cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.7rygnt
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus lobbii Hook.f. & Thomson ex Ettingsh."
 ]
}

 The dataset includes 23 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023442-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kaavw7
title: Occurrence Download

description: A dataset containing 3993 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Israel, Jordan, Türkiye, Syrian Arab Republic, Lebanon, Iraq, Palestine, State of, Iran (Islamic Republic of))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Triticum turgidum subsp. dicoccoides (Asch. & Graebn.) Thell."
 ]
}

 The dataset includes 3993 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023440-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m42khz
title: Occurrence Download

description: A dataset containing 2382 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Psilotum nudum (L.) P.Beauv."
 ]
}

 The dataset includes 2382 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023443-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.265sxs
title: Occurrence Download

description: A dataset containing 57581 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "TaxonKey is Cervus elaphus Linnaeus, 1758",
 "Year 1990-2009"
 ]
}

 The dataset includes 57581 records from 288 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023411-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/cvwqq6
title: Time limitation for occupation of indigenous land in Brazil

description: The Constitution of the Federative Republic of Brazil of 1988 dedicates its own chapter to treat indigenous peoples, where it can be observed, from art. 231, norms that recognize and protect the category in social organization, customs, norms, beliefs, norms that deal and originate with the occupation, being up to the Union, they are provided in the disciplines of the Magna Law, on demarcation, protection and respect the goods that are typical of the original peoples highlighted.
DOI: 10.15468/dl.kc7wfm
title: Occurrence Download

description: A dataset containing 3306891 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae)",
 "Country is New Zealand",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 0"
 ]
}

 The dataset includes 3306891 records from 377 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023425-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cxwcjh
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Myristica teysmannii Miq."
 ]
}

 The dataset includes 20 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023448-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7940113
title: pySCION

description: pySCION Earth System Model code
description: -update readme file
DOI: 10.5281/zenodo.7912906
title: pySCION

description: pySCION Earth System Model code
DOI: 10.34740/kaggle/dsv/10722092
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.34740/kaggle/dsv/10722103
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.6084/m9.figshare.28389050.v1
title: 5-7 precipitation reconstruction

description: We developed a May–July precipitation reconstruction for the northeastern Qinghai-Tibetan Plateau using a well-replicated ensemble tree-ring-width chronology from 350 Qilian juniper samples, spanning 845–2023 CE.
DOI: 10.6084/m9.figshare.28389050
title: 5-7 precipitation reconstruction

description: We developed a May–July precipitation reconstruction for the northeastern Qinghai-Tibetan Plateau using a well-replicated ensemble tree-ring-width chronology from 350 Qilian juniper samples, spanning 845–2023 CE.
DOI: 10.48597/4fpn-nuwr
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/vgps-jjtx
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/c6nk-s5tt
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/t85t-xfdb
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/teq8-4yqs
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/xvzg-4uen
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/32zv-3j58
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/5hdf-dygt
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/a3pe-6b9e
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/qt7k-3da7
title: VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/xxxv-j2tc
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/qtj7-xr34
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/7peg-7ft3
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/7uak-xzgs
title: VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/fbha-zz3g
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/pjp6-qk2k
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/rfeb-82kz
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/fr5q-bsdk
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/k75f-9bj2
title: VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/nydd-waxe
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/e2kg-8dp8
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/nmps-k8hv
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/u8xn-hh46
title: VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/5n3x-a6x9
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/668c-57aj
title: VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/82cz-ttfy
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/byj5-qcdx
title: VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/nuxw-4sc7
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/pc2u-373m
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/qf5z-ad3b
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/wmjw-xugj
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/2yx7-ubc8
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/3hzh-493q
title: VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/fm2r-pxh6
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/g7jm-a2gx
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.48597/wxwt-jh9z
title: Measurement of VOC (hydrocarbons) at Hohenpeissenberg

description: Measurement of VOC (hydrocarbons) at Hohenpeissenberg
DOI: 10.21227/byy9-5179
title: Supplementary Data S1: 1000 Hotels

description: The dataset for this study comprises detailed records from 1,000 economy hotel chain projects across 29 countries, including the 19 G20 member states, the European Union, 5 African Union tropical climate countries, and 5 G20 cold climate countries. These countries were chosen to capture a wide range of climatic, economic, and geographic diversity. The data covers 10 globally dominant economy hotel chains, ensuring representation of significant architectural and operational trends in the hospitality industry. The dataset includes two main types of data: primary geometric attributes and secondary contextual factors. The primary data captures critical geometric and design metrics, such as Building Footprint Area (m²), Total Building Area (m²), Number of Building Floors, Total Number of Hotel Rooms, Building Perimeter (m), Building Shape Factor (Surface Area/Volume), and Building Plan Geometry Classifications (Level 1 and Level 2). The secondary data includes contextual and operational factors such as Country, GDP (Trillion USD), GDP per Capita (USD), Continent, Main ASHRAE Climate Zones, Climate Zone Descriptions, Hotel Name, Location, Main Chain Hotel Floor Count, and Room Heights (Indoor and Outdoor).
DOI: 10.15468/dl.2dtkr8
title: Occurrence Download

description: A dataset containing 24605 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (DEU.9_1, DEU.5_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Amphibia, Reptilia)"
 ]
}

 The dataset includes 24605 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023427-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6092/unibo/amsacta/8187
title: Additional datasets for Red beetroot fermentation by microbial consortia

description: The file RedBeetroot_Fermentation_MicrobialConsortia_ImprovedAromaticFeatures_Data.ods contains three sheets. The "lactic_acid" sheet provides data on the concentrations of organic acids (D-lactic, L-lactic, and acetic acid) and residual sugars, measured using enzymatic reactions with three commercial starters ("LAB mix", "Ln. mesenter.", and "Kefir") or spontaneous fermentation ("Spontaneous"). The data are presented as mean concentrations (g/L ± standard deviation) at the end of fermentation. The "cells" sheet quantifies lactic acid bacteria (LAB) in the brine after fermentation, with mean counts (Log CFU/mL ± standard deviation) of yeasts and LAB on MRS agar. The "correlations" sheet contains Spearman rank correlation data among process parameters and volatile organic compounds (VOCs).
DOI: 10.5281/zenodo.14849905
title: Traffic Sign Recognition YOLOv8

description: Description:







The Traffic Sign Recognition Dataset is designed to support the development of deep learning models, particularly for object detection and classification. The dataset includes images of various traffic signs, each annotated with bounding boxes and corresponding class labels. These images have been preprocessed for uniform size and pixel normalization, ensuring optimal training conditions for models like YOLOv8. The dataset captures a wide variety of traffic signs, making it ideal for tasks related to traffic safety and autonomous vehicle systems.







Download Dataset









Applications

The dataset is well-suited for training models in autonomous driving, transportation safety, and smart traffic management systems. It can be used in various environments to improve the recognition of traffic signs in different weather conditions, lighting, and angles. This diversity enhances the dataset’s robustness, making it useful for real-time object detection tasks in smart city infrastructure and road safety systems.

Dataset Structure



Image Annotations: Each traffic sign in the dataset is mark with bounding boxes, allowing precise localization during model training.

Class Labels: The dataset includes class labels for different types of traffic signs, such as stop signs, speed limits, and warnings.

Preprocessing: The images have been resize and normalize to maintain consistent pixel values, which aids in model training.


Use Cases

This dataset can be extend beyond traffic sign recognition. It can be used in conjunction with other datasets for multi-class object detection in road environments, facilitating the development of comprehensive autonomous navigation systems. Additionally, the dataset supports research into improving model accuracy in challenging conditions such as low-light, occluded signs, or varying weather.

Why Use YOLOv8?

YOLOv8 is a highly efficient model for object detection, offering speed and accuracy improvements over its predecessors. The architecture of YOLOv8 enables quick real-time detection of traffic signs with high precision, making this dataset particularly suitable for applications where rapid recognition is essential.

Future Improvements

Further development of this dataset could involve incorporating more challenging real-world conditions, such as damage or obscured signs, or training models to handle multiple sign types in a single image. The dataset can also be expand to cover more countries, including region-specific signs and regulations.

This dataset is sourced from KAggle.
DOI: 10.5281/zenodo.14849906
title: Traffic Sign Recognition YOLOv8

description: Description:







The Traffic Sign Recognition Dataset is designed to support the development of deep learning models, particularly for object detection and classification. The dataset includes images of various traffic signs, each annotated with bounding boxes and corresponding class labels. These images have been preprocessed for uniform size and pixel normalization, ensuring optimal training conditions for models like YOLOv8. The dataset captures a wide variety of traffic signs, making it ideal for tasks related to traffic safety and autonomous vehicle systems.







Download Dataset









Applications

The dataset is well-suited for training models in autonomous driving, transportation safety, and smart traffic management systems. It can be used in various environments to improve the recognition of traffic signs in different weather conditions, lighting, and angles. This diversity enhances the dataset’s robustness, making it useful for real-time object detection tasks in smart city infrastructure and road safety systems.

Dataset Structure



Image Annotations: Each traffic sign in the dataset is mark with bounding boxes, allowing precise localization during model training.

Class Labels: The dataset includes class labels for different types of traffic signs, such as stop signs, speed limits, and warnings.

Preprocessing: The images have been resize and normalize to maintain consistent pixel values, which aids in model training.


Use Cases

This dataset can be extend beyond traffic sign recognition. It can be used in conjunction with other datasets for multi-class object detection in road environments, facilitating the development of comprehensive autonomous navigation systems. Additionally, the dataset supports research into improving model accuracy in challenging conditions such as low-light, occluded signs, or varying weather.

Why Use YOLOv8?

YOLOv8 is a highly efficient model for object detection, offering speed and accuracy improvements over its predecessors. The architecture of YOLOv8 enables quick real-time detection of traffic signs with high precision, making this dataset particularly suitable for applications where rapid recognition is essential.

Future Improvements

Further development of this dataset could involve incorporating more challenging real-world conditions, such as damage or obscured signs, or training models to handle multiple sign types in a single image. The dataset can also be expand to cover more countries, including region-specific signs and regulations.

This dataset is sourced from KAggle.
DOI: 10.5281/zenodo.14849801
title: Are there potential benefits from using microbial products to control ammonia and greenhouse gases released from pig slurry?

description: The raw data of NH3, CO2 and CH4 emissions of pig slurry samples under microbial inocculant treatments (A1, A2) and their controls (C1, C2). Please see the details in the related publication.
DOI: 10.5281/zenodo.14849802
title: Are there potential benefits from using microbial products to control ammonia and greenhouse gases released from pig slurry?

description: The raw data of NH3, CO2 and CH4 emissions of pig slurry samples under microbial inocculant treatments (A1, A2) and their controls (C1, C2). Please see the details in the related publication.
DOI: 10.57755/f97g-5j91
title: Insights into the physical and functional properties of human blood cells and breast cancer cells modified by chlo-rine-containing derivatives of 2'-hydroxychalcone - data

description: <p>

<span lang="EN-US">Chlorine-substituted
2’-hydroxychalcones exhibit significant antimicrobial and antiproliferative
properties, particularly against MCF-7 and MDA-MB-231 breast cancer cells. This
study aimed to evaluate their effects on normal circulatory system cells, including
erythrocytes, peripheral blood mononuclear cells (PBMCs), platelets, and human microvascular
endothelial cells (HMEC-1). The chloro-chalcones did not induce hemolysis but
caused morphological alterations in erythrocytes. They exhibited minimal cytotoxicity
toward PBMCs at concentrations substantially exceeding their half-maximal inhibitory
concentrations for breast cancer cells and demonstrated significantly lower toxicity
toward HMEC-1 cells. Furthermore, the compounds had negligible effects on
platelet mitochondrial activity, but markedly inhibited collagen-induced
platelet aggregation. Their anti-proliferative properties were associated with
the induction of apoptosis and mitophagy, primarily through mitochondrial
depolarization and oxidative stress modulation. However, they did not affect
cell cycle progression. The specific mechanism of anti-proliferative action
varied depending on the position of the chlorine substitution, influencing
either apoptosis via mitochondrial dysfunction or mitophagy-mediated
degradation of impaired mitochondria. These findings underscore the selective
activity of chloro-chalcones, effectively targeting malignant cells while
sparing normal circulatory cells. Their capacity to disrupt cancer cell
metabolism with minimal toxicity to circulatory cells highlights their
potential as promising candidates for anticancer therapy, warranting further
investigation.</span></p>
DOI: 10.5517/ccdc.csd.cc2f93b6
title: CCDC 2243077: Experimental Crystal Structure Determination

description: Related Article: Krishna Kumar Mahto, Beauty Kumari, Mukesh Kumar, Khursheed Ahmad|2025|Inorg.Chim.Acta|578|122536|doi:10.1016/j.ica.2025.122536
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.cc2k0gdg
title: CCDC 2353935: Experimental Crystal Structure Determination

description: Related Article: Jeong Yeon Yoo, Chanwoo Kim, Ji Hoon Kwon, Hana Cho, Kiyoung Jeong, Wonwoo Park, Younghun Kim, Dongwhan Lee, Seung Bum Park|2025|Chem.Commun.|61|2285|doi:10.1039/D4CC06630H
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.cc2k0gfh
title: CCDC 2353936: Experimental Crystal Structure Determination

description: Related Article: Jeong Yeon Yoo, Chanwoo Kim, Ji Hoon Kwon, Hana Cho, Kiyoung Jeong, Wonwoo Park, Younghun Kim, Dongwhan Lee, Seung Bum Park|2025|Chem.Commun.|61|2285|doi:10.1039/D4CC06630H
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.cc2k0ggj
title: CCDC 2353937: Experimental Crystal Structure Determination

description: Related Article: Jeong Yeon Yoo, Chanwoo Kim, Ji Hoon Kwon, Hana Cho, Kiyoung Jeong, Wonwoo Park, Younghun Kim, Dongwhan Lee, Seung Bum Park|2025|Chem.Commun.|61|2285|doi:10.1039/D4CC06630H
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.