cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.pmmf29
title: Occurrence Download

description: A dataset containing 9184 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Notonomus clivinoides, Temognatha maculiventris, Goedetrechus mendumae, Goedetrechus parallelus, Idacarabus cordicollis, Lamprima aenea, Matthewsius rossi, Matthewsius illawarrensis, Idacarabus troglodytes, Aulacopris matthewsi, Nebria fragilis, Hoplogonus vanderschoori, Hoplogonus bornemisszai, Nurus brevis, Diorygopyx duplodentatus, Lissotes latidens, Nurus atlas, Lissotes menalcas, Tasmanotrechus cockerilli, Acrotona horrida, Castiarina insculpta, Catadromus lacordairei, Hoplogonus simsoni, Pachycerus rugosus, Temognatha flavocincta, Temognatha sanguinipennis, Temognatha tricolorata, Cormodes darwinii, Enchymus, Enchymus humeralis, Enchymus punctonotatus, Hybomorphus melanosomus, Promethis, Promethister, Promethis angulata, Promethis crassihornis, Promethis crenatostriata, Promethis evanescens, Promethis formosana, Promethis guangxiana, Promethis kaoshana, Promethis kurosawai, Promethis oshimana, Promethis pauperula, Promethis piluensis, Promethis punctatostriata, Promethis rectangula, Promethis sinuatocollis, Promethis sulcigera, Promethis taiwana, Promethis tonkinensis, Promethis valgipes subvalgipes, Promethis valgipes valgipes, Promethis vietnamica, Promethis valgipes, Trigonophorus, Trigonophorus, Trigonophorus)"
 ]
}

 The dataset includes 9184 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063702-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mzxfg4
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Liolaemus pseudolemniscatus Lamborot & Ortiz, 1990"
 ]
}

 The dataset includes 43 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057318-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kk88rgfr55
title: Banana Bunch Harvesting Expert Dataset

description: Dataset overview: This dataset includes images of banana bunches collected from various fields on the island of Madeira, Portugal. The dataset includes four different fields through 2022: Câmara de Lobos on July 25th, Lugar de Baixo on May 17th and 31st, Ponto do Sol on May 20th, June 3rd, August 22nd and October 26th, Santo António on June 7th. The dataset includes a total of 400 samples collected by banana harvesting cutters, opinion based on decision made using digital images. 

Data collection process: The dataset was gathered in 2022 using a variety of smartphone cameras, which includes Samsung Galaxy A12, Galaxy Note 9, One Plus 9 and Iphone 12. The wide range of cameras used to gather images in various environments has improved the resilience and generalizability of possible machine learning models.

Labels and captions: The bunch of bananas visible in the final images underwent a laborious human labelling process in order to receive the correct caption. The generated label collection is used to compare the images, which could offer crucial baseline information for developing and testing object detection models.

Data usage: The main objective of the study is to evaluate how well banana bunch harvesting decisions made by cutters using digital image inspection compared to those made in the field. This dataset can be used by researchers for tasks including model training, benchmarking, and perform model tuning.
DOI: 10.17632/kk88rgfr55.1
title: Banana Bunch Harvesting Expert Dataset

description: Dataset overview: This dataset includes images of banana bunches collected from various fields on the island of Madeira, Portugal. The dataset includes four different fields through 2022: Câmara de Lobos on July 25th, Lugar de Baixo on May 17th and 31st, Ponto do Sol on May 20th, June 3rd, August 22nd and October 26th, Santo António on June 7th. The dataset includes a total of 400 samples collected by banana harvesting cutters, opinion based on decision made using digital images. 

Data collection process: The dataset was gathered in 2022 using a variety of smartphone cameras, which includes Samsung Galaxy A12, Galaxy Note 9, One Plus 9 and Iphone 12. The wide range of cameras used to gather images in various environments has improved the resilience and generalizability of possible machine learning models.

Labels and captions: The bunch of bananas visible in the final images underwent a laborious human labelling process in order to receive the correct caption. The generated label collection is used to compare the images, which could offer crucial baseline information for developing and testing object detection models.

Data usage: The main objective of the study is to evaluate how well banana bunch harvesting decisions made by cutters using digital image inspection compared to those made in the field. This dataset can be used by researchers for tasks including model training, benchmarking, and perform model tuning.
DOI: 10.17632/bhsnrps3w7
title: source data

description: nervous system and cancer
DOI: 10.17632/bhsnrps3w7.1
title: source data

description: nervous system and cancer
DOI: 10.17632/fkkfx4vdm3
title: AgriLeafNet: Fruit Tree Leaf Dataset for Agricultural Research

description: AgriLeafNet is a labeled dataset of 24,888 annotated (1,504 original and 23384 augmented) high-resolution RGB fruit tree leaves images spread across 12 different species gathered in a rural village in Bangladesh. All the photographs were taken with a Samsung A22 5G phone with a standardized white A4 paper background and photographed to capture the two sides of every leaf so that representation is as exhaustive as possible. The collection was made into folders specific to each class and all the images are labeled carrying the corresponding scientific name. They are Syzygium cumini (Jamun), Litchi chinensis (Litchi), Mangifera indica (Mango), Ziziphus zizyphus (Jujube), Psidium guajava (Guava), Elaeocarpus serratus (Olive), Baccaurea motleyana (Burmese Grape), Phyllanthus acidus (Otaheite Gooseberry), Citrus limon (Lemon), Citrus maxima (Pomelo), Annona muricata (Soursop), and The selected classes are based on the agricultural importance and morphological peculiarities.

This data can be used to provide a strong benchmark of machine learning models in agriculture training and validation. It helps researchers to generate precision farming, biodiversity tracking, and auto classification of leaves tools and it provides cost-effective, infinite, and smartphone-based frames of data collection. AgriLeafNet should be used in classification of fruits leaves, plant disease identification, environmental study, and production of artificial intelligence-based agricultural innovations, which makes the tool a useful resource to researchers and practitioners in the science of plant, ecology and agricultural engineering.
DOI: 10.17632/fkkfx4vdm3.1
title: AgriLeafNet: Fruit Tree Leaf Dataset for Agricultural Research

description: AgriLeafNet is a labeled dataset of 24,888 annotated (1,504 original and 23384 augmented) high-resolution RGB fruit tree leaves images spread across 12 different species gathered in a rural village in Bangladesh. All the photographs were taken with a Samsung A22 5G phone with a standardized white A4 paper background and photographed to capture the two sides of every leaf so that representation is as exhaustive as possible. The collection was made into folders specific to each class and all the images are labeled carrying the corresponding scientific name. They are Syzygium cumini (Jamun), Litchi chinensis (Litchi), Mangifera indica (Mango), Ziziphus zizyphus (Jujube), Psidium guajava (Guava), Elaeocarpus serratus (Olive), Baccaurea motleyana (Burmese Grape), Phyllanthus acidus (Otaheite Gooseberry), Citrus limon (Lemon), Citrus maxima (Pomelo), Annona muricata (Soursop), and The selected classes are based on the agricultural importance and morphological peculiarities.

This data can be used to provide a strong benchmark of machine learning models in agriculture training and validation. It helps researchers to generate precision farming, biodiversity tracking, and auto classification of leaves tools and it provides cost-effective, infinite, and smartphone-based frames of data collection. AgriLeafNet should be used in classification of fruits leaves, plant disease identification, environmental study, and production of artificial intelligence-based agricultural innovations, which makes the tool a useful resource to researchers and practitioners in the science of plant, ecology and agricultural engineering.
DOI: 10.17632/d45hf6nxfv.1
title: HCsig
DOI: 10.17632/ksrbjk3n7x
title: goethite

description: Spectroscopic data describe the characteristics of goethite.
DOI: 10.17632/ksrbjk3n7x.1
title: goethite

description: Spectroscopic data describe the characteristics of goethite.
DOI: 10.15468/dl.7hurty
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chiloglanis niger Roberts, 1989",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((54.7419 -4.6698,55.1382 -5.0908,55.1382 -3.2684,55.0126 -3.2506,54.7023 -3.7189,54.8472 -4.0798,54.7419 -4.6698))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057319-250525065834625/datasets/export for details.
DOI: 10.17632/c5hx856kcw
title: Stoichiometry and relative abundance of microorganisms

description: Stoichiometry and relative abundance of microorganisms.
DOI: 10.17632/c5hx856kcw.1
title: Stoichiometry and relative abundance of microorganisms

description: Stoichiometry and relative abundance of microorganisms.
DOI: 10.17632/53v6j8s5kp
title: A study of A bacterial host factor confines phage localization for excluding the infected compartment through cell division. Pollak Fiyaksel et al.

description: This dataset supports the research described in our manuscript:
"A bacterial host factor confines phage localization for excluding the infected compartment through cell division."

Our hypothesis was that Bacillus subtilis actively restricts bacteriophage infection through a host-driven mechanism. The data demonstrates that the bacterial protein YjbH binds to the injected phage DNA and recruits the cell division machinery to isolate the infected region via asymmetric division, ultimately confining the spread of viral components.

The dataset includes:

* Raw and processed images from fluorescence and confocal microscopy, tracking phage infection dynamics.
* Quantifications of plaque size, lysis curves, and protein–DNA binding assays (e.g., EMSA, ddPCR).
* Western blot data for protein expression validation.
* Cryo-ET tomograms showing asymmetric septa formation.
* DNA-protein interaction data, including co-IP and MS results.

Each dataset is labeled by figure and panel, and includes a short description to facilitate interpretation.
DOI: 10.17632/53v6j8s5kp.1
title: A study of A bacterial host factor confines phage localization for excluding the infected compartment through cell division. Pollak Fiyaksel et al.

description: This dataset supports the research described in our manuscript:
"A bacterial host factor confines phage localization for excluding the infected compartment through cell division."

Our hypothesis was that Bacillus subtilis actively restricts bacteriophage infection through a host-driven mechanism. The data demonstrates that the bacterial protein YjbH binds to the injected phage DNA and recruits the cell division machinery to isolate the infected region via asymmetric division, ultimately confining the spread of viral components.

The dataset includes:

* Raw and processed images from fluorescence and confocal microscopy, tracking phage infection dynamics.
* Quantifications of plaque size, lysis curves, and protein–DNA binding assays (e.g., EMSA, ddPCR).
* Western blot data for protein expression validation.
* Cryo-ET tomograms showing asymmetric septa formation.
* DNA-protein interaction data, including co-IP and MS results.

Each dataset is labeled by figure and panel, and includes a short description to facilitate interpretation.
DOI: 10.17632/y4bvcrch65
title: Dataset for particle size composition and organic C and total N contents within soil aggregates
DOI: 10.17632/y4bvcrch65.1
title: Dataset for particle size composition and organic C and total N contents within soil aggregates
DOI: 10.5281/zenodo.15699405
title: SAAINT-DB: a comprehensive structural antibody database for antibody modeling and design

description: This repository includes SAAINT-parser-processed structure models and unprocessed PDB mmcifs for SAAINT-DB.

processed_pdb_models.tar.gz: the SAAINT-parser-processed structure models (in the PDB format)

unprocessed_mmcifs.tar.gz: the original, unprocessed PDB structures (in the mmCIF format)
DOI: 10.15468/dl.ac5muy
title: Occurrence Download

description: A dataset containing 306138 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lupinus arcticus S.Watson, Salix arctica Pall., Parnassia palustris L., Stellaria borealis Bigelow, Carex vaginata Tausch)",
 "Geometry POLYGON ((-180 45.53015, 180 45.53015, 180 83.62313, -180 83.62313, -180 45.53015))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "CoordinateUncertaintyInMeters is less than or equal to 50000"
 ]
}

 The dataset includes 306138 records from 494 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057293-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ax9kuh
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cocconeis pediculus Ehrenberg, 1838",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.5701 -24.0715,25.5395 -24.0715,25.5395 -28.5786,30.5701 -28.5786,30.5701 -24.0715))"
 ]
}

 The dataset includes 35 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057320-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/br35zjsgyk
title: UHI_Gdansk

description: Basic data from the monitoring installation.
DOI: 10.17632/br35zjsgyk.1
title: UHI_Gdansk

description: Basic data from the monitoring installation.
DOI: 10.15468/dl.ssa2wj
title: Occurrence Download

description: A dataset containing 8654 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Buteo rufinus (Cretzschmar, 1829)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 8654 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057321-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gmzbe3
title: Occurrence Download

description: A dataset containing 111974536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is Observation.org, Nature data from around the World",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 111974536 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057286-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6rz334dbxk
title: Research Data

description: 该数据集提供了炉渣和粉煤灰基粘合剂的实验性能测试数据，包括：
设置时间 炉渣和粉煤灰样品的
机械强度 （压缩和弯曲）
全范围 XRD 光谱 28 天固化样品的
FTIR 表征数据 28 天固化样品的
DOI: 10.17632/6rz334dbxk.1
title: Research Data

description: 该数据集提供了炉渣和粉煤灰基粘合剂的实验性能测试数据，包括：
设置时间 炉渣和粉煤灰样品的
机械强度 （压缩和弯曲）
全范围 XRD 光谱 28 天固化样品的
FTIR 表征数据 28 天固化样品的
DOI: 10.17632/kmvv6mx43j.2
title: Tourist Attractions in Bangladesh

description: Tourist attractions in Bangladesh dataset is the information and statistics about the popular destinations and landmarks that attract tourists in Bangladesh. This dataset contains information about visitor numbers, demographics, visitor satisfaction, popular landmarks, museums, national parks, historical sites, amusement parks, and economic impact.
There are 495 sub-districts or upazilas in Bangladesh. The information regarding the upazilas was collected from the official outlet of the Bangladesh government, which is responsible for providing any official administrative information. Then, with the help of Google Place API, we gather information on attractive places on each upazila based on three keywords: points of interest, historical places, and tourist places. We collected information on about ten places for each keyword in each upazila.
The collected data was JSON-encoded. Each record contains features like title, which is the place's name, upazila name, longitude, latitude, rating, rating count, category, id, and keyword. However, the raw dataset was not in proper format with suitable information. After processing all the records by data integration, validation, feature extraction, outlier removal, and prepossessing, we finally have 13,813 records about tourist spots across Bangladesh. To increase the portability and usability on different systems, the entire dataset was also converted to the format of JSON and SQL format. The size of the SQL file is 1.98 MB and the JSON file is 5.00 MB.
DOI: 10.5525/gla.researchdata.1245
title: Outdoor play: Investigating parental perceptions of risks, benefits and competencies of children aged 2-5 years during the COVID-19 pandemic

description: This study aims to get a better understanding of how parents or carers living in Scotland perceive outdoor play situations in terms of their risks and benefits for children aged 2-5 years. This will help inform the development of outdoor play and learning opportunities for young children in the community as well as in childcare settings. 

The dataset has three parts: 
1. Socio-demographic information about parent and child. 
2. General play related questions relating how often they took their child out to play, and under what weather conditions.
3. Parental assessment of risk and benefits for 10 visually presented outdoor situations that parents may experience with their child(ren). Parents judged the risk and benefit of each situation using Likert style responses (Completely disagree – Completely agree) and under what conditions they would allow their child to do this activity. For each of the 10 situations parents answer up to 7 questions. The questions are the same for each situation but depending on their responses, additional questions could display throughout.
DOI: 10.5525/gla.researchdata.1246
title: Outdoor play: Investigating Early Learning and Childcare practitioner perceptions of risks, benefits and competencies of children aged 2-5 years during the COVID-19 pandemic

description: This study aims to get a better understanding of how practitioners in Scotland delivering Early Learning and Childcare perceive outdoor play situations in terms of their risks and benefits for children aged 2-5 years. This will help inform the development of outdoor play and learning opportunities for young children in the community as well as in childcare settings. 

The dataset has three parts: 
1. Demographic information about the practitioner and their childcare setting, including age, gender, and general experience related questions in the childcare sector, predominant age group of responsibility, and childcare setting (urban. Town, rural etc).
2. Practitioner assessment of risk and benefits for 10 visually presented outdoor situations that practitioners may experience with their child(ren) in their childcare centre. Practitioners judged the risk and benefit of each situation using Likert style responses (Completely disagree – Completely agree) and under what conditions they would allow their child to do this activity. For each of the 10 situations practitioners answer up to 7 questions. The questions are the same for each situation but depending on their responses, additional questions could display throughout.
DOI: 10.17632/27knvfh68s
title: LAOS data for Investigating PolySTAT’s Role in Clot Contraction and Fibrin Network Mechanics

description: Raw LAOS data for fibrin clot rheology.
DOI: 10.17632/27knvfh68s.1
title: LAOS data for Investigating PolySTAT’s Role in Clot Contraction and Fibrin Network Mechanics

description: Raw LAOS data for fibrin clot rheology.
DOI: 10.17632/fnwjyg6fwh
title: The dataset of "Chemical Characteristics of Wintertime PM2.5 at a Background Site in Northwest China During Urban Emission Reduction: Synergistic Effects of Biomass/Firework Burning and Liquid-phase Oxidation"

description: Raw data of chemical compositions of PM2.5 and meteorological parameters at a background site near a megacity in the Guanzhong Plain, China, from December 23, 2021, to January 23, 2022.
DOI: 10.17632/fnwjyg6fwh.1
title: The dataset of "Chemical Characteristics of Wintertime PM2.5 at a Background Site in Northwest China During Urban Emission Reduction: Synergistic Effects of Biomass/Firework Burning and Liquid-phase Oxidation"

description: Raw data of chemical compositions of PM2.5 and meteorological parameters at a background site near a megacity in the Guanzhong Plain, China, from December 23, 2021, to January 23, 2022.
DOI: 10.5281/zenodo.15699299
title: Transcriptomic analysis of lettuce seedlings in response to cytokinins and oxidative stress

description: This dataset includes transcriptomic profiles of Lactuca sativa (cv. Salinas) seedlings generated via high-depth RNA sequencing in two independent experiments:










GSE281401, refered ad CK— Seedlings treated with exogenous cytokinins (6-benzylaminopurine at 5 µM or 50 µM) for 6 and 24 hours. The aim of this experiment is to investigate cytokinin-responsive transcriptional networks, with a particular focus on Cytokinin Response Factors (CRFs), which play a crucial role in modulating plant growth and abiotic stress responses.




GSE281402, refered as OS— Seedlings exposed to hydrogen peroxide (H₂O₂) for 1 and 3 hours to explore the transcriptional response to oxidative stress.







Expression data are presented in the format of counts, TMM-normalized counts, TPM, FPKM

DEGS and Network Analysis results carried out using the CK dataset are also presented

A file containing manually curated annottations for CK, OS and photosynthesis pathways and the lettuce Transcription factors used for the interpretation of the results is included

Raw sequence files (SRA) are currently under embargo and will be publicly released on July 31, 2025 within the GSE281401 and GSE281402 GEO datasets


Both experiments include time course sampling and three biological replicates per condition. This dataset provides an integrated framework for investigating hormonal and environmental signal integration in Asteraceae crops. More experimental details in seq_template - CK (version 1).xlsb.xlsx and seq_template - OS (version 1).xlsb.xlsx
DOI: 10.17632/d37bjgd4cf
title: JDS26263

description: JDS26263 Supplementary Tables