cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2ursrb
title: Occurrence Download

description: A dataset containing 1048 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bougainvillea spectabilis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 1048 records from 228 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011191-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18428142
title: NWA1685_LL4 LIBS data

description: LIBS data gathered on a meteorite sample NWA1685_LL4 during the EXCITE measurement campaign at CEITEC BUT. All measurement details are collected in the metadata.txt text file.
DOI: 10.5281/zenodo.18428143
title: NWA1685_LL4 LIBS data

description: LIBS data gathered on a meteorite sample NWA1685_LL4 during the EXCITE measurement campaign at CEITEC BUT. All measurement details are collected in the metadata.txt text file.
DOI: 10.5281/zenodo.17165967
title: MACO: An Open-Image Data Set with Multiannotation Strategies for Building Material Counting

description: Building material management is fundamental to engineering projects. Currently, the counting of building materials is primarily conducted manually, a process that is time-consuming and prone to errors. Recent advances in computer vision and deep learning have significantly facilitated automation in the construction industry. However, the effectiveness of deep learning approaches hinges heavily on large-scale datasets with accurate, manually annotated data. Existing construction-domain image datasets are ill-suited for material counting tasks, and this lack of large-scale, publicly available datasets has become a major barrier to progress in this area. To address this gap, this study introduces and publicly releases a new large-scale image dataset, called Material Counting in Construction (MACO), collected directly from construction sites. The MACO dataset comprises 4,426 images and 563,595 annotated objects, covering seven common building materials and diverse real-world construction scenarios. To enhance the utility of MACO and to support more downstream tasks, three popular annotation techniques were employed: horizontal bounding boxes (HBBs), oriented bounding boxes (OBBs), and segmentation masks. This dataset is the first large-scale, multi-material, multi-annotation, and high-density open dataset (with 127.3 annotations per image) among existing construction datasets. The validity of MACO is confirmed through benchmarking with two state-of-the-art one-stage object detection algorithms, achieving a maximum mAP50 of 91.6%. This provides a robust benchmark for method selection in similar tasks. In addition, to compare the “one-material-one-model” and “multi-material-one-model” paradigms, experiments were conducted on models trained on single versus multiple materials. Results indicate that the multi-material counting model exhibits performance degradation due to cross-material feature interference, suggesting that developing a generalized detection and counting model requires further research. Overall, MACO is designed to advance intelligent construction site management, including material detection and counting, specification measurement, and robotic grasping and assembly.

 

 
description: We’d love to hear how you’re using the MACO dataset!

Please take our 1-minute survey: https://forms.gle/ngvm7PuKcpQFZJwK9

If you are based in China and have difficulty accessing Google, please use this alternative form: https://www.wjx.top/vm/OtWj18N.aspx

Your input helps us improve data quality and support the open-science community.
DOI: 10.5281/zenodo.17165968
title: MACO: An Open-Image Data Set with Multiannotation Strategies for Building Material Counting

description: Building material management is fundamental to engineering projects. Currently, the counting of building materials is primarily conducted manually, a process that is time-consuming and prone to errors. Recent advances in computer vision and deep learning have significantly facilitated automation in the construction industry. However, the effectiveness of deep learning approaches hinges heavily on large-scale datasets with accurate, manually annotated data. Existing construction-domain image datasets are ill-suited for material counting tasks, and this lack of large-scale, publicly available datasets has become a major barrier to progress in this area. To address this gap, this study introduces and publicly releases a new large-scale image dataset, called Material Counting in Construction (MACO), collected directly from construction sites. The MACO dataset comprises 4,426 images and 563,595 annotated objects, covering seven common building materials and diverse real-world construction scenarios. To enhance the utility of MACO and to support more downstream tasks, three popular annotation techniques were employed: horizontal bounding boxes (HBBs), oriented bounding boxes (OBBs), and segmentation masks. This dataset is the first large-scale, multi-material, multi-annotation, and high-density open dataset (with 127.3 annotations per image) among existing construction datasets. The validity of MACO is confirmed through benchmarking with two state-of-the-art one-stage object detection algorithms, achieving a maximum mAP50 of 91.6%. This provides a robust benchmark for method selection in similar tasks. In addition, to compare the “one-material-one-model” and “multi-material-one-model” paradigms, experiments were conducted on models trained on single versus multiple materials. Results indicate that the multi-material counting model exhibits performance degradation due to cross-material feature interference, suggesting that developing a generalized detection and counting model requires further research. Overall, MACO is designed to advance intelligent construction site management, including material detection and counting, specification measurement, and robotic grasping and assembly.

 

 
description: We’d love to hear how you’re using the MACO dataset!

Please take our 1-minute survey: https://forms.gle/ngvm7PuKcpQFZJwK9

If you are based in China and have difficulty accessing Google, please use this alternative form: https://www.wjx.top/vm/OtWj18N.aspx

Your input helps us improve data quality and support the open-science community.
DOI: 10.15468/dl.rs8q78
title: Occurrence Download

description: A dataset containing 1102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ceodes umbellifera",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 1102 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011194-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dpv6u2
title: Occurrence Download

description: A dataset containing 172625 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pyrus calleryana",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 172625 records from 206 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011146-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18467797
title: Structural basis for substrate recognition by the pain-associated neuronal polyamine transporter SLC45A4

description: This repository contains molecular dynamics simulation data investigating the structural dynamics of SLC45A4, a neuronal polyamine transporter linked to chronic pain regulation. The dataset includes .xtc trajectories, .gro structure files, and .tpr input files derived from 1 μs all-atom simulations of five distinct systems: the apo protein in detergent and nanodisc environments, and the transporter bound to the substrates putrescine, spermidine, and spermine within a mixed lipid bilayer. These files were utilized for the analysis and conclusions presented in the associated manuscript.
DOI: 10.5281/zenodo.18467798
title: Structural basis for substrate recognition by the pain-associated neuronal polyamine transporter SLC45A4

description: This repository contains molecular dynamics simulation data investigating the structural dynamics of SLC45A4, a neuronal polyamine transporter linked to chronic pain regulation. The dataset includes .xtc trajectories, .gro structure files, and .tpr input files derived from 1 μs all-atom simulations of five distinct systems: the apo protein in detergent and nanodisc environments, and the transporter bound to the substrates putrescine, spermidine, and spermine within a mixed lipid bilayer. These files were utilized for the analysis and conclusions presented in the associated manuscript.
DOI: 10.15468/dl.gnanxv
title: Occurrence Download

description: A dataset containing 835 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mythimna separata",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 835 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011196-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.txx7kt
title: Occurrence Download

description: A dataset containing 10107 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Taxa occurrence (presence/absence) and composition of benthic macroinvertebrates from trawl surveys (2003-2017) conducted on Lake Victoria, Uganda"
 ]
}

 The dataset includes 10107 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011197-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.2908/env_egss_mon
title: Production, value added and exports in the environmental goods and service sector
title: Produktion, Wertschöpfung und Ausfuhren des Sektors Umweltgüter und -dienstleistungen
title: Production, valeur ajoutée et exportations du secteur des biens et services environnementaux

description: Die EGSS-Konten erfassen Daten über die Produktion von Waren und Dienstleistungen, die die Umwelt schützen und zur nachhaltigen Bewirtschaftung natürlicher Ressourcen beitragen. EGSS ist mit den Konzepten im Rahmen der Volkswirtschaftlichen Gesamtrechnungen kohärent und untersucht Themen wie grünes Wachstum und grüne Beschäftigung. Damit unterstützt es die Entwicklung und Überwachung von umwelt- und ressourcenbezogenen Politiken und Zielen.
description: The EGSS accounts collects data on the production of goods and services intended to protect the environment and sustainably manage natural resources. EGSS is aligned with the concepts and boundaries of national accounts and explores issues such as green growth and green employment. With that, it supports the development and monitoring of environmental and resource-related policies and targets.
description: Les comptes SBSE permettent de collecter des données sur la production de biens et de services destinés à protéger l'environnement et à gérer durablement les ressources naturelles. Les SBSE sont alignés sur les concepts et les limites des comptes nationaux et explorent des questions telles que la croissance verte et l'emploi vert. Ainsi, ils soutiennent le développement et le suivi des politiques et des objectifs liés à l'environnement et aux ressources.
DOI: 10.5281/zenodo.18479041
title: EUR and AFR simulations under the OutOfAfrica_2T12 demographic model for RAiSD-AI evaluation

description: datasets for EUR and AFR

200 samples

demographic model parameters obtained from stdpopsim (OutOfAfrica_2T12)
DOI: 10.5281/zenodo.18479042
title: EUR and AFR simulations under the OutOfAfrica_2T12 demographic model for RAiSD-AI evaluation

description: datasets for EUR and AFR

200 samples

demographic model parameters obtained from stdpopsim (OutOfAfrica_2T12)
DOI: 10.15468/dl.7vaqft
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Colignonia ovalifolia",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 91 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011198-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c67xtu
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Myotis pilosus",
 "Year 1970-2024"
 ]
}

 The dataset includes 22 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011201-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18468335
title: Light and Rhizobial Symbiosis Synergistically Enhance Nitrogen Use Efficiency and Soil Function in Mediterranean Legumes

description: This dataset contains plant and soil data from a field experiment where we used two shade-adapted and two sun-adapted species that were grown both in full light and shade with and without inoculation. We provide data on plant performance and soil variations under the mentioned experimental conditions.  Complete metadata is provided to support reuse in microbial ecology, phylogeny, and plant–microbiome studies.
DOI: 10.5281/zenodo.18468334
title: Light and Rhizobial Symbiosis Synergistically Enhance Nitrogen Use Efficiency and Soil Function in Mediterranean Legumes

description: This dataset contains plant and soil data from a field experiment where we used two shade-adapted and two sun-adapted species that were grown both in full light and shade with and without inoculation. We provide data on plant performance and soil variations under the mentioned experimental conditions.  Complete metadata is provided to support reuse in microbial ecology, phylogeny, and plant–microbiome studies.
DOI: 10.15468/dl.52ubfq
title: Occurrence Download

description: A dataset containing 29762 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Heracleum sosnowskyi)",
 "OccurrenceStatus is Present",
 "SpeciesKey is not null",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2026",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 },
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation)"
 ]
}

 The dataset includes 29762 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011200-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qvnb4c
title: Occurrence Download

description: A dataset containing 10897 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Loxostege sticticalis",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 10897 records from 143 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011199-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18297901
title: The dataset, the code, the models, and the namelist files for "Gray-Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator"

description: The dataset for training and testing, the FNO code, the trained FNO model, and the namelist files are used in the paper: Gray-Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator.

The FNO code is also available at https://github.com/FreyLuo/FNO-CBL. 

The LES data can be found at https://doi.org/10.5281/zenodo.18326720.
DOI: 10.5281/zenodo.18297902
title: The dataset, the code, the models, and the namelist files for "Gray-Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator"

description: The dataset for training and testing, the trained FNO model, and the namelist files are used in the paper: Gray-Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator.

Version v2 adds the code and the FNO-I1OI model.

The code is also available at https://github.com/FreyLuo/FNO-CBL. 

The LES data can be found at https://doi.org/10.5281/zenodo.18326720.
DOI: 10.15468/dl.ntu77h
title: Occurrence Download

description: A dataset containing 114634 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations, Museum of Comparative Zoology, Harvard University, Chinese Institute of Biology - Chinese Academy of Sciences, CAS Herpetology (HERP))",
 "Geometry POLYGON((100.41401 24.34203,99.96191 24.8893,99.26235 26.21823,98.90067 29.13069,96.00725 31.92894,92.75214 32.9759,91.53386 32.7094,91.14662 32.15111,91.15035 32.13924,91.01514 31.36143,91.6338 30.48103,93.18045 30.74278,95.46473 29.93376,98.08214 28.19675,98.43905 24.84171,100.41401 24.34203))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chordata"
 ]
}

 The dataset includes 114634 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011164-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yr9dnh
title: Occurrence Download

description: A dataset containing 317 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Commicarpus australis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 317 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011203-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wpsfax
title: Occurrence Download

description: A dataset containing 1086 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phalera flavescens",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 1086 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011204-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17301221
title: The LES data for "Gray-Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator"

description: The LES data is used in the paper: Gray-Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator. 

The FNO code is available at https://github.com/FreyLuo/FNO-CBL.  

The model and the other data can be found at https://doi.org/10.5281/zenodo.18297902.
DOI: 10.5281/zenodo.18326720
title: The LES data for "Gray-Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator"

description: The LES data is used in the paper: Gray-Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator. 

The FNO code is available at https://github.com/FreyLuo/FNO-CBL.  

The model and the other data can be found at https://doi.org/10.5281/zenodo.18297902.
DOI: 10.5281/zenodo.18479251
title: Gordon Ritter Farm (LiDAR) November 19 2025

description: This orthomosaic of Sånta Rita-Sumai, Guam was created from 240 images taken on November 19 2025, at 150ft AGL, with a DJI Matrice 350 . The mission was planned using the DJI Pilot application and post-processed using DJI Terra.

This project was made possible through funding by the following grant(s): NACD (Award No. HQ00342020012).
description: Disclaimer: The following aerial imagery is intended solely for research purposes related to the assessment of the captured area. It is strictly prohibited to distribute this imagery to any unauthorized individuals or third-party entities without prior authorization. All drone imagery was obtained in accordance with FAA guidelines and applicable local laws, with all necessary authorizations secured to ensure safe and legal operation.

The University of Guam Drone Corps funded by NASA Guam Space Grant and NASA Guam EPSCoR retain all rights to the aerial imagery, including but not limited to copyrights and intellectual property rights. Any unauthorized modification, reproduction, or distribution of the imagery is strictly prohibited.

By using this data, you acknowledge and agree to abide by the terms and conditions set forth in this disclaimer.

 

 

Privacy Disclaimer: The aerial imagery captured for assessment may inadvertently include images of private property and individuals. While every effort has been made to respect privacy, it is possible that identifiable information or sensitive locations may be visible in the imagery. University of Guam Drone Corps and related parties disclaim any responsibility for the unintentional collection or dissemination of such information. The recipient(s) of this imagery are urged to handle it with due care and take necessary precautions to protect the privacy rights of individuals and comply with applicable privacy laws and regulations.
DOI: 10.5517/ccdc.csd.cc2c81pd
title: CCDC 2182483: Experimental Crystal Structure Determination

description: Related Article: Xun Tian, Ailin Pan, Ya Du, Lening Zhang, Cuirong Qin, Shengzu Duan, Li-Juan Yang, Xiaodong Yang|2025|CSD Communication|||
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.cc2gzjww
title: CCDC 2293469: Experimental Crystal Structure Determination

description: Related Article: Ailin Pan, Yamiao Meng, Xun Tian, Shengzu Duan, Yonggang Jiang, guogang deng, Bart Limburg, Hongbin Zhang, Patrick Walsh, Xiaodong Yang|2025|CSD Communication|||
description: Related Article: Ailin Pan, Madeline E. Rotella, Yamiao Meng, Xun Tian, Shengzu Duan, Yonggang Jiang, Guogang Deng, Bart Limburg, Hongbin Zhang, Marisa C. Kozlowski, Patrick J. Walsh, Xiaodong Yang|2025|Chemical Science|17|412|doi:10.1039/d5sc00268k
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.