cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13164574
title: Dataset of "Photoelectrochemical generation of H2O2 using hematite (α-Fe2O3) and gas diffusion electrode (GDE)"

description: In contrast to the industrial-scale production of H2O2 the electrochemical or photoelectrochemical synthesis is environmentally friendly. In the present work, the photoelectrochemical generation of H2O2 was studied by combining the hematite (α-Fe2O3/FTO/glass) photoanode and gas diffusion electrode (GDE) modified by incorporation of tin (II) phthalocyanine (SnPc) in its hydrophilic layer. The experiments were carried out in a photoelectrochemical cell with two compartments separated by a proton exchange membrane under applied bias and AM1.5 irradiation (100 mW/cm2). The generated amount of H2O2 was determined by chemical analysis (visible light spectrophotometry) of the electrolyte. As a tool to determine the efficiency of such a process, the Faradaic efficiency (FE) was calculated. The best configuration used air as an inlet gas for GDE and phosphate buffer (pH 6.4) as an electrolyte in the cathodic compartment. The combination of hematite and GDE (with SnPc) was the most effective in H2O2 photoelectrochemical generation. The highest value of FE was 52.4 % for GDE (O2 reduction to H2O2) and 0.4 % for hematite photoanode (H2O oxidation to H2O2).
DOI: 10.5281/zenodo.15071078
title: A Multi-Year Forecast Dataset of Arctic Sea Ice Concentration Based on Spectral Analysis and Modeling

description: 1. Data Description

The data include both codes, forecasted and observed Arctic SIC. The forecasted data, contains the predicted SIC from January, 1, 2024 to December, 31, 2027. The observed data is SIC in 2024 is provided by the University of Bremen (https://seaice.uni-bremen.de/sea-ice-concentration/amsre-amsr2/.), which can be used for validation.

2. SIC data



Observed Data:



Temporal Coverage: January 1, 2024, to December 31, 2024.

Data Formats: NetCDF format.

Naming Convention: Files are named according to the date in the format YYYYMMDD.tif.



Forecasted Data:



Temporal Coverage: January 1, 2024, to December 31, 2027.

 Data Formats: GeoTIFF format.






Projection System: Northern Polar Stereographic.

Spatial Resolution: 6.25 km.


3. code



Data Formats: py format.

Function:





SIC_plot.py: Visualizes sea ice concentration using NetCDF data, applying a polar stereographic projection, custom colormap, and land/coastline features for better geographic context.  




SIM_plot.py: Compares forecasted and observed sea ice margin using binary segmentation, erosion, and contour extraction, overlaying results on a polar stereographic map with land masking.


SIC_forecast.py: Implements long-term sea ice concentration forecasting by combining Least Squares (LS) trend modeling with an AutoRegressive (AR) model for residual correction. LS fits a quadratic trend with periodic components, while AR captures temporal dependencies in residuals, improving forecast accuracy. The method extrapolates future SIC values by predicting both trend and residual components.
DOI: 10.5281/zenodo.15087160
title: A Multi-Year Forecast Dataset of Arctic Sea Ice Concentration Based on Spectral Analysis and Modeling

description: 1. Data Description

The data include both codes, forecasted and observed Arctic SIC. The forecasted data, contains the predicted SIC from January, 1, 2024 to December, 31, 2035. The observed data is SIC in 2024 is provided by the University of Bremen (https://seaice.uni-bremen.de/sea-ice-concentration/amsre-amsr2/.), which can be used for validation.

2. SIC data



Observed Data:



Temporal Coverage: January 1, 2024, to December 31, 2024.

Data Formats: NetCDF format.

Naming Convention: Files are named according to the date in the format YYYYMMDD.tif.



Forecasted Data:



Temporal Coverage: January 1, 2024, to December 31, 2035.

 Data Formats: GeoTIFF format.






Projection System: Northern Polar Stereographic.

Spatial Resolution: 6.25 km.


3. code



Data Formats: py format.

Function:





SIC_plot.py: Visualizes sea ice concentration using NetCDF data, applying a polar stereographic projection, custom colormap, and land/coastline features for better geographic context.  




SIM_plot.py: Compares forecasted and observed sea ice margin using binary segmentation, erosion, and contour extraction, overlaying results on a polar stereographic map with land masking.


SIC_forecast.py: Implements long-term sea ice concentration forecasting by combining Least Squares (LS) trend modeling with an AutoRegressive (AR) model for residual correction. LS fits a quadratic trend with periodic components, while AR captures temporal dependencies in residuals, improving forecast accuracy. The method extrapolates future SIC values by predicting both trend and residual components.
DOI: 10.5281/zenodo.15468486
title: A Multi-Year Forecast Dataset of Arctic Sea Ice Concentration Based on Spectral Analysis and Modeling

description: 1. Data Description

The data include both codes, forecasted and observed Arctic SIC. The forecasted data, contains the predicted SIC from January, 1, 2020 to December, 31, 2029. The observed data is SIC from 2013 to 2024 is obtained by the University of Bremen (https://seaice.uni-bremen.de/sea-ice-concentration/amsre-amsr2/).

2. SIC data



Observed Data:



Temporal Coverage: January 1, 2013, to December 31, 2024.

Data Formats: NetCDF, format.

Naming Convention: Files are named according to the date in the format asi-AMSR2-n6250-YYYYMMDD-v5.4.nc.



Forecasted Data:



Temporal Coverage: January 1, 2020, to December 31, 2029.

 Data Formats: GeoTIFF format.






Projection System: Northern Polar Stereographic.

Spatial Resolution: 6.25 km.


3. code





This repository provides a complete pipeline for long-term daily Arctic sea ice concentration (SIC) prediction based on time series modeling. The included code consists of four major functional components:

🔧 1. Data Preprocessing





preprocess.py: This script processes raw NetCDF-format Sea Ice Concentration (SIC) data from the Bremen University dataset.





The preprocessed data is also provided as input_data for direct use in 2Time Series Forecasting.


📈 2. Time Series Forecasting





forecast.py: Main forecasting script that uses a hybrid model (Least Squares + AutoRegressive).




ls.py: Performs least squares fitting using a quadratic trend and dual seasonal (365.2 and 182.6 days) components.




ar.py: Applies an AutoRegressive model to residuals for further prediction refinement.


ls_ar.py: Combines LS and AR forecasts.




The output is a predicted .npy file containing daily SIC forecasts for a full year.


🗺 3. GeoTIFF Export





npy2tif.py: Converts the predicted SIC .npy data into daily GeoTIFF images using georeferencing from a sample NetCDF file and a North Polar Stereographic projection.
DOI: 10.5281/zenodo.15543305
title: Titanium Substitution Facilitating Oxygen and Manganese Redox in Sodium Layered Oxide Cathode

description: This dataset contains original PNG data from original research within the project EBEAM. Precisely, there are six final, complex Figures, below:

Figure 1. Structure and composition of the NLMO and NLMTO. a,b) Rietveld refinement of XRD patterns. Extra peaks at 17-30° represent the existence of superstructure. c-e) Structure models. The prismatic Na coordination in d) and ABBA repeat stacking sequence of O in c) indicate the P2 structure. The models in e) show the special Li‐Mn and Li‐Mn‐Ti superstructures. f–g) EDS mapping images (Scale bar, 50 nm). h) Ti 2p XPS spectra.

Figure 2. Structure evolution of the NLMO and NLMTO. a,b) Charge–discharge curves in the 1st cycle and corresponding in situ XRD patterns

Figure 3. Charge compensation of the NLMO and NLMTO. a) The initial chargé-discharge curves. Three labeled states (Pristine, 4.5 and 1.5 V) were selected to test ex situ XPS. The charge compensation mechanism is also summarized in (a). b,c) Ex situ XPS O 1s spectra. d) The mole ratio of oxidized O2minus (On-minus) among the total oxygen signals. e) Ex situ XPS Mn 2p3-2 spectra.

Figure 4. Electrochemical behaviors of the NLMO and NLMTO. a-c) The charge curves at 0.1C (1C is 200 mA g minus1), corresponding V versus dQ-dV patterns, and Q versus dV-dQ patterns. The labeled Mn‐redox and O‐redox in (b) and (c) are used to calculate their capacity contribution. d,e) Rate capacity and cycling performance at 0.5C. f,g) The calculated capacity of Mn‐redox and O‐redox driven from differential curves. h) Resistance from the SEI layer (RSEI) and charge transfer (RCT).

Figure 5. Bulk and interphase structures of the NLMO and NLMTO after cycling. a) XRD patterns. b) STEM images. c,d) TEM images. e,f) O1s and F 1s XPS spectra. The XPS signals are composed of species from both the bulk structure and SEI layer.

Figure 6. DFT calculations of the NLMO and NLMTO. a) The total, O 2p, and Mn 3d density of states (DOS). The Fermi energy (EF) level is set to be 0 eV. b) Schematic illustration of the energy levels. The red, blue, and orange arrow lines represent the locations of the top Mn4plus 3d t2g, O2minus 2p, and the bottom Mn4+ 3d eg orbitals respectively.

Funding: 

This work was supported by following grants,

National Natural Science Foundation of China (Grant No. 52071225 and 51672181), the Department of Human Resources and Social Security of Jiangsu Province (100 Talents Project); 

European Union’s Horizon Europe Research and Innovation Program under grant agreement No.101087143 (Electron Beam Emergent Additive Manufacturing (EBEAM));

REFRESH Research Excellence for Region Sustainability, and High-tech Industries project No. CZ.10.03.01/00/22 003/0000048 via an operational program transition.
DOI: 10.6084/m9.figshare.30673856
title: Data

description: Original Data
DOI: 10.6084/m9.figshare.30673856.v1
title: Data

description: Original Data
DOI: 10.15468/dl.59zmpx
title: Occurrence Download

description: A dataset containing 161797176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "Year 2020-2020",
 "Year 2000-2000"
 ]
 }
 ]
}

 The dataset includes 161797176 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001781-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17404293
title: DeOTA-IoT: Techniques Catalog for Designing Over-the-Air Update Systems for IoT
DOI: 10.15468/dl.hvt4a3
title: Occurrence Download

description: A dataset containing 5246 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Phylloscopus affinis",
 "Year 2024-2025"
 ]
}

 The dataset includes 5246 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001803-251120083545085/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.30674240
title: Differential Effects of Multidimensional Perfectionism on Burnout and Loneliness Among Delhi NCR Corporate Employees: A Path Analysis

description: Perfectionism can be a double-edged sword: while striving for excellence may drive performance, certain perfectionistic tendencies can erode employees’ resources and well-being. Guided by Conservation of Resources Theory, this study explored how rigid, self-critical, and narcissistic perfectionism relate to burnout and workplace loneliness among 875 full-time corporate employees (ages 21–40) in India’s Delhi National Capital Region. Participants completed the Big Three Perfectionism Scale–Short Form, Oldenburg Burnout Inventory, and De Jong Gierveld Loneliness Scale-6 in a masked, cross-sectional design. Path analyses in AMOS 26.0 revealed that self-critical perfectionism was the strongest predictor of overall burnout (β = .595, p &lt; .001), which in turn predicted both disengagement (β = .574, p &lt; .001) and emotional exhaustion (β = .461, p &lt; .001). Narcissistic perfectionism showed more moderate links to burnout (β = .232, p &lt; .001) but emerged as the primary driver of workplace loneliness (β = .125, p &lt; .001). Rigid perfectionism had no significant effects. Contrary to expectation, burnout did not mediate the perfectionism–loneliness relationship (β = –.016, p = .282), suggesting that self-critical and narcissistic perfectionism deplete distinct resource pools, self-regulatory versus social, through separate pathways. The full model accounted for 54 percent of variance in burnout, 77 percent in disengagement, 65 percent in exhaustion, and 21 percent in loneliness. These findings suggest that interventions aimed at reducing self-critical evaluations and tempering interpersonal perfectionistic demands, rather than targeting rigid standards, may yield the greatest benefits for employee health and social well-being.
DOI: 10.6084/m9.figshare.30674240.v2
title: Differential Effects of Multidimensional Perfectionism on Burnout and Loneliness Among Delhi NCR Corporate Employees: A Path Analysis

description: Perfectionism can be a double-edged sword: while striving for excellence may drive performance, certain perfectionistic tendencies can erode employees’ resources and well-being. Guided by Conservation of Resources Theory, this study explored how rigid, self-critical, and narcissistic perfectionism relate to burnout and workplace loneliness among 875 full-time corporate employees (ages 21–40) in India’s Delhi National Capital Region. Participants completed the Big Three Perfectionism Scale–Short Form, Oldenburg Burnout Inventory, and De Jong Gierveld Loneliness Scale-6 in a masked, cross-sectional design. Path analyses in AMOS 26.0 revealed that self-critical perfectionism was the strongest predictor of overall burnout (β = .595, p &lt; .001), which in turn predicted both disengagement (β = .574, p &lt; .001) and emotional exhaustion (β = .461, p &lt; .001). Narcissistic perfectionism showed more moderate links to burnout (β = .232, p &lt; .001) but emerged as the primary driver of workplace loneliness (β = .125, p &lt; .001). Rigid perfectionism had no significant effects. Contrary to expectation, burnout did not mediate the perfectionism–loneliness relationship (β = –.016, p = .282), suggesting that self-critical and narcissistic perfectionism deplete distinct resource pools, self-regulatory versus social, through separate pathways. The full model accounted for 54 percent of variance in burnout, 77 percent in disengagement, 65 percent in exhaustion, and 21 percent in loneliness. These findings suggest that interventions aimed at reducing self-critical evaluations and tempering interpersonal perfectionistic demands, rather than targeting rigid standards, may yield the greatest benefits for employee health and social well-being.
DOI: 10.6084/m9.figshare.30674240.v1
title: Relationship of Perfectionism with Burnout and Loneliness Amongst Young Adults in Urban Delhi NCR Corporate Workplaces

description: This study investigates the intricate relationship between perfectionism, burnout characterized by exhaustion and disengagement, and loneliness among young adults in multinational companies (MNCs). The data was collected from 873 participants, aged 21-40 years working in Delhi NCR corporate workplaces, through purposive sampling using validated psychometric tools; including the Big Three Perfectionism Scale-Short Form, the Oldenburg Burnout Inventory, and the DeJong Gierveld Loneliness Scale. By using a path analysis model, the results revealed that self-critical perfectionism has the strongest association with burnout, particularly with emotional exhaustion and disengagement. Narcissistic perfectionism moderately predicts burnout and loneliness, highlighting its dual impact on emotional well-being and social relationships. Rigid perfectionism, however, shows weaker associations, suggesting its limited role in predicting adverse outcomes. Burnout significantly predicts disengagement and exhaustion, while its relationship with loneliness is weaker. The findings aim to bridge gaps in the literature by examining not only individual-level consequences but also organizational implications, emphasizing the urgent need for interventions fostering sustainable work environments contributing to the growing discourse on mental health in workplaces, advocating for balanced perfectionistic pursuits that promote productivity without compromising psychological well-being.
DOI: 10.15468/dl.9mbh4z
title: Occurrence Download

description: A dataset containing 335 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Tayassuidae"
 ]
}

 The dataset includes 335 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001805-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17668218
title: OD and CFU of Yersinia ruckeri

description: This dataset includes the measurements used to calculate colony-forming units (CFUs). These CFU values were used to quantify the infection pressure applied during the rainbow trout challenge experiments.
DOI: 10.5281/zenodo.17668219
title: OD and CFU of Yersinia ruckeri

description: This dataset includes the measurements used to calculate colony-forming units (CFUs). These CFU values were used to quantify the infection pressure applied during the rainbow trout challenge experiments.
DOI: 10.15468/dl.un8rer
title: Occurrence Download

description: A dataset containing 288 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amplexidiscus fenestrafer"
 ]
}

 The dataset includes 288 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001806-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e8fjcw
title: Occurrence Download

description: A dataset listing the 332 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((148.58175 -32.18890,148.60601 -32.16936,148.46813 -32.13880,148.32966 -32.14732,148.24326 -32.37050,148.20894 -32.43046,148.20125 -32.52780,148.26634 -32.55624,148.58589 -32.55873,148.81904 -32.55724,148.91845 -32.51932,148.99242 -32.44694,149.03325 -32.32901,149.00012 -32.27099,148.76223 -32.23646,148.71193 -32.24197,148.66459 -32.26999,148.62139 -32.28750,148.57524 -32.28950,148.55748 -32.28450,148.56636 -32.24697,148.58056 -32.21794,148.58175 -32.18890))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset's 332 records were derived from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001804-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8376848
title: 30 m resolution global forest burned area dataset of 2020

description: Global forest burned area data was produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 5° x 5° tiles spanning the range 180W–180E and 80N–60S.

contacts: zhangzhaoming@aircas.ac.cn/zhangzm@radi.ac.cn

Please cite this paper: Zhaoming Zhang, Baoye Qi, Guojin He, et al. High resolution global forest burned area changes monitoring using Landsat 7/8 images. GEO-SPATIAL INFORMATION SCIENCE, 2025. DOI: 10.1080/10095020.2025.2483429
DOI: 10.5281/zenodo.8376849
title: 30 m resolution global forest burned area dataset of 2020

description: Global forest burned area data was produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 5° x 5° tiles spanning the range 180W–180E and 80N–60S.

contacts: zhangzhaoming@aircas.ac.cn/zhangzm@radi.ac.cn

Please cite this paper: Zhaoming Zhang, Baoye Qi, Guojin He, et al. High resolution global forest burned area changes monitoring using Landsat 7/8 images. GEO-SPATIAL INFORMATION SCIENCE, 2025. DOI: 10.1080/10095020.2025.2483429
DOI: 10.15468/dl.jthmnn
title: Occurrence Download

description: A dataset containing 7021 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carex glacialis",
 "Year 1000-2025"
 ]
}

 The dataset includes 7021 records from 156 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001809-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17668277
title: EDP Data
DOI: 10.5281/zenodo.17668278
title: EDP Data
DOI: 10.15468/dl.bqcrby
title: Occurrence Download

description: A dataset containing 3651 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-53799.0",
 "TaxonKey is Carex glacialis",
 "Year 1000-2025"
 ]
}

 The dataset includes 3651 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001811-251120083545085/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.30674246
title: Lipocalin sequences and treefiles

description: Sequences and molecular phylogeny of lipocalin proteins sampled from different species.
DOI: 10.6084/m9.figshare.30674246.v1
title: Lipocalin sequences and treefiles

description: Sequences and molecular phylogeny of lipocalin proteins sampled from different species.
DOI: 10.6084/m9.figshare.30674243
title: Remittances in Zacatecas, Mexico (2013-2023)

description: This database contains data on remittances flows in Zacatecas, Mexico, for the period 2013-2023.
DOI: 10.6084/m9.figshare.30674243.v1
title: Remittances in Zacatecas, Mexico (2013-2023)

description: This database contains data on remittances flows in Zacatecas, Mexico, for the period 2013-2023.
DOI: 10.15468/dl.xjj2x4
title: Occurrence Download

description: A dataset containing 11614 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Adansonia digitata",
 "Year 1000-2025"
 ]
}

 The dataset includes 11614 records from 217 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001812-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.