediment (~1 g) was wet-sieved using a 20 µm mesh with buffered water. The <20 µm fraction was concentrated, dried, and homogenised before isotopic analysis. Measurements were performed at the Stable Isotope Facility, School of Earth and Environmental Sciences, Cardiff University (UK).
DOI: 10.6084/m9.figshare.30731381
title: <b>D</b><b>arwin’s Naturalization Conundrum Depends </b><b>o</b><b>n Soil Nutrient Concentration</b>

description: Data and Code for Huang, R., Dong, C., et al. "Darwin’s Naturalization Conundrum Depends on Soil Nutrient Concentration."
DOI: 10.6084/m9.figshare.30731381.v2
title: <b>D</b><b>arwin’s Naturalization Conundrum Depends </b><b>o</b><b>n Soil Nutrient Concentration</b>

description: Data and Code for Huang, R., Dong, C., et al. "Darwin’s Naturalization Conundrum Depends on Soil Nutrient Concentration."
DOI: 10.6084/m9.figshare.30731381.v1
title: <b>D</b><b>arwin’s Naturalization Conundrum Depends </b><b>o</b><b>n Soil Nutrient Concentration</b>

description: Data and Code for Huang, R., Dong, C., et al. "Darwin’s Naturalization Conundrum Depends on Soil Nutrient Concentration."
DOI: 10.5281/zenodo.16044409
title: Coccolith mass data from IODP Site U1476 during the Piacenzian

description: This dataset provides relative coccolith mass measurements from IODP Site U1476 in the Mozambique Channel. Sediments were collected during the IODP Expedition 361: South African Climate (30 January–31 March 2016). Species identification, counting, and morphometric analysis were performed using the SYRACO (SYstème de Reconnaissance Automatique de Coccolithes) software at the European Centre for Research and Teaching in Environmental Geosciences (CEREGE, France). SYRACO applies neural network algorithms to classify coccoliths and extract quantitative parameters from segmented digital images, enabling high-throughput and standardised assessment of coccolith assemblages (Beaufort et al., 2014).
DOI: 10.5281/zenodo.16044410
title: Coccolith mass data from IODP Site U1476 during the Piacenzian

description: This dataset provides relative coccolith mass measurements from IODP Site U1476 in the Mozambique Channel. Sediments were collected during the IODP Expedition 361: South African Climate (30 January–31 March 2016). Species identification, counting, and morphometric analysis were performed using the SYRACO (SYstème de Reconnaissance Automatique de Coccolithes) software at the European Centre for Research and Teaching in Environmental Geosciences (CEREGE, France). SYRACO applies neural network algorithms to classify coccoliths and extract quantitative parameters from segmented digital images, enabling high-throughput and standardised assessment of coccolith assemblages (Beaufort et al., 2014).
DOI: 10.34740/kaggle/ds/8862198
title: BRACS-WSI-Group-MT-Type-DCIS
DOI: 10.5281/zenodo.17747990
title: Survey on fetal dose estimation methods in Europe in the framework of EU-funded SONORA Project (Towards Safe, Optimized and Personalized Radiology and Radiotherapy procedures for pregnant patients)

description: In the framework of the EU-funded "SONORA" project (Towards Safe, Optimized and persoNalized radiOlogy and RAdiotherapy procedures for pregnant patients), funded under the PIANOFORTE Open Call 2023, a survey was created through Google Form and shared in December 2024 among the PIANOFORTE stakeholders, European and National scientific societies of Medical Physics, Radiology, and Dosimetry (as Eurados). The survey aimed to investigate the current practices related to fetal dose estimation, procedure optimization, and risk communication to pregnant patients undergoing diagnostic imaging procedures across various institutions. The objective was to assess existing methodologies and identify areas requiring harmonization or improvement. Specifically, the survey aimed: 1) to identify the tools and methods used for fetal dose estimation in many DIR procedures performed on pregnant patients: Computed Tomography (CT), Radiography (RX), and Fluoroscopy/Angiography (RF/XA) procedures, and other modalities; 2) to identify the ranges of the dose to the fetus, for the different exposure modalities;  3) to investigate the optimization strategies to reduce fetal dose; 4) to investigate how communications take place between physician and the pregnant mother about the radiological risk to the unborn child. These files are related to the full questionnaire launched and the collected responses.
DOI: 10.5281/zenodo.17747991
title: Survey on fetal dose estimation methods in Europe in the framework of EU-funded SONORA Project (Towards Safe, Optimized and Personalized Radiology and Radiotherapy procedures for pregnant patients)

description: In the framework of the EU-funded "SONORA" project (Towards Safe, Optimized and persoNalized radiOlogy and RAdiotherapy procedures for pregnant patients), funded under the PIANOFORTE Open Call 2023, a survey was created through Google Form and shared in December 2024 among the PIANOFORTE stakeholders, European and National scientific societies of Medical Physics, Radiology, and Dosimetry (as Eurados). The survey aimed to investigate the current practices related to fetal dose estimation, procedure optimization, and risk communication to pregnant patients undergoing diagnostic imaging procedures across various institutions. The objective was to assess existing methodologies and identify areas requiring harmonization or improvement. Specifically, the survey aimed: 1) to identify the tools and methods used for fetal dose estimation in many DIR procedures performed on pregnant patients: Computed Tomography (CT), Radiography (RX), and Fluoroscopy/Angiography (RF/XA) procedures, and other modalities; 2) to identify the ranges of the dose to the fetus, for the different exposure modalities;  3) to investigate the optimization strategies to reduce fetal dose; 4) to investigate how communications take place between physician and the pregnant mother about the radiological risk to the unborn child. These files are related to the full questionnaire launched and the collected responses.
DOI: 10.6084/m9.figshare.30739376
title: Table S2

description: Table S2.The proteins identified in MFGM from raw and treated milk sample of the four species
DOI: 10.6084/m9.figshare.30739376.v1
title: Table S2

description: Table S2.The proteins identified in MFGM from raw and treated milk sample of the four species
DOI: 10.5281/zenodo.16043901
title: Coccolith calcium carbonate at IODP Site U1476 during the Piacenzian

description: This dataset provides the relative carbonate contribution of coccoliths at IODP Site U1476 in the Mozambique Channel. Sediments were collected during the IODP Expedition 361: South African Climate (30 January–31 March 2016). Species identification, counting, and morphometric analysis were performed using the SYRACO (SYstème de Reconnaissance Automatique de Coccolithes) software at the European Centre for Research and Teaching in Environmental Geosciences (CEREGE, France). SYRACO applies neural network algorithms to classify coccoliths and extract quantitative parameters from segmented digital images, enabling high-throughput and standardised assessment of coccolith assemblages (Beaufort et al., 2014).
DOI: 10.5281/zenodo.16043902
title: Coccolith calcium carbonate at IODP Site U1476 during the Piacenzian

description: This dataset provides the relative carbonate contribution of coccoliths at IODP Site U1476 in the Mozambique Channel. Sediments were collected during the IODP Expedition 361: South African Climate (30 January–31 March 2016). Species identification, counting, and morphometric analysis were performed using the SYRACO (SYstème de Reconnaissance Automatique de Coccolithes) software at the European Centre for Research and Teaching in Environmental Geosciences (CEREGE, France). SYRACO applies neural network algorithms to classify coccoliths and extract quantitative parameters from segmented digital images, enabling high-throughput and standardised assessment of coccolith assemblages (Beaufort et al., 2014).
DOI: 10.5281/zenodo.16043100
title: Relative abundances of coccolithophores at IODP Site U1476 during the Piacenzian

description: This dataset provides the relative abundances of coccolithophores (coccoliths) from IODP Site U1476, drilled in the Mozambique Channel during IODP Expedition 361: South African Climate (30 January–31 March 2016). Species identification and counting were performed using the SYRACO (SYstème de Reconnaissance Automatique de Coccolithes) software at the European Centre for Research and Teaching in Environmental Geosciences (CEREGE, France). SYRACO applies neural network algorithms to classify coccoliths and extract quantitative parameters from segmented digital images, enabling high-throughput and standardised assessment of coccolith assemblages (Beaufort et al., 2014).
DOI: 10.5281/zenodo.16043101
title: Relative abundances of coccolithophores at IODP Site U1476 during the Piacenzian

description: This dataset provides the relative abundances of coccolithophores (coccoliths) from IODP Site U1476, drilled in the Mozambique Channel during IODP Expedition 361: South African Climate (30 January–31 March 2016). Species identification and counting were performed using the SYRACO (SYstème de Reconnaissance Automatique de Coccolithes) software at the European Centre for Research and Teaching in Environmental Geosciences (CEREGE, France). SYRACO applies neural network algorithms to classify coccoliths and extract quantitative parameters from segmented digital images, enabling high-throughput and standardised assessment of coccolith assemblages (Beaufort et al., 2014).
DOI: 10.5281/zenodo.17750679
title: ULB Bunny dataset

description: ULB Bunny Dataset

Description

The ULB Bunny Dataset is an 80-view 360° multiview Blender rendering of a metallic Stanford Bunny placed in a Lambertian scene.  This dataset is intended for research on non-Lambertian surface reconstruction, Gaussian Splatting, and material-aware rendering.  All views are rendered under controlled lighting to ensure consistency and reproducibility.

---

Contents

The dataset includes:

- 80 PNG images    High-resolution renders from uniformly sampled viewpoints covering a full 360°.

- COLMAP reconstruction files   Provided in the Gaussian Splatting–compatible format.

Directory structure:

-- images/     *.png  (80 images)

-- sparse/     -- 0/          cameras.bin          images.bin          points3D.bin

Format





Images: PNG, undistorted, one image per camera viewpoint




Camera calibration: Stored in cameras.bin




Camera poses: Stored in images.bin




Sparse point cloud: Stored in points3D.bin



The structure follows the standard COLMAP sparse model expected by most Gaussian Splatting implementations.

Intended Use

This dataset is suitable for:





Evaluating non-Lambertian surface reconstruction




Testing 3D Gaussian Splatting and material-aware Gaussian variants




Benchmarking differentiable rendering algorithms




Studying view-dependent effects under controlled conditions



 

Citation

If you use this dataset, please cite:

Dubar, E., Dury, S., Bonatto, D., & Lafruit, G. (2025). ULB Bunny Dataset. Zenodo. https://doi.org/10.5281/zenodo.17750680

@dataset{ulb_bunny_dataset_2025,  author       = {Eva Dubar and Sarah Dury and Daniele Bonatto and Gauthier Lafruit},  title        = {ULB Bunny Dataset},  year         = {2025},  publisher    = {Zenodo},  doi          = {10.5281/zenodo.17750680},  url          = {https://doi.org/10.5281/zenodo.17750680}}
DOI: 10.5281/zenodo.17750680
title: ULB Bunny dataset

description: ULB Bunny Dataset

Description

The ULB Bunny Dataset is an 80-view 360° multiview Blender rendering of a metallic Stanford Bunny placed in a Lambertian scene.  This dataset is intended for research on non-Lambertian surface reconstruction, Gaussian Splatting, and material-aware rendering.  All views are rendered under controlled lighting to ensure consistency and reproducibility.

---

Contents

The dataset includes:

- 80 PNG images    High-resolution renders from uniformly sampled viewpoints covering a full 360°.

- COLMAP reconstruction files   Provided in the Gaussian Splatting–compatible format.

Directory structure:

-- images/     *.png  (80 images)

-- sparse/     -- 0/          cameras.bin          images.bin          points3D.bin

Format





Images: PNG, undistorted, one image per camera viewpoint




Camera calibration: Stored in cameras.bin




Camera poses: Stored in images.bin




Sparse point cloud: Stored in points3D.bin



The structure follows the standard COLMAP sparse model expected by most Gaussian Splatting implementations.

Intended Use

This dataset is suitable for:





Evaluating non-Lambertian surface reconstruction




Testing 3D Gaussian Splatting and material-aware Gaussian variants




Benchmarking differentiable rendering algorithms




Studying view-dependent effects under controlled conditions



 

Citation

If you use this dataset, please cite:

Dubar, E., Dury, S., Bonatto, D., & Lafruit, G. (2025). ULB Bunny Dataset. Zenodo. https://doi.org/10.5281/zenodo.17750680

@dataset{ulb_bunny_dataset_2025,  author       = {Eva Dubar and Sarah Dury and Daniele Bonatto and Gauthier Lafruit},  title        = {ULB Bunny Dataset},  year         = {2025},  publisher    = {Zenodo},  doi          = {10.5281/zenodo.17750680},  url          = {https://doi.org/10.5281/zenodo.17750680}}
DOI: 10.15468/dl.fjbjgb
title: Occurrence Download

description: A dataset containing 20791 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is Craterellus cornucopioides",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 20791 records from 307 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014122-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.idzlqw
title: Occurrence Download

description: A dataset containing 39955 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Andorra",
 "Country is Armenia",
 "Country is Austria",
 "Country is Belarus",
 "Country is Belgium",
 "Country is Bosnia and Herzegovina",
 "Country is Bulgaria",
 "Country is China",
 "Country is Croatia",
 "Country is Czechia",
 "Country is Estonia",
 "Country is France",
 "Country is Germany",
 "Country is Greece",
 "Country is Hungary",
 "Country is India",
 "Country is Japan",
 "Country is Kazakhstan",
 "Country is Kyrgyzstan",
 "Country is Latvia",
 "Country is Lithuania",
 "Country is North Macedonia",
 "Country is Mongolia",
 "Country is Montenegro",
 "Country is Netherlands",
 "Country is Poland",
 "Country is Portugal",
 "Country is Serbia",
 "Country is Slovakia",
 "Country is Slovenia",
 "Country is Switzerland",
 "Country is Tajikistan",
 "Country is Türkiye",
 "Country is Uzbekistan",
 "Country is Ukraine",
 "Country is United Kingdom of Great Britain and Northern Ireland"
 ]
 },
 "TaxonKey is Salix viminalis L."
 ]
}

 The dataset includes 39955 records from 272 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003352-171025204727650/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5q7emr
title: Occurrence Download

description: A dataset containing 1235627 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tringa flavipes (J.F.Gmelin, 1789)"
 ]
}

 The dataset includes 1235627 records from 296 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078134-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8mpzvh
title: Occurrence Download

description: A dataset containing 198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Madagascar",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Microcebus griseorufus Kollman, 1910"
 ]
}

 The dataset includes 198 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0195054-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fdn8t8
title: Occurrence Download

description: A dataset containing 1048 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Brillantaisia lamium (Nees) Benth.)"
 ]
}

 The dataset includes 1048 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0163678-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7odxxn
title: Occurrence Download

description: A dataset containing 280 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tillandsia filifolia Schltdl. & Cham."
 ]
}

 The dataset includes 280 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003353-150912100344238/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ct7srh
title: Occurrence Download

description: A dataset containing 446622 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2023-02-15",
 "EventDate is less than or equal to 2023-03-17",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 446622 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0163670-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vnpqnw
title: Occurrence Download

description: A dataset containing 265 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ophichthus puncticeps (Kaup, 1859)"
 ]
}

 The dataset includes 265 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035234-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.