cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xrhcbbjx4c
title: Catena Research data

description: Soil DNA extraction and sequencing: Total genomic DNA was extracted utilizing the Omega Soil DNA Kit (M5636-02, Omega Bio-Tek, Norcross, GA) following the manufacturer’s protocol. The quantity and quality of the extracted DNA were evaluated using a NanoDrop NC2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and agarose gel electrophoresis, respectively. The ITS1 region of the fungal rRNA gene was amplified employing the forward primer ITS5F (5ʹ-GGAAGTAAAAGTCGTAACAAGG-3ʹ) and the reverse primer ITS2R (5ʹ-GCTGCGTTCTTCATCGATGC-3ʹ). To facilitate multiplex sequencing, sample-specific 7-bp barcodes were incorporated into the primers. Following amplification, PCR products underwent purification using Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified with the Quant-iTPicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA). Post-individual quantification, the amplicons were evenly pooled, and pair-end 2 × 250-bp sequencing was executed on the Illumina NovaSeq platform utilizing the NovaSeq 6000 SP Reagent Kit (500 cycles, Illumina, San Diego, CA) at Shanghai Personal Biotechnology Co., Ltd., Shanghai, China.
Determination of soil properties: All soil samples underwent air-drying, and soil water content (SWC) was assessed through weight measurements. Soil pH and EC were ascertained using a pH meter (SevenExcellence-S470, Mettler-Toledo, Columbus, OH) following extraction at soil-to-deionized water ratios of 1:2.5 and 1:5 w/v, respectively. Total soil salts were quantified utilizing the weight method (Bado et al., 2016). Soil total organic carbon (SOC) content was determined via dichromate oxidation and titration with ferrous ammonium sulfate (Gul et al., 2015). The total N (TN) content was measured by a semi-micro Kjeldahl digestion procedure, soil available phosphorus (AP) and available potassium (AK) contents were determined by digestion with H2SO4 and HClO4 followed by extraction with 0.5 mol L-1 NaHCO3 (Bi et al., 2020). AK and AP were assessed using inductively coupled plasma-optical emission spectrometry (ICP-OES, Optima 5300DV, Perkin Elmer, Waltham, MA). Major cations (Na+, K+, Mg2+, and Ca2+) and anions (Cl-and SO42-) were identified through ion chromatography (IC, 886 Basic Plus, Metrohm AG, Herisau, Switzerland) with a detection limit of 0.02 mg L-1.
DOI: 10.17632/xrhcbbjx4c.1
title: Catena Research data

description: Soil DNA extraction and sequencing: Total genomic DNA was extracted utilizing the Omega Soil DNA Kit (M5636-02, Omega Bio-Tek, Norcross, GA) following the manufacturer’s protocol. The quantity and quality of the extracted DNA were evaluated using a NanoDrop NC2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and agarose gel electrophoresis, respectively. The ITS1 region of the fungal rRNA gene was amplified employing the forward primer ITS5F (5ʹ-GGAAGTAAAAGTCGTAACAAGG-3ʹ) and the reverse primer ITS2R (5ʹ-GCTGCGTTCTTCATCGATGC-3ʹ). To facilitate multiplex sequencing, sample-specific 7-bp barcodes were incorporated into the primers. Following amplification, PCR products underwent purification using Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified with the Quant-iTPicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA). Post-individual quantification, the amplicons were evenly pooled, and pair-end 2 × 250-bp sequencing was executed on the Illumina NovaSeq platform utilizing the NovaSeq 6000 SP Reagent Kit (500 cycles, Illumina, San Diego, CA) at Shanghai Personal Biotechnology Co., Ltd., Shanghai, China.
Determination of soil properties: All soil samples underwent air-drying, and soil water content (SWC) was assessed through weight measurements. Soil pH and EC were ascertained using a pH meter (SevenExcellence-S470, Mettler-Toledo, Columbus, OH) following extraction at soil-to-deionized water ratios of 1:2.5 and 1:5 w/v, respectively. Total soil salts were quantified utilizing the weight method (Bado et al., 2016). Soil total organic carbon (SOC) content was determined via dichromate oxidation and titration with ferrous ammonium sulfate (Gul et al., 2015). The total N (TN) content was measured by a semi-micro Kjeldahl digestion procedure, soil available phosphorus (AP) and available potassium (AK) contents were determined by digestion with H2SO4 and HClO4 followed by extraction with 0.5 mol L-1 NaHCO3 (Bi et al., 2020). AK and AP were assessed using inductively coupled plasma-optical emission spectrometry (ICP-OES, Optima 5300DV, Perkin Elmer, Waltham, MA). Major cations (Na+, K+, Mg2+, and Ca2+) and anions (Cl-and SO42-) were identified through ion chromatography (IC, 886 Basic Plus, Metrohm AG, Herisau, Switzerland) with a detection limit of 0.02 mg L-1.
DOI: 10.57760/sciencedb.j00003.00040
title: Motion intention prediction for lumbar exoskeletons based on attention-enhanced sEMG inference
title: Motion intention prediction for lumbar exoskeletons based on attention-enhanced sEMG inference

description: Regarding the electromyographic data of back movements
description: Regarding the electromyographic data of back movements
DOI: 10.17632/xshgtczwrp
title: GCA-Lead Partitioning on Birnessite during Pre-Phase Transition

description: Data of "Lead Partitioning on Birnessite during Pre-Phase Transition"
DOI: 10.17632/xshgtczwrp.1
title: GCA-Lead Partitioning on Birnessite during Pre-Phase Transition

description: Data of "Lead Partitioning on Birnessite during Pre-Phase Transition"
DOI: 10.17632/44bhn5r6c6
title: Dataset: Carbon Dioxide Emission Statistics- Exhaust Gas Boiler reduction and emission forecasting

description: This dataset contains CO₂ emissions and operational metrics from four natural gas-fired power plants in Bangladesh (2015–2022). It includes monthly fuel use, electricity generation, and derived CO₂ emissions calculated using IPCC (2006) methods, along with short-term measurements of emission reductions achieved by Exhaust Gas Boilers (EGB). The dataset also provides forecasted monthly emissions for 2023–2026 using six time-series models (ARIMA, ETS, TBATS, Holt, Holt-Winters, and ELM), enabling comparative evaluation of predictive accuracy. This resource supports emissions modeling, operational efficiency studies, and environmental policy analysis.
DOI: 10.17632/44bhn5r6c6.2
title: Dataset: Carbon Dioxide Emission Statistics- Exhaust Gas Boiler reduction and emission forecasting

description: This dataset contains CO₂ emissions and operational metrics from four natural gas-fired power plants in Bangladesh (2015–2022). It includes monthly fuel use, electricity generation, and derived CO₂ emissions calculated using IPCC (2006) methods, along with short-term measurements of emission reductions achieved by Exhaust Gas Boilers (EGB). The dataset also provides forecasted monthly emissions for 2023–2026 using six time-series models (ARIMA, ETS, TBATS, Holt, Holt-Winters, and ELM), enabling comparative evaluation of predictive accuracy. This resource supports emissions modeling, operational efficiency studies, and environmental policy analysis.
DOI: 10.17632/86dkwp3zkh
title: Catena data

description: Determination of soil properties: All soil samples underwent air-drying, and soil water content (SWC) was assessed through weight measurements. Soil pH and EC were ascertained using a pH meter (SevenExcellence-S470, Mettler-Toledo, Columbus, OH) following extraction at soil-to-deionized water ratios of 1:2.5 and 1:5 w/v, respectively. Total soil salts were quantified utilizing the weight method (Bado et al., 2016). Soil total organic carbon (SOC) content was determined via dichromate oxidation and titration with ferrous ammonium sulfate (Gul et al., 2015). The total N (TN) content was measured by a semi-micro Kjeldahl digestion procedure, soil available phosphorus (AP) and available potassium (AK) contents were determined by digestion with H2SO4 and HClO4 followed by extraction with 0.5 mol L-1 NaHCO3 (Bi et al., 2020). AK and AP were assessed using inductively coupled plasma-optical emission spectrometry (ICP-OES, Optima 5300DV, Perkin Elmer, Waltham, MA). Major cations (Na+, K+, Mg2+, and Ca2+) and anions (Cl-and SO42-) were identified through ion chromatography (IC, 886 Basic Plus, Metrohm AG, Herisau, Switzerland) with a detection limit of 0.02 mg L-1.
Soil DNA extraction and sequencing: Total genomic DNA was extracted utilizing the Omega Soil DNA Kit (M5636-02, Omega Bio-Tek, Norcross, GA) following the manufacturer’s protocol. The quantity and quality of the extracted DNA were evaluated using a NanoDrop NC2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and agarose gel electrophoresis, respectively. The ITS1 region of the fungal rRNA gene was amplified employing the forward primer ITS5F (5ʹ-GGAAGTAAAAGTCGTAACAAGG-3ʹ) and the reverse primer ITS2R (5ʹ-GCTGCGTTCTTCATCGATGC-3ʹ). To facilitate multiplex sequencing, sample-specific 7-bp barcodes were incorporated into the primers. Following amplification, PCR products underwent purification using Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified with the Quant-iTPicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA). Post-individual quantification, the amplicons were evenly pooled, and pair-end 2 × 250-bp sequencing was executed on the Illumina NovaSeq platform utilizing the NovaSeq 6000 SP Reagent Kit (500 cycles, Illumina, San Diego, CA) at Shanghai Personal Biotechnology Co., Ltd., Shanghai, China.
DOI: 10.17632/86dkwp3zkh.1
title: Catena data

description: Determination of soil properties: All soil samples underwent air-drying, and soil water content (SWC) was assessed through weight measurements. Soil pH and EC were ascertained using a pH meter (SevenExcellence-S470, Mettler-Toledo, Columbus, OH) following extraction at soil-to-deionized water ratios of 1:2.5 and 1:5 w/v, respectively. Total soil salts were quantified utilizing the weight method (Bado et al., 2016). Soil total organic carbon (SOC) content was determined via dichromate oxidation and titration with ferrous ammonium sulfate (Gul et al., 2015). The total N (TN) content was measured by a semi-micro Kjeldahl digestion procedure, soil available phosphorus (AP) and available potassium (AK) contents were determined by digestion with H2SO4 and HClO4 followed by extraction with 0.5 mol L-1 NaHCO3 (Bi et al., 2020). AK and AP were assessed using inductively coupled plasma-optical emission spectrometry (ICP-OES, Optima 5300DV, Perkin Elmer, Waltham, MA). Major cations (Na+, K+, Mg2+, and Ca2+) and anions (Cl-and SO42-) were identified through ion chromatography (IC, 886 Basic Plus, Metrohm AG, Herisau, Switzerland) with a detection limit of 0.02 mg L-1.
Soil DNA extraction and sequencing: Total genomic DNA was extracted utilizing the Omega Soil DNA Kit (M5636-02, Omega Bio-Tek, Norcross, GA) following the manufacturer’s protocol. The quantity and quality of the extracted DNA were evaluated using a NanoDrop NC2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and agarose gel electrophoresis, respectively. The ITS1 region of the fungal rRNA gene was amplified employing the forward primer ITS5F (5ʹ-GGAAGTAAAAGTCGTAACAAGG-3ʹ) and the reverse primer ITS2R (5ʹ-GCTGCGTTCTTCATCGATGC-3ʹ). To facilitate multiplex sequencing, sample-specific 7-bp barcodes were incorporated into the primers. Following amplification, PCR products underwent purification using Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified with the Quant-iTPicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA). Post-individual quantification, the amplicons were evenly pooled, and pair-end 2 × 250-bp sequencing was executed on the Illumina NovaSeq platform utilizing the NovaSeq 6000 SP Reagent Kit (500 cycles, Illumina, San Diego, CA) at Shanghai Personal Biotechnology Co., Ltd., Shanghai, China.
DOI: 10.17632/h3t4nbgfbz
title: HSV-2 and cGAS-STING
DOI: 10.17632/h3t4nbgfbz.1
title: HSV-2 and cGAS-STING
DOI: 10.6084/m9.figshare.30731222
title: raw data with environment drivers

description: raw data
DOI: 10.6084/m9.figshare.30731222.v1
title: raw data with environment drivers

description: raw data
DOI: 10.17632/hy4836vk8h
title: PAHs data in peatlands in Great Khingan Mountain, China

description: PAHs contents and accumulation rates in five peatlands in northern Great Khingan Mountain, Northeast China.
DOI: 10.17632/hy4836vk8h.1
title: PAHs data in peatlands in Great Khingan Mountain, China

description: PAHs contents and accumulation rates in five peatlands in northern Great Khingan Mountain, Northeast China.
DOI: 10.17632/trzg5f8z3c
title: Water Resources Research Data

description: We provided all the data and the final results.
DOI: 10.17632/trzg5f8z3c.1
title: Water Resources Research Data

description: We provided all the data and the final results.
DOI: 10.17632/93srbbjk8w
title: ColorBlindness_Perception_UniversityStudents_2025

description: # Dataset: Color Blindness Perception Among University Students (2025)

## Overview
This dataset contains anonymized survey responses from higher-education students enrolled in design-related academic programs (Video Games, Animation, Digital Design, and Audiovisual Creation). The study explores students’ awareness, knowledge, and perceptions of color blindness (Color Vision Deficiency, CVD), as well as the presence of accessibility-related content in their academic training.

The dataset accompanies the research article **“Perception of Color Vision Deficiency in Higher Education Design Programs” (2025)**.

All responses have been anonymized. Free-text responses and non-essential scoring columns have been removed to ensure compliance with privacy and ethical standards.

## File Structure
- `ColorBlindness_Perception_UniversityStudents_2025.csv` 
- `ColorBlindness_Perception_UniversityStudents_2025.xlsx`

Both files contain the same data in different formats.

## Number of Participants
- **N = 387** higher-education students from creative-media fields.

## Variables Included
The dataset contains:
- Sociodemographic variables 
- Academic program and training background 
- Prior knowledge of color blindness 
- Self-reported CVD status 
- Awareness of accessibility tools 
- Perception of received training 
- Opinions about incorporating accessibility into curricula

A full Data Dictionary is provided below.

## Ethical Compliance
- The study received formal approval from the **UDIT University Ethics Committee**, ensuring compliance with ethical research standards.
- No direct identifiers are present (names, emails, student IDs, IPs, etc.).
- All free-text responses and potentially identifying information have been removed.
- The dataset is fully anonymized and compliant with FAIR data principles and general data protection regulations.


## Citation
If you use this dataset, please cite:

**Campos-Baello, C., & Alonso-Urbano, D. (2025). Color Blindness Perception Among University Students (Dataset). Mendeley Data**

## License
CC BY 4.0 — You are free to use, adapt, and share this dataset with attribution.
DOI: 10.17632/93srbbjk8w.3
title: ColorBlindness_Perception_UniversityStudents_2025

description: # Dataset: Color Blindness Perception Among University Students (2025)

## Overview
This dataset contains anonymized survey responses from higher-education students enrolled in design-related academic programs (Video Games, Animation, Digital Design, and Audiovisual Creation). The study explores students’ awareness, knowledge, and perceptions of color blindness (Color Vision Deficiency, CVD), as well as the presence of accessibility-related content in their academic training.

The dataset accompanies the research article **“Perception of Color Vision Deficiency in Higher Education Design Programs” (2025)**.

All responses have been anonymized. Free-text responses and non-essential scoring columns have been removed to ensure compliance with privacy and ethical standards.

## File Structure
- `ColorBlindness_Perception_UniversityStudents_2025.csv` 
- `ColorBlindness_Perception_UniversityStudents_2025.xlsx`

Both files contain the same data in different formats.

## Number of Participants
- **N = 387** higher-education students from creative-media fields.

## Variables Included
The dataset contains:
- Sociodemographic variables 
- Academic program and training background 
- Prior knowledge of color blindness 
- Self-reported CVD status 
- Awareness of accessibility tools 
- Perception of received training 
- Opinions about incorporating accessibility into curricula

A full Data Dictionary is provided below.

## Ethical Compliance
- The study received formal approval from the **UDIT University Ethics Committee**, ensuring compliance with ethical research standards.
- No direct identifiers are present (names, emails, student IDs, IPs, etc.).
- All free-text responses and potentially identifying information have been removed.
- The dataset is fully anonymized and compliant with FAIR data principles and general data protection regulations.


## Citation
If you use this dataset, please cite:

**Campos-Baello, C., & Alonso-Urbano, D. (2025). Color Blindness Perception Among University Students (Dataset). Mendeley Data**

## License
CC BY 4.0 — You are free to use, adapt, and share this dataset with attribution.
DOI: 10.17632/rg4n829bb8
title: Spatiotemporal Dynamics of Multi-Organ Regeneration Following COVID-19-Induced Injury in Syrian Hamsters

description: Phenotypic data of Syrian hamsters infected with SARS-CoV-2, including body weight, lung index, pathological score, viral load, and blood routine, etc
DOI: 10.17632/rg4n829bb8.1
title: Spatiotemporal Dynamics of Multi-Organ Regeneration Following COVID-19-Induced Injury in Syrian Hamsters

description: Phenotypic data of Syrian hamsters infected with SARS-CoV-2, including body weight, lung index, pathological score, viral load, and blood routine, etc
DOI: 10.17632/k2bsgd8y66.1
title: Rapid Evolution and Transmission of Porcine Sapelovirus: A Looming Threat to Swine Herds

description: Genes of Porcine Sapelovirus prevalent in Northeast China from 2015 to 2022
DOI: 10.17632/j2gjwhnsny.1
title: Figure

description: This data contains images illustrating the conceptual framework model hypothesis, the Results of the path analysis, a Word Cloud Analysis, and a Cluster Analysis.
DOI: 10.17632/bsgfd7tvg9
title: Modeling the Impacts of Worker Fatigue on Order Batching and Sequencing in a Put-Wall-assisted Based Order Fulfillment System

description: Dataset: Worker Fatigue in Put-Wall Order Fulfillment System Description
This repository contains supplementary materials for the paper "Modeling the Impacts of Worker Fatigue on Order Batching and Sequencing in a Put-Wall-assisted Based Order Fulfillment System". The dataset includes algorithm pseudocode and test instances used in our computational experiments.
Repository Contents
1. Algorithm Pseudocode (pseudocode.txt)
This text file contains detailed pseudocode for the four benchmark heuristic algorithms implemented and compared in our study:CBSA，SGA，and GVNS.
Each algorithm section includes: Input/output specifications，Key parameters and fatigue modeling components，Main procedural steps with detailed comments，Fatigue accumulation and recovery mechanisms
2. Test Instances (test_instances.zip)
Compressed archive containing order fulfillment test instances of varying complexity:
Small-Scale Instances ( 10 set of instances);large-Scale Instances ( 5 set of instances);
DOI: 10.17632/bsgfd7tvg9.1
title: Modeling the Impacts of Worker Fatigue on Order Batching and Sequencing in a Put-Wall-assisted Based Order Fulfillment System

description: Dataset: Worker Fatigue in Put-Wall Order Fulfillment System Description
This repository contains supplementary materials for the paper "Modeling the Impacts of Worker Fatigue on Order Batching and Sequencing in a Put-Wall-assisted Based Order Fulfillment System". The dataset includes algorithm pseudocode and test instances used in our computational experiments.
Repository Contents
1. Algorithm Pseudocode (pseudocode.txt)
This text file contains detailed pseudocode for the four benchmark heuristic algorithms implemented and compared in our study:CBSA，SGA，and GVNS.
Each algorithm section includes: Input/output specifications，Key parameters and fatigue modeling components，Main procedural steps with detailed comments，Fatigue accumulation and recovery mechanisms
2. Test Instances (test_instances.zip)
Compressed archive containing order fulfillment test instances of varying complexity:
Small-Scale Instances ( 10 set of instances);large-Scale Instances ( 5 set of instances);
DOI: 10.17632/jzdhg7gj63
title: Soil carbon pool

description: To investigate the potential impacts of bracket type, soil depth, and panel zone location on soil properties within the photovoltaic power station, a systematic soil survey was conducted at the Talatan photovoltaic power station from 2023 to 2024. Sampling encompassed the freeze-thaw period (March-April), non-freezing period (May-October), and freezing period (November-February). The survey adhered to the technical principles of “randomization, “equal quantity”, and “multi-point mixing” as outlined in the “Specifications for Soil Observation Indicators in Terrestrial Ecosystems” (Pan et al., 2019) (Fig. 1c, d). During the investigation process, three sampling points were randomly selected within each type of photovoltaic bracket—Fixed-angle (FABs), Semi-tracking (STBs), and Tracking (TBs)—based on their spatial distribution.
DOI: 10.17632/jzdhg7gj63.1
title: Soil carbon pool

description: To investigate the potential impacts of bracket type, soil depth, and panel zone location on soil properties within the photovoltaic power station, a systematic soil survey was conducted at the Talatan photovoltaic power station from 2023 to 2024. Sampling encompassed the freeze-thaw period (March-April), non-freezing period (May-October), and freezing period (November-February). The survey adhered to the technical principles of “randomization, “equal quantity”, and “multi-point mixing” as outlined in the “Specifications for Soil Observation Indicators in Terrestrial Ecosystems” (Pan et al., 2019) (Fig. 1c, d). During the investigation process, three sampling points were randomly selected within each type of photovoltaic bracket—Fixed-angle (FABs), Semi-tracking (STBs), and Tracking (TBs)—based on their spatial distribution.
DOI: 10.48670/moi-00235
title: Global Ocean Heat Content (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing
DOI: 10.5281/zenodo.7382639
title: Wikidata subset from 2018 dumps created with WDSub at Biohackathon 2022 - Turtle format

description: Subset of Wikidata obtained using this Shape Expression: https://github.com/kg-subsetting/datasets-biohackathon2022/blob/main/GeneWiki/GeneWiki.shex


And the wdsub tool version 0.0.28: https://github.com/weso/wdsub


The input dump is: wikidata-20180115-all


And the dumpformat is TURTLE
DOI: 10.5281/zenodo.7309738
title: Wikidata subset from 2018 dumps created with WDSub at Biohackathon 2022 - Turtle format

description: Subset of Wikidata obtained using this Shape Expression: https://github.com/kg-subsetting/datasets-biohackathon2022/blob/main/GeneWiki/GeneWiki.shex


And the wdsub tool version 0.0.28: https://github.com/weso/wdsub


The input dump is: wikidata-20180115-all


And the dumpformat is TURTLE
DOI: 10.48670/moi-00233
title: Global Ocean Heat Content (0-300m) from Reanalysis & Multi-Observations Reprocessing
DOI: 10.5281/zenodo.7371769
title: GeneWiki subset created with WDSub at Biohackathon 2022

description: Subset of Wikidata obtained using this Shape Expression: https://github.com/kg-subsetting/datasets-biohackathon2022/blob/main/GeneWiki/GeneWiki.shex


And the wdsub tool version 0.0.28: https://github.com/weso/wdsub


The input dump is: wikidata-20180115-all


And the dumpformat is JSON
DOI: 10.5281/zenodo.7305577
title: GeneWiki subset created with WDSub at Biohackathon 2022

description: Subset of Wikidata obtained using this Shape Expression: https://github.com/kg-subsetting/datasets-biohackathon2022/blob/main/GeneWiki/GeneWiki.shex


And the wdsub tool version 0.0.28: https://github.com/weso/wdsub


The input dump is: wikidata-20180115-all


And the dumpformat is JSON