cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tt6rbx7s3h.3
title: Extended Evaluation of SnowPole Detection for Machine-Perceivable Infrastructure for Nordic Winter Conditions: A Comparative Study of Object Detection Models

description: In this study, we present an extensive evaluation of state-of-the-art YOLO object detection architectures for identifying snow poles in LiDAR-derived imagery captured under challenging Nordic conditions. Building upon our previous work on the SnowPole Detection dataset [1] and our LiDAR–GNSS-based localization framework [2], we expand the benchmark to include six YOLO models—YOLOv5s, YOLOv7-tiny, YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv11n—evaluated across multiple input modalities. Specifically, we assess single-channel modalities (Reflectance, Signal, Near-Infrared) and six pseudo-color combinations derived by mapping these channels to RGB representations. Each model’s performance is quantified using Precision, Recall, mAP@50, mAP@50–95, and GPU inference latency. To facilitate systematic comparison, we define a composite Rank Score that integrates detection accuracy and real-time performance in a weighted formulation. Experimental results show that YOLOv9t consistently achieves the highest detection accuracy, while YOLOv11n provides the best trade-off between accuracy and inference speed, making it a promising candidate for real-time applications on embedded platforms. Among input modalities, pseudo-color combinations—particularly those fusing Near-Infrared, Signal, and Reflectance channels—outperformed single modalities across most configurations, achieving the highest Rank Scores and mAP metrics. Therefore, we recommend using multimodal LiDAR representations such as Combination 4 and Combination 5 to maximize detection robustness in practical deployments. All datasets, benchmarking code, and trained models are publicly avail- able to support reproducibility and further research through our GitHub repository (a).

References
[1] Durga Prasad Bavirisetti, Gabriel Hanssen Kiss, Petter Arnesen, Hanne Seter, Shaira Tabassum, and Frank Lindseth. Snowpole detection: A comprehensive dataset for detection and localization using lidar imaging in nordic winter conditions. Data in Brief, 59:111403, 2025.
[2] Durga Prasad Bavirisetti, Gabriel Hanssen Kiss, and Frank Lindseth. A pole detection and geospatial localization framework using lidar-gnss data fusion. In 2024 27th International Conference on Information Fusion (FUSION), pages 1–8. IEEE, 2024.
(a) https://github.com/MuhammadIbneRafiq/Extended-evaluation-snowpole-lidar-dataset
DOI: 10.5281/zenodo.14377364
title: Neoantigen-5b-HLA-Binding-Novel-Peptides

description: Neoantigens are peptides derived from tumor-specific mutations, which are recognized by the immune system as foreign and can stimulate an immune response against cancer cells. Identifying these neoantigens is a crucial step in the development of personalized cancer immunotherapies, as they serve as targets for T-cell mediated immune responses. However, predicting which peptides from the tumor genome will bind effectively to major histocompatibility complex (MHC) molecules—key proteins that present antigens to immune cells—remains a significant challenge.

This tutorial outlines a comprehensive workflow for the identification, prediction, and validation of potential neoantigens. We begin by using the Immune Epitope Database (IEDB) to predict the binding affinity of peptide sequences to MHC molecules. IEDB provides powerful tools to model how peptides interact with different MHC alleles, helping to prioritize peptides that are most likely to be presented by the immune system. Next, we validate these peptides using PepQuery, a tool that allows for the comparison of predicted neoantigens with experimental proteomics data, providing an additional layer of confidence in their relevance. Finally, we categorize the peptides into strong and weak binders, based on their predicted affinity, which helps in identifying the most promising candidates for cancer immunotherapy.
DOI: 10.5281/zenodo.14377365
title: Neoantigen-5b-HLA-Binding-Novel-Peptides

description: Neoantigens are peptides derived from tumor-specific mutations, which are recognized by the immune system as foreign and can stimulate an immune response against cancer cells. Identifying these neoantigens is a crucial step in the development of personalized cancer immunotherapies, as they serve as targets for T-cell mediated immune responses. However, predicting which peptides from the tumor genome will bind effectively to major histocompatibility complex (MHC) molecules—key proteins that present antigens to immune cells—remains a significant challenge.

This tutorial outlines a comprehensive workflow for the identification, prediction, and validation of potential neoantigens. We begin by using the Immune Epitope Database (IEDB) to predict the binding affinity of peptide sequences to MHC molecules. IEDB provides powerful tools to model how peptides interact with different MHC alleles, helping to prioritize peptides that are most likely to be presented by the immune system. Next, we validate these peptides using PepQuery, a tool that allows for the comparison of predicted neoantigens with experimental proteomics data, providing an additional layer of confidence in their relevance. Finally, we categorize the peptides into strong and weak binders, based on their predicted affinity, which helps in identifying the most promising candidates for cancer immunotherapy.
DOI: 10.17632/pzj5v56ycs.1
title: advisory tool
DOI: 10.17632/jjhykb7v9w
title: Comprehensive Mango Leaf Images Dataset for Multi-Class Disease Classification and Automated Plant Disease Detection

description: This dataset features high-resolution images of mango (Mangifera indica) leaves collected from the Rajbari and Pabna districts in Bangladesh. Each image was initially captured using a smartphone camera, then resized for efficient processing. A structured augmentation pipeline was applied to enhance the dataset, involving brightness, contrast and color adjustments, along with geometric transformations such as cropping, scaling, flipping, rotation and shifting.The final augmented dataset is categorized into six classes based on leaf health and disease status: Healthy, Anthracnose, Dried Leaves, Gall Midge, Powdery Mildew and Senescent Leaves. This diverse and well-labeled dataset serves as a valuable resource for training, testing and validating machine learning and deep learning models aimed at automated detection and classification of mango leaf diseases. It holds significant potential for supporting farmers and researchers in the development of effective crop management solutions.

Dataset Classes and Image Counts:
Anthracnose: 1000 images 
Dried: 1000 images 
Gall Midge: 1000 images 
Healthy: 1000 images
Powdery Mildew: 1000 images 
Senescent Leaves: 1000 images

Image Details:
Original image resolution: 4096 x 3072 pixels
Resized image resolution: 480 x 560 pixels
Image format: JPG
Color mode: RGB
Collection device: Smartphone camera
Location: Rajbari and Pabna districts, Bangladesh
DOI: 10.17632/jjhykb7v9w.2
title: Comprehensive Mango Leaf Images Dataset for Multi-Class Disease Classification and Automated Plant Disease Detection

description: This dataset features high-resolution images of mango (Mangifera indica) leaves collected from the Rajbari and Pabna districts in Bangladesh. Each image was initially captured using a smartphone camera, then resized for efficient processing. A structured augmentation pipeline was applied to enhance the dataset, involving brightness, contrast and color adjustments, along with geometric transformations such as cropping, scaling, flipping, rotation and shifting.The final augmented dataset is categorized into six classes based on leaf health and disease status: Healthy, Anthracnose, Dried Leaves, Gall Midge, Powdery Mildew and Senescent Leaves. This diverse and well-labeled dataset serves as a valuable resource for training, testing and validating machine learning and deep learning models aimed at automated detection and classification of mango leaf diseases. It holds significant potential for supporting farmers and researchers in the development of effective crop management solutions.

Dataset Classes and Image Counts:
Anthracnose: 1000 images 
Dried: 1000 images 
Gall Midge: 1000 images 
Healthy: 1000 images
Powdery Mildew: 1000 images 
Senescent Leaves: 1000 images

Image Details:
Original image resolution: 4096 x 3072 pixels
Resized image resolution: 480 x 560 pixels
Image format: JPG
Color mode: RGB
Collection device: Smartphone camera
Location: Rajbari and Pabna districts, Bangladesh
DOI: 10.15468/dl.hxk6fg
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argemone mexicana L.",
 "Country is Zambia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081648-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nwxchm
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argemone mexicana L.",
 "Country is Zimbabwe",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081649-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zyrv3dfftm
title: African Continental Free Trade Agreement and Inclusive Growth in Sub-Saharan African

description: This dataset compiles annual panel data for multiple Sub‑Saharan African countries covering the period 2003–2022. It captures key macroeconomic, social, and trade indicators to analyze the impact of the African Continental Free Trade Agreement (AfCFTA) on inclusive growth. Variables include:
Economic indicators: GDP growth rate, agricultural value addition, employment share by sector (agriculture, industry, services), inflation, GDP per capita

Social indicators: poverty rate, education level, adolescent fertility, gender equality index, access to water, health expectancy, social inclusion index

Trade & policy indicators: agricultural imports/exports, non‑tariff measures, average tariff rate, trade quality index, foreign direct investment inflows, government effectiveness

Energy & inequality: energy consumption per capita, Gini coefficient, human resource index, public equity score, multi‑poverty metrics, population growth
The dataset is suitable for econometric modeling (e.g., difference-in-differences, panel regressions) to assess AfCFTA's contribution toward sustainable and equitable economic growth in SSA.
DOI: 10.17632/zyrv3dfftm.1
title: African Continental Free Trade Agreement and Inclusive Growth in Sub-Saharan African

description: This dataset compiles annual panel data for multiple Sub‑Saharan African countries covering the period 2003–2022. It captures key macroeconomic, social, and trade indicators to analyze the impact of the African Continental Free Trade Agreement (AfCFTA) on inclusive growth. Variables include:
Economic indicators: GDP growth rate, agricultural value addition, employment share by sector (agriculture, industry, services), inflation, GDP per capita

Social indicators: poverty rate, education level, adolescent fertility, gender equality index, access to water, health expectancy, social inclusion index

Trade & policy indicators: agricultural imports/exports, non‑tariff measures, average tariff rate, trade quality index, foreign direct investment inflows, government effectiveness

Energy & inequality: energy consumption per capita, Gini coefficient, human resource index, public equity score, multi‑poverty metrics, population growth
The dataset is suitable for econometric modeling (e.g., difference-in-differences, panel regressions) to assess AfCFTA's contribution toward sustainable and equitable economic growth in SSA.
DOI: 10.15468/dl.vmaj7e
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argemone mexicana L.",
 "Country is Yemen",
 "HasCoordinate is true"
 ]
}

 The dataset includes 27 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081647-250525065834625/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.29103797.v2
title: Phylogenetic trees used in BEREN paper

description: This contains the two phylogenetic trees used in the supplementary analysis of BEREN.1) Baltic_NCLDV_Markers_Total_PolB: A tree of polB from total NCLDV community in Baltic. This contains reference sequences.2) Baltic_NCLDV_Polb_genome_tree: This is a tree of genome polBs found in GVMAGs in the Baltic. This is reference-free.3) MCP_viro.aln: Alignment for MCP tree built for recovered Preplasmiviricota viruses.4) MCP_viro.nwk: Tree for ecovered Preplasmiviricota viruses.5) NCLDV_PolB.aln: Alignment for PolB marker tree with reference sequences.
DOI: 10.6084/m9.figshare.29438624
title: Viperin seems to play all role in the rainbow trout (<i>Oncorhynchus mykiss</i>) systemic and local immune responses against the viral haemorrhagic septicemic virus

description: Raw data about RNAseq results
DOI: 10.6084/m9.figshare.29438624.v1
title: Viperin seems to play all role in the rainbow trout (<i>Oncorhynchus mykiss</i>) systemic and local immune responses against the viral haemorrhagic septicemic virus

description: Raw data about RNAseq results
DOI: 10.17632/hk54wrgncd
title: This dataset contains 3D morphology measurements and adsorption kinetics data of microplastics (PE, PP, PS) interacting with naphthalene, acquired using digital holographic microscopy (DHM)

description: This dataset contains experimental data acquired for the research titled "Simultaneous Measurements of Microplastic 3D Morphology and Naphthalene Adsorption Based on Digital Holographic Microscopy". The dataset includes quantitative measurements of 3D morphology and adsorption kinetics of three types of microplastics—polyethylene (PE), polypropylene (PP), and polystyrene (PS)—in aqueous environments.
Using a digital holographic microscopy (DHM) system, we captured phase reconstruction images to obtain precise 3D morphological parameters such as particle height, surface roughness, and size distribution. In parallel, the adsorption behavior of naphthalene onto microplastics was investigated under different conditions, including varying concentrations and contact times.
The dataset comprises:
1. 3D phase-based morphology data of microplastics (height maps, reconstructed profiles).
2. Calibration data linking refractive index shifts to solution concentration.
3. Adsorption kinetics data based on both pseudo-first-order and pseudo-second-order models.
4. Experimental parameters including temperature, initial concentration, and material properties.
This dataset can support further research in environmental monitoring, pollutant-microplastic interactions, and the development of DHM-based quantitative analysis methods.
DOI: 10.17632/hk54wrgncd.1
title: This dataset contains 3D morphology measurements and adsorption kinetics data of microplastics (PE, PP, PS) interacting with naphthalene, acquired using digital holographic microscopy (DHM)

description: This dataset contains experimental data acquired for the research titled "Simultaneous Measurements of Microplastic 3D Morphology and Naphthalene Adsorption Based on Digital Holographic Microscopy". The dataset includes quantitative measurements of 3D morphology and adsorption kinetics of three types of microplastics—polyethylene (PE), polypropylene (PP), and polystyrene (PS)—in aqueous environments.
Using a digital holographic microscopy (DHM) system, we captured phase reconstruction images to obtain precise 3D morphological parameters such as particle height, surface roughness, and size distribution. In parallel, the adsorption behavior of naphthalene onto microplastics was investigated under different conditions, including varying concentrations and contact times.
The dataset comprises:
1. 3D phase-based morphology data of microplastics (height maps, reconstructed profiles).
2. Calibration data linking refractive index shifts to solution concentration.
3. Adsorption kinetics data based on both pseudo-first-order and pseudo-second-order models.
4. Experimental parameters including temperature, initial concentration, and material properties.
This dataset can support further research in environmental monitoring, pollutant-microplastic interactions, and the development of DHM-based quantitative analysis methods.
DOI: 10.15468/dl.9eu7bx
title: Occurrence Download

description: A dataset containing 6708 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "StateProvince is one of (Mississippi, MIssissippi)",
 "TaxonKey is one of (Paspalum setaceum Michx., Croton michauxii G.L.Webster, Croton michaelii V.W.Steinm., Diodia teres Walter, Dichanthelium commutatum (Schult.) Gould, Pinus palustris Mill., Rhus glabra L., Trichostema dichotomum L., Polygonella gracilis (Nutt.) Meisn., Lespedeza hirta (L.) Hornem., Commelina erecta Chapm., 1860, Rubus trivialis Michx., Vitis rotundifolia Michx., Opuntia humifusa (Raf.) Raf., Gaylussacia dumosa (Andrews) A.Gray, Paspalum notatum Flüggé, Rhus copallina L., Stylosanthes biflora (L.) Britton, Sterns & Poggenb., Tradescantia ohiensis Raf., Cnidoscolus stimulosus (Michx.) Engelm. & A.Gray, Crotalaria rotundifolia J.F.Gmel., Croton argyranthemus Michx., Cyperus retrorsus Chapm., Eriogonum tomentosum Michx., Galactia volubilis (L.) Britton, Pinus elliottii Engelm., Rumex hastatus D.Don, Euphorbia floridana Chapm., Dalea pinnata (J.F.Gmel.) Barneby)"
 ]
}

 The dataset includes 6708 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081650-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d76mjk
title: Occurrence Download

description: A dataset listing the 114 species recorded in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Botanical Database of Southern Africa (BODATSA): Botanical Collections",
 "GadmGid is ZAF.9.5.3_1",
 "Geometry POLYGON((20.42616 -34.03762,20.45506 -34.03762,20.45506 -34.00084,20.42616 -34.00084,20.42616 -34.03762))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 114 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081640-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/yyg9cw67hk
title: Ancestry_detour_dogs

description: Data file contains the behavioral data of purebred dogs, who were tested in a detour task around a V-shaped fence. 
The file also contains the designation of dog breeds according to the cladogram of Parker et al., 2017.
DOI: 10.17632/yyg9cw67hk.2
title: Ancestry_detour_dogs

description: Data file contains the behavioral data of purebred dogs, who were tested in a detour task around a V-shaped fence. 
The file also contains the designation of dog breeds according to the cladogram of Parker et al., 2017.
DOI: 10.15468/dl.mf22tf
title: Occurrence Download

description: A dataset containing 231 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 Melignomon eisentrauti Louette, 1981",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 231 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081651-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13012/b2idb-6252312_v1
title: Desiccation rate of white-tailed deer (Odocoileus virginianus) retropharyngeal lymph nodes

description: This dataset contains measurements of water loss as white-tailed deer (Odocoileus virginianus) retroypharyngeal lymph nodes air-dried in a refrigerator for 31 days. Daily weights for lymph nodes are recorded every 24 hours, as are the variables "firmness" and "surface wetness". "Firmness" is a categorical variable measuring how much the tissue deforms to the touch (soft, medium, or hard). "Surface wetness" is the amount of visible moisture on the outside of the lymph node (all, some, or none). Lymph node weights were measured until their weights stabilized for 3 consecutive days at two decimal places (ex. 3.02, 3.02, 3.02) or until the weights fluctuated only by 0.01 (ex. 3.02, 3.03, 3.02). Lymph nodes were from northern Illinois white-tailed deer collected as part of the Illinois Department of Natural Resources' ongoing chronic wasting disease (CWD) management efforts.
DOI: 10.17632/7jhmmp6y7s
title: Large zinc isotope variations in eastern Pacific seamount basalts as a result of mantle metasomatism

description: This dataset includes major, trace element data, Sr-Nd-Pb isotope data and Zn-Fe isotope data of near-EPR 5-15°N seamount lavas, as well as major element abundances corrected for the effect of fractional crystallization to Mg# = ~72, modeling result of the Zn isotope variation during mantle melting, and estimation result of the age of the enriched source component. In recent years, zinc (Zn) isotope systematics was proposed as a novel tracer for additions of near-surface materials to the mantle, while Zn isotope fractionation during magmatic processes was considered insignificant. Here we show that lavas from seamounts near the East Pacific Rise (5°-15°N) define the largest δ66Zn variation (from 0.19 ± 0.03‰ to 0.52‰ ± 0.02‰) observed in unaltered oceanic basalts. The high-δ66Zn lavas have low CaO contents and CaO/Al2O3 ratios, clearly arguing against an origin related to additions of recycled carbonates. Furthermore, high-δ66Zn lavas have higher abundances of highly incompatible elements (e.g., high La/Sm and Sm/Yb) as well as more enriched radiogenic Sr-Nd-Pb isotope compositions. These characteristics link their origin with a compositionally enriched mantle component of ancient low-degree melt metasomatic origin. We suggest that the low-degree melt metasomatism in the seismic low-velocity zone beneath ocean basins may cause significant chemical and isotopic (e.g., Zn and Fe) variations in the upper mantle at present and in the past. Our findings further indicate that the high-δ66Zn signatures in terrestrial basalts most likely originate from additions of low-degree metasomatic melts to their mantle sources. We also demonstrate that widespread interpretation that invokes subducted carbonates to explain mantle-derived melts with heavy Zn isotope compositions is inconsistent with multiple observations.