cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17591827
title: Arctic Ocean Phytoplankton Functional Types from 2003 to 2024: Daily Outputs

description: Overview

This dataset includes the daily Phytoplankton Functional Types (PFTs) data on a Pan-Arctic scale from 2003 to 2024 (April to August). The data were produced by the Takuvik International Research Laboratory (Université Laval and CNRS, Québec, Canada) using the algorithm developed by Orkney et al. (2020), explained in detail in the “Supplemental Material” section of the article. The original algorithm was written in the R programming language and was provided by its author. It has been optimised for faster performance. 








Variables: Phytoplankton Functional Types (PFTs) 










Temporal resolutions: daily product. 










Spatial resolutions: 4 km  




To distinguish major phytoplankton functional types, the algorithm uses MODIS-Aqua ocean colour reflectance data as input. 

The original algorithm was developed and calibrated for the Labrador and Barents Seas, and additional work of validation is required when applying it to other Arctic regions.

Organization

There are 22 zipped files, one per year for the period 2003-2024, from April to August. Each zip file contains 154 daily data files in .parquet format, except for 2022, which contains 139 daily data files because there is no input MODIS-Aqua reflectance data between 1 April and 15 April.

Name convention

Filename: DATE.parquet

DATE in format yyyymmdd

yyyy = Four-digit year (e.g, 2003)

mm = Two-digit month from 04 (April) to 08 (August)

dd = Two-digit day, from 01 to 31

Output file

Each daily parquet file contains: 




Column Name
Type
Description 
Units


date
DATE
The date of observation 
-


year
DOUBLE 
The year of observation 
-


longitude
DOUBLE
The longitude
decimal degrees 


latitude
DOUBLE
The latitude 
decimal degrees 


pixel_class
VARCHAR
The pixel classification 
-


chlorophyll_guesses
DOUBLE




Chlorophyll-a concentration estimation 


mg/m3


rrs_412
DOUBLE




Remote sensing reflectance at 412 nm 


sr-1 


rrs_443
DOUBLE




Remote sensing reflectance at 443 nm 


sr-1


rrs_469 
DOUBLE




Remote sensing reflectance at 469 nm 


sr-1


rrs_488 
DOUBLE




Remote sensing reflectance at 488 nm 


sr-1


rrs_531
DOUBLE




Remote sensing reflectance at 531 nm 


sr-1


rrs_547
DOUBLE




Remote sensing reflectance at 547 nm 


sr-1


rrs_555
DOUBLE




Remote sensing reflectance at 555 nm 


sr-1


rrs_645 
DOUBLE




Remote sensing reflectance at 645 nm 


sr-1


rrs_667 
DOUBLE




Remote sensing reflectance at 667 nm 


sr-1






rrs_678






DOUBLE






Remote sensing reflectance at 678 nm 






sr-1 






 

The pixel classification (pixel_class) is as follows:



coccolithophores

sediment

cdom

phaeocystis

ocean

diatoms
DOI: 10.15468/dl.kw3a3h
title: Occurrence Download

description: A dataset containing 1217 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is Europe",
 "Geometry POLYGON((-16.61068 32.84322,51.55816 34.0489,57.17663 51.39817,44.21098 51.27468,31.91068 52.09017,26.73771 49.98986,22.61489 50.4566,17.36413 48.23961,16.56989 46.93094,13.42475 46.5144,12.32217 46.90644,11.34208 46.88193,9.26963 47.01509,7.98303 45.21078,6.79144 45.21078,8.45967 49.08346,3.27623 50.4538,-0.72174 50.90742,-13.48964 45.13823,-16.61068 32.84322))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Digitaria sanguinalis"
 ]
}

 The dataset includes 1217 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005157-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30691979
title: <b>Integrative Network Pharmacology and Molecular Docking Approaches in Herbal Medicine Research. A Systematic Review of Applications, Advances, and Translational Potential</b>

description: Network pharmacology and molecular docking have emerged as important in modern herbal drug discovery, offering systems-level understanding into phytochemical interactions and therapeutic mechanisms. This review aimed to evaluate the application of integrated network pharmacology and molecular docking in herbal medicine research
DOI: 10.6084/m9.figshare.30691979.v2
title: <b>Integrative Network Pharmacology and Molecular Docking Approaches in Herbal Medicine Research. A Systematic Review of Applications, Advances, and Translational Potential</b>

description: Network pharmacology and molecular docking have emerged as important in modern herbal drug discovery, offering systems-level understanding into phytochemical interactions and therapeutic mechanisms. This review aimed to evaluate the application of integrated network pharmacology and molecular docking in herbal medicine research
DOI: 10.6084/m9.figshare.30691979.v1
title: <b>Integrative Network Pharmacology and Molecular Docking Approaches in Herbal Medicine Research. A Systematic Review of Applications, Advances, and Translational Potential</b>

description: Network pharmacology and molecular docking have emerged as important in modern herbal drug discovery, offering systems-level understanding into phytochemical interactions and therapeutic mechanisms. This review aimed to evaluate the application of integrated network pharmacology and molecular docking in herbal medicine research
DOI: 10.15468/dl.u3q8d4
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Vector collection_OH surveillance project Estonia"
 ]
}

 The dataset includes 100 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005159-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30692063
title: User Experience in Virtual Self-Disclosure: Appraising Natural, Urban, and Artificial VR Environments

description: Data for the study titled 'User Experience in Virtual Self-Disclosure: Appraising Natural, Urban, and Artificial VR Environments'
DOI: 10.6084/m9.figshare.30692063.v1
title: User Experience in Virtual Self-Disclosure: Appraising Natural, Urban, and Artificial VR Environments

description: Data for the study titled 'User Experience in Virtual Self-Disclosure: Appraising Natural, Urban, and Artificial VR Environments'
DOI: 10.4121/9dea333c-7439-4c39-9f74-b7904d6d4a83
title: Classification Machine Learning Models for Early Detection of Toxic Petroleum Hydrocarbons in Groundwater

description: Jupyter notebook containing scripts for training and evaluating machine learning models for detecting petroleum hydrocarbons in groundwater using in-situ water quality parameters. Also included are the IPYNB file for visualizing simulation results and the python module used.
DOI: 10.4121/9dea333c-7439-4c39-9f74-b7904d6d4a83.v1
title: Classification Machine Learning Models for Early Detection of Toxic Petroleum Hydrocarbons in Groundwater

description: Jupyter notebook containing scripts for training and evaluating machine learning models for detecting petroleum hydrocarbons in groundwater using in-situ water quality parameters. Also included are the IPYNB file for visualizing simulation results and the python module used.
DOI: 10.5281/zenodo.17695853
title: SimuTCR
DOI: 10.5281/zenodo.17695854
title: SimuTCR
DOI: 10.6084/m9.figshare.30692069
title: Macau is a “treasure trove”

description: A 25-Year Analysis of Chinese Cross-Border Wealth Spillover Effects
DOI: 10.6084/m9.figshare.30692069.v1
title: Macau is a “treasure trove”

description: A 25-Year Analysis of Chinese Cross-Border Wealth Spillover Effects
DOI: 10.5281/zenodo.17695898
title: CGMGC-Cat Gut Microbial Genome Collection

description: The Cat Gut Microbial Genome Collection (CGMGC) is a comprehensive multi-kingdom genomic resource of the feline gut microbiome. It comprises over 40,000 high-quality genomes spanning 874 prokaryotic species, 6 fungal species, and 5,543 viral operational taxonomic units (vOTUs), assembled from 873 metagenomic samples across five continents. The CGMGC captures extensive host-specific microbial diversity and serves as a foundational dataset for studying carnivore biology, host-microbe co-evolution, and evidence-based veterinary medicine under the "One Health" framework.
DOI: 10.5281/zenodo.17695899
title: CGMGC-Cat Gut Microbial Genome Collection

description: The Cat Gut Microbial Genome Collection (CGMGC) is a comprehensive multi-kingdom genomic resource of the feline gut microbiome. It comprises over 40,000 high-quality genomes spanning 874 prokaryotic species, 6 fungal species, and 5,543 viral operational taxonomic units (vOTUs), assembled from 873 metagenomic samples across five continents. The CGMGC captures extensive host-specific microbial diversity and serves as a foundational dataset for studying carnivore biology, host-microbe co-evolution, and evidence-based veterinary medicine under the "One Health" framework.
DOI: 10.5519/qd.ai6z53az
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.15468/dl.uhr4jc
title: Occurrence Download

description: A dataset containing 109636 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Elaeocarpus"
 ]
}

 The dataset includes 109636 records from 400 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005132-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17696088
title: Dataset: immunofluorescence microscopy images of hTERT-RPE1 primary cilia under TIAM2, WDR19 and dual-siRNA knockdown

description: This dataset contains the raw and processed confocal immunofluorescence microscopy images used in the manuscript “Co-occurrence of rare variants implicates gene pairs in cytoskeletal pathways and is associated with increased severity for ASD”. Images include ARL13B, γ-tubulin, and acetylated α-tubulin staining of primary cilia under control, negative siRNA, TIAM2, WDR19, and dual-siRNA knockdown conditions.
DOI: 10.5281/zenodo.17696087
title: Dataset: immunofluorescence microscopy images of hTERT-RPE1 primary cilia under TIAM2, WDR19 and dual-siRNA knockdown

description: This dataset contains the raw and processed confocal immunofluorescence microscopy images used in the manuscript “Co-occurrence of rare variants implicates gene pairs in cytoskeletal pathways and is associated with increased severity for ASD”. Images include ARL13B, γ-tubulin, and acetylated α-tubulin staining of primary cilia under control, negative siRNA, TIAM2, WDR19, and dual-siRNA knockdown conditions.
DOI: 10.57967/hf/7073
title: wikipedia-2017-bm25
DOI: 10.17632/bsc44zsfdd
title: Production and carbon footprint of microbial oil from waste lemon peel extract - supplementary material

description: Supplementary data and underlying data supporting the finding described in "Production and carbon footprint of microbial oil from waste lemon peel extract".

Abstract
Background
The agricultural sector is one of the leading producers of agro-industrial solid organic waste. This waste is mainly disposed of by incineration or landfilled, representing a huge loss of potential resources, which could be used for the production of high-value chemicals. In this study, a fermentation process for the production of microbial oil from waste lemon extract (LE), an aqueous side stream deriving from waste lemon peel and pulp processing, was developed and assessed for impacts. Microbial oil can have many and diverse applications, from plasticisers in plastic and rubber compounds to moisturizers in cosmetic formulation.
Methods and results
Characterization of LE revealed that its autoclaving process is effective for increasing the concentration of readily available glucose and fructose, reaching 28.77 ± 0.08 g L-1 and 25.68 ± 0.27 g L-1. Nitrogen content was measured too, revealing a C/N ratio of 85, optimal for triggering lipid accumulation in the selected microbial cell factory. Therefore, the oleaginous yeast Cutaneotrichosporon oleaginosum was cultivated in an unmodified LE-based medium in 2 L bioreactors, resulting in a lipid accumulation of 0.47 ± 0.08 goil gCDW-1. Finally, a new lipid extraction method using green solvents was developed, which allowed to extract and purify 11.29 g of oils, corresponding to 35% of the cell dry weight.
The carbon footprint of this laboratory-scale production was estimated to be 71 - 434 kgCO2eq kg-1 microbial oil, with electricity consumption of the fermentation step as the main factor. Simulation of the process in a 300L fermenter suggests that the electricity consumption, and therefore the overall impact, can be drastically reduced with scale-up.

Conclusions
The proposed process is promising in terms of production and has the advantage of not being in competition with edible resources and land use. However, the microbial oil yield and the extraction process must be optimized to make the process sustainable.
DOI: 10.17632/bsc44zsfdd.3
title: Production and carbon footprint of microbial oil from waste lemon peel extract - supplementary material

description: Supplementary data and underlying data supporting the finding described in "Production and carbon footprint of microbial oil from waste lemon peel extract".

Abstract
Background
The agricultural sector is one of the leading producers of agro-industrial solid organic waste. This waste is mainly disposed of by incineration or landfilled, representing a huge loss of potential resources, which could be used for the production of high-value chemicals. In this study, a fermentation process for the production of microbial oil from waste lemon extract (LE), an aqueous side stream deriving from waste lemon peel and pulp processing, was developed and assessed for impacts. Microbial oil can have many and diverse applications, from plasticisers in plastic and rubber compounds to moisturizers in cosmetic formulation.
Methods and results
Characterization of LE revealed that its autoclaving process is effective for increasing the concentration of readily available glucose and fructose, reaching 28.77 ± 0.08 g L-1 and 25.68 ± 0.27 g L-1. Nitrogen content was measured too, revealing a C/N ratio of 85, optimal for triggering lipid accumulation in the selected microbial cell factory. Therefore, the oleaginous yeast Cutaneotrichosporon oleaginosum was cultivated in an unmodified LE-based medium in 2 L bioreactors, resulting in a lipid accumulation of 0.47 ± 0.08 goil gCDW-1. Finally, a new lipid extraction method using green solvents was developed, which allowed to extract and purify 11.29 g of oils, corresponding to 35% of the cell dry weight.
The carbon footprint of this laboratory-scale production was estimated to be 71 - 434 kgCO2eq kg-1 microbial oil, with electricity consumption of the fermentation step as the main factor. Simulation of the process in a 300L fermenter suggests that the electricity consumption, and therefore the overall impact, can be drastically reduced with scale-up.

Conclusions
The proposed process is promising in terms of production and has the advantage of not being in competition with edible resources and land use. However, the microbial oil yield and the extraction process must be optimized to make the process sustainable.
DOI: 10.4121/0a23147e-ba85-4ba2-a058-ba199c65d711
title: Hybrid Machine Learning Models for Estimating Petroleum Hydrocarbon Concentration in Groundwater

description: Jupyter notebook containing scripts for training and evaluating machine learning models including long short-term memory network and classical regression models for continuous monitoring of petroleum hydrocarbons in groundwater. The design of a hybrid Kalman filter was also included, as well as the Jupyter notebooks for visualizing the results.
DOI: 10.4121/0a23147e-ba85-4ba2-a058-ba199c65d711.v1
title: Hybrid Machine Learning Models for Estimating Petroleum Hydrocarbon Concentration in Groundwater

description: Jupyter notebook containing scripts for training and evaluating machine learning models including long short-term memory network and classical regression models for continuous monitoring of petroleum hydrocarbons in groundwater. The design of a hybrid Kalman filter was also included, as well as the Jupyter notebooks for visualizing the results.
DOI: 10.15468/dl.e64eg2
title: Occurrence Download

description: A dataset containing 13715 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Living Specimen)",
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Indigofera"
 ]
}

 The dataset includes 13715 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005162-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.urkay9
title: Occurrence Download

description: A dataset containing 2630 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Trifolium repens"
 ]
}

 The dataset includes 2630 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005163-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qsqe7a
title: Occurrence Download

description: A dataset containing 591 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ziziphus jujuba spinosa"
 ]
}

 The dataset includes 591 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005164-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zyysnh
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ligusticum chuanxiong"
 ]
}

 The dataset includes 76 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005165-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17638167
title: A Global 30 m Landsat-based Dataset of Forest Fire Patches (GlobMap FFP v1.0) from 1984 to 2022

description: Forest fires exert profound ecological and socioeconomic impacts globally. Characterizing their long-term effects and evolvingregimes requires consistent, high-resolution event-level fire records over extended periods. The Landsat archive provides a unique foundation for such efforts, offering fine spatial detail with globally coherent, multi-decadal observations. Yet, it remains challenging to generate a globally consistent, Landsat-based fire product with event-level characterization. Here we present a 30 m global forest fire patch dataset spanning 1984-2022, developed from the full Landsat archive to ensure comprehensive fire characterization. We first condensed multi-temporal burned signals from Landsat archive on Google Earth Engine (GEE) using a pixel-based image compositing approach. This approach also reduces noise from clouds and shadows while ensuring high computational efficiency using GEE. We then mapped burned area using artificial neural network modeling across global forests. Finally, we delineated individual fire patches through spatial–temporal clustering and extracted their key attributes. This dataset offers a valuable resource for quantifying fire impacts and advancing the understanding of contemporary and future fire regimes in global forests.

The dataset is organized into seven “.zip” packages corresponding to compositing periods and is provide in 5° x 5°Sinusoidal grid tiles (666 tiles in global forests). Each 30 m tile (18,533 x 18,533 pixels) includes three raster-format GeoTIFF files: (1) fire patch ID, (2) burned year, and (3) QA level. Files follow the naming convention: “ScarID_ScarTM3DV03_GEEScarTMSinv02_Clean.A3000001.h[HH]v[VV].[FILE_TYPE].[PERIOD].tif”, where [HH] and [VV] denote the horizontal and vertical tile indices. [FILE_TYPE] corresponds to one of the three file types: “ScarID”, “ScarYear”, and “QualityFlag”, representing fire patch ID, burned year, and QA level, respectively. The actual year of fire is obtained by adding 1980 to “ScarYear”. Only QA levels 1 and 2 are included in the distributed dataset.
DOI: 10.5281/zenodo.17638166
title: A Global 30 m Landsat-based Dataset of Forest Fire Patches (GlobMap FFP v1.0) from 1984 to 2022

description: Forest fires exert profound ecological and socioeconomic impacts globally. Characterizing their long-term effects and evolvingregimes requires consistent, high-resolution event-level fire records over extended periods. The Landsat archive provides a unique foundation for such efforts, offering fine spatial detail with globally coherent, multi-decadal observations. Yet, it remains challenging to generate a globally consistent, Landsat-based fire product with event-level characterization. Here we present a 30 m global forest fire patch dataset spanning 1984-2022, developed from the full Landsat archive to ensure comprehensive fire characterization. We first condensed multi-temporal burned signals from Landsat archive on Google Earth Engine (GEE) using a pixel-based image compositing approach. This approach also reduces noise from clouds and shadows while ensuring high computational efficiency using GEE. We then mapped burned area using artificial neural network modeling across global forests. Finally, we delineated individual fire patches through spatial–temporal clustering and extracted their key attributes. This dataset offers a valuable resource for quantifying fire impacts and advancing the understanding of contemporary and future fire regimes in global forests.

The dataset is organized into seven “.zip” packages corresponding to compositing periods and is provide in 5° x 5°Sinusoidal grid tiles (666 tiles in global forests). Each 30 m tile (18,533 x 18,533 pixels) includes three raster-format GeoTIFF files: (1) fire patch ID, (2) burned year, and (3) QA level. Files follow the naming convention: “ScarID_ScarTM3DV03_GEEScarTMSinv02_Clean.A3000001.h[HH]v[VV].[FILE_TYPE].[PERIOD].tif”, where [HH] and [VV] denote the horizontal and vertical tile indices. [FILE_TYPE] corresponds to one of the three file types: “ScarID”, “ScarYear”, and “QualityFlag”, representing fire patch ID, burned year, and QA level, respectively. The actual year of fire is obtained by adding 1980 to “ScarYear”. Only QA levels 1 and 2 are included in the distributed dataset.
DOI: 10.5281/zenodo.17695976
title: Extended Data for "Exploring the Experiences of Students with Physical Disabilities at a University in KwaZulu-Natal, South Africa"

description: This repository contains the extended data associated with the manuscript titled “Exploring the Experiences of Students with Physical Disabilities at a University in KwaZulu-Natal, South Africa”, submitted to F1000Research.

The extended data include study materials used during data collection, namely:





Interview guide




Participant Information Sheet




Participant Consent form template




Ethical clearance document 



These materials are provided to support transparency, reproducibility, and methodological clarity.No identifiable or original participant data are included due to ethical restrictions.